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This dataset belongs to the \"classification on both numerical and categorical features\" benchmark. \n \n Original link: https://openml.org/d/42192 \n \n Original description: \n \nnominal features and target for COMPAS"},"dataset_features":{"kind":"string","value":"{0: [0 - sex (nominal)],\n 1: [1 - age (numeric)],\n 2: [2 - juv_misd_count (numeric)],\n 3: [3 - priors_count (numeric)],\n 4: [4 - age_cat_25-45 (nominal)],\n 5: [5 - age_cat_Greaterthan45 (nominal)],\n 6: [6 - age_cat_Lessthan25 (nominal)],\n 7: [7 - race_African-American (nominal)],\n 8: [8 - race_Caucasian (nominal)],\n 9: [9 - c_charge_degree_F (nominal)],\n 10: [10 - c_charge_degree_M (nominal)],\n 11: [11 - twoyearrecid (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 2483.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 2483.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 12.0,\n 'NumberOfInstances': 4966.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 3.0,\n 'NumberOfSymbolicFeatures': 9.0,\n 'cost_matrix': nan,\n 'did': 45039,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'compas-two-years',\n 'source_data': '45039',\n 'status': 'active',\n 'target_feature': 'twoyearrecid',\n 'task_type': 'Supervised Classification',\n 'tid': 361286,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"compas-two-years"},"attribute_names":{"kind":"list like","value":["sex","age","juv_misd_count","priors_count","age_cat_25-45","age_cat_Greaterthan45","age_cat_Lessthan25","race_African-American","race_Caucasian","c_charge_degree_F","c_charge_degree_M"],"string":"[\n \"sex\",\n \"age\",\n \"juv_misd_count\",\n \"priors_count\",\n \"age_cat_25-45\",\n \"age_cat_Greaterthan45\",\n \"age_cat_Lessthan25\",\n \"race_African-American\",\n \"race_Caucasian\",\n \"c_charge_degree_F\",\n \"c_charge_degree_M\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[true,false,false,false,true,true,true,true,true,true,true],"string":"[\n true,\n false,\n false,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2848,"string":"2,848"}}},{"rowIdx":2718,"cells":{"uid":{"kind":"number","value":361149,"string":"361,149"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_SPT_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Sports Actions Dataset (Micro)**\n***\nThe 100-Sports dataset(https://www.kaggle.com/datasets/gpiosenka/sports-classification) is a collection of sports images covering 73 different sports. Images are 224x224x3 in size and in .jpg format. Images were gathered from internet searches. The images were scanned with a duplicate image detector program and all duplicate images were removed. For Meta-Album, the dataset is preprocessed and images are resized into 128x128 pixels using Open-CV. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.SPT \n**Meta Album URL**: [https://meta-album.github.io/datasets/SPT.html](https://meta-album.github.io/datasets/SPT.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: SPT \n**Dataset Name**: Sports Actions \n**Short Description**: 100 Sports Dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: human actions, sports \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC0 1.0 Public Domain \n**License URL(original data release)**: https://www.kaggle.com/gpiosenka/sports-classification\nhttps://creativecommons.org/publicdomain/zero/1.0/\n \n**License (Meta-Album data release)**: CC0 1.0 Public Domain \n**License URL (Meta-Album data release)**: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/) \n\n**Source**: 100 Sports Image Classification \n**Source URL**: https://www.kaggle.com/gpiosenka/sports-classification \n \n**Original Author**: Gerald Piosenka \n**Original contact**: https://www.kaggle.com/gpiosenka/contact \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{100-sports,\n title={100 Sports Image Classification},\n author={Gerald Piosenka},\n url={https://www.kaggle.com/datasets/gpiosenka/sports-classification},\n publisher= {Kaggle}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44284) [[Extended]](https://www.openml.org/d/44319)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44240,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_SPT_Micro',\n 'source_data': '44240',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361149,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_SPT_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2849,"string":"2,849"}}},{"rowIdx":2719,"cells":{"uid":{"kind":"number","value":361057,"string":"361,057"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"wine"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark.\n Original source: https://archive.ics.uci.edu/ml/datasets/wine+quality Please give credit to the original source if you use this dataset."},"dataset_features":{"kind":"string","value":"{0: [0 - fixed acidity (numeric)],\n 1: [1 - volatile acidity (numeric)],\n 2: [2 - citric acid (numeric)],\n 3: [3 - residual sugar (numeric)],\n 4: [4 - chlorides (numeric)],\n 5: [5 - free sulfur dioxide (numeric)],\n 6: [6 - total sulfur dioxide (numeric)],\n 7: [7 - density (numeric)],\n 8: [8 - pH (numeric)],\n 9: [9 - sulphates (numeric)],\n 10: [10 - alcohol (numeric)],\n 11: [11 - quality (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 1277.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 1277.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 12.0,\n 'NumberOfInstances': 2554.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 11.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 44091,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'wine',\n 'source_data': '44091',\n 'status': 'active',\n 'target_feature': 'quality',\n 'task_type': 'Supervised Classification',\n 'tid': 361057,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"wine"},"attribute_names":{"kind":"list like","value":["fixed acidity","volatile acidity","citric acid","residual sugar","chlorides","free sulfur dioxide","total sulfur dioxide","density","pH","sulphates","alcohol"],"string":"[\n \"fixed acidity\",\n \"volatile acidity\",\n \"citric acid\",\n \"residual sugar\",\n \"chlorides\",\n \"free sulfur dioxide\",\n \"total sulfur dioxide\",\n \"density\",\n \"pH\",\n \"sulphates\",\n \"alcohol\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2850,"string":"2,850"}}},{"rowIdx":2720,"cells":{"uid":{"kind":"number","value":146572,"string":"146,572"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"scene"},"dataset_description":{"kind":"string","value":"**Author**: Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. \n**Source**: [Mulan](http://mulan.sourceforge.net/datasets-mlc.html) \n**Please cite**: \n\n### Description\n\nScene recognition dataset - It contains characteristics about images and their classes. \nThe original dataset is a multi-label classification problem with 6 different labels: {Beach, Sunset, FallFoliage, Field, Mountain, Urban}.\nThe current dataset is a binary classification problem considering just the 'Urban' label.\n\n### Sources\n\nMatthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown.\nLearning multi-label scene classification.\nPattern Recognition, 37(9):1757-1771, 2004. \n\n### Dataset Information\n\nMulti-label classification problem, based on real-world images. \nInstances: 2407 \nFeatures: 294 numerical features with values between [0,1] \nClasses/Labels: 2 {Urban, Nor Urban} \nNo missing values"},"dataset_features":{"kind":"string","value":"{0: [0 - attr1 (numeric)],\n 1: [1 - attr2 (numeric)],\n 2: [2 - attr3 (numeric)],\n 3: [3 - attr4 (numeric)],\n 4: [4 - attr5 (numeric)],\n 5: [5 - attr6 (numeric)],\n 6: [6 - attr7 (numeric)],\n 7: [7 - attr8 (numeric)],\n 8: [8 - attr9 (numeric)],\n 9: [9 - attr10 (numeric)],\n 10: [10 - attr11 (numeric)],\n 11: [11 - attr12 (numeric)],\n 12: [12 - attr13 (numeric)],\n 13: [13 - attr14 (numeric)],\n 14: [14 - attr15 (numeric)],\n 15: [15 - attr16 (numeric)],\n 16: [16 - attr17 (numeric)],\n 17: [17 - attr18 (numeric)],\n 18: [18 - attr19 (numeric)],\n 19: [19 - attr20 (numeric)],\n 20: [20 - attr21 (numeric)],\n 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true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2851,"string":"2,851"}}},{"rowIdx":2721,"cells":{"uid":{"kind":"number","value":361158,"string":"361,158"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_MD_5_BIS_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album OmniPrint-MD-5-bis Dataset (Micro)**\n***\nOmniPrint-MD-5-bis dataset consists of 28 240 images (128x128, RGB) from 706 categories. The images are synthesized with OmniPrint, and no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, the strength of random perspective transformation is 0.04, left/right/top/bottom margins are all 20% of the image size, the strength of pre-rasterization elastic transformation is 0.035, random translation is activated both horizontally and vertically, image blending method is Poisson Image Editing, rotation is within -60 and 60 degrees, horizontal shear is within -0.5 and 0.5, the foreground is filled with a random color, the background consists of images downloaded from Pexels(https://www.pexels.com/). \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: OCR.MD_5_BIS \n**Meta Album URL**: [https://meta-album.github.io/datasets/MD_5_BIS.html](https://meta-album.github.io/datasets/MD_5_BIS.html) \n**Domain ID**: OCR \n**Domain Name**: Optical Character Recognition \n**Dataset ID**: MD_5_BIS \n**Dataset Name**: OmniPrint-MD-5-bis \n**Short Description**: Character images with a specific set of nuisance parameters \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: ocr \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY 4.0 \n**License URL(original data release)**: https://creativecommons.org/licenses/by/4.0/\n \n**License (Meta-Album data release)**: CC BY 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: OmniPrint \n**Source URL**: https://github.com/SunHaozhe/OmniPrint \n \n**Original Author**: Haozhe Sun \n**Original contact**: sunhaozhe275940200@gmail.com \n\n**Meta Album author**: Haozhe Sun \n**Created Date**: 25 June 2021 \n**Contact Name**: Haozhe Sun \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{sun2021omniprint,\n title={OmniPrint: A Configurable Printed Character Synthesizer},\n author={Haozhe Sun and Wei-Wei Tu and Isabelle M Guyon},\n booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},\n year={2021},\n url={https://openreview.net/forum?id=R07XwJPmgpl}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44296)"},"dataset_features":{"kind":"string","value":"{0: [0 - 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margin_top (numeric)],\n 25: [25 - offset_horizontal (numeric)],\n 26: [26 - offset_vertical (numeric)],\n 27: [27 - original_image_height_resolution (numeric)],\n 28: [28 - original_image_width_resolution (numeric)],\n 29: [29 - perspective_params (string)],\n 30: [30 - pre_elastic (numeric)],\n 31: [31 - rotation (numeric)],\n 32: [32 - shear_x (numeric)],\n 33: [33 - stroke_fill (string)],\n 34: [34 - SUPER_CATEGORY (string)],\n 35: [35 - family_name (string)],\n 36: [36 - style_name (string)],\n 37: [37 - postscript_name (string)],\n 38: [38 - variable_font_weight (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': nan,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': nan,\n 'NumberOfClasses': 0.0,\n 'NumberOfFeatures': 39.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 24.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 44252,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_MD_5_BIS_Micro',\n 'source_data': '44252',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361158,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_MD_5_BIS_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","text","font_file","background","background_image_crop_x","background_image_crop_x_plus_width","background_image_crop_y","background_image_crop_y_plus_height","background_image_name","background_image_original_height","background_image_original_width","background_image_resized_height","background_image_resized_width","font_size","font_weight","foreground","image_blending_method","image_height_resolution","image_mode","image_width_resolution","margin_bottom","margin_left","margin_right","margin_top","offset_horizontal","offset_vertical","original_image_height_resolution","original_image_width_resolution","perspective_params","pre_elastic","rotation","shear_x","stroke_fill","SUPER_CATEGORY","family_name","style_name","postscript_name","variable_font_weight"],"string":"[\n \"FILE_NAME\",\n \"text\",\n \"font_file\",\n \"background\",\n \"background_image_crop_x\",\n \"background_image_crop_x_plus_width\",\n \"background_image_crop_y\",\n \"background_image_crop_y_plus_height\",\n \"background_image_name\",\n \"background_image_original_height\",\n \"background_image_original_width\",\n \"background_image_resized_height\",\n \"background_image_resized_width\",\n \"font_size\",\n \"font_weight\",\n \"foreground\",\n \"image_blending_method\",\n \"image_height_resolution\",\n \"image_mode\",\n \"image_width_resolution\",\n \"margin_bottom\",\n \"margin_left\",\n \"margin_right\",\n \"margin_top\",\n \"offset_horizontal\",\n \"offset_vertical\",\n \"original_image_height_resolution\",\n \"original_image_width_resolution\",\n \"perspective_params\",\n \"pre_elastic\",\n \"rotation\",\n \"shear_x\",\n \"stroke_fill\",\n \"SUPER_CATEGORY\",\n \"family_name\",\n \"style_name\",\n \"postscript_name\",\n \"variable_font_weight\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2853,"string":"2,853"}}},{"rowIdx":2722,"cells":{"uid":{"kind":"number","value":361150,"string":"361,150"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_TEX_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Textures Dataset (Micro)**\n***\nThe original Textures dataset is a combination of 4 texture datasets: KTH-TIPS and KTH-TIPS 2 (https://www.csc.kth.se/cvap/databases/kth-tips/index.html), Kylberg Textures Dataset (http://www.cb.uu.se/~gustaf/texture/) and UIUC Textures Dataset. The data in all four datasets is collected in laboratory conditions, i.e., images were captured in a controlled environment with configurable brightness, luminosity, scale and angle. The KTH-TIPS dataset was collected by Mario Fritz and KTH-TIPS 2 dataset was collected by P. Mallikarjuna and Alireza Tavakoli Targhi, created in 2004 and 2006 respectively. Both of these datasets were prepared under the supervision of Eric Hayman and Barbara Caputo. The data for Kylberg Textures Dataset and UIUC Textures Dataset data was collected by the original authors of these datasets in September 2010 and August 2005 respectively. The Meta-Album Textures dataset is a preprocessed version of the original dataset (combination of 4 datasets). All the images are preprocessed by first cropping into perfect squared images and then resized into 128x128 with an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX.html](https://meta-album.github.io/datasets/TEX.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX \n**Dataset Name**: Textures \n**Short Description**: Textures dataset from KTH-TIPS, Kylberg and UIUC \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY NC 4.0, public for research, cite paper to use dataset \n**License URL(original data release)**: https://creativecommons.org/licenses/by-nc/4.0/\nhttps://kylberg.org/wp-content/uploads/2020/05/KylbergTextureDocumentation-1.0.pdf\n \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: KTH-TIPS, Kylberg, UIUC \n**Source URL**: https://www.csc.kth.se/cvap/databases/kth-tips/index.html \nhttps://www.cb.uu.se/~gustaf/texture/ \n \n**Original Author**: Eric Hayman, Barbara Caputo, Mario Fritz, P. Mallikarjuna, Alireza Tavakoli Targhi, Jean Ponce, Gustaf Kylberg, Svetlana Lazebnik, Cordelia Schmid \n**Original contact**: KTH-TIPS: hayman@nada.kth.se, Kylberg: gustaf.kylberg@gmail.com, UIUC: Jean.Ponce@inria.fr \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{Fritz2004THEKD,\n title={THE KTH-TIPS database},\n author={Mario Fritz and E. Hayman and B. Caputo and J. Eklundh},\n year={2004},\n url = {https://www.csc.kth.se/cvap/databases/kth-tips/index.html}\n}\n\n@inproceedings{Mallikarjuna2006THEK2,\n title={THE KTH-TIPS 2 database},\n author={P. Mallikarjuna and Alireza Tavakoli Targhi and Mario Fritz and E. Hayman and B. Caputo and J. Eklundh},\n year={2006},\n url = {https://www.csc.kth.se/cvap/databases/kth-tips/index.html}\n}\n\n@TECHREPORT{Kylberg2011c,\n author = {Gustaf Kylberg},\n title = {The Kylberg Texture Dataset v. 1.0},\n institution = {Centre for Image Analysis, Swedish University of Agricultural Sciences\n\tand Uppsala University, Uppsala, Sweden},\n year = {2011},\n type = {External report (Blue series)},\n number = {35},\n month = {September},\n url = {http://www.cb.uu.se/~gustaf/texture/}\n}\n\n@article{lazebnik:inria-00548530,\n TITLE = {{A sparse texture representation using local affine regions}},\n AUTHOR = {Lazebnik, Svetlana and Schmid, Cordelia and Ponce, Jean},\n URL = {https://hal.inria.fr/inria-00548530},\n JOURNAL = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},\n PUBLISHER = {{Institute of Electrical and Electronics Engineers}},\n VOLUME = {27},\n NUMBER = {8},\n PAGES = {1265--1278},\n YEAR = {2005},\n MONTH = Aug,\n DOI = {10.1109/TPAMI.2005.151},\n KEYWORDS = {Index Terms- Image processing and computer vision ; feature measurement ; pattern recognition ; texture},\n PDF = {https://hal.inria.fr/inria-00548530/file/lana_pami_final.pdf},\n HAL_ID = {inria-00548530},\n HAL_VERSION = {v1},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44288) [[Extended]](https://www.openml.org/d/44322)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44244,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_TEX_Micro',\n 'source_data': '44244',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361150,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_TEX_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2854,"string":"2,854"}}},{"rowIdx":2723,"cells":{"uid":{"kind":"number","value":361155,"string":"361,155"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_MED_LF_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Medicinal Leaf Dataset (Micro)**\n***\nThe Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 800 images in total, captured with a mobile phone camera. The original resolution is 1 600x1 200 px. We create Medleaf for Meta-Album by cropping them at the center and resize to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.MED_LF \n**Meta Album URL**: [https://meta-album.github.io/datasets/MED_LF.html](https://meta-album.github.io/datasets/MED_LF.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: MED_LF \n**Dataset Name**: Medicinal Leaf \n**Short Description**: Healthy Medicinal Leaf \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: medicinal leaf, plants, plant diseases \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY 4.0 \n**License URL(original data release)**: https://data.mendeley.com/datasets/nnytj2v3n5/1\nhttps://creativecommons.org/licenses/by/4.0/\n \n**License (Meta-Album data release)**: CC BY 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: Medicinal Leaf Dataset \n**Source URL**: https://data.mendeley.com/datasets/nnytj2v3n5/1 \n \n**Original Author**: S Roopashree, J Anitha \n**Original contact**: \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n @article{s_j_2020, \n title={Medicinal Leaf Dataset}, \n url={https://data.mendeley.com/datasets/nnytj2v3n5/1}, \n author={S, Roopashree and J, Anitha}, \n year={2020}, \n month={Oct},\n doi={10.17632/nnytj2v3n5.1},\n version={1},\n publisher={Mendeley Data} \n} \n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44299) [[Extended]](https://www.openml.org/d/44332)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44314,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_MED_LF_Micro',\n 'source_data': '44314',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361155,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_MED_LF_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2855,"string":"2,855"}}},{"rowIdx":2724,"cells":{"uid":{"kind":"number","value":361151,"string":"361,151"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_CRS_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Cars Dataset (Micro)**\n***\nThe original Cars dataset (https://ai.stanford.edu/~jkrause/cars/car_dataset.html) was collected in 2013, and it contains more than 16 000 images from 196 classes of cars. Most images are on the road, but some have different backgrounds, and each image has only one car. Each class can have 48 to 136 images of variable resolutions. The preprocess version for this dataset was obtained by creating square images either duplicating the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image. In this case, cropping was not applied to create the square images since following this technique results in losing too much information from the cars. Then, the square images were resized into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.CRS \n**Meta Album URL**: [https://meta-album.github.io/datasets/CRS.html](https://meta-album.github.io/datasets/CRS.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: CRS \n**Dataset Name**: Cars \n**Short Description**: Dataset with images of different car models \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: vehicles, cars \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: ImageNet License \n**License URL(original data release)**: https://ai.stanford.edu/~jkrause/cars/car_dataset.html\nhttps://www.image-net.org/download.php\n \n**License (Meta-Album data release)**: ImageNet License \n**License URL (Meta-Album data release)**: [https://www.image-net.org/download.php](https://www.image-net.org/download.php) \n\n**Source**: Stanford Cars Dataset \n**Source URL**: https://ai.stanford.edu/~jkrause/cars/car_dataset.html \n \n**Original Author**: Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei \n**Original contact**: jkrause@cs.stanford.edu \n\n**Meta Album author**: Philip Boser \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{KrauseStarkDengFei-Fei_3DRR2013,\n title = {3D Object Representations for Fine-Grained Categorization},\n booktitle = {4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13)},\n year = {2013},\n address = {Sydney, Australia},\n author = {Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44289) [[Extended]](https://www.openml.org/d/44323)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44245,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_CRS_Micro',\n 'source_data': '44245',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361151,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_CRS_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2856,"string":"2,856"}}},{"rowIdx":2725,"cells":{"uid":{"kind":"number","value":361147,"string":"361,147"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_RESISC_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album RESISC Dataset (Micro)**\n***\nRESISC45 dataset(https://gcheng-nwpu.github.io/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating many image variations. Even though RESISC45 does not propose a label hierarchy, it can be created from other common aerial image label organization scheme. We have preprocessed RESISC for Meta-Album by resizing the dataset to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: REM_SEN.RESISC \n**Meta Album URL**: [https://meta-album.github.io/datasets/RESISC.html](https://meta-album.github.io/datasets/RESISC.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RESISC \n**Dataset Name**: RESISC \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC-BY-NC 4.0 \n**License URL(original data release)**: https://creativecommons.org/licenses/by-nc/4.0/\n \n**License (Meta-Album data release)**: CC-BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: NWPU-RESISC45 Dataset \n**Source URL**: https://gcheng-nwpu.github.io/ \n \n**Original Author**: Gong Cheng, Junwei Han, and Xiaoqiang Lu \n**Original contact**: chenggong1119@gmail.com \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{DBLP:journals/corr/ChengHL17,\n author = {Gong Cheng and Junwei Han and Xiaoqiang Lu},\n title = {Remote Sensing Image Scene Classification: Benchmark and State of the Art},\n journal = {CoRR},\n volume = {abs/1703.00121},\n year = {2017},\n url = {http://arxiv.org/abs/1703.00121},\n eprinttype = {arXiv},\n eprint = {1703.00121},\n timestamp = {Mon, 02 Dec 2019 09:32:19 +0100},\n biburl = {https://dblp.org/rec/journals/corr/ChengHL17.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44290) [[Extended]](https://www.openml.org/d/44324)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44246,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_RESISC_Micro',\n 'source_data': '44246',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361147,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_RESISC_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2857,"string":"2,857"}}},{"rowIdx":2726,"cells":{"uid":{"kind":"number","value":361163,"string":"361,163"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_APL_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Airplanes Dataset (Micro)**\n***\nThe original Airplanes dataset (https://zenodo.org/record/3464319) comprises more than 9 000 remote sensing images acquired from Google Earth satellite imagery, including 21 different types of aircraft from around 36 airports. All the images were carefully labeled by seven specialists in the field of remote sensing image interpretation. Each class can have 230 to 846 images, where each image contains only one complete aircraft, and they have variable resolutions. To preprocess this dataset, we cropped the images from either side to make them square, and then we resized them into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.APL \n**Meta Album URL**: [https://meta-album.github.io/datasets/APL.html](https://meta-album.github.io/datasets/APL.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: APL \n**Dataset Name**: Airplanes \n**Short Description**: Airplanes dataset with different aiplane models \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: vehicles, airplanes \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://zenodo.org/record/3464319\nhttps://creativecommons.org/licenses/by/4.0/legalcode\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) \n\n**Source**: Muti-type Aircraft of Remote Sensing Images: MTARSI \n**Source URL**: https://zenodo.org/record/3464319 \n \n**Original Author**: Wu, Zhize \n**Original contact**: \n\n**Meta Album author**: Philip Boser \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@dataset{wu_zhize_2019_3464319,\n author = {Wu, Zhize},\n title = {{Muti-type Aircraft of Remote Sensing Images: MTARSI}},\n month = may,\n year = 2019,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.3464319},\n url = {https://doi.org/10.5281/zenodo.3464319}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44295) [[Extended]](https://www.openml.org/d/44329)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44251,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_APL_Micro',\n 'source_data': '44251',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361163,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_APL_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2858,"string":"2,858"}}},{"rowIdx":2727,"cells":{"uid":{"kind":"number","value":361064,"string":"361,064"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"kdd_ipums_la_97-small"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nBinarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun."},"dataset_features":{"kind":"string","value":"{0: [0 - value (numeric)],\n 1: [1 - rent (numeric)],\n 2: [2 - ftotinc (numeric)],\n 3: [3 - momloc (numeric)],\n 4: [4 - famsize (numeric)],\n 5: [5 - nchild (numeric)],\n 6: [6 - eldch (numeric)],\n 7: [7 - yngch (numeric)],\n 8: [8 - nsibs (numeric)],\n 9: [9 - age (numeric)],\n 10: [10 - occscore (numeric)],\n 11: [11 - sei (numeric)],\n 12: [12 - inctot (numeric)],\n 13: [13 - incwage (numeric)],\n 14: [14 - incbus (numeric)],\n 15: [15 - incfarm (numeric)],\n 16: [16 - incss (numeric)],\n 17: [17 - incwelfr (numeric)],\n 18: [18 - incother (numeric)],\n 19: [19 - poverty (numeric)],\n 20: [20 - binaryClass (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 2594.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 2594.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 21.0,\n 'NumberOfInstances': 5188.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 20.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 44124,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'kdd_ipums_la_97-small',\n 'source_data': '44124',\n 'status': 'active',\n 'target_feature': 'binaryClass',\n 'task_type': 'Supervised Classification',\n 'tid': 361064,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"kdd_ipums_la_97-small"},"attribute_names":{"kind":"list like","value":["value","rent","ftotinc","momloc","famsize","nchild","eldch","yngch","nsibs","age","occscore","sei","inctot","incwage","incbus","incfarm","incss","incwelfr","incother","poverty"],"string":"[\n \"value\",\n \"rent\",\n \"ftotinc\",\n \"momloc\",\n \"famsize\",\n \"nchild\",\n \"eldch\",\n \"yngch\",\n \"nsibs\",\n \"age\",\n \"occscore\",\n \"sei\",\n \"inctot\",\n \"incwage\",\n \"incbus\",\n \"incfarm\",\n \"incss\",\n \"incwelfr\",\n \"incother\",\n \"poverty\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2859,"string":"2,859"}}},{"rowIdx":2728,"cells":{"uid":{"kind":"number","value":361159,"string":"361,159"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_TEX_DTD_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Textures-DTD Dataset (Micro)**\n***\nThe Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data was annotated using Amazon Mechanical Turk. The data collection process is mentioned on the dataset overview page. For Meta-Album meta-dataset, this dataset is preprocessed by cropping the images to square images and then resizing them to 128x128 using Open-CV with an anti-aliasing filter. This dataset has 47 class labels. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX_DTD \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX_DTD.html](https://meta-album.github.io/datasets/TEX_DTD.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX_DTD \n**Dataset Name**: Textures-DTD \n**Short Description**: Textures dataset from Describable Textures Dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Describable Textures Dataset (DTD), University of Oxford, England \n**Source URL**: https://www.robots.ox.ac.uk/~vgg/data/dtd/ \n \n**Original Author**: Mircea Cimpoi, Subhransu354Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi \n**Original contact**: {mircea, vedaldi}@robots.ox.ac.uk \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@InProceedings{cimpoi14describing,\n\tAuthor = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi},\n\tTitle = {Describing Textures in the Wild},\n\tBooktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},\n\tYear = {2014}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44294) [[Extended]](https://www.openml.org/d/44328)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44250,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_TEX_DTD_Micro',\n 'source_data': '44250',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361159,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_TEX_DTD_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2860,"string":"2,860"}}},{"rowIdx":2729,"cells":{"uid":{"kind":"number","value":361172,"string":"361,172"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_MD_6_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album OmniPrint-MD-6 Dataset (Micro)**\n***\nOmniPrint-MD-6 dataset consists of 28 120 images (128x128, RGB) from 703 categories. The images are synthesized with OmniPrint, no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, the strength of random perspective transformation is 0.04, left/right/top/bottom margins are all 20% of the image size, the strength of pre-rasterization elastic transformation is 0.035, random translation is activated both horizontally and vertically, image blending method is Poisson Image Editing, rotation is within -60 and 60 degrees, horizontal shear is within -0.5 and 0.5, both foreground and background are images taken from a personal mobile phone. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: OCR.MD_6 \n**Meta Album URL**: [https://meta-album.github.io/datasets/MD_6.html](https://meta-album.github.io/datasets/MD_6.html) \n**Domain ID**: OCR \n**Domain Name**: Optical Character Recognition \n**Dataset ID**: MD_6 \n**Dataset Name**: OmniPrint-MD-6 \n**Short Description**: Character images with a specific set of nuisance parameters \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: ocr \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY 4.0 \n**License URL(original data release)**: https://creativecommons.org/licenses/by/4.0/\n \n**License (Meta-Album data release)**: CC BY 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: OmniPrint \n**Source URL**: https://github.com/SunHaozhe/OmniPrint \n \n**Original Author**: Haozhe Sun \n**Original contact**: sunhaozhe275940200@gmail.com \n\n**Meta Album author**: Haozhe Sun \n**Created Date**: 25 June 2021 \n**Contact Name**: Haozhe Sun \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{sun2021omniprint,\n title={OmniPrint: A Configurable Printed Character Synthesizer},\n author={Haozhe Sun and Wei-Wei Tu and Isabelle M Guyon},\n booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},\n year={2021},\n url={https://openreview.net/forum?id=R07XwJPmgpl}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44310)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - text (string)],\n 2: [2 - CATEGORY (numeric)],\n 3: [3 - font_file (string)],\n 4: [4 - background (string)],\n 5: [5 - background_image_crop_x (numeric)],\n 6: [6 - background_image_crop_x_plus_width (numeric)],\n 7: [7 - background_image_crop_y (numeric)],\n 8: [8 - background_image_crop_y_plus_height (numeric)],\n 9: [9 - background_image_name (string)],\n 10: [10 - background_image_original_height (numeric)],\n 11: [11 - background_image_original_width (numeric)],\n 12: [12 - background_image_resized_height (numeric)],\n 13: [13 - background_image_resized_width (numeric)],\n 14: [14 - font_size (numeric)],\n 15: [15 - font_weight (numeric)],\n 16: [16 - foreground (string)],\n 17: [17 - foreground_image_crop_x (numeric)],\n 18: [18 - foreground_image_crop_x_plus_width (numeric)],\n 19: [19 - foreground_image_crop_y (numeric)],\n 20: [20 - foreground_image_crop_y_plus_height (numeric)],\n 21: [21 - foreground_image_name (string)],\n 22: [22 - foreground_image_original_height (numeric)],\n 23: [23 - foreground_image_original_width (numeric)],\n 24: [24 - foreground_image_resized_height (numeric)],\n 25: [25 - foreground_image_resized_width (numeric)],\n 26: [26 - image_blending_method (string)],\n 27: [27 - image_height_resolution (numeric)],\n 28: [28 - image_mode (string)],\n 29: [29 - image_width_resolution (numeric)],\n 30: [30 - margin_bottom (numeric)],\n 31: [31 - margin_left (numeric)],\n 32: [32 - margin_right (numeric)],\n 33: [33 - margin_top (numeric)],\n 34: [34 - offset_horizontal (numeric)],\n 35: [35 - offset_vertical (numeric)],\n 36: [36 - original_image_height_resolution (numeric)],\n 37: [37 - original_image_width_resolution (numeric)],\n 38: [38 - perspective_params (string)],\n 39: [39 - pre_elastic (numeric)],\n 40: [40 - rotation (numeric)],\n 41: [41 - shear_x (numeric)],\n 42: [42 - SUPER_CATEGORY (string)],\n 43: [43 - family_name (string)],\n 44: [44 - style_name (string)],\n 45: [45 - postscript_name (string)],\n 46: [46 - variable_font_weight (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': nan,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': nan,\n 'NumberOfClasses': 0.0,\n 'NumberOfFeatures': 47.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 32.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 44280,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_MD_6_Micro',\n 'source_data': '44280',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361172,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_MD_6_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","text","font_file","background","background_image_crop_x","background_image_crop_x_plus_width","background_image_crop_y","background_image_crop_y_plus_height","background_image_name","background_image_original_height","background_image_original_width","background_image_resized_height","background_image_resized_width","font_size","font_weight","foreground","foreground_image_crop_x","foreground_image_crop_x_plus_width","foreground_image_crop_y","foreground_image_crop_y_plus_height","foreground_image_name","foreground_image_original_height","foreground_image_original_width","foreground_image_resized_height","foreground_image_resized_width","image_blending_method","image_height_resolution","image_mode","image_width_resolution","margin_bottom","margin_left","margin_right","margin_top","offset_horizontal","offset_vertical","original_image_height_resolution","original_image_width_resolution","perspective_params","pre_elastic","rotation","shear_x","SUPER_CATEGORY","family_name","style_name","postscript_name","variable_font_weight"],"string":"[\n \"FILE_NAME\",\n \"text\",\n \"font_file\",\n \"background\",\n \"background_image_crop_x\",\n \"background_image_crop_x_plus_width\",\n \"background_image_crop_y\",\n \"background_image_crop_y_plus_height\",\n \"background_image_name\",\n \"background_image_original_height\",\n \"background_image_original_width\",\n \"background_image_resized_height\",\n \"background_image_resized_width\",\n \"font_size\",\n \"font_weight\",\n \"foreground\",\n \"foreground_image_crop_x\",\n \"foreground_image_crop_x_plus_width\",\n \"foreground_image_crop_y\",\n \"foreground_image_crop_y_plus_height\",\n \"foreground_image_name\",\n \"foreground_image_original_height\",\n \"foreground_image_original_width\",\n \"foreground_image_resized_height\",\n \"foreground_image_resized_width\",\n \"image_blending_method\",\n \"image_height_resolution\",\n \"image_mode\",\n \"image_width_resolution\",\n \"margin_bottom\",\n \"margin_left\",\n \"margin_right\",\n \"margin_top\",\n \"offset_horizontal\",\n \"offset_vertical\",\n \"original_image_height_resolution\",\n \"original_image_width_resolution\",\n \"perspective_params\",\n \"pre_elastic\",\n \"rotation\",\n \"shear_x\",\n \"SUPER_CATEGORY\",\n \"family_name\",\n \"style_name\",\n \"postscript_name\",\n \"variable_font_weight\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2861,"string":"2,861"}}},{"rowIdx":2730,"cells":{"uid":{"kind":"number","value":361168,"string":"361,168"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_BTS_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Boats Dataset (Micro)**\n***\nThe original version of the Meta-Album boats dataset is called MARVEL dataset (https://github.com/avaapm/marveldataset2016). It has more than 138 000 images of 26 different maritime vessels in their natural background. Each class can have 1 802 to 8 930 images of variable resolutions. To preprocess this dataset, we either duplicate the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image to create square images. No cropping was applied because the boats occupy most of the image, and applying this technique will lead to incomplete images. Finally, the square images were resized into 128x128 px using an anti-aliasing filter \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.BTS \n**Meta Album URL**: [https://meta-album.github.io/datasets/BTS.html](https://meta-album.github.io/datasets/BTS.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: BTS \n**Dataset Name**: Boats \n**Short Description**: Dataset with images of different boats \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: vehicles, boats \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Cite paper to use dataset \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: MARVEL: A LARGE-SCALE IMAGE DATASET FOR MARITIME VESSELS \n**Source URL**: https://github.com/avaapm/marveldataset2016 \n \n**Original Author**: Gundogdu E., Solmaz B, Yucesoy V., Koc A. \n**Original contact**: \n\n**Meta Album author**: Dustin Carrion \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@InProceedings{MARVEL,\n author=\"Gundogdu, Erhan and Solmaz, Berkan and Yucesoy, Veysel and Koc, Aykut\",\n editor=\"Lai, Shang-Hong and Lepetit, Vincent and Nishino, Ko and Sato, Yoichi\",\n title=\"MARVEL: A Large-Scale Image Dataset for Maritime Vessels\",\n booktitle=\"Computer Vision -- ACCV 2016\",\n year=\"2017\",\n publisher=\"Springer International Publishing\",\n address=\"Cham\",\n pages=\"165--180\",\n isbn=\"978-3-319-54193-8\"\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44309) [[Extended]](https://www.openml.org/d/44343)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44279,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_BTS_Micro',\n 'source_data': '44279',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361168,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_BTS_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2862,"string":"2,862"}}},{"rowIdx":2731,"cells":{"uid":{"kind":"number","value":361157,"string":"361,157"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_BCT_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album DIBaS Dataset (Micro)**\n***\nThe Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each class, we decided to split each image into several smaller images before resizing them to 128x128. We then obtained a preprocessed dataset of 4 060 images with at least 108 images for each class. This dataset was also preprocessed with blob normalization techniques, which is quite unusual for this type of image. The goal of this transformation was to reduce the importance of color in decision-making for a bias-aware challenge. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.BCT \n**Meta Album URL**: [https://meta-album.github.io/datasets/BCT.html](https://meta-album.github.io/datasets/BCT.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopic \n**Dataset ID**: BCT \n**Dataset Name**: DIBaS \n**Short Description**: Digital Image of Bacterial Species (DIBaS) \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: microscopic, bacteria \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Public for researchers \n**License URL(original data release)**: \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Digital Image of Bacterial Species (DIBaS) \n**Source URL**: http://misztal.edu.pl/software/databases/dibas/ \nhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184554 \nhttps://github.com/gallardorafael/DIBaS-Dataset \n \n**Original Author**: Bartosz Zielinski, Anna Plichta, Krzysztof Misztal, Przemyslaw Spurek, Monika Brzychczy-Wloch, Dorota Ochonska \n**Original contact**: krzysztof.misztal@uj.edu.pl \n\n**Meta Album author**: Romain Mussard \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{10.1371/journal.pone.0184554,\n doi = {10.1371/journal.pone.0184554},\n author = {Zielinski, Bartosz AND Plichta, Anna AND Misztal, Krzysztof AND Spurek, Przemyslaw AND Brzychczy-Wloch, Monika AND Ochonska, Dorota},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Deep learning approach to bacterial colony classification},\n year = {2017},\n month = {09},\n volume = {12},\n url = {https://doi.org/10.1371/journal.pone.0184554},\n pages = {1-14},\n number = {9}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44281) [[Extended]](https://www.openml.org/d/44316)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44237,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_BCT_Micro',\n 'source_data': '44237',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361157,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_BCT_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2864,"string":"2,864"}}},{"rowIdx":2732,"cells":{"uid":{"kind":"number","value":361161,"string":"361,161"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_INS_2_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Insects2 Dataset (Micro)**\n***\nThe pest insects dataset was originally created as a large scale benchmark dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102). It contains more than 75 000 images belongs to 102 categories. It also has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upper-level category. The preprocessed version is made from the original dataset by cropping the images in perfect squares and then resizing them into the required images size of 128x128. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: SM_AM.INS2 \n**Meta Album URL**: [https://meta-album.github.io/datasets/INS_2.html](https://meta-album.github.io/datasets/INS_2.html) \n**Domain ID**: SM_AM \n**Domain Name**: Small Aninamls \n**Dataset ID**: INS_2 \n**Dataset Name**: Insects2 \n**Short Description**: Insects dataset for Insect Pest Recognition \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: insects, ecology \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Free for academic usage, cite to use dataset \n**License URL(original data release)**: https://github.com/xpwu95/IP102\n \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition \n**Source URL**: https://github.com/xpwu95/IP102 \n \n**Original Author**: Xiaoping Wu, Chi Zhan, Yukun Lai, Ming-Ming Cheng, Jufeng Yang \n**Original contact**: xpwu95@163.com \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{Wu2019Insect,\n title={IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition},\n author={Xiaoping Wu and Chi Zhan and Yukun Lai and Ming-Ming Cheng and Jufeng Yang},\n booktitle={IEEE CVPR},\n pages={8787--8796},\n year={2019},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44292) [[Extended]](https://www.openml.org/d/44326)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44248,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_INS_2_Micro',\n 'source_data': '44248',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361161,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_INS_2_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2865,"string":"2,865"}}},{"rowIdx":2733,"cells":{"uid":{"kind":"number","value":361165,"string":"361,165"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_DOG_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Dogs Dataset (Micro)**\n***\nResearchers from Stanford University created the original Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). It contains more than 20 000 images belonging to 120 breeds of dogs worldwide. The images and annotations came from ImageNet for the task of fine-grained image categorization. The number of images per class and the resolution is not balanced. Each class can have 148 to 252 images with a resolution from 100x105 to 2 448x3 264 px. This dataset has a little inter-class variation and a large intra-class variation due to color, pose, and occlusion. Most of the images in this dataset are taken in man-made environments leading to a significant background variation. The preprocessed version of this dataset is prepared from the original dataset by cropping the images from either side to make squared images. In case an image has a resolution lower than 128 px, the squared images are done by either duplicating the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image. These square images are then resized into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: LR_AM.DOG \n**Meta Album URL**: [https://meta-album.github.io/datasets/DOG.html](https://meta-album.github.io/datasets/DOG.html) \n**Domain ID**: LR_AM \n**Domain Name**: Large Aninamls \n**Dataset ID**: DOG \n**Dataset Name**: Dogs \n**Short Description**: Dogs dataset with different breeds of dogs \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: dogs, animals \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Cite to use dataset, open for research \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Stanford Dogs Dataset \n**Source URL**: http://vision.stanford.edu/aditya86/ImageNetDogs/ \n \n**Original Author**: Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, Li Fei-Fei \n**Original contact**: aditya86@cs.stanford.edu \n\n**Meta Album author**: Dustin Carrion \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{KhoslaYaoJayadevaprakashFeiFei_FGVC2011,\n author = {Aditya Khosla and Nityananda Jayadevaprakash and Bangpeng Yao and Li Fei-Fei},\n title = {Novel Dataset for Fine-Grained Image Categorization},\n booktitle = {First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition},\n year = {2011},\n month = {June},\n address = {Colorado Springs, CO}\n}\n\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44298) [[Extended]](https://www.openml.org/d/44331)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 19.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 760.0,\n 'NumberOfInstancesWithMissingValues': 760.0,\n 'NumberOfMissingValues': 760.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44313,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_DOG_Micro',\n 'source_data': '44313',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361165,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_DOG_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2866,"string":"2,866"}}},{"rowIdx":2734,"cells":{"uid":{"kind":"number","value":361160,"string":"361,160"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_NET_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album PlantNet Dataset (Micro)**\n***\nMeta-Album PlantNet dataset is created by sampling the Pl@ntNet-300k dataset (https://openreview.net/forum?id=eLYinD0TtIt), itself a sampling of the Pl@ntNet Project's repository. The images and labels which enter this database are sourced by citizen botanists from around the world, then confirmed using a weighted reliability score from others users, such that each image has been reviewed by 2.03 citizen botanists on average. Of the 1 081 classes in the original Pl@ntNet-300k dataset, PLT_NET retains the 25 most populous classes, belonging to 21 genera, for a total of 120 688 images total, with min 2 914, max 9 011 image distribution per class. Each image contains a colored 128x128 image of a plant or a piece or a plant from the corresponding class (or in some cases sketches of plants or plant cells on microscope slides), scaled from the initial variable width using the INTER_AREA anti-aliasing filter from Open-CV. Almost all images were initially square; cropping by taking the largest possible square with center at the middle of the initial image was applied otherwise. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT.PLT_NET \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_NET.html](https://meta-album.github.io/datasets/PLT_NET.html) \n**Domain ID**: PLT \n**Domain Name**: Plants \n**Dataset ID**: PLT_NET \n**Dataset Name**: PlantNet \n**Short Description**: Plants Dataset with different species of plants \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: ecology, plants, plant species \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://zenodo.org/record/4726653\nhttps://creativecommons.org/licenses/by/4.0/legalcode\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) \n\n**Source**: PlantNet \n**Source URL**: https://plantnet.org/en/2021/03/30/a-plntnet-dataset-for-machine-learning-researchers/ \n \n**Original Author**: Garcin, Camille and Joly, Alexis and Bonnet, Pierre and Lombardo, Jean-Christophe and Affouard, Antoine and Chouet, Mathias and Servajean, Maximilien and Salmon, Joseph and Lorieul, Titouan \n**Original contact**: camille.garcin@inria.fr \n\n**Meta Album author**: Felix Herron \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{garcin2021plntnetk,\n title={Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution},\n author={Camille Garcin and alexis joly and Pierre Bonnet and Antoine Affouard and Jean-Christophe Lombardo and Mathias Chouet and Maximilien Servajean and Titouan Lorieul and Joseph Salmon},\n booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},\n year={2021},\n url={https://openreview.net/forum?id=eLYinD0TtIt}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44293) [[Extended]](https://www.openml.org/d/44327)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44249,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_NET_Micro',\n 'source_data': '44249',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361160,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_NET_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2867,"string":"2,867"}}},{"rowIdx":2735,"cells":{"uid":{"kind":"number","value":361146,"string":"361,146"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_MD_MIX_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album OmniPrint-MD-mix Dataset (Micro)**\n***\nOmniPrint-MD-mix dataset consists of 28 240 images (128x128, RGB) from 706 categories. The images are synthesized with OmniPrint, and no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, the strength of random perspective transformation is 0.04, left/right/top/bottom margins are all 20% of the image size, the strength of pre-rasterization elastic transformation is 0.035, random translation is activated both horizontally and vertically, rotation is within -60 and 60 degrees, horizontal shear is within -0.5 and 0.5, brightness is within 0.8333 and 1.2, contrast is within 0.8333 and 1.2, color enhancement is within 0.8333 and 1.2. The other parameters vary between images. We designed 20 settings, each setting is used to synthesize 2 images. All images/textures consists of photos taken by a personal mobile phone. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: OCR.MD_MIX \n**Meta Album URL**: [https://meta-album.github.io/datasets/MD_MIX.html](https://meta-album.github.io/datasets/MD_MIX.html) \n**Domain ID**: OCR \n**Domain Name**: Optical Character Recognition \n**Dataset ID**: MD_MIX \n**Dataset Name**: OmniPrint-MD-mix \n**Short Description**: Character images with a specific set of nuisance parameters \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: ocr \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY 4.0 \n**License URL(original data release)**: https://creativecommons.org/licenses/by/4.0/\n \n**License (Meta-Album data release)**: CC BY 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: OmniPrint \n**Source URL**: https://github.com/SunHaozhe/OmniPrint \n \n**Original Author**: Haozhe Sun \n**Original contact**: sunhaozhe275940200@gmail.com \n\n**Meta Album author**: Haozhe Sun \n**Created Date**: 25 June 2021 \n**Contact Name**: Haozhe Sun \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{sun2021omniprint,\n title={OmniPrint: A Configurable Printed Character Synthesizer},\n author={Haozhe Sun and Wei-Wei Tu and Isabelle M Guyon},\n booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},\n year={2021},\n url={https://openreview.net/forum?id=R07XwJPmgpl}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44287)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - text (string)],\n 2: [2 - CATEGORY (numeric)],\n 3: [3 - font_file (string)],\n 4: [4 - background (string)],\n 5: [5 - background_color (string)],\n 6: [6 - brightness (numeric)],\n 7: [7 - color_enhance (numeric)],\n 8: [8 - contrast (numeric)],\n 9: [9 - font_size (numeric)],\n 10: [10 - font_weight (numeric)],\n 11: [11 - foreground (string)],\n 12: [12 - image_blending_method (string)],\n 13: [13 - image_height_resolution (numeric)],\n 14: [14 - image_mode (string)],\n 15: [15 - image_width_resolution (numeric)],\n 16: [16 - margin_bottom (numeric)],\n 17: [17 - margin_left (numeric)],\n 18: [18 - margin_right (numeric)],\n 19: [19 - margin_top (numeric)],\n 20: [20 - offset_horizontal (numeric)],\n 21: [21 - offset_vertical (numeric)],\n 22: [22 - original_image_height_resolution (numeric)],\n 23: [23 - original_image_width_resolution (numeric)],\n 24: [24 - perspective_params (string)],\n 25: [25 - pre_elastic (numeric)],\n 26: [26 - rotation (numeric)],\n 27: [27 - shear_x (numeric)],\n 28: [28 - stroke_fill (string)],\n 29: [29 - SUPER_CATEGORY (string)],\n 30: [30 - family_name (string)],\n 31: [31 - style_name (string)],\n 32: [32 - postscript_name (string)],\n 33: [33 - variable_font_weight (nominal)],\n 34: [34 - outline (string)],\n 35: [35 - outline_size (numeric)],\n 36: [36 - morph_gradient_kernel_shape (string)],\n 37: [37 - morph_gradient_kernel_size (numeric)],\n 38: [38 - foreground_image_crop_x (numeric)],\n 39: [39 - foreground_image_crop_x_plus_width (numeric)],\n 40: [40 - foreground_image_crop_y (numeric)],\n 41: [41 - foreground_image_crop_y_plus_height (numeric)],\n 42: [42 - foreground_image_name (string)],\n 43: [43 - foreground_image_original_height (numeric)],\n 44: [44 - foreground_image_original_width (numeric)],\n 45: [45 - foreground_image_resized_height (numeric)],\n 46: [46 - foreground_image_resized_width (numeric)],\n 47: [47 - outline_image_crop_x (numeric)],\n 48: [48 - outline_image_crop_x_plus_width (numeric)],\n 49: [49 - outline_image_crop_y (numeric)],\n 50: [50 - outline_image_crop_y_plus_height (numeric)],\n 51: [51 - outline_image_name (string)],\n 52: [52 - outline_image_original_height (numeric)],\n 53: [53 - outline_image_original_width (numeric)],\n 54: [54 - outline_image_resized_height (numeric)],\n 55: [55 - outline_image_resized_width (numeric)],\n 56: [56 - background_polygon_fill_color (string)],\n 57: [57 - background_polygon_outline_color (string)],\n 58: [58 - background_random_color_composition_params (string)],\n 59: [59 - background_image_crop_x (numeric)],\n 60: [60 - background_image_crop_x_plus_width (numeric)],\n 61: [61 - background_image_crop_y (numeric)],\n 62: [62 - background_image_crop_y_plus_height (numeric)],\n 63: [63 - background_image_name (string)],\n 64: [64 - background_image_original_height (numeric)],\n 65: [65 - background_image_original_width (numeric)],\n 66: [66 - background_image_resized_height (numeric)],\n 67: [67 - background_image_resized_width (numeric)],\n 68: [68 - shear_y (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': nan,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': nan,\n 'NumberOfClasses': 0.0,\n 'NumberOfFeatures': 69.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 18840.0,\n 'NumberOfNumericFeatures': 46.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 44243,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_MD_MIX_Micro',\n 'source_data': '44243',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361146,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_MD_MIX_Micro"},"attribute_names":{"kind":"list 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\"FILE_NAME\",\n \"text\",\n \"font_file\",\n \"background\",\n \"background_color\",\n \"brightness\",\n \"color_enhance\",\n \"contrast\",\n \"font_size\",\n \"font_weight\",\n \"foreground\",\n \"image_blending_method\",\n \"image_height_resolution\",\n \"image_mode\",\n \"image_width_resolution\",\n \"margin_bottom\",\n \"margin_left\",\n \"margin_right\",\n \"margin_top\",\n \"offset_horizontal\",\n \"offset_vertical\",\n \"original_image_height_resolution\",\n \"original_image_width_resolution\",\n \"perspective_params\",\n \"pre_elastic\",\n \"rotation\",\n \"shear_x\",\n \"stroke_fill\",\n \"SUPER_CATEGORY\",\n \"family_name\",\n \"style_name\",\n \"postscript_name\",\n \"variable_font_weight\",\n \"outline\",\n \"outline_size\",\n \"morph_gradient_kernel_shape\",\n \"morph_gradient_kernel_size\",\n \"foreground_image_crop_x\",\n \"foreground_image_crop_x_plus_width\",\n \"foreground_image_crop_y\",\n \"foreground_image_crop_y_plus_height\",\n \"foreground_image_name\",\n \"foreground_image_original_height\",\n \"foreground_image_original_width\",\n \"foreground_image_resized_height\",\n \"foreground_image_resized_width\",\n \"outline_image_crop_x\",\n \"outline_image_crop_x_plus_width\",\n \"outline_image_crop_y\",\n \"outline_image_crop_y_plus_height\",\n \"outline_image_name\",\n \"outline_image_original_height\",\n \"outline_image_original_width\",\n \"outline_image_resized_height\",\n \"outline_image_resized_width\",\n \"background_polygon_fill_color\",\n \"background_polygon_outline_color\",\n \"background_random_color_composition_params\",\n \"background_image_crop_x\",\n \"background_image_crop_x_plus_width\",\n \"background_image_crop_y\",\n \"background_image_crop_y_plus_height\",\n \"background_image_name\",\n \"background_image_original_height\",\n \"background_image_original_width\",\n \"background_image_resized_height\",\n \"background_image_resized_width\",\n \"shear_y\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2868,"string":"2,868"}}},{"rowIdx":2736,"cells":{"uid":{"kind":"number","value":361156,"string":"361,156"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_ACT_40_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Stanford 40 Actions Dataset (Micro)**\n***\nThe Stanford 40 Actions dataset(http://vision.stanford.edu/Datasets/40actions.html) contains images of humans performing 40 actions. There are 9 532 images in total with 180-300 images per action class. The dataset is designed for understanding human actions in still images. For Meta-Album, the dataset is preprocessed and images are resized into 128x128 pixels using Open-CV. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.ACT_40 \n**Meta Album URL**: [https://meta-album.github.io/datasets/ACT_40.html](https://meta-album.github.io/datasets/ACT_40.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: ACT_40 \n**Dataset Name**: Stanford 40 Actions \n**Short Description**: Stanford 40 Actions dataset with images of humans performing 40 actions. \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: human actions \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Cite paper to use dataset \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Stanford 40 Actions \n**Source URL**: http://vision.stanford.edu/Datasets/40actions.html \n \n**Original Author**: B. Yao, X. Jiang, A. Khosla, A.L. Lin, L.J. Guibas, and L. Fei-Fei. \n**Original contact**: bangpeng@cs.stanford.edu \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@INPROCEEDINGS{6126386,\n author={Yao, Bangpeng and Jiang, Xiaoye and Khosla, Aditya and Lin, Andy Lai and Guibas, Leonidas and Fei-Fei, Li},\n booktitle={2011 International Conference on Computer Vision}, \n title={Human action recognition by learning bases of action attributes and parts}, \n year={2011},\n pages={1331-1338},\n doi={10.1109/ICCV.2011.6126386}\n}\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44291) [[Extended]](https://www.openml.org/d/44325)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44247,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_ACT_40_Micro',\n 'source_data': '44247',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361156,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_ACT_40_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2869,"string":"2,869"}}},{"rowIdx":2737,"cells":{"uid":{"kind":"number","value":361041,"string":"361,041"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"eye_movements"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"regression on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/\n\nCompetition 1 (preprocessed data)\nA straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.\n\nThe dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.\n\n\n* Features are in columns, feature vectors in rows.\n* Each assignment is a time sequence of 22-dimensional feature vectors.\n* The first column is the line number, second the assignment number and the next 22 columns (3 to 24) are the different features. Columns 25 to 27 contain extra information about the example. The training data set contains the classification label in the 28th column: \"0\" for irrelevant, \"1\" for relevant and \"2\" for the correct answer.\n* Each example (row) represents a single word. You are asked to return the classification of each read sentence.\n* The 22 features provided are commonly used in psychological studies on eye movement. All of them are not necessarily relevant in this context.\n\nThe objective of the Challenge is to predict the classification labels (I, R, C).\n\n\n\nPlease see the technical report for information of eye movements, experimental setup, baseline methods and references:\n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. [PDF]\n\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nFEATURES\n\nThe values in columns marked with an asterisk (*) are same for all occurances of the word.\n\nCOL\tNAME\t\tDESCRIPTION\n1\t#line\t\tLine number\n2\t#assg\t\tAssignment Number\n3\tfixcount\tNumber of fixations to the word\n4*\tfirstPassCnt\tNumber of fixations to the word when it is first encountered\n5*\tP1stFixation\t'1' if fixation occured when the sentence the word was in was encountered the first time\n6*\tP2stFixation\t'1' if fixation occured when the sentence the word was in was encountered the second time\n7*\tprevFixDur\tDuration of previous fixation\n8*\tfirstfixDur\tDuration of the first fixation when the word is first encountered\n9*\tfirstPassFixDur\tSum of durations of fixations when the word is first encountered\n10*\tnextFixDur\tDuration of the next fixation when gaze initially moves from the word\n11\tfirstSaccLen\tLength of the first saccade\n12\tlastSaccLen\tDistance between fixation on the word and the next fixation\n13\tprevFixPos\tDistance between the first fixation preceding the word and the beginning ot the word\n14\tlandingPos\tDistance between the first fixation on the word and the beginning of the word\n15\tleavingPos\tDistance between the last fixation on the word and the beginning of the word\n16\ttotalFixDur\tSum of all durations of fixations to the word\n17\tmeanFixDur\tMean duration of the fixations to the word\n18*\tnRegressFrom\tNumber of regressions leaving from the word\n19*\tregressLen\tSum of durations of regressions initiating from this word\n20*\tnextWordRegress\t'1' if a regression initiated from the following word\n21*\tregressDur\tSum of durations of the fixations on the word during regression\n22\tpupilDiamMax\tMaximum pupil diameter\n23\tpupilDiamLag\tMaximum pupil diameter 0.5 - 1.5 seconds after the beginning of fixation\n24\ttimePrtctg\tFirst fixation duration divided by the total number of fixations\n25\tnWordsInTitle\tNumber of word in the sentence (title) this word is in\n26\ttitleNo\t\tTitle number\n27\twordNo\t\tWord number (ordinal) in this title\n28\tlabel\t\tClassification for training data ('0'=irrelevant, '1'=relevant, '2'=correct)"},"dataset_features":{"kind":"string","value":"{0: [0 - lineNo (numeric)],\n 1: [1 - assgNo (numeric)],\n 2: [2 - prevFixDur (numeric)],\n 3: [3 - firstfixDur (numeric)],\n 4: [4 - firstPassFixDur (nominal)],\n 5: [5 - nextFixDur (nominal)],\n 6: [6 - firstSaccLen (numeric)],\n 7: [7 - lastSaccLen (numeric)],\n 8: [8 - prevFixPos (numeric)],\n 9: [9 - landingPos (numeric)],\n 10: [10 - leavingPos (numeric)],\n 11: [11 - totalFixDur (numeric)],\n 12: [12 - meanFixDur (numeric)],\n 13: [13 - regressLen (numeric)],\n 14: [14 - regressDur (numeric)],\n 15: [15 - pupilDiamMax (numeric)],\n 16: [16 - pupilDiamLag (numeric)],\n 17: [17 - timePrtctg (numeric)],\n 18: [18 - titleNo (numeric)],\n 19: [19 - wordNo (nominal)],\n 20: [20 - label (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 3804.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 3804.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 21.0,\n 'NumberOfInstances': 7608.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 17.0,\n 'NumberOfSymbolicFeatures': 4.0,\n 'cost_matrix': nan,\n 'did': 44078,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'eye_movements',\n 'source_data': '44078',\n 'status': 'active',\n 'target_feature': 'label',\n 'task_type': 'Supervised Classification',\n 'tid': 361041,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"eye_movements"},"attribute_names":{"kind":"list like","value":["lineNo","assgNo","prevFixDur","firstfixDur","firstPassFixDur","nextFixDur","firstSaccLen","lastSaccLen","prevFixPos","landingPos","leavingPos","totalFixDur","meanFixDur","regressLen","regressDur","pupilDiamMax","pupilDiamLag","timePrtctg","titleNo","wordNo"],"string":"[\n \"lineNo\",\n \"assgNo\",\n \"prevFixDur\",\n \"firstfixDur\",\n \"firstPassFixDur\",\n \"nextFixDur\",\n \"firstSaccLen\",\n \"lastSaccLen\",\n \"prevFixPos\",\n \"landingPos\",\n \"leavingPos\",\n \"totalFixDur\",\n \"meanFixDur\",\n \"regressLen\",\n \"regressDur\",\n \"pupilDiamMax\",\n \"pupilDiamLag\",\n \"timePrtctg\",\n \"titleNo\",\n \"wordNo\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,true,true,false,false,false,false,false,false,false,false,false,false,false,false,false,true],"string":"[\n false,\n false,\n false,\n false,\n true,\n true,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2870,"string":"2,870"}}},{"rowIdx":2738,"cells":{"uid":{"kind":"number","value":4688,"string":"4,688"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"AP_Ovary_Kidney"},"dataset_description":{"kind":"string","value":"**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nGEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC curve, etc.) for classification, feature selection or clustering algorithms.\n\nThis repository was inspired by an increasing need in machine learning / bioinformatics communities for a collection of microarray classification problems that could be used by different researches. This way many different classification or feature selection techniques can finally be compared to eachother on the same set of problems.\n\nOrigin of data\n\nEach gene expression sample in GEMLeR repository comes from a large publicly available expO (Expression Project For Oncology) repository by International Genomics Consortium.\n\nThe goal of expO and its consortium supporters is to procure tissue samples under standard conditions and perform gene expression analyses on a clinically annotated set of deidentified tumor samples. The tumor data is updated with clinical outcomes and is released into the public domain without intellectual property restriction. The availability of this information translates into direct benefits for patients, researchers and pharma alike.\n\nSource: expO website\nAlthough there are various other sources of gene expression data available, a decision to use data from expO repository was made because of:\n- consistency of tissue samples processing procedure\n- same microarray platform used for all samples\n- availability of additional information for combined genotype-phenotype studies\n- availability of a large number of samples for different tumor types\n\nIn case of publishing material based on GEMLeR datasets, then, please note the assistance you received by using this repository. This will help others to obtain the same datasets and replicate your experiments. Please cite as follows when referring to this repository:\n\nStiglic, G., & Kokol, P. (2010). Stability of Ranked Gene Lists in Large Microarray Analysis Studies. Journal of biomedicine biotechnology, 2010, 616358.\n\nYou are also welcome to acknowledge the contribution of expO (Expression Project For Oncology) and International Genomics Consortium for providing their gene expression samples to the public."},"dataset_features":{"kind":"string","value":"{0: [0 - ID_REF (numeric)],\n 1: [1 - 1007_s_at (numeric)],\n 2: [2 - 121_at (numeric)],\n 3: [3 - 1405_i_at (numeric)],\n 4: [4 - 1487_at (numeric)],\n 5: [5 - 1552256_a_at (numeric)],\n 6: [6 - 1552257_a_at (numeric)],\n 7: [7 - 1552274_at (numeric)],\n 8: [8 - 1552275_s_at (numeric)],\n 9: [9 - 1552348_at (numeric)],\n 10: [10 - 1552349_a_at (numeric)],\n 11: [11 - 1552362_a_at (numeric)],\n 12: [12 - 1552365_at (numeric)],\n 13: [13 - 1552367_a_at (numeric)],\n 14: [14 - 1552368_at (numeric)],\n 15: [15 - 1552426_a_at (numeric)],\n 16: [16 - 1552455_at (numeric)],\n 17: [17 - 1552519_at (numeric)],\n 18: [18 - 1552566_at (numeric)],\n 19: [19 - 1552610_a_at (numeric)],\n 20: [20 - 1552621_at 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200622_x_at (numeric)],\n 446: [446 - 200623_s_at (numeric)],\n 447: [447 - 200624_s_at (numeric)],\n 448: [448 - 200625_s_at (numeric)],\n 449: [449 - 200626_s_at (numeric)],\n 450: [450 - 200627_at (numeric)],\n 451: [451 - 200628_s_at (numeric)],\n 452: [452 - 200629_at (numeric)],\n 453: [453 - 200630_x_at (numeric)],\n 454: [454 - 200631_s_at (numeric)],\n 455: [455 - 200632_s_at (numeric)],\n 456: [456 - 200633_at (numeric)],\n 457: [457 - 200634_at (numeric)],\n 458: [458 - 200635_s_at (numeric)],\n 459: [459 - 200636_s_at (numeric)],\n 460: [460 - 200637_s_at (numeric)],\n 461: [461 - 200638_s_at (numeric)],\n 462: [462 - 200639_s_at (numeric)],\n 463: [463 - 200640_at (numeric)],\n 464: [464 - 200641_s_at (numeric)],\n 465: [465 - 200642_at (numeric)],\n 466: [466 - 200643_at (numeric)],\n 467: [467 - 200644_at (numeric)],\n 468: [468 - 200645_at (numeric)],\n 469: [469 - 200647_x_at (numeric)],\n 470: [470 - 200648_s_at (numeric)],\n 471: [471 - 200649_at (numeric)],\n 472: [472 - 200650_s_at (numeric)],\n 473: [473 - 200651_at (numeric)],\n 474: [474 - 200652_at (numeric)],\n 475: [475 - 200653_s_at (numeric)],\n 476: [476 - 200654_at (numeric)],\n 477: [477 - 200655_s_at (numeric)],\n 478: [478 - 200656_s_at (numeric)],\n 479: [479 - 200657_at (numeric)],\n 480: [480 - 200658_s_at (numeric)],\n 481: [481 - 200659_s_at (numeric)],\n 482: [482 - 200660_at (numeric)],\n 483: [483 - 200661_at (numeric)],\n 484: [484 - 200662_s_at (numeric)],\n 485: [485 - 200663_at (numeric)],\n 486: [486 - 200664_s_at (numeric)],\n 487: [487 - 200665_s_at (numeric)],\n 488: [488 - 200666_s_at (numeric)],\n 489: [489 - 200667_at (numeric)],\n 490: [490 - 200668_s_at (numeric)],\n 491: [491 - 200669_s_at (numeric)],\n 492: [492 - 200670_at (numeric)],\n 493: [493 - 200673_at (numeric)],\n 494: [494 - 200674_s_at (numeric)],\n 495: [495 - 200675_at (numeric)],\n 496: [496 - 200677_at (numeric)],\n 497: [497 - 200678_x_at (numeric)],\n 498: [498 - 200679_x_at (numeric)],\n 499: [499 - 200680_x_at (numeric)],\n 500: [500 - 200681_at (numeric)],\n 501: [501 - 200682_s_at (numeric)],\n 502: [502 - 200683_s_at (numeric)],\n 503: [503 - 200686_s_at (numeric)],\n 504: [504 - 200687_s_at (numeric)],\n 505: [505 - 200689_x_at (numeric)],\n 506: [506 - 200691_s_at (numeric)],\n 507: [507 - 200692_s_at (numeric)],\n 508: [508 - 200693_at (numeric)],\n 509: [509 - 200694_s_at (numeric)],\n 510: [510 - 200695_at (numeric)],\n 511: [511 - 200696_s_at (numeric)],\n 512: [512 - 200697_at (numeric)],\n 513: [513 - 200698_at (numeric)],\n 514: [514 - 200699_at (numeric)],\n 515: [515 - 200700_s_at (numeric)],\n 516: [516 - 200701_at (numeric)],\n 517: [517 - 200703_at (numeric)],\n 518: [518 - 200704_at (numeric)],\n 519: [519 - 200705_s_at (numeric)],\n 520: [520 - 200706_s_at (numeric)],\n 521: [521 - 200707_at (numeric)],\n 522: [522 - 200708_at (numeric)],\n 523: [523 - 200709_at (numeric)],\n 524: [524 - 200710_at (numeric)],\n 525: [525 - 200711_s_at (numeric)],\n 526: [526 - 200712_s_at (numeric)],\n 527: [527 - 200713_s_at (numeric)],\n 528: [528 - 200714_x_at (numeric)],\n 529: [529 - 200715_x_at (numeric)],\n 530: [530 - 200716_x_at (numeric)],\n 531: [531 - 200717_x_at (numeric)],\n 532: [532 - 200718_s_at (numeric)],\n 533: [533 - 200722_s_at (numeric)],\n 534: [534 - 200723_s_at (numeric)],\n 535: [535 - 200725_x_at (numeric)],\n 536: [536 - 200726_at (numeric)],\n 537: [537 - 200727_s_at (numeric)],\n 538: [538 - 200728_at (numeric)],\n 539: [539 - 200729_s_at (numeric)],\n 540: [540 - 200731_s_at (numeric)],\n 541: [541 - 200732_s_at (numeric)],\n 542: [542 - 200733_s_at (numeric)],\n 543: [543 - 200734_s_at (numeric)],\n 544: [544 - 200735_x_at (numeric)],\n 545: [545 - 200736_s_at (numeric)],\n 546: [546 - 200737_at (numeric)],\n 547: [547 - 200738_s_at (numeric)],\n 548: [548 - 200739_s_at (numeric)],\n 549: [549 - 200740_s_at (numeric)],\n 550: [550 - 200741_s_at (numeric)],\n 551: [551 - 200742_s_at (numeric)],\n 552: [552 - 200743_s_at (numeric)],\n 553: [553 - 200744_s_at (numeric)],\n 554: [554 - 200745_s_at (numeric)],\n 555: [555 - 200746_s_at (numeric)],\n 556: [556 - 200747_s_at (numeric)],\n 557: [557 - 200748_s_at (numeric)],\n 558: [558 - 200749_at (numeric)],\n 559: [559 - 200750_s_at (numeric)],\n 560: [560 - 200751_s_at (numeric)],\n 561: [561 - 200753_x_at (numeric)],\n 562: [562 - 200754_x_at (numeric)],\n 563: [563 - 200755_s_at (numeric)],\n 564: [564 - 200756_x_at (numeric)],\n 565: [565 - 200757_s_at (numeric)],\n 566: [566 - 200758_s_at (numeric)],\n 567: [567 - 200759_x_at (numeric)],\n 568: [568 - 200760_s_at (numeric)],\n 569: [569 - 200761_s_at (numeric)],\n 570: [570 - 200762_at (numeric)],\n 571: [571 - 200763_s_at (numeric)],\n 572: [572 - 200764_s_at (numeric)],\n 573: [573 - 200765_x_at (numeric)],\n 574: [574 - 200766_at (numeric)],\n 575: [575 - 200768_s_at (numeric)],\n 576: [576 - 200770_s_at (numeric)],\n 577: [577 - 200771_at (numeric)],\n 578: [578 - 200772_x_at (numeric)],\n 579: [579 - 200773_x_at (numeric)],\n 580: [580 - 200774_at (numeric)],\n 581: [581 - 200775_s_at (numeric)],\n 582: [582 - 200776_s_at (numeric)],\n 583: [583 - 200777_s_at (numeric)],\n 584: [584 - 200778_s_at (numeric)],\n 585: [585 - 200779_at (numeric)],\n 586: [586 - 200780_x_at (numeric)],\n 587: [587 - 200781_s_at (numeric)],\n 588: [588 - 200782_at (numeric)],\n 589: [589 - 200783_s_at (numeric)],\n 590: [590 - 200786_at (numeric)],\n 591: [591 - 200787_s_at (numeric)],\n 592: [592 - 200788_s_at (numeric)],\n 593: [593 - 200789_at (numeric)],\n 594: [594 - 200790_at (numeric)],\n 595: [595 - 200791_s_at (numeric)],\n 596: [596 - 200792_at (numeric)],\n 597: [597 - 200793_s_at (numeric)],\n 598: [598 - 200794_x_at (numeric)],\n 599: [599 - 200795_at (numeric)],\n 600: [600 - 200797_s_at (numeric)],\n 601: [601 - 200798_x_at (numeric)],\n 602: [602 - 200799_at (numeric)],\n 603: [603 - 200800_s_at (numeric)],\n 604: [604 - 200801_x_at (numeric)],\n 605: [605 - 200802_at (numeric)],\n 606: [606 - 200803_s_at (numeric)],\n 607: [607 - 200804_at (numeric)],\n 608: [608 - 200805_at (numeric)],\n 609: [609 - 200806_s_at (numeric)],\n 610: [610 - 200807_s_at (numeric)],\n 611: [611 - 200808_s_at (numeric)],\n 612: [612 - 200809_x_at (numeric)],\n 613: [613 - 200810_s_at (numeric)],\n 614: [614 - 200811_at (numeric)],\n 615: [615 - 200812_at (numeric)],\n 616: [616 - 200814_at (numeric)],\n 617: [617 - 200816_s_at (numeric)],\n 618: [618 - 200817_x_at (numeric)],\n 619: [619 - 200818_at (numeric)],\n 620: [620 - 200819_s_at (numeric)],\n 621: [621 - 200820_at (numeric)],\n 622: [622 - 200821_at (numeric)],\n 623: [623 - 200822_x_at (numeric)],\n 624: [624 - 200823_x_at (numeric)],\n 625: [625 - 200824_at (numeric)],\n 626: [626 - 200825_s_at (numeric)],\n 627: [627 - 200826_at (numeric)],\n 628: [628 - 200827_at (numeric)],\n 629: [629 - 200828_s_at (numeric)],\n 630: [630 - 200829_x_at (numeric)],\n 631: [631 - 200830_at (numeric)],\n 632: [632 - 200831_s_at (numeric)],\n 633: [633 - 200832_s_at (numeric)],\n 634: [634 - 200833_s_at (numeric)],\n 635: [635 - 200834_s_at (numeric)],\n 636: [636 - 200837_at (numeric)],\n 637: [637 - 200838_at (numeric)],\n 638: [638 - 200839_s_at (numeric)],\n 639: [639 - 200840_at (numeric)],\n 640: [640 - 200842_s_at (numeric)],\n 641: [641 - 200843_s_at (numeric)],\n 642: [642 - 200844_s_at (numeric)],\n 643: [643 - 200845_s_at (numeric)],\n 644: [644 - 200846_s_at (numeric)],\n 645: [645 - 200847_s_at (numeric)],\n 646: [646 - 200848_at (numeric)],\n 647: [647 - 200849_s_at (numeric)],\n 648: [648 - 200850_s_at (numeric)],\n 649: [649 - 200851_s_at (numeric)],\n 650: [650 - 200852_x_at (numeric)],\n 651: [651 - 200853_at (numeric)],\n 652: [652 - 200858_s_at (numeric)],\n 653: [653 - 200859_x_at (numeric)],\n 654: [654 - 200860_s_at (numeric)],\n 655: [655 - 200862_at (numeric)],\n 656: [656 - 200863_s_at (numeric)],\n 657: [657 - 200864_s_at (numeric)],\n 658: [658 - 200866_s_at (numeric)],\n 659: [659 - 200867_at (numeric)],\n 660: [660 - 200868_s_at (numeric)],\n 661: [661 - 200869_at (numeric)],\n 662: [662 - 200870_at (numeric)],\n 663: [663 - 200871_s_at (numeric)],\n 664: [664 - 200872_at (numeric)],\n 665: [665 - 200873_s_at (numeric)],\n 666: [666 - 200875_s_at (numeric)],\n 667: [667 - 200876_s_at (numeric)],\n 668: [668 - 200877_at (numeric)],\n 669: [669 - 200878_at (numeric)],\n 670: [670 - 200880_at (numeric)],\n 671: [671 - 200881_s_at (numeric)],\n 672: [672 - 200882_s_at (numeric)],\n 673: [673 - 200883_at (numeric)],\n 674: [674 - 200884_at (numeric)],\n 675: [675 - 200885_at (numeric)],\n 676: [676 - 200886_s_at (numeric)],\n 677: [677 - 200887_s_at (numeric)],\n 678: [678 - 200888_s_at (numeric)],\n 679: [679 - 200889_s_at (numeric)],\n 680: [680 - 200890_s_at (numeric)],\n 681: [681 - 200891_s_at (numeric)],\n 682: [682 - 200892_s_at (numeric)],\n 683: [683 - 200893_at (numeric)],\n 684: [684 - 200894_s_at (numeric)],\n 685: [685 - 200895_s_at (numeric)],\n 686: [686 - 200896_x_at (numeric)],\n 687: [687 - 200897_s_at (numeric)],\n 688: [688 - 200898_s_at (numeric)],\n 689: [689 - 200899_s_at (numeric)],\n 690: [690 - 200900_s_at (numeric)],\n 691: [691 - 200901_s_at (numeric)],\n 692: [692 - 200902_at (numeric)],\n 693: [693 - 200903_s_at (numeric)],\n 694: [694 - 200904_at (numeric)],\n 695: [695 - 200905_x_at (numeric)],\n 696: [696 - 200906_s_at (numeric)],\n 697: [697 - 200907_s_at (numeric)],\n 698: [698 - 200908_s_at (numeric)],\n 699: [699 - 200909_s_at (numeric)],\n 700: [700 - 200910_at (numeric)],\n 701: [701 - 200911_s_at (numeric)],\n 702: [702 - 200912_s_at (numeric)],\n 703: [703 - 200914_x_at (numeric)],\n 704: [704 - 200915_x_at (numeric)],\n 705: [705 - 200916_at (numeric)],\n 706: [706 - 200918_s_at (numeric)],\n 707: [707 - 200919_at (numeric)],\n 708: [708 - 200920_s_at (numeric)],\n 709: [709 - 200921_s_at (numeric)],\n 710: [710 - 200922_at (numeric)],\n 711: [711 - 200923_at (numeric)],\n 712: [712 - 200924_s_at (numeric)],\n 713: [713 - 200925_at (numeric)],\n 714: [714 - 200926_at (numeric)],\n 715: [715 - 200927_s_at (numeric)],\n 716: [716 - 200929_at (numeric)],\n 717: [717 - 200931_s_at (numeric)],\n 718: [718 - 200932_s_at (numeric)],\n 719: [719 - 200933_x_at (numeric)],\n 720: [720 - 200934_at (numeric)],\n 721: [721 - 200935_at (numeric)],\n 722: [722 - 200936_at (numeric)],\n 723: [723 - 200937_s_at (numeric)],\n 724: [724 - 200940_s_at (numeric)],\n 725: [725 - 200941_at (numeric)],\n 726: [726 - 200942_s_at (numeric)],\n 727: [727 - 200943_at (numeric)],\n 728: [728 - 200944_s_at (numeric)],\n 729: [729 - 200945_s_at (numeric)],\n 730: [730 - 200946_x_at (numeric)],\n 731: [731 - 200947_s_at (numeric)],\n 732: [732 - 200948_at (numeric)],\n 733: [733 - 200949_x_at (numeric)],\n 734: [734 - 200950_at (numeric)],\n 735: [735 - 200953_s_at (numeric)],\n 736: [736 - 200954_at (numeric)],\n 737: [737 - 200955_at (numeric)],\n 738: [738 - 200956_s_at (numeric)],\n 739: [739 - 200957_s_at (numeric)],\n 740: [740 - 200958_s_at (numeric)],\n 741: [741 - 200959_at (numeric)],\n 742: [742 - 200960_x_at (numeric)],\n 743: [743 - 200961_at (numeric)],\n 744: [744 - 200962_at (numeric)],\n 745: [745 - 200963_x_at (numeric)],\n 746: [746 - 200964_at (numeric)],\n 747: [747 - 200965_s_at (numeric)],\n 748: [748 - 200966_x_at (numeric)],\n 749: [749 - 200967_at (numeric)],\n 750: [750 - 200968_s_at (numeric)],\n 751: [751 - 200969_at (numeric)],\n 752: [752 - 200970_s_at (numeric)],\n 753: [753 - 200971_s_at (numeric)],\n 754: [754 - 200972_at (numeric)],\n 755: [755 - 200973_s_at (numeric)],\n 756: [756 - 200974_at (numeric)],\n 757: [757 - 200975_at (numeric)],\n 758: [758 - 200976_s_at (numeric)],\n 759: [759 - 200977_s_at (numeric)],\n 760: [760 - 200978_at (numeric)],\n 761: [761 - 200979_at (numeric)],\n 762: [762 - 200980_s_at (numeric)],\n 763: [763 - 200981_x_at (numeric)],\n 764: [764 - 200982_s_at (numeric)],\n 765: [765 - 200983_x_at (numeric)],\n 766: [766 - 200984_s_at (numeric)],\n 767: [767 - 200985_s_at (numeric)],\n 768: [768 - 200986_at (numeric)],\n 769: [769 - 200989_at (numeric)],\n 770: [770 - 200990_at (numeric)],\n 771: [771 - 200991_s_at (numeric)],\n 772: [772 - 200992_at (numeric)],\n 773: [773 - 200993_at (numeric)],\n 774: [774 - 200994_at (numeric)],\n 775: [775 - 200996_at (numeric)],\n 776: [776 - 200997_at (numeric)],\n 777: [777 - 200998_s_at (numeric)],\n 778: [778 - 200999_s_at (numeric)],\n 779: [779 - 201000_at (numeric)],\n 780: [780 - 201001_s_at (numeric)],\n 781: [781 - 201002_s_at (numeric)],\n 782: [782 - 201003_x_at (numeric)],\n 783: [783 - 201004_at (numeric)],\n 784: [784 - 201005_at (numeric)],\n 785: [785 - 201007_at (numeric)],\n 786: [786 - 201008_s_at (numeric)],\n 787: [787 - 201009_s_at (numeric)],\n 788: [788 - 201010_s_at (numeric)],\n 789: [789 - 201011_at (numeric)],\n 790: [790 - 201012_at (numeric)],\n 791: [791 - 201013_s_at (numeric)],\n 792: [792 - 201014_s_at (numeric)],\n 793: [793 - 201015_s_at (numeric)],\n 794: [794 - 201016_at (numeric)],\n 795: [795 - 201017_at (numeric)],\n 796: [796 - 201018_at (numeric)],\n 797: [797 - 201019_s_at (numeric)],\n 798: [798 - 201020_at (numeric)],\n 799: [799 - 201021_s_at (numeric)],\n 800: [800 - 201022_s_at (numeric)],\n 801: [801 - 201023_at (numeric)],\n 802: [802 - 201024_x_at (numeric)],\n 803: [803 - 201028_s_at (numeric)],\n 804: [804 - 201029_s_at (numeric)],\n 805: [805 - 201030_x_at (numeric)],\n 806: [806 - 201031_s_at (numeric)],\n 807: [807 - 201032_at (numeric)],\n 808: [808 - 201033_x_at (numeric)],\n 809: [809 - 201034_at (numeric)],\n 810: [810 - 201036_s_at (numeric)],\n 811: [811 - 201037_at (numeric)],\n 812: [812 - 201039_s_at (numeric)],\n 813: [813 - 201041_s_at (numeric)],\n 814: [814 - 201042_at (numeric)],\n 815: [815 - 201043_s_at (numeric)],\n 816: [816 - 201044_x_at (numeric)],\n 817: [817 - 201046_s_at (numeric)],\n 818: [818 - 201047_x_at (numeric)],\n 819: [819 - 201049_s_at (numeric)],\n 820: [820 - 201050_at (numeric)],\n 821: [821 - 201051_at (numeric)],\n 822: [822 - 201052_s_at (numeric)],\n 823: [823 - 201053_s_at (numeric)],\n 824: [824 - 201054_at (numeric)],\n 825: [825 - 201057_s_at (numeric)],\n 826: [826 - 201058_s_at (numeric)],\n 827: [827 - 201059_at (numeric)],\n 828: [828 - 201060_x_at (numeric)],\n 829: [829 - 201061_s_at (numeric)],\n 830: [830 - 201063_at (numeric)],\n 831: [831 - 201064_s_at (numeric)],\n 832: [832 - 201065_s_at (numeric)],\n 833: [833 - 201066_at (numeric)],\n 834: [834 - 201067_at (numeric)],\n 835: [835 - 201068_s_at (numeric)],\n 836: [836 - 201069_at (numeric)],\n 837: [837 - 201070_x_at (numeric)],\n 838: [838 - 201071_x_at (numeric)],\n 839: [839 - 201074_at (numeric)],\n 840: [840 - 201075_s_at (numeric)],\n 841: [841 - 201077_s_at (numeric)],\n 842: [842 - 201078_at (numeric)],\n 843: [843 - 201079_at (numeric)],\n 844: [844 - 201084_s_at (numeric)],\n 845: [845 - 201086_x_at (numeric)],\n 846: [846 - 201088_at (numeric)],\n 847: [847 - 201089_at (numeric)],\n 848: [848 - 201090_x_at (numeric)],\n 849: [849 - 201091_s_at (numeric)],\n 850: [850 - 201092_at (numeric)],\n 851: [851 - 201093_x_at (numeric)],\n 852: [852 - 201094_at (numeric)],\n 853: [853 - 201095_at (numeric)],\n 854: [854 - 201096_s_at (numeric)],\n 855: [855 - 201097_s_at (numeric)],\n 856: [856 - 201098_at (numeric)],\n 857: [857 - 201099_at (numeric)],\n 858: [858 - 201100_s_at (numeric)],\n 859: [859 - 201102_s_at (numeric)],\n 860: [860 - 201103_x_at (numeric)],\n 861: [861 - 201104_x_at (numeric)],\n 862: [862 - 201105_at (numeric)],\n 863: [863 - 201106_at (numeric)],\n 864: [864 - 201108_s_at (numeric)],\n 865: [865 - 201109_s_at (numeric)],\n 866: [866 - 201110_s_at (numeric)],\n 867: [867 - 201111_at (numeric)],\n 868: [868 - 201112_s_at (numeric)],\n 869: [869 - 201113_at (numeric)],\n 870: [870 - 201114_x_at (numeric)],\n 871: [871 - 201115_at (numeric)],\n 872: [872 - 201116_s_at (numeric)],\n 873: [873 - 201117_s_at (numeric)],\n 874: [874 - 201118_at (numeric)],\n 875: [875 - 201119_s_at (numeric)],\n 876: [876 - 201120_s_at (numeric)],\n 877: [877 - 201121_s_at (numeric)],\n 878: [878 - 201122_x_at (numeric)],\n 879: [879 - 201123_s_at (numeric)],\n 880: [880 - 201125_s_at (numeric)],\n 881: [881 - 201126_s_at (numeric)],\n 882: [882 - 201127_s_at (numeric)],\n 883: [883 - 201128_s_at (numeric)],\n 884: [884 - 201129_at (numeric)],\n 885: [885 - 201131_s_at (numeric)],\n 886: [886 - 201132_at (numeric)],\n 887: [887 - 201133_s_at (numeric)],\n 888: [888 - 201134_x_at (numeric)],\n 889: [889 - 201135_at (numeric)],\n 890: [890 - 201136_at (numeric)],\n 891: [891 - 201137_s_at (numeric)],\n 892: [892 - 201138_s_at (numeric)],\n 893: [893 - 201139_s_at (numeric)],\n 894: [894 - 201140_s_at (numeric)],\n 895: [895 - 201141_at (numeric)],\n 896: [896 - 201144_s_at (numeric)],\n 897: [897 - 201145_at (numeric)],\n 898: [898 - 201146_at (numeric)],\n 899: [899 - 201147_s_at (numeric)],\n 900: [900 - 201148_s_at (numeric)],\n 901: [901 - 201149_s_at (numeric)],\n 902: [902 - 201150_s_at (numeric)],\n 903: [903 - 201151_s_at (numeric)],\n 904: [904 - 201152_s_at (numeric)],\n 905: [905 - 201153_s_at (numeric)],\n 906: [906 - 201154_x_at (numeric)],\n 907: [907 - 201155_s_at (numeric)],\n 908: [908 - 201156_s_at (numeric)],\n 909: [909 - 201157_s_at (numeric)],\n 910: [910 - 201160_s_at (numeric)],\n 911: [911 - 201161_s_at (numeric)],\n 912: [912 - 201162_at (numeric)],\n 913: [913 - 201163_s_at (numeric)],\n 914: [914 - 201164_s_at (numeric)],\n 915: [915 - 201165_s_at (numeric)],\n 916: [916 - 201166_s_at (numeric)],\n 917: [917 - 201168_x_at (numeric)],\n 918: [918 - 201170_s_at (numeric)],\n 919: [919 - 201172_x_at (numeric)],\n 920: [920 - 201173_x_at (numeric)],\n 921: [921 - 201174_s_at (numeric)],\n 922: [922 - 201175_at (numeric)],\n 923: [923 - 201176_s_at (numeric)],\n 924: [924 - 201177_s_at (numeric)],\n 925: [925 - 201178_at 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[1413 - 201823_s_at (numeric)],\n 1414: [1414 - 201825_s_at (numeric)],\n 1415: [1415 - 201828_x_at (numeric)],\n 1416: [1416 - 201829_at (numeric)],\n 1417: [1417 - 201830_s_at (numeric)],\n 1418: [1418 - 201831_s_at (numeric)],\n 1419: [1419 - 201832_s_at (numeric)],\n 1420: [1420 - 201833_at (numeric)],\n 1421: [1421 - 201834_at (numeric)],\n 1422: [1422 - 201835_s_at (numeric)],\n 1423: [1423 - 201837_s_at (numeric)],\n 1424: [1424 - 201839_s_at (numeric)],\n 1425: [1425 - 201840_at (numeric)],\n 1426: [1426 - 201841_s_at (numeric)],\n 1427: [1427 - 201842_s_at (numeric)],\n 1428: [1428 - 201843_s_at (numeric)],\n 1429: [1429 - 201844_s_at (numeric)],\n 1430: [1430 - 201845_s_at (numeric)],\n 1431: [1431 - 201846_s_at (numeric)],\n 1432: [1432 - 201847_at (numeric)],\n 1433: [1433 - 201848_s_at (numeric)],\n 1434: [1434 - 201849_at (numeric)],\n 1435: [1435 - 201850_at (numeric)],\n 1436: [1436 - 201852_x_at (numeric)],\n 1437: [1437 - 201853_s_at (numeric)],\n 1438: [1438 - 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201999_s_at (numeric)],\n 1541: [1541 - 202000_at (numeric)],\n 1542: [1542 - 202001_s_at (numeric)],\n 1543: [1543 - 202003_s_at (numeric)],\n 1544: [1544 - 202004_x_at (numeric)],\n 1545: [1545 - 202005_at (numeric)],\n 1546: [1546 - 202006_at (numeric)],\n 1547: [1547 - 202007_at (numeric)],\n 1548: [1548 - 202011_at (numeric)],\n 1549: [1549 - 202016_at (numeric)],\n 1550: [1550 - 202017_at (numeric)],\n 1551: [1551 - 202018_s_at (numeric)],\n 1552: [1552 - 202020_s_at (numeric)],\n 1553: [1553 - 202021_x_at (numeric)],\n 1554: [1554 - 202022_at (numeric)],\n 1555: [1555 - 202023_at (numeric)],\n 1556: [1556 - 202024_at (numeric)],\n 1557: [1557 - 202026_at (numeric)],\n 1558: [1558 - 202028_s_at (numeric)],\n 1559: [1559 - 202029_x_at (numeric)],\n 1560: [1560 - 202031_s_at (numeric)],\n 1561: [1561 - 202033_s_at (numeric)],\n 1562: [1562 - 202034_x_at (numeric)],\n 1563: [1563 - 202036_s_at (numeric)],\n 1564: [1564 - 202037_s_at (numeric)],\n 1565: [1565 - 202038_at 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[1693 - 202261_at (numeric)],\n 1694: [1694 - 202262_x_at (numeric)],\n 1695: [1695 - 202263_at (numeric)],\n 1696: [1696 - 202265_at (numeric)],\n 1697: [1697 - 202266_at (numeric)],\n 1698: [1698 - 202267_at (numeric)],\n 1699: [1699 - 202268_s_at (numeric)],\n 1700: [1700 - 202269_x_at (numeric)],\n 1701: [1701 - 202270_at (numeric)],\n 1702: [1702 - 202271_at (numeric)],\n 1703: [1703 - 202273_at (numeric)],\n 1704: [1704 - 202274_at (numeric)],\n 1705: [1705 - 202275_at (numeric)],\n 1706: [1706 - 202276_at (numeric)],\n 1707: [1707 - 202277_at (numeric)],\n 1708: [1708 - 202279_at (numeric)],\n 1709: [1709 - 202282_at (numeric)],\n 1710: [1710 - 202283_at (numeric)],\n 1711: [1711 - 202284_s_at (numeric)],\n 1712: [1712 - 202286_s_at (numeric)],\n 1713: [1713 - 202289_s_at (numeric)],\n 1714: [1714 - 202290_at (numeric)],\n 1715: [1715 - 202291_s_at (numeric)],\n 1716: [1716 - 202295_s_at (numeric)],\n 1717: [1717 - 202296_s_at (numeric)],\n 1718: [1718 - 202297_s_at (numeric)],\n 1719: [1719 - 202298_at (numeric)],\n 1720: [1720 - 202299_s_at (numeric)],\n 1721: [1721 - 202300_at (numeric)],\n 1722: [1722 - 202302_s_at (numeric)],\n 1723: [1723 - 202304_at (numeric)],\n 1724: [1724 - 202305_s_at (numeric)],\n 1725: [1725 - 202306_at (numeric)],\n 1726: [1726 - 202307_s_at (numeric)],\n 1727: [1727 - 202309_at (numeric)],\n 1728: [1728 - 202310_s_at (numeric)],\n 1729: [1729 - 202311_s_at (numeric)],\n 1730: [1730 - 202313_at (numeric)],\n 1731: [1731 - 202314_at (numeric)],\n 1732: [1732 - 202318_s_at (numeric)],\n 1733: [1733 - 202322_s_at (numeric)],\n 1734: [1734 - 202324_s_at (numeric)],\n 1735: [1735 - 202325_s_at (numeric)],\n 1736: [1736 - 202326_at (numeric)],\n 1737: [1737 - 202330_s_at (numeric)],\n 1738: [1738 - 202333_s_at (numeric)],\n 1739: [1739 - 202334_s_at (numeric)],\n 1740: [1740 - 202336_s_at (numeric)],\n 1741: [1741 - 202340_x_at (numeric)],\n 1742: [1742 - 202341_s_at (numeric)],\n 1743: [1743 - 202342_s_at (numeric)],\n 1744: [1744 - 202343_x_at (numeric)],\n 1745: [1745 - 202344_at (numeric)],\n 1746: [1746 - 202345_s_at (numeric)],\n 1747: [1747 - 202346_at (numeric)],\n 1748: [1748 - 202347_s_at (numeric)],\n 1749: [1749 - 202350_s_at (numeric)],\n 1750: [1750 - 202351_at (numeric)],\n 1751: [1751 - 202352_s_at (numeric)],\n 1752: [1752 - 202357_s_at (numeric)],\n 1753: [1753 - 202361_at (numeric)],\n 1754: [1754 - 202362_at (numeric)],\n 1755: [1755 - 202363_at (numeric)],\n 1756: [1756 - 202364_at (numeric)],\n 1757: [1757 - 202365_at (numeric)],\n 1758: [1758 - 202369_s_at (numeric)],\n 1759: [1759 - 202370_s_at (numeric)],\n 1760: [1760 - 202371_at (numeric)],\n 1761: [1761 - 202372_at (numeric)],\n 1762: [1762 - 202373_s_at (numeric)],\n 1763: [1763 - 202376_at (numeric)],\n 1764: [1764 - 202377_at (numeric)],\n 1765: [1765 - 202378_s_at (numeric)],\n 1766: [1766 - 202379_s_at (numeric)],\n 1767: [1767 - 202381_at (numeric)],\n 1768: [1768 - 202382_s_at (numeric)],\n 1769: [1769 - 202387_at (numeric)],\n 1770: [1770 - 202388_at (numeric)],\n 1771: [1771 - 202391_at (numeric)],\n 1772: [1772 - 202393_s_at (numeric)],\n 1773: [1773 - 202395_at (numeric)],\n 1774: [1774 - 202396_at (numeric)],\n 1775: [1775 - 202397_at (numeric)],\n 1776: [1776 - 202399_s_at (numeric)],\n 1777: [1777 - 202403_s_at (numeric)],\n 1778: [1778 - 202404_s_at (numeric)],\n 1779: [1779 - 202406_s_at (numeric)],\n 1780: [1780 - 202408_s_at (numeric)],\n 1781: [1781 - 202409_at (numeric)],\n 1782: [1782 - 202410_x_at (numeric)],\n 1783: [1783 - 202411_at (numeric)],\n 1784: [1784 - 202412_s_at (numeric)],\n 1785: [1785 - 202413_s_at (numeric)],\n 1786: [1786 - 202416_at (numeric)],\n 1787: [1787 - 202417_at (numeric)],\n 1788: [1788 - 202418_at (numeric)],\n 1789: [1789 - 202420_s_at (numeric)],\n 1790: [1790 - 202421_at (numeric)],\n 1791: [1791 - 202423_at (numeric)],\n 1792: [1792 - 202424_at (numeric)],\n 1793: [1793 - 202427_s_at (numeric)],\n 1794: [1794 - 202428_x_at (numeric)],\n 1795: [1795 - 202429_s_at (numeric)],\n 1796: [1796 - 202430_s_at (numeric)],\n 1797: [1797 - 202431_s_at (numeric)],\n 1798: [1798 - 202432_at (numeric)],\n 1799: [1799 - 202435_s_at (numeric)],\n 1800: [1800 - 202436_s_at (numeric)],\n 1801: [1801 - 202437_s_at (numeric)],\n 1802: [1802 - 202440_s_at (numeric)],\n 1803: [1803 - 202441_at (numeric)],\n 1804: [1804 - 202442_at (numeric)],\n 1805: [1805 - 202443_x_at (numeric)],\n 1806: [1806 - 202446_s_at (numeric)],\n 1807: [1807 - 202447_at (numeric)],\n 1808: [1808 - 202449_s_at (numeric)],\n 1809: [1809 - 202450_s_at (numeric)],\n 1810: [1810 - 202451_at (numeric)],\n 1811: [1811 - 202454_s_at (numeric)],\n 1812: [1812 - 202457_s_at (numeric)],\n 1813: [1813 - 202458_at (numeric)],\n 1814: [1814 - 202459_s_at (numeric)],\n 1815: [1815 - 202460_s_at (numeric)],\n 1816: [1816 - 202462_s_at (numeric)],\n 1817: [1817 - 202464_s_at (numeric)],\n 1818: [1818 - 202465_at (numeric)],\n 1819: [1819 - 202466_at (numeric)],\n 1820: [1820 - 202467_s_at (numeric)],\n 1821: [1821 - 202468_s_at (numeric)],\n 1822: [1822 - 202469_s_at (numeric)],\n 1823: [1823 - 202471_s_at (numeric)],\n 1824: [1824 - 202475_at (numeric)],\n 1825: [1825 - 202478_at (numeric)],\n 1826: [1826 - 202481_at (numeric)],\n 1827: [1827 - 202483_s_at (numeric)],\n 1828: [1828 - 202484_s_at (numeric)],\n 1829: [1829 - 202487_s_at (numeric)],\n 1830: [1830 - 202488_s_at (numeric)],\n 1831: [1831 - 202489_s_at (numeric)],\n 1832: [1832 - 202491_s_at (numeric)],\n 1833: [1833 - 202495_at (numeric)],\n 1834: [1834 - 202497_x_at (numeric)],\n 1835: [1835 - 202499_s_at (numeric)],\n 1836: [1836 - 202501_at (numeric)],\n 1837: [1837 - 202502_at (numeric)],\n 1838: [1838 - 202503_s_at (numeric)],\n 1839: [1839 - 202504_at (numeric)],\n 1840: [1840 - 202505_at (numeric)],\n 1841: [1841 - 202506_at (numeric)],\n 1842: [1842 - 202510_s_at (numeric)],\n 1843: [1843 - 202511_s_at (numeric)],\n 1844: [1844 - 202514_at (numeric)],\n 1845: [1845 - 202515_at (numeric)],\n 1846: [1846 - 202517_at (numeric)],\n 1847: [1847 - 202519_at (numeric)],\n 1848: [1848 - 202520_s_at (numeric)],\n 1849: [1849 - 202522_at (numeric)],\n 1850: [1850 - 202524_s_at (numeric)],\n 1851: [1851 - 202525_at (numeric)],\n 1852: [1852 - 202527_s_at (numeric)],\n 1853: [1853 - 202528_at (numeric)],\n 1854: [1854 - 202529_at (numeric)],\n 1855: [1855 - 202531_at (numeric)],\n 1856: [1856 - 202536_at (numeric)],\n 1857: [1857 - 202537_s_at (numeric)],\n 1858: [1858 - 202538_s_at (numeric)],\n 1859: [1859 - 202539_s_at (numeric)],\n 1860: [1860 - 202540_s_at (numeric)],\n 1861: [1861 - 202542_s_at (numeric)],\n 1862: [1862 - 202543_s_at (numeric)],\n 1863: [1863 - 202544_at (numeric)],\n 1864: [1864 - 202545_at (numeric)],\n 1865: [1865 - 202546_at (numeric)],\n 1866: [1866 - 202548_s_at (numeric)],\n 1867: [1867 - 202550_s_at (numeric)],\n 1868: [1868 - 202551_s_at (numeric)],\n 1869: [1869 - 202552_s_at (numeric)],\n 1870: [1870 - 202553_s_at (numeric)],\n 1871: [1871 - 202554_s_at (numeric)],\n 1872: [1872 - 202555_s_at (numeric)],\n 1873: [1873 - 202557_at (numeric)],\n 1874: [1874 - 202559_x_at (numeric)],\n 1875: [1875 - 202560_s_at (numeric)],\n 1876: [1876 - 202562_s_at (numeric)],\n 1877: [1877 - 202564_x_at (numeric)],\n 1878: [1878 - 202565_s_at (numeric)],\n 1879: [1879 - 202567_at (numeric)],\n 1880: [1880 - 202575_at (numeric)],\n 1881: [1881 - 202579_x_at (numeric)],\n 1882: [1882 - 202581_at (numeric)],\n 1883: [1883 - 202582_s_at (numeric)],\n 1884: [1884 - 202587_s_at (numeric)],\n 1885: [1885 - 202589_at (numeric)],\n 1886: [1886 - 202591_s_at (numeric)],\n 1887: [1887 - 202592_at (numeric)],\n 1888: [1888 - 202593_s_at (numeric)],\n 1889: [1889 - 202594_at (numeric)],\n 1890: [1890 - 202595_s_at (numeric)],\n 1891: [1891 - 202596_at (numeric)],\n 1892: [1892 - 202597_at (numeric)],\n 1893: [1893 - 202598_at (numeric)],\n 1894: [1894 - 202599_s_at (numeric)],\n 1895: [1895 - 202600_s_at (numeric)],\n 1896: [1896 - 202602_s_at (numeric)],\n 1897: [1897 - 202603_at (numeric)],\n 1898: [1898 - 202605_at (numeric)],\n 1899: [1899 - 202606_s_at (numeric)],\n 1900: [1900 - 202609_at (numeric)],\n 1901: [1901 - 202613_at (numeric)],\n 1902: [1902 - 202614_at (numeric)],\n 1903: [1903 - 202615_at (numeric)],\n 1904: [1904 - 202619_s_at (numeric)],\n 1905: [1905 - 202620_s_at (numeric)],\n 1906: [1906 - 202623_at (numeric)],\n 1907: [1907 - 202625_at (numeric)],\n 1908: [1908 - 202626_s_at (numeric)],\n 1909: [1909 - 202627_s_at (numeric)],\n 1910: [1910 - 202628_s_at (numeric)],\n 1911: [1911 - 202633_at (numeric)],\n 1912: [1912 - 202634_at (numeric)],\n 1913: [1913 - 202635_s_at (numeric)],\n 1914: [1914 - 202636_at (numeric)],\n 1915: [1915 - 202637_s_at (numeric)],\n 1916: [1916 - 202641_at (numeric)],\n 1917: [1917 - 202642_s_at (numeric)],\n 1918: [1918 - 202643_s_at (numeric)],\n 1919: [1919 - 202644_s_at (numeric)],\n 1920: [1920 - 202646_s_at (numeric)],\n 1921: [1921 - 202649_x_at (numeric)],\n 1922: [1922 - 202651_at (numeric)],\n 1923: [1923 - 202653_s_at (numeric)],\n 1924: [1924 - 202655_at (numeric)],\n 1925: [1925 - 202656_s_at (numeric)],\n 1926: [1926 - 202657_s_at (numeric)],\n 1927: [1927 - 202658_at (numeric)],\n 1928: [1928 - 202659_at (numeric)],\n 1929: [1929 - 202660_at (numeric)],\n 1930: [1930 - 202662_s_at (numeric)],\n 1931: [1931 - 202664_at (numeric)],\n 1932: [1932 - 202666_s_at (numeric)],\n 1933: [1933 - 202668_at (numeric)],\n 1934: [1934 - 202669_s_at (numeric)],\n 1935: [1935 - 202670_at (numeric)],\n 1936: [1936 - 202671_s_at (numeric)],\n 1937: [1937 - 202672_s_at (numeric)],\n 1938: [1938 - 202673_at (numeric)],\n 1939: [1939 - 202675_at (numeric)],\n 1940: [1940 - 202677_at (numeric)],\n 1941: [1941 - 202678_at (numeric)],\n 1942: [1942 - 202679_at (numeric)],\n 1943: [1943 - 202682_s_at (numeric)],\n 1944: [1944 - 202686_s_at (numeric)],\n 1945: [1945 - 202687_s_at (numeric)],\n 1946: [1946 - 202688_at (numeric)],\n 1947: [1947 - 202690_s_at (numeric)],\n 1948: [1948 - 202693_s_at (numeric)],\n 1949: [1949 - 202697_at (numeric)],\n 1950: [1950 - 202698_x_at (numeric)],\n 1951: [1951 - 202704_at (numeric)],\n 1952: [1952 - 202705_at (numeric)],\n 1953: [1953 - 202708_s_at (numeric)],\n 1954: [1954 - 202709_at (numeric)],\n 1955: [1955 - 202710_at (numeric)],\n 1956: [1956 - 202712_s_at (numeric)],\n 1957: [1957 - 202715_at (numeric)],\n 1958: [1958 - 202717_s_at (numeric)],\n 1959: [1959 - 202718_at (numeric)],\n 1960: [1960 - 202719_s_at (numeric)],\n 1961: [1961 - 202720_at (numeric)],\n 1962: [1962 - 202721_s_at (numeric)],\n 1963: [1963 - 202722_s_at (numeric)],\n 1964: [1964 - 202724_s_at (numeric)],\n 1965: [1965 - 202727_s_at (numeric)],\n 1966: [1966 - 202729_s_at (numeric)],\n 1967: [1967 - 202731_at (numeric)],\n 1968: [1968 - 202732_at (numeric)],\n 1969: [1969 - 202733_at (numeric)],\n 1970: [1970 - 202736_s_at (numeric)],\n 1971: [1971 - 202737_s_at (numeric)],\n 1972: [1972 - 202740_at (numeric)],\n 1973: [1973 - 202741_at (numeric)],\n 1974: [1974 - 202742_s_at (numeric)],\n 1975: [1975 - 202743_at (numeric)],\n 1976: [1976 - 202746_at (numeric)],\n 1977: [1977 - 202747_s_at (numeric)],\n 1978: [1978 - 202748_at (numeric)],\n 1979: [1979 - 202749_at (numeric)],\n 1980: [1980 - 202753_at (numeric)],\n 1981: [1981 - 202754_at (numeric)],\n 1982: [1982 - 202758_s_at (numeric)],\n 1983: [1983 - 202759_s_at (numeric)],\n 1984: [1984 - 202760_s_at (numeric)],\n 1985: [1985 - 202761_s_at (numeric)],\n 1986: [1986 - 202765_s_at (numeric)],\n 1987: [1987 - 202766_s_at (numeric)],\n 1988: [1988 - 202768_at (numeric)],\n 1989: [1989 - 202769_at (numeric)],\n 1990: [1990 - 202770_s_at (numeric)],\n 1991: [1991 - 202771_at (numeric)],\n 1992: [1992 - 202772_at (numeric)],\n 1993: [1993 - 202776_at (numeric)],\n 1994: [1994 - 202777_at (numeric)],\n 1995: [1995 - 202778_s_at (numeric)],\n 1996: [1996 - 202779_s_at (numeric)],\n 1997: [1997 - 202780_at (numeric)],\n 1998: [1998 - 202783_at (numeric)],\n 1999: [1999 - 202784_s_at (numeric)],\n 2000: [2000 - 202785_at (numeric)],\n 2001: [2001 - 202786_at (numeric)],\n 2002: [2002 - 202788_at (numeric)],\n 2003: [2003 - 202790_at (numeric)],\n 2004: [2004 - 202794_at (numeric)],\n 2005: [2005 - 202795_x_at (numeric)],\n 2006: [2006 - 202796_at (numeric)],\n 2007: [2007 - 202797_at (numeric)],\n 2008: [2008 - 202798_at (numeric)],\n 2009: [2009 - 202800_at (numeric)],\n 2010: [2010 - 202803_s_at (numeric)],\n 2011: [2011 - 202804_at (numeric)],\n 2012: [2012 - 202806_at (numeric)],\n 2013: [2013 - 202808_at (numeric)],\n 2014: [2014 - 202809_s_at (numeric)],\n 2015: [2015 - 202810_at (numeric)],\n 2016: [2016 - 202813_at (numeric)],\n 2017: [2017 - 202814_s_at (numeric)],\n 2018: [2018 - 202816_s_at (numeric)],\n 2019: [2019 - 202820_at (numeric)],\n 2020: [2020 - 202822_at (numeric)],\n 2021: [2021 - 202824_s_at (numeric)],\n 2022: [2022 - 202825_at (numeric)],\n 2023: [2023 - 202826_at (numeric)],\n 2024: [2024 - 202829_s_at (numeric)],\n 2025: [2025 - 202830_s_at (numeric)],\n 2026: [2026 - 202831_at (numeric)],\n 2027: [2027 - 202832_at (numeric)],\n 2028: [2028 - 202833_s_at (numeric)],\n 2029: [2029 - 202834_at (numeric)],\n 2030: [2030 - 202836_s_at (numeric)],\n 2031: [2031 - 202838_at (numeric)],\n 2032: [2032 - 202839_s_at (numeric)],\n 2033: [2033 - 202842_s_at (numeric)],\n 2034: [2034 - 202844_s_at (numeric)],\n 2035: [2035 - 202845_s_at (numeric)],\n 2036: [2036 - 202846_s_at (numeric)],\n 2037: [2037 - 202850_at (numeric)],\n 2038: [2038 - 202853_s_at (numeric)],\n 2039: [2039 - 202854_at (numeric)],\n 2040: [2040 - 202855_s_at (numeric)],\n 2041: [2041 - 202856_s_at (numeric)],\n 2042: [2042 - 202857_at (numeric)],\n 2043: [2043 - 202858_at (numeric)],\n 2044: [2044 - 202859_x_at (numeric)],\n 2045: [2045 - 202861_at (numeric)],\n 2046: [2046 - 202862_at (numeric)],\n 2047: [2047 - 202864_s_at (numeric)],\n 2048: [2048 - 202868_s_at (numeric)],\n 2049: [2049 - 202869_at (numeric)],\n 2050: [2050 - 202870_s_at (numeric)],\n 2051: [2051 - 202872_at (numeric)],\n 2052: [2052 - 202874_s_at (numeric)],\n 2053: [2053 - 202876_s_at (numeric)],\n 2054: [2054 - 202877_s_at (numeric)],\n 2055: [2055 - 202878_s_at (numeric)],\n 2056: [2056 - 202882_x_at (numeric)],\n 2057: [2057 - 202887_s_at (numeric)],\n 2058: [2058 - 202888_s_at (numeric)],\n 2059: [2059 - 202890_at (numeric)],\n 2060: [2060 - 202893_at (numeric)],\n 2061: [2061 - 202897_at (numeric)],\n 2062: [2062 - 202899_s_at (numeric)],\n 2063: [2063 - 202900_s_at (numeric)],\n 2064: [2064 - 202901_x_at (numeric)],\n 2065: [2065 - 202902_s_at (numeric)],\n 2066: [2066 - 202905_x_at (numeric)],\n 2067: [2067 - 202906_s_at (numeric)],\n 2068: [2068 - 202907_s_at (numeric)],\n 2069: [2069 - 202908_at (numeric)],\n 2070: [2070 - 202909_at (numeric)],\n 2071: [2071 - 202910_s_at (numeric)],\n 2072: [2072 - 202911_at (numeric)],\n 2073: [2073 - 202912_at (numeric)],\n 2074: [2074 - 202917_s_at (numeric)],\n 2075: [2075 - 202918_s_at (numeric)],\n 2076: [2076 - 202920_at (numeric)],\n 2077: [2077 - 202922_at (numeric)],\n 2078: [2078 - 202923_s_at (numeric)],\n 2079: [2079 - 202925_s_at (numeric)],\n 2080: [2080 - 202926_at (numeric)],\n 2081: [2081 - 202929_s_at (numeric)],\n 2082: [2082 - 202930_s_at (numeric)],\n 2083: [2083 - 202931_x_at (numeric)],\n 2084: [2084 - 202932_at (numeric)],\n 2085: [2085 - 202933_s_at (numeric)],\n 2086: [2086 - 202934_at (numeric)],\n 2087: [2087 - 202935_s_at (numeric)],\n 2088: [2088 - 202936_s_at (numeric)],\n 2089: [2089 - 202939_at (numeric)],\n 2090: [2090 - 202941_at (numeric)],\n 2091: [2091 - 202942_at (numeric)],\n 2092: [2092 - 202946_s_at (numeric)],\n 2093: [2093 - 202947_s_at (numeric)],\n 2094: [2094 - 202948_at (numeric)],\n 2095: [2095 - 202949_s_at (numeric)],\n 2096: [2096 - 202950_at (numeric)],\n 2097: [2097 - 202951_at (numeric)],\n 2098: [2098 - 202953_at (numeric)],\n 2099: [2099 - 202954_at (numeric)],\n 2100: [2100 - 202956_at (numeric)],\n 2101: [2101 - 202957_at (numeric)],\n 2102: [2102 - 202960_s_at (numeric)],\n 2103: [2103 - 202961_s_at (numeric)],\n 2104: [2104 - 202962_at (numeric)],\n 2105: [2105 - 202963_at (numeric)],\n 2106: [2106 - 202965_s_at (numeric)],\n 2107: [2107 - 202967_at (numeric)],\n 2108: [2108 - 202969_at (numeric)],\n 2109: [2109 - 202971_s_at (numeric)],\n 2110: [2110 - 202972_s_at (numeric)],\n 2111: [2111 - 202973_x_at (numeric)],\n 2112: [2112 - 202974_at (numeric)],\n 2113: [2113 - 202975_s_at (numeric)],\n 2114: [2114 - 202976_s_at (numeric)],\n 2115: [2115 - 202981_x_at (numeric)],\n 2116: [2116 - 202982_s_at (numeric)],\n 2117: [2117 - 202983_at (numeric)],\n 2118: [2118 - 202985_s_at (numeric)],\n 2119: [2119 - 202986_at (numeric)],\n 2120: [2120 - 202988_s_at (numeric)],\n 2121: [2121 - 202990_at (numeric)],\n 2122: [2122 - 202991_at (numeric)],\n 2123: [2123 - 202992_at (numeric)],\n 2124: [2124 - 202994_s_at (numeric)],\n 2125: [2125 - 202995_s_at (numeric)],\n 2126: [2126 - 202996_at (numeric)],\n 2127: [2127 - 202998_s_at (numeric)],\n 2128: [2128 - 203000_at (numeric)],\n 2129: [2129 - 203002_at (numeric)],\n 2130: [2130 - 203006_at (numeric)],\n 2131: [2131 - 203007_x_at (numeric)],\n 2132: [2132 - 203008_x_at (numeric)],\n 2133: [2133 - 203009_at (numeric)],\n 2134: [2134 - 203011_at (numeric)],\n 2135: [2135 - 203012_x_at (numeric)],\n 2136: [2136 - 203020_at (numeric)],\n 2137: [2137 - 203021_at (numeric)],\n 2138: [2138 - 203022_at (numeric)],\n 2139: [2139 - 203024_s_at (numeric)],\n 2140: [2140 - 203028_s_at (numeric)],\n 2141: [2141 - 203029_s_at (numeric)],\n 2142: [2142 - 203033_x_at (numeric)],\n 2143: [2143 - 203034_s_at (numeric)],\n 2144: [2144 - 203037_s_at (numeric)],\n 2145: [2145 - 203038_at (numeric)],\n 2146: [2146 - 203039_s_at (numeric)],\n 2147: [2147 - 203041_s_at (numeric)],\n 2148: [2148 - 203042_at (numeric)],\n 2149: [2149 - 203043_at (numeric)],\n 2150: [2150 - 203044_at (numeric)],\n 2151: [2151 - 203045_at (numeric)],\n 2152: [2152 - 203046_s_at 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2204: [2204 - 203178_at (numeric)],\n 2205: [2205 - 203180_at (numeric)],\n 2206: [2206 - 203182_s_at (numeric)],\n 2207: [2207 - 203184_at (numeric)],\n 2208: [2208 - 203185_at (numeric)],\n 2209: [2209 - 203186_s_at (numeric)],\n 2210: [2210 - 203188_at (numeric)],\n 2211: [2211 - 203189_s_at (numeric)],\n 2212: [2212 - 203190_at (numeric)],\n 2213: [2213 - 203196_at (numeric)],\n 2214: [2214 - 203200_s_at (numeric)],\n 2215: [2215 - 203207_s_at (numeric)],\n 2216: [2216 - 203208_s_at (numeric)],\n 2217: [2217 - 203211_s_at (numeric)],\n 2218: [2218 - 203213_at (numeric)],\n 2219: [2219 - 203214_x_at (numeric)],\n 2220: [2220 - 203215_s_at (numeric)],\n 2221: [2221 - 203216_s_at (numeric)],\n 2222: [2222 - 203217_s_at (numeric)],\n 2223: [2223 - 203221_at (numeric)],\n 2224: [2224 - 203224_at (numeric)],\n 2225: [2225 - 203225_s_at (numeric)],\n 2226: [2226 - 203226_s_at (numeric)],\n 2227: [2227 - 203227_s_at (numeric)],\n 2228: [2228 - 203228_at (numeric)],\n 2229: [2229 - 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2255: [2255 - 203302_at (numeric)],\n 2256: [2256 - 203303_at (numeric)],\n 2257: [2257 - 203304_at (numeric)],\n 2258: [2258 - 203305_at (numeric)],\n 2259: [2259 - 203306_s_at (numeric)],\n 2260: [2260 - 203310_at (numeric)],\n 2261: [2261 - 203312_x_at (numeric)],\n 2262: [2262 - 203313_s_at (numeric)],\n 2263: [2263 - 203315_at (numeric)],\n 2264: [2264 - 203316_s_at (numeric)],\n 2265: [2265 - 203318_s_at (numeric)],\n 2266: [2266 - 203319_s_at (numeric)],\n 2267: [2267 - 203320_at (numeric)],\n 2268: [2268 - 203321_s_at (numeric)],\n 2269: [2269 - 203323_at (numeric)],\n 2270: [2270 - 203324_s_at (numeric)],\n 2271: [2271 - 203325_s_at (numeric)],\n 2272: [2272 - 203329_at (numeric)],\n 2273: [2273 - 203333_at (numeric)],\n 2274: [2274 - 203335_at (numeric)],\n 2275: [2275 - 203336_s_at (numeric)],\n 2276: [2276 - 203338_at (numeric)],\n 2277: [2277 - 203341_at (numeric)],\n 2278: [2278 - 203343_at (numeric)],\n 2279: [2279 - 203344_s_at (numeric)],\n 2280: [2280 - 203345_s_at 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- 203535_at (numeric)],\n 2358: [2358 - 203537_at (numeric)],\n 2359: [2359 - 203538_at (numeric)],\n 2360: [2360 - 203542_s_at (numeric)],\n 2361: [2361 - 203543_s_at (numeric)],\n 2362: [2362 - 203545_at (numeric)],\n 2363: [2363 - 203547_at (numeric)],\n 2364: [2364 - 203548_s_at (numeric)],\n 2365: [2365 - 203549_s_at (numeric)],\n 2366: [2366 - 203550_s_at (numeric)],\n 2367: [2367 - 203554_x_at (numeric)],\n 2368: [2368 - 203556_at (numeric)],\n 2369: [2369 - 203557_s_at (numeric)],\n 2370: [2370 - 203559_s_at (numeric)],\n 2371: [2371 - 203560_at (numeric)],\n 2372: [2372 - 203561_at (numeric)],\n 2373: [2373 - 203566_s_at (numeric)],\n 2374: [2374 - 203567_s_at (numeric)],\n 2375: [2375 - 203568_s_at (numeric)],\n 2376: [2376 - 203569_s_at (numeric)],\n 2377: [2377 - 203570_at (numeric)],\n 2378: [2378 - 203571_s_at (numeric)],\n 2379: [2379 - 203572_s_at (numeric)],\n 2380: [2380 - 203574_at (numeric)],\n 2381: [2381 - 203576_at (numeric)],\n 2382: [2382 - 203581_at 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[2690 - 204285_s_at (numeric)],\n 2691: [2691 - 204288_s_at (numeric)],\n 2692: [2692 - 204290_s_at (numeric)],\n 2693: [2693 - 204295_at (numeric)],\n 2694: [2694 - 204298_s_at (numeric)],\n 2695: [2695 - 204301_at (numeric)],\n 2696: [2696 - 204304_s_at (numeric)],\n 2697: [2697 - 204306_s_at (numeric)],\n 2698: [2698 - 204319_s_at (numeric)],\n 2699: [2699 - 204320_at (numeric)],\n 2700: [2700 - 204326_x_at (numeric)],\n 2701: [2701 - 204331_s_at (numeric)],\n 2702: [2702 - 204332_s_at (numeric)],\n 2703: [2703 - 204337_at (numeric)],\n 2704: [2704 - 204342_at (numeric)],\n 2705: [2705 - 204345_at (numeric)],\n 2706: [2706 - 204347_at (numeric)],\n 2707: [2707 - 204348_s_at (numeric)],\n 2708: [2708 - 204351_at (numeric)],\n 2709: [2709 - 204352_at (numeric)],\n 2710: [2710 - 204359_at (numeric)],\n 2711: [2711 - 204362_at (numeric)],\n 2712: [2712 - 204363_at (numeric)],\n 2713: [2713 - 204364_s_at (numeric)],\n 2714: [2714 - 204366_s_at (numeric)],\n 2715: [2715 - 204368_at 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2869: [2869 - 204803_s_at (numeric)],\n 2870: [2870 - 204805_s_at (numeric)],\n 2871: [2871 - 204806_x_at (numeric)],\n 2872: [2872 - 204808_s_at (numeric)],\n 2873: [2873 - 204818_at (numeric)],\n 2874: [2874 - 204820_s_at (numeric)],\n 2875: [2875 - 204821_at (numeric)],\n 2876: [2876 - 204822_at (numeric)],\n 2877: [2877 - 204825_at (numeric)],\n 2878: [2878 - 204829_s_at (numeric)],\n 2879: [2879 - 204832_s_at (numeric)],\n 2880: [2880 - 204834_at (numeric)],\n 2881: [2881 - 204836_at (numeric)],\n 2882: [2882 - 204839_at (numeric)],\n 2883: [2883 - 204842_x_at (numeric)],\n 2884: [2884 - 204844_at (numeric)],\n 2885: [2885 - 204845_s_at (numeric)],\n 2886: [2886 - 204846_at (numeric)],\n 2887: [2887 - 204848_x_at (numeric)],\n 2888: [2888 - 204849_at (numeric)],\n 2889: [2889 - 204855_at (numeric)],\n 2890: [2890 - 204857_at (numeric)],\n 2891: [2891 - 204872_at (numeric)],\n 2892: [2892 - 204875_s_at (numeric)],\n 2893: [2893 - 204881_s_at (numeric)],\n 2894: [2894 - 204885_s_at 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208709_s_at (numeric)],\n 3760: [3760 - 208711_s_at (numeric)],\n 3761: [3761 - 208712_at (numeric)],\n 3762: [3762 - 208713_at (numeric)],\n 3763: [3763 - 208714_at (numeric)],\n 3764: [3764 - 208716_s_at (numeric)],\n 3765: [3765 - 208717_at (numeric)],\n 3766: [3766 - 208718_at (numeric)],\n 3767: [3767 - 208719_s_at (numeric)],\n 3768: [3768 - 208720_s_at (numeric)],\n 3769: [3769 - 208722_s_at (numeric)],\n 3770: [3770 - 208723_at (numeric)],\n 3771: [3771 - 208724_s_at (numeric)],\n 3772: [3772 - 208726_s_at (numeric)],\n 3773: [3773 - 208727_s_at (numeric)],\n 3774: [3774 - 208728_s_at (numeric)],\n 3775: [3775 - 208729_x_at (numeric)],\n 3776: [3776 - 208730_x_at (numeric)],\n 3777: [3777 - 208731_at (numeric)],\n 3778: [3778 - 208732_at (numeric)],\n 3779: [3779 - 208734_x_at (numeric)],\n 3780: [3780 - 208735_s_at (numeric)],\n 3781: [3781 - 208736_at (numeric)],\n 3782: [3782 - 208737_at (numeric)],\n 3783: [3783 - 208738_x_at (numeric)],\n 3784: [3784 - 208739_x_at 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(numeric)],\n 3810: [3810 - 208773_s_at (numeric)],\n 3811: [3811 - 208774_at (numeric)],\n 3812: [3812 - 208775_at (numeric)],\n 3813: [3813 - 208777_s_at (numeric)],\n 3814: [3814 - 208778_s_at (numeric)],\n 3815: [3815 - 208779_x_at (numeric)],\n 3816: [3816 - 208780_x_at (numeric)],\n 3817: [3817 - 208781_x_at (numeric)],\n 3818: [3818 - 208782_at (numeric)],\n 3819: [3819 - 208783_s_at (numeric)],\n 3820: [3820 - 208785_s_at (numeric)],\n 3821: [3821 - 208786_s_at (numeric)],\n 3822: [3822 - 208787_at (numeric)],\n 3823: [3823 - 208788_at (numeric)],\n 3824: [3824 - 208789_at (numeric)],\n 3825: [3825 - 208791_at (numeric)],\n 3826: [3826 - 208792_s_at (numeric)],\n 3827: [3827 - 208794_s_at (numeric)],\n 3828: [3828 - 208795_s_at (numeric)],\n 3829: [3829 - 208796_s_at (numeric)],\n 3830: [3830 - 208798_x_at (numeric)],\n 3831: [3831 - 208799_at (numeric)],\n 3832: [3832 - 208800_at (numeric)],\n 3833: [3833 - 208801_at (numeric)],\n 3834: [3834 - 208804_s_at (numeric)],\n 3835: [3835 - 208805_at (numeric)],\n 3836: [3836 - 208808_s_at (numeric)],\n 3837: [3837 - 208809_s_at (numeric)],\n 3838: [3838 - 208810_at (numeric)],\n 3839: [3839 - 208811_s_at (numeric)],\n 3840: [3840 - 208812_x_at (numeric)],\n 3841: [3841 - 208813_at (numeric)],\n 3842: [3842 - 208815_x_at (numeric)],\n 3843: [3843 - 208816_x_at (numeric)],\n 3844: [3844 - 208817_at (numeric)],\n 3845: [3845 - 208818_s_at (numeric)],\n 3846: [3846 - 208819_at (numeric)],\n 3847: [3847 - 208820_at (numeric)],\n 3848: [3848 - 208821_at (numeric)],\n 3849: [3849 - 208822_s_at (numeric)],\n 3850: [3850 - 208824_x_at (numeric)],\n 3851: [3851 - 208825_x_at (numeric)],\n 3852: [3852 - 208826_x_at (numeric)],\n 3853: [3853 - 208827_at (numeric)],\n 3854: [3854 - 208828_at (numeric)],\n 3855: [3855 - 208829_at (numeric)],\n 3856: [3856 - 208833_s_at (numeric)],\n 3857: [3857 - 208834_x_at (numeric)],\n 3858: [3858 - 208835_s_at (numeric)],\n 3859: [3859 - 208836_at (numeric)],\n 3860: [3860 - 208837_at (numeric)],\n 3861: [3861 - 208838_at (numeric)],\n 3862: [3862 - 208839_s_at (numeric)],\n 3863: [3863 - 208841_s_at (numeric)],\n 3864: [3864 - 208842_s_at (numeric)],\n 3865: [3865 - 208843_s_at (numeric)],\n 3866: [3866 - 208845_at (numeric)],\n 3867: [3867 - 208846_s_at (numeric)],\n 3868: [3868 - 208847_s_at (numeric)],\n 3869: [3869 - 208848_at (numeric)],\n 3870: [3870 - 208850_s_at (numeric)],\n 3871: [3871 - 208851_s_at (numeric)],\n 3872: [3872 - 208852_s_at (numeric)],\n 3873: [3873 - 208853_s_at (numeric)],\n 3874: [3874 - 208854_s_at (numeric)],\n 3875: [3875 - 208855_s_at (numeric)],\n 3876: [3876 - 208856_x_at (numeric)],\n 3877: [3877 - 208857_s_at (numeric)],\n 3878: [3878 - 208858_s_at (numeric)],\n 3879: [3879 - 208861_s_at (numeric)],\n 3880: [3880 - 208862_s_at (numeric)],\n 3881: [3881 - 208863_s_at (numeric)],\n 3882: [3882 - 208864_s_at (numeric)],\n 3883: [3883 - 208865_at (numeric)],\n 3884: [3884 - 208868_s_at (numeric)],\n 3885: [3885 - 208869_s_at (numeric)],\n 3886: [3886 - 208870_x_at (numeric)],\n 3887: [3887 - 208872_s_at (numeric)],\n 3888: [3888 - 208873_s_at (numeric)],\n 3889: [3889 - 208874_x_at (numeric)],\n 3890: [3890 - 208880_s_at (numeric)],\n 3891: [3891 - 208881_x_at (numeric)],\n 3892: [3892 - 208882_s_at (numeric)],\n 3893: [3893 - 208884_s_at (numeric)],\n 3894: [3894 - 208885_at (numeric)],\n 3895: [3895 - 208886_at (numeric)],\n 3896: [3896 - 208887_at (numeric)],\n 3897: [3897 - 208890_s_at (numeric)],\n 3898: [3898 - 208891_at (numeric)],\n 3899: [3899 - 208892_s_at (numeric)],\n 3900: [3900 - 208893_s_at (numeric)],\n 3901: [3901 - 208894_at (numeric)],\n 3902: [3902 - 208895_s_at (numeric)],\n 3903: [3903 - 208898_at (numeric)],\n 3904: [3904 - 208899_x_at (numeric)],\n 3905: [3905 - 208901_s_at (numeric)],\n 3906: [3906 - 208904_s_at (numeric)],\n 3907: [3907 - 208905_at (numeric)],\n 3908: [3908 - 208906_at (numeric)],\n 3909: [3909 - 208907_s_at (numeric)],\n 3910: [3910 - 208908_s_at (numeric)],\n 3911: [3911 - 208909_at (numeric)],\n 3912: [3912 - 208910_s_at (numeric)],\n 3913: [3913 - 208911_s_at (numeric)],\n 3914: [3914 - 208912_s_at (numeric)],\n 3915: [3915 - 208916_at (numeric)],\n 3916: [3916 - 208918_s_at (numeric)],\n 3917: [3917 - 208919_s_at (numeric)],\n 3918: [3918 - 208920_at (numeric)],\n 3919: [3919 - 208921_s_at (numeric)],\n 3920: [3920 - 208923_at (numeric)],\n 3921: [3921 - 208924_at (numeric)],\n 3922: [3922 - 208925_at (numeric)],\n 3923: [3923 - 208926_at (numeric)],\n 3924: [3924 - 208928_at (numeric)],\n 3925: [3925 - 208929_x_at (numeric)],\n 3926: [3926 - 208932_at (numeric)],\n 3927: [3927 - 208933_s_at (numeric)],\n 3928: [3928 - 208934_s_at (numeric)],\n 3929: [3929 - 208936_x_at (numeric)],\n 3930: [3930 - 208937_s_at (numeric)],\n 3931: [3931 - 208939_at (numeric)],\n 3932: [3932 - 208942_s_at (numeric)],\n 3933: [3933 - 208943_s_at (numeric)],\n 3934: [3934 - 208944_at (numeric)],\n 3935: [3935 - 208946_s_at (numeric)],\n 3936: [3936 - 208948_s_at (numeric)],\n 3937: [3937 - 208949_s_at (numeric)],\n 3938: [3938 - 208950_s_at (numeric)],\n 3939: [3939 - 208951_at (numeric)],\n 3940: [3940 - 208952_s_at (numeric)],\n 3941: [3941 - 208953_at (numeric)],\n 3942: [3942 - 208956_x_at (numeric)],\n 3943: [3943 - 208959_s_at (numeric)],\n 3944: [3944 - 208960_s_at (numeric)],\n 3945: [3945 - 208961_s_at (numeric)],\n 3946: [3946 - 208965_s_at (numeric)],\n 3947: [3947 - 208966_x_at (numeric)],\n 3948: [3948 - 208967_s_at (numeric)],\n 3949: [3949 - 208968_s_at (numeric)],\n 3950: [3950 - 208969_at (numeric)],\n 3951: [3951 - 208970_s_at (numeric)],\n 3952: [3952 - 208972_s_at (numeric)],\n 3953: [3953 - 208974_x_at (numeric)],\n 3954: [3954 - 208975_s_at (numeric)],\n 3955: [3955 - 208977_x_at (numeric)],\n 3956: [3956 - 208978_at (numeric)],\n 3957: [3957 - 208979_at (numeric)],\n 3958: [3958 - 208980_s_at (numeric)],\n 3959: [3959 - 208981_at (numeric)],\n 3960: [3960 - 208982_at (numeric)],\n 3961: [3961 - 208983_s_at (numeric)],\n 3962: [3962 - 208984_x_at (numeric)],\n 3963: [3963 - 208985_s_at (numeric)],\n 3964: [3964 - 208986_at (numeric)],\n 3965: [3965 - 208988_at (numeric)],\n 3966: [3966 - 208990_s_at (numeric)],\n 3967: [3967 - 208991_at (numeric)],\n 3968: [3968 - 208992_s_at (numeric)],\n 3969: [3969 - 208993_s_at (numeric)],\n 3970: [3970 - 208996_s_at (numeric)],\n 3971: [3971 - 208997_s_at (numeric)],\n 3972: [3972 - 208998_at (numeric)],\n 3973: [3973 - 208999_at (numeric)],\n 3974: [3974 - 209001_s_at (numeric)],\n 3975: [3975 - 209004_s_at (numeric)],\n 3976: [3976 - 209005_at (numeric)],\n 3977: [3977 - 209006_s_at (numeric)],\n 3978: [3978 - 209007_s_at (numeric)],\n 3979: [3979 - 209008_x_at (numeric)],\n 3980: [3980 - 209009_at (numeric)],\n 3981: [3981 - 209014_at (numeric)],\n 3982: [3982 - 209015_s_at (numeric)],\n 3983: [3983 - 209016_s_at (numeric)],\n 3984: [3984 - 209017_s_at (numeric)],\n 3985: [3985 - 209019_s_at (numeric)],\n 3986: [3986 - 209022_at (numeric)],\n 3987: [3987 - 209023_s_at (numeric)],\n 3988: [3988 - 209024_s_at (numeric)],\n 3989: [3989 - 209025_s_at (numeric)],\n 3990: [3990 - 209026_x_at (numeric)],\n 3991: [3991 - 209027_s_at (numeric)],\n 3992: [3992 - 209030_s_at (numeric)],\n 3993: [3993 - 209031_at (numeric)],\n 3994: [3994 - 209033_s_at (numeric)],\n 3995: [3995 - 209034_at (numeric)],\n 3996: [3996 - 209035_at (numeric)],\n 3997: [3997 - 209036_s_at (numeric)],\n 3998: [3998 - 209040_s_at (numeric)],\n 3999: [3999 - 209043_at (numeric)],\n 4000: [4000 - 209044_x_at (numeric)],\n 4001: [4001 - 209045_at (numeric)],\n 4002: [4002 - 209046_s_at (numeric)],\n 4003: [4003 - 209047_at (numeric)],\n 4004: [4004 - 209048_s_at (numeric)],\n 4005: [4005 - 209049_s_at (numeric)],\n 4006: [4006 - 209050_s_at (numeric)],\n 4007: [4007 - 209056_s_at (numeric)],\n 4008: [4008 - 209057_x_at (numeric)],\n 4009: [4009 - 209058_at (numeric)],\n 4010: [4010 - 209059_s_at (numeric)],\n 4011: [4011 - 209063_x_at (numeric)],\n 4012: [4012 - 209064_x_at (numeric)],\n 4013: [4013 - 209066_x_at (numeric)],\n 4014: [4014 - 209067_s_at (numeric)],\n 4015: [4015 - 209068_at (numeric)],\n 4016: [4016 - 209069_s_at (numeric)],\n 4017: [4017 - 209070_s_at (numeric)],\n 4018: [4018 - 209071_s_at (numeric)],\n 4019: [4019 - 209074_s_at (numeric)],\n 4020: [4020 - 209075_s_at (numeric)],\n 4021: [4021 - 209076_s_at (numeric)],\n 4022: [4022 - 209077_at (numeric)],\n 4023: [4023 - 209079_x_at (numeric)],\n 4024: [4024 - 209080_x_at (numeric)],\n 4025: [4025 - 209081_s_at (numeric)],\n 4026: [4026 - 209082_s_at (numeric)],\n 4027: [4027 - 209083_at (numeric)],\n 4028: [4028 - 209086_x_at (numeric)],\n 4029: [4029 - 209087_x_at (numeric)],\n 4030: [4030 - 209089_at (numeric)],\n 4031: [4031 - 209090_s_at (numeric)],\n 4032: [4032 - 209091_s_at (numeric)],\n 4033: [4033 - 209092_s_at (numeric)],\n 4034: [4034 - 209094_at (numeric)],\n 4035: [4035 - 209095_at (numeric)],\n 4036: [4036 - 209096_at (numeric)],\n 4037: [4037 - 209099_x_at (numeric)],\n 4038: [4038 - 209100_at (numeric)],\n 4039: [4039 - 209101_at (numeric)],\n 4040: [4040 - 209102_s_at (numeric)],\n 4041: [4041 - 209103_s_at (numeric)],\n 4042: [4042 - 209104_s_at (numeric)],\n 4043: [4043 - 209108_at (numeric)],\n 4044: [4044 - 209109_s_at (numeric)],\n 4045: [4045 - 209110_s_at (numeric)],\n 4046: [4046 - 209111_at (numeric)],\n 4047: [4047 - 209112_at (numeric)],\n 4048: [4048 - 209114_at (numeric)],\n 4049: [4049 - 209115_at (numeric)],\n 4050: [4050 - 209116_x_at (numeric)],\n 4051: [4051 - 209118_s_at (numeric)],\n 4052: [4052 - 209120_at (numeric)],\n 4053: [4053 - 209121_x_at (numeric)],\n 4054: [4054 - 209122_at (numeric)],\n 4055: [4055 - 209123_at (numeric)],\n 4056: [4056 - 209124_at (numeric)],\n 4057: [4057 - 209125_at (numeric)],\n 4058: [4058 - 209126_x_at (numeric)],\n 4059: [4059 - 209129_at (numeric)],\n 4060: [4060 - 209130_at (numeric)],\n 4061: [4061 - 209134_s_at (numeric)],\n 4062: [4062 - 209135_at (numeric)],\n 4063: [4063 - 209138_x_at (numeric)],\n 4064: [4064 - 209139_s_at (numeric)],\n 4065: [4065 - 209140_x_at (numeric)],\n 4066: [4066 - 209141_at (numeric)],\n 4067: [4067 - 209142_s_at (numeric)],\n 4068: [4068 - 209143_s_at (numeric)],\n 4069: [4069 - 209146_at (numeric)],\n 4070: [4070 - 209147_s_at (numeric)],\n 4071: [4071 - 209150_s_at (numeric)],\n 4072: [4072 - 209153_s_at (numeric)],\n 4073: [4073 - 209154_at (numeric)],\n 4074: [4074 - 209155_s_at (numeric)],\n 4075: [4075 - 209156_s_at (numeric)],\n 4076: [4076 - 209157_at (numeric)],\n 4077: [4077 - 209160_at (numeric)],\n 4078: [4078 - 209163_at (numeric)],\n 4079: [4079 - 209164_s_at (numeric)],\n 4080: [4080 - 209166_s_at (numeric)],\n 4081: [4081 - 209167_at (numeric)],\n 4082: [4082 - 209170_s_at (numeric)],\n 4083: [4083 - 209171_at (numeric)],\n 4084: [4084 - 209173_at (numeric)],\n 4085: [4085 - 209177_at (numeric)],\n 4086: [4086 - 209180_at (numeric)],\n 4087: [4087 - 209181_s_at (numeric)],\n 4088: [4088 - 209182_s_at (numeric)],\n 4089: [4089 - 209183_s_at (numeric)],\n 4090: [4090 - 209184_s_at (numeric)],\n 4091: [4091 - 209185_s_at (numeric)],\n 4092: [4092 - 209186_at (numeric)],\n 4093: [4093 - 209187_at (numeric)],\n 4094: [4094 - 209189_at (numeric)],\n 4095: [4095 - 209190_s_at (numeric)],\n 4096: [4096 - 209191_at (numeric)],\n 4097: [4097 - 209194_at (numeric)],\n 4098: [4098 - 209199_s_at (numeric)],\n 4099: [4099 - 209200_at (numeric)],\n 4100: [4100 - 209201_x_at (numeric)],\n 4101: [4101 - 209205_s_at (numeric)],\n 4102: [4102 - 209209_s_at (numeric)],\n 4103: [4103 - 209210_s_at (numeric)],\n 4104: [4104 - 209211_at (numeric)],\n 4105: [4105 - 209212_s_at (numeric)],\n 4106: [4106 - 209213_at (numeric)],\n 4107: [4107 - 209214_s_at (numeric)],\n 4108: [4108 - 209216_at (numeric)],\n 4109: [4109 - 209218_at (numeric)],\n 4110: [4110 - 209219_at (numeric)],\n 4111: [4111 - 209220_at (numeric)],\n 4112: [4112 - 209222_s_at (numeric)],\n 4113: [4113 - 209224_s_at (numeric)],\n 4114: [4114 - 209225_x_at (numeric)],\n 4115: [4115 - 209226_s_at (numeric)],\n 4116: [4116 - 209228_x_at (numeric)],\n 4117: [4117 - 209230_s_at (numeric)],\n 4118: [4118 - 209233_at (numeric)],\n 4119: [4119 - 209238_at (numeric)],\n 4120: [4120 - 209239_at (numeric)],\n 4121: [4121 - 209240_at (numeric)],\n 4122: [4122 - 209242_at (numeric)],\n 4123: [4123 - 209243_s_at (numeric)],\n 4124: [4124 - 209248_at (numeric)],\n 4125: [4125 - 209249_s_at (numeric)],\n 4126: [4126 - 209250_at (numeric)],\n 4127: [4127 - 209251_x_at (numeric)],\n 4128: [4128 - 209255_at (numeric)],\n 4129: [4129 - 209259_s_at (numeric)],\n 4130: [4130 - 209260_at (numeric)],\n 4131: [4131 - 209262_s_at (numeric)],\n 4132: [4132 - 209263_x_at (numeric)],\n 4133: [4133 - 209265_s_at (numeric)],\n 4134: [4134 - 209267_s_at (numeric)],\n 4135: [4135 - 209270_at (numeric)],\n 4136: [4136 - 209272_at (numeric)],\n 4137: [4137 - 209273_s_at (numeric)],\n 4138: [4138 - 209274_s_at (numeric)],\n 4139: [4139 - 209276_s_at (numeric)],\n 4140: [4140 - 209277_at (numeric)],\n 4141: [4141 - 209278_s_at (numeric)],\n 4142: [4142 - 209281_s_at (numeric)],\n 4143: [4143 - 209283_at (numeric)],\n 4144: [4144 - 209284_s_at (numeric)],\n 4145: [4145 - 209285_s_at (numeric)],\n 4146: [4146 - 209288_s_at (numeric)],\n 4147: [4147 - 209289_at (numeric)],\n 4148: [4148 - 209290_s_at (numeric)],\n 4149: [4149 - 209291_at (numeric)],\n 4150: [4150 - 209292_at (numeric)],\n 4151: [4151 - 209293_x_at (numeric)],\n 4152: [4152 - 209295_at (numeric)],\n 4153: [4153 - 209296_at (numeric)],\n 4154: [4154 - 209301_at (numeric)],\n 4155: [4155 - 209302_at (numeric)],\n 4156: [4156 - 209303_at (numeric)],\n 4157: [4157 - 209304_x_at (numeric)],\n 4158: [4158 - 209305_s_at (numeric)],\n 4159: [4159 - 209306_s_at (numeric)],\n 4160: [4160 - 209307_at (numeric)],\n 4161: [4161 - 209309_at (numeric)],\n 4162: [4162 - 209312_x_at (numeric)],\n 4163: [4163 - 209313_at (numeric)],\n 4164: [4164 - 209318_x_at (numeric)],\n 4165: [4165 - 209323_at (numeric)],\n 4166: [4166 - 209326_at (numeric)],\n 4167: [4167 - 209329_x_at (numeric)],\n 4168: [4168 - 209330_s_at (numeric)],\n 4169: [4169 - 209332_s_at (numeric)],\n 4170: [4170 - 209335_at (numeric)],\n 4171: [4171 - 209337_at (numeric)],\n 4172: [4172 - 209339_at (numeric)],\n 4173: [4173 - 209340_at (numeric)],\n 4174: [4174 - 209341_s_at (numeric)],\n 4175: [4175 - 209343_at (numeric)],\n 4176: [4176 - 209344_at (numeric)],\n 4177: [4177 - 209348_s_at (numeric)],\n 4178: [4178 - 209351_at (numeric)],\n 4179: [4179 - 209355_s_at (numeric)],\n 4180: [4180 - 209356_x_at (numeric)],\n 4181: [4181 - 209357_at (numeric)],\n 4182: [4182 - 209358_at (numeric)],\n 4183: [4183 - 209360_s_at (numeric)],\n 4184: [4184 - 209362_at (numeric)],\n 4185: [4185 - 209365_s_at (numeric)],\n 4186: [4186 - 209366_x_at (numeric)],\n 4187: [4187 - 209369_at (numeric)],\n 4188: [4188 - 209373_at (numeric)],\n 4189: [4189 - 209374_s_at (numeric)],\n 4190: [4190 - 209376_x_at (numeric)],\n 4191: [4191 - 209377_s_at (numeric)],\n 4192: [4192 - 209380_s_at (numeric)],\n 4193: [4193 - 209383_at (numeric)],\n 4194: [4194 - 209384_at (numeric)],\n 4195: [4195 - 209385_s_at (numeric)],\n 4196: [4196 - 209386_at (numeric)],\n 4197: [4197 - 209387_s_at (numeric)],\n 4198: [4198 - 209388_at (numeric)],\n 4199: [4199 - 209389_x_at (numeric)],\n 4200: [4200 - 209392_at (numeric)],\n 4201: [4201 - 209393_s_at (numeric)],\n 4202: [4202 - 209395_at (numeric)],\n 4203: [4203 - 209396_s_at (numeric)],\n 4204: [4204 - 209397_at (numeric)],\n 4205: [4205 - 209398_at (numeric)],\n 4206: [4206 - 209403_at (numeric)],\n 4207: [4207 - 209404_s_at (numeric)],\n 4208: [4208 - 209408_at (numeric)],\n 4209: [4209 - 209409_at (numeric)],\n 4210: [4210 - 209421_at (numeric)],\n 4211: [4211 - 209422_at (numeric)],\n 4212: [4212 - 209424_s_at (numeric)],\n 4213: [4213 - 209425_at (numeric)],\n 4214: [4214 - 209426_s_at (numeric)],\n 4215: [4215 - 209435_s_at (numeric)],\n 4216: [4216 - 209436_at (numeric)],\n 4217: [4217 - 209437_s_at (numeric)],\n 4218: [4218 - 209439_s_at (numeric)],\n 4219: [4219 - 209443_at (numeric)],\n 4220: [4220 - 209447_at (numeric)],\n 4221: [4221 - 209448_at (numeric)],\n 4222: [4222 - 209451_at (numeric)],\n 4223: [4223 - 209452_s_at (numeric)],\n 4224: [4224 - 209458_x_at (numeric)],\n 4225: [4225 - 209459_s_at (numeric)],\n 4226: [4226 - 209460_at (numeric)],\n 4227: [4227 - 209465_x_at (numeric)],\n 4228: [4228 - 209466_x_at (numeric)],\n 4229: [4229 - 209469_at (numeric)],\n 4230: [4230 - 209470_s_at (numeric)],\n 4231: [4231 - 209471_s_at (numeric)],\n 4232: [4232 - 209472_at (numeric)],\n 4233: [4233 - 209473_at (numeric)],\n 4234: [4234 - 209476_at (numeric)],\n 4235: [4235 - 209478_at (numeric)],\n 4236: [4236 - 209479_at (numeric)],\n 4237: [4237 - 209480_at (numeric)],\n 4238: [4238 - 209481_at (numeric)],\n 4239: [4239 - 209482_at (numeric)],\n 4240: [4240 - 209484_s_at (numeric)],\n 4241: [4241 - 209485_s_at (numeric)],\n 4242: [4242 - 209486_at (numeric)],\n 4243: [4243 - 209487_at (numeric)],\n 4244: [4244 - 209488_s_at (numeric)],\n 4245: [4245 - 209492_x_at (numeric)],\n 4246: [4246 - 209493_at (numeric)],\n 4247: [4247 - 209494_s_at (numeric)],\n 4248: [4248 - 209496_at (numeric)],\n 4249: [4249 - 209498_at (numeric)],\n 4250: [4250 - 209499_x_at (numeric)],\n 4251: [4251 - 209500_x_at (numeric)],\n 4252: [4252 - 209501_at (numeric)],\n 4253: [4253 - 209503_s_at (numeric)],\n 4254: [4254 - 209504_s_at (numeric)],\n 4255: [4255 - 209505_at (numeric)],\n 4256: [4256 - 209507_at (numeric)],\n 4257: [4257 - 209508_x_at (numeric)],\n 4258: [4258 - 209510_at (numeric)],\n 4259: [4259 - 209511_at (numeric)],\n 4260: [4260 - 209513_s_at (numeric)],\n 4261: [4261 - 209515_s_at (numeric)],\n 4262: [4262 - 209517_s_at (numeric)],\n 4263: [4263 - 209523_at (numeric)],\n 4264: [4264 - 209524_at (numeric)],\n 4265: [4265 - 209529_at (numeric)],\n 4266: [4266 - 209539_at (numeric)],\n 4267: [4267 - 209540_at (numeric)],\n 4268: [4268 - 209541_at (numeric)],\n 4269: [4269 - 209542_x_at (numeric)],\n 4270: [4270 - 209543_s_at (numeric)],\n 4271: [4271 - 209545_s_at (numeric)],\n 4272: [4272 - 209546_s_at (numeric)],\n 4273: [4273 - 209549_s_at (numeric)],\n 4274: [4274 - 209550_at (numeric)],\n 4275: [4275 - 209552_at (numeric)],\n 4276: [4276 - 209555_s_at (numeric)],\n 4277: [4277 - 209558_s_at (numeric)],\n 4278: [4278 - 209560_s_at (numeric)],\n 4279: [4279 - 209563_x_at (numeric)],\n 4280: [4280 - 209566_at (numeric)],\n 4281: [4281 - 209567_at (numeric)],\n 4282: [4282 - 209568_s_at (numeric)],\n 4283: [4283 - 209569_x_at (numeric)],\n 4284: [4284 - 209570_s_at (numeric)],\n 4285: [4285 - 209572_s_at (numeric)],\n 4286: [4286 - 209576_at (numeric)],\n 4287: [4287 - 209579_s_at (numeric)],\n 4288: [4288 - 209580_s_at (numeric)],\n 4289: [4289 - 209581_at (numeric)],\n 4290: [4290 - 209583_s_at (numeric)],\n 4291: [4291 - 209586_s_at (numeric)],\n 4292: [4292 - 209590_at (numeric)],\n 4293: [4293 - 209591_s_at (numeric)],\n 4294: [4294 - 209596_at (numeric)],\n 4295: [4295 - 209598_at (numeric)],\n 4296: [4296 - 209602_s_at (numeric)],\n 4297: [4297 - 209604_s_at (numeric)],\n 4298: [4298 - 209605_at (numeric)],\n 4299: [4299 - 209606_at (numeric)],\n 4300: [4300 - 209607_x_at (numeric)],\n 4301: [4301 - 209609_s_at (numeric)],\n 4302: [4302 - 209612_s_at (numeric)],\n 4303: [4303 - 209613_s_at (numeric)],\n 4304: [4304 - 209616_s_at (numeric)],\n 4305: [4305 - 209619_at (numeric)],\n 4306: [4306 - 209621_s_at (numeric)],\n 4307: [4307 - 209623_at (numeric)],\n 4308: [4308 - 209627_s_at (numeric)],\n 4309: [4309 - 209628_at (numeric)],\n 4310: [4310 - 209630_s_at (numeric)],\n 4311: [4311 - 209631_s_at (numeric)],\n 4312: [4312 - 209632_at (numeric)],\n 4313: [4313 - 209633_at (numeric)],\n 4314: [4314 - 209641_s_at (numeric)],\n 4315: [4315 - 209644_x_at (numeric)],\n 4316: [4316 - 209651_at (numeric)],\n 4317: [4317 - 209652_s_at (numeric)],\n 4318: [4318 - 209654_at 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[4395 - 209869_at (numeric)],\n 4396: [4396 - 209875_s_at (numeric)],\n 4397: [4397 - 209882_at (numeric)],\n 4398: [4398 - 209884_s_at (numeric)],\n 4399: [4399 - 209888_s_at (numeric)],\n 4400: [4400 - 209890_at (numeric)],\n 4401: [4401 - 209894_at (numeric)],\n 4402: [4402 - 209896_s_at (numeric)],\n 4403: [4403 - 209897_s_at (numeric)],\n 4404: [4404 - 209898_x_at (numeric)],\n 4405: [4405 - 209899_s_at (numeric)],\n 4406: [4406 - 209900_s_at (numeric)],\n 4407: [4407 - 209901_x_at (numeric)],\n 4408: [4408 - 209903_s_at (numeric)],\n 4409: [4409 - 209904_at (numeric)],\n 4410: [4410 - 209905_at (numeric)],\n 4411: [4411 - 209906_at (numeric)],\n 4412: [4412 - 209911_x_at (numeric)],\n 4413: [4413 - 209913_x_at (numeric)],\n 4414: [4414 - 209919_x_at (numeric)],\n 4415: [4415 - 209921_at (numeric)],\n 4416: [4416 - 209924_at (numeric)],\n 4417: [4417 - 209932_s_at (numeric)],\n 4418: [4418 - 209933_s_at (numeric)],\n 4419: [4419 - 209937_at (numeric)],\n 4420: [4420 - 209939_x_at (numeric)],\n 4421: [4421 - 209942_x_at (numeric)],\n 4422: [4422 - 209944_at (numeric)],\n 4423: [4423 - 209946_at (numeric)],\n 4424: [4424 - 209950_s_at (numeric)],\n 4425: [4425 - 209953_s_at (numeric)],\n 4426: [4426 - 209955_s_at (numeric)],\n 4427: [4427 - 209959_at (numeric)],\n 4428: [4428 - 209960_at (numeric)],\n 4429: [4429 - 209966_x_at (numeric)],\n 4430: [4430 - 209967_s_at (numeric)],\n 4431: [4431 - 209969_s_at (numeric)],\n 4432: [4432 - 209970_x_at (numeric)],\n 4433: [4433 - 209971_x_at (numeric)],\n 4434: [4434 - 209974_s_at (numeric)],\n 4435: [4435 - 209977_at (numeric)],\n 4436: [4436 - 209980_s_at (numeric)],\n 4437: [4437 - 209987_s_at (numeric)],\n 4438: [4438 - 209988_s_at (numeric)],\n 4439: [4439 - 209993_at (numeric)],\n 4440: [4440 - 209994_s_at (numeric)],\n 4441: [4441 - 210002_at (numeric)],\n 4442: [4442 - 210014_x_at (numeric)],\n 4443: [4443 - 210020_x_at (numeric)],\n 4444: [4444 - 210024_s_at (numeric)],\n 4445: [4445 - 210026_s_at (numeric)],\n 4446: [4446 - 210027_s_at (numeric)],\n 4447: [4447 - 210029_at (numeric)],\n 4448: [4448 - 210036_s_at (numeric)],\n 4449: [4449 - 210042_s_at (numeric)],\n 4450: [4450 - 210046_s_at (numeric)],\n 4451: [4451 - 210052_s_at (numeric)],\n 4452: [4452 - 210058_at (numeric)],\n 4453: [4453 - 210064_s_at (numeric)],\n 4454: [4454 - 210065_s_at (numeric)],\n 4455: [4455 - 210072_at (numeric)],\n 4456: [4456 - 210074_at (numeric)],\n 4457: [4457 - 210084_x_at (numeric)],\n 4458: [4458 - 210088_x_at (numeric)],\n 4459: [4459 - 210092_at (numeric)],\n 4460: [4460 - 210093_s_at (numeric)],\n 4461: [4461 - 210095_s_at (numeric)],\n 4462: [4462 - 210096_at (numeric)],\n 4463: [4463 - 210097_s_at (numeric)],\n 4464: [4464 - 210101_x_at (numeric)],\n 4465: [4465 - 210105_s_at (numeric)],\n 4466: [4466 - 210107_at (numeric)],\n 4467: [4467 - 210111_s_at (numeric)],\n 4468: [4468 - 210119_at (numeric)],\n 4469: [4469 - 210125_s_at (numeric)],\n 4470: [4470 - 210130_s_at (numeric)],\n 4471: [4471 - 210131_x_at (numeric)],\n 4472: [4472 - 210136_at (numeric)],\n 4473: [4473 - 210137_s_at (numeric)],\n 4474: [4474 - 210139_s_at (numeric)],\n 4475: [4475 - 210142_x_at (numeric)],\n 4476: [4476 - 210143_at (numeric)],\n 4477: [4477 - 210145_at (numeric)],\n 4478: [4478 - 210149_s_at (numeric)],\n 4479: [4479 - 210150_s_at (numeric)],\n 4480: [4480 - 210155_at (numeric)],\n 4481: [4481 - 210156_s_at (numeric)],\n 4482: [4482 - 210163_at (numeric)],\n 4483: [4483 - 210165_at (numeric)],\n 4484: [4484 - 210168_at (numeric)],\n 4485: [4485 - 210170_at (numeric)],\n 4486: [4486 - 210178_x_at (numeric)],\n 4487: [4487 - 210183_x_at (numeric)],\n 4488: [4488 - 210186_s_at (numeric)],\n 4489: [4489 - 210201_x_at (numeric)],\n 4490: [4490 - 210208_x_at (numeric)],\n 4491: [4491 - 210211_s_at (numeric)],\n 4492: [4492 - 210212_x_at (numeric)],\n 4493: [4493 - 210213_s_at (numeric)],\n 4494: [4494 - 210220_at (numeric)],\n 4495: [4495 - 210230_at (numeric)],\n 4496: [4496 - 210231_x_at (numeric)],\n 4497: [4497 - 210239_at (numeric)],\n 4498: [4498 - 210250_x_at (numeric)],\n 4499: [4499 - 210260_s_at (numeric)],\n 4500: [4500 - 210266_s_at (numeric)],\n 4501: [4501 - 210275_s_at (numeric)],\n 4502: [4502 - 210276_s_at (numeric)],\n 4503: [4503 - 210283_x_at (numeric)],\n 4504: [4504 - 210289_at (numeric)],\n 4505: [4505 - 210293_s_at (numeric)],\n 4506: [4506 - 210296_s_at (numeric)],\n 4507: [4507 - 210297_s_at (numeric)],\n 4508: [4508 - 210298_x_at (numeric)],\n 4509: [4509 - 210299_s_at (numeric)],\n 4510: [4510 - 210301_at (numeric)],\n 4511: [4511 - 210312_s_at (numeric)],\n 4512: [4512 - 210314_x_at (numeric)],\n 4513: [4513 - 210317_s_at (numeric)],\n 4514: [4514 - 210319_x_at (numeric)],\n 4515: [4515 - 210337_s_at (numeric)],\n 4516: [4516 - 210338_s_at (numeric)],\n 4517: [4517 - 210346_s_at (numeric)],\n 4518: [4518 - 210347_s_at (numeric)],\n 4519: [4519 - 210355_at (numeric)],\n 4520: [4520 - 210356_x_at (numeric)],\n 4521: [4521 - 210368_at (numeric)],\n 4522: [4522 - 210371_s_at (numeric)],\n 4523: [4523 - 210372_s_at (numeric)],\n 4524: [4524 - 210374_x_at (numeric)],\n 4525: [4525 - 210375_at (numeric)],\n 4526: [4526 - 210377_at (numeric)],\n 4527: [4527 - 210386_s_at (numeric)],\n 4528: [4528 - 210387_at (numeric)],\n 4529: [4529 - 210395_x_at (numeric)],\n 4530: [4530 - 210396_s_at (numeric)],\n 4531: [4531 - 210397_at (numeric)],\n 4532: [4532 - 210402_at (numeric)],\n 4533: [4533 - 210403_s_at (numeric)],\n 4534: [4534 - 210406_s_at (numeric)],\n 4535: [4535 - 210413_x_at (numeric)],\n 4536: [4536 - 210418_s_at (numeric)],\n 4537: [4537 - 210425_x_at (numeric)],\n 4538: [4538 - 210427_x_at (numeric)],\n 4539: [4539 - 210428_s_at (numeric)],\n 4540: [4540 - 210431_at (numeric)],\n 4541: [4541 - 210432_s_at (numeric)],\n 4542: [4542 - 210434_x_at (numeric)],\n 4543: [4543 - 210438_x_at (numeric)],\n 4544: [4544 - 210445_at (numeric)],\n 4545: [4545 - 210453_x_at (numeric)],\n 4546: [4546 - 210460_s_at (numeric)],\n 4547: [4547 - 210466_s_at (numeric)],\n 4548: [4548 - 210470_x_at (numeric)],\n 4549: [4549 - 210473_s_at (numeric)],\n 4550: [4550 - 210495_x_at (numeric)],\n 4551: [4551 - 210501_x_at (numeric)],\n 4552: [4552 - 210502_s_at (numeric)],\n 4553: [4553 - 210505_at (numeric)],\n 4554: [4554 - 210510_s_at (numeric)],\n 4555: [4555 - 210511_s_at (numeric)],\n 4556: [4556 - 210512_s_at (numeric)],\n 4557: [4557 - 210513_s_at (numeric)],\n 4558: [4558 - 210514_x_at (numeric)],\n 4559: [4559 - 210517_s_at (numeric)],\n 4560: [4560 - 210519_s_at (numeric)],\n 4561: [4561 - 210524_x_at (numeric)],\n 4562: [4562 - 210527_x_at (numeric)],\n 4563: [4563 - 210532_s_at (numeric)],\n 4564: [4564 - 210538_s_at (numeric)],\n 4565: [4565 - 210540_s_at (numeric)],\n 4566: [4566 - 210541_s_at (numeric)],\n 4567: [4567 - 210543_s_at (numeric)],\n 4568: [4568 - 210544_s_at (numeric)],\n 4569: [4569 - 210546_x_at (numeric)],\n 4570: [4570 - 210554_s_at (numeric)],\n 4571: [4571 - 210559_s_at (numeric)],\n 4572: [4572 - 210561_s_at (numeric)],\n 4573: [4573 - 210563_x_at (numeric)],\n 4574: [4574 - 210564_x_at (numeric)],\n 4575: [4575 - 210574_s_at (numeric)],\n 4576: [4576 - 210580_x_at (numeric)],\n 4577: [4577 - 210582_s_at (numeric)],\n 4578: [4578 - 210588_x_at (numeric)],\n 4579: [4579 - 210592_s_at (numeric)],\n 4580: [4580 - 210601_at (numeric)],\n 4581: [4581 - 210602_s_at (numeric)],\n 4582: [4582 - 210605_s_at (numeric)],\n 4583: [4583 - 210609_s_at (numeric)],\n 4584: [4584 - 210616_s_at (numeric)],\n 4585: [4585 - 210619_s_at (numeric)],\n 4586: [4586 - 210624_s_at (numeric)],\n 4587: [4587 - 210633_x_at (numeric)],\n 4588: [4588 - 210638_s_at (numeric)],\n 4589: [4589 - 210645_s_at (numeric)],\n 4590: [4590 - 210646_x_at (numeric)],\n 4591: [4591 - 210648_x_at (numeric)],\n 4592: [4592 - 210649_s_at (numeric)],\n 4593: [4593 - 210652_s_at (numeric)],\n 4594: [4594 - 210664_s_at (numeric)],\n 4595: [4595 - 210665_at (numeric)],\n 4596: [4596 - 210676_x_at (numeric)],\n 4597: [4597 - 210679_x_at (numeric)],\n 4598: [4598 - 210686_x_at (numeric)],\n 4599: [4599 - 210691_s_at (numeric)],\n 4600: [4600 - 210715_s_at (numeric)],\n 4601: [4601 - 210717_at (numeric)],\n 4602: [4602 - 210719_s_at (numeric)],\n 4603: [4603 - 210733_at (numeric)],\n 4604: [4604 - 210735_s_at (numeric)],\n 4605: [4605 - 210739_x_at (numeric)],\n 4606: [4606 - 210740_s_at (numeric)],\n 4607: [4607 - 210749_x_at (numeric)],\n 4608: [4608 - 210754_s_at (numeric)],\n 4609: [4609 - 210756_s_at (numeric)],\n 4610: [4610 - 210757_x_at (numeric)],\n 4611: [4611 - 210759_s_at (numeric)],\n 4612: [4612 - 210761_s_at (numeric)],\n 4613: [4613 - 210762_s_at (numeric)],\n 4614: [4614 - 210764_s_at (numeric)],\n 4615: [4615 - 210766_s_at (numeric)],\n 4616: [4616 - 210774_s_at (numeric)],\n 4617: [4617 - 210776_x_at (numeric)],\n 4618: [4618 - 210785_s_at (numeric)],\n 4619: [4619 - 210788_s_at (numeric)],\n 4620: [4620 - 210794_s_at (numeric)],\n 4621: [4621 - 210807_s_at (numeric)],\n 4622: [4622 - 210809_s_at (numeric)],\n 4623: [4623 - 210817_s_at (numeric)],\n 4624: [4624 - 210825_s_at (numeric)],\n 4625: [4625 - 210827_s_at (numeric)],\n 4626: [4626 - 210830_s_at (numeric)],\n 4627: [4627 - 210831_s_at (numeric)],\n 4628: [4628 - 210832_x_at (numeric)],\n 4629: [4629 - 210835_s_at (numeric)],\n 4630: [4630 - 210839_s_at (numeric)],\n 4631: [4631 - 210840_s_at (numeric)],\n 4632: [4632 - 210844_x_at (numeric)],\n 4633: [4633 - 210845_s_at (numeric)],\n 4634: [4634 - 210854_x_at (numeric)],\n 4635: [4635 - 210858_x_at (numeric)],\n 4636: [4636 - 210869_s_at (numeric)],\n 4637: [4637 - 210881_s_at (numeric)],\n 4638: [4638 - 210889_s_at (numeric)],\n 4639: [4639 - 210891_s_at (numeric)],\n 4640: [4640 - 210896_s_at (numeric)],\n 4641: [4641 - 210904_s_at (numeric)],\n 4642: [4642 - 210907_s_at (numeric)],\n 4643: [4643 - 210908_s_at (numeric)],\n 4644: [4644 - 210915_x_at (numeric)],\n 4645: [4645 - 210927_x_at (numeric)],\n 4646: [4646 - 210929_s_at (numeric)],\n 4647: [4647 - 210930_s_at (numeric)],\n 4648: [4648 - 210933_s_at (numeric)],\n 4649: [4649 - 210944_s_at (numeric)],\n 4650: [4650 - 210946_at (numeric)],\n 4651: [4651 - 210949_s_at (numeric)],\n 4652: [4652 - 210950_s_at (numeric)],\n 4653: [4653 - 210962_s_at (numeric)],\n 4654: [4654 - 210968_s_at (numeric)],\n 4655: [4655 - 210970_s_at (numeric)],\n 4656: [4656 - 210972_x_at (numeric)],\n 4657: [4657 - 210973_s_at (numeric)],\n 4658: [4658 - 210976_s_at (numeric)],\n 4659: [4659 - 210978_s_at (numeric)],\n 4660: [4660 - 210980_s_at (numeric)],\n 4661: [4661 - 210982_s_at (numeric)],\n 4662: [4662 - 210983_s_at (numeric)],\n 4663: [4663 - 210986_s_at (numeric)],\n 4664: [4664 - 210987_x_at (numeric)],\n 4665: [4665 - 210993_s_at (numeric)],\n 4666: [4666 - 210996_s_at (numeric)],\n 4667: [4667 - 210997_at (numeric)],\n 4668: [4668 - 210998_s_at (numeric)],\n 4669: [4669 - 210999_s_at (numeric)],\n 4670: [4670 - 211015_s_at (numeric)],\n 4671: [4671 - 211023_at (numeric)],\n 4672: [4672 - 211025_x_at (numeric)],\n 4673: [4673 - 211026_s_at (numeric)],\n 4674: [4674 - 211028_s_at (numeric)],\n 4675: [4675 - 211036_x_at (numeric)],\n 4676: [4676 - 211038_s_at (numeric)],\n 4677: [4677 - 211040_x_at (numeric)],\n 4678: [4678 - 211042_x_at (numeric)],\n 4679: [4679 - 211043_s_at (numeric)],\n 4680: [4680 - 211047_x_at (numeric)],\n 4681: [4681 - 211048_s_at (numeric)],\n 4682: [4682 - 211058_x_at (numeric)],\n 4683: [4683 - 211060_x_at (numeric)],\n 4684: [4684 - 211063_s_at (numeric)],\n 4685: [4685 - 211065_x_at (numeric)],\n 4686: [4686 - 211066_x_at (numeric)],\n 4687: [4687 - 211068_x_at (numeric)],\n 4688: [4688 - 211069_s_at (numeric)],\n 4689: [4689 - 211070_x_at (numeric)],\n 4690: [4690 - 211071_s_at (numeric)],\n 4691: [4691 - 211072_x_at (numeric)],\n 4692: [4692 - 211073_x_at (numeric)],\n 4693: [4693 - 211074_at (numeric)],\n 4694: [4694 - 211075_s_at (numeric)],\n 4695: [4695 - 211090_s_at (numeric)],\n 4696: [4696 - 211098_x_at (numeric)],\n 4697: [4697 - 211122_s_at (numeric)],\n 4698: [4698 - 211126_s_at (numeric)],\n 4699: [4699 - 211136_s_at (numeric)],\n 4700: [4700 - 211138_s_at (numeric)],\n 4701: [4701 - 211148_s_at (numeric)],\n 4702: [4702 - 211160_x_at (numeric)],\n 4703: [4703 - 211161_s_at (numeric)],\n 4704: [4704 - 211184_s_at (numeric)],\n 4705: [4705 - 211185_s_at (numeric)],\n 4706: [4706 - 211202_s_at (numeric)],\n 4707: [4707 - 211208_s_at (numeric)],\n 4708: [4708 - 211239_s_at (numeric)],\n 4709: [4709 - 211240_x_at (numeric)],\n 4710: [4710 - 211251_x_at (numeric)],\n 4711: [4711 - 211257_x_at (numeric)],\n 4712: [4712 - 211270_x_at (numeric)],\n 4713: [4713 - 211271_x_at (numeric)],\n 4714: [4714 - 211275_s_at (numeric)],\n 4715: [4715 - 211276_at (numeric)],\n 4716: [4716 - 211277_x_at (numeric)],\n 4717: [4717 - 211284_s_at (numeric)],\n 4718: [4718 - 211285_s_at (numeric)],\n 4719: [4719 - 211296_x_at (numeric)],\n 4720: [4720 - 211297_s_at (numeric)],\n 4721: [4721 - 211298_s_at (numeric)],\n 4722: [4722 - 211316_x_at (numeric)],\n 4723: [4723 - 211317_s_at (numeric)],\n 4724: [4724 - 211318_s_at (numeric)],\n 4725: [4725 - 211323_s_at (numeric)],\n 4726: [4726 - 211325_x_at (numeric)],\n 4727: [4727 - 211340_s_at (numeric)],\n 4728: [4728 - 211343_s_at (numeric)],\n 4729: [4729 - 211345_x_at (numeric)],\n 4730: [4730 - 211357_s_at (numeric)],\n 4731: [4731 - 211366_x_at (numeric)],\n 4732: [4732 - 211368_s_at (numeric)],\n 4733: [4733 - 211372_s_at (numeric)],\n 4734: [4734 - 211375_s_at (numeric)],\n 4735: [4735 - 211376_s_at (numeric)],\n 4736: [4736 - 211378_x_at (numeric)],\n 4737: [4737 - 211382_s_at (numeric)],\n 4738: [4738 - 211395_x_at (numeric)],\n 4739: [4739 - 211404_s_at (numeric)],\n 4740: [4740 - 211416_x_at (numeric)],\n 4741: [4741 - 211417_x_at (numeric)],\n 4742: [4742 - 211423_s_at (numeric)],\n 4743: [4743 - 211429_s_at (numeric)],\n 4744: [4744 - 211430_s_at (numeric)],\n 4745: [4745 - 211445_x_at (numeric)],\n 4746: [4746 - 211450_s_at (numeric)],\n 4747: [4747 - 211452_x_at (numeric)],\n 4748: [4748 - 211454_x_at (numeric)],\n 4749: [4749 - 211456_x_at (numeric)],\n 4750: [4750 - 211458_s_at (numeric)],\n 4751: [4751 - 211464_x_at (numeric)],\n 4752: [4752 - 211465_x_at (numeric)],\n 4753: [4753 - 211470_s_at (numeric)],\n 4754: [4754 - 211474_s_at (numeric)],\n 4755: [4755 - 211475_s_at (numeric)],\n 4756: [4756 - 211478_s_at (numeric)],\n 4757: [4757 - 211487_x_at (numeric)],\n 4758: [4758 - 211494_s_at (numeric)],\n 4759: [4759 - 211495_x_at (numeric)],\n 4760: [4760 - 211501_s_at (numeric)],\n 4761: [4761 - 211503_s_at (numeric)],\n 4762: [4762 - 211505_s_at (numeric)],\n 4763: [4763 - 211506_s_at (numeric)],\n 4764: [4764 - 211509_s_at (numeric)],\n 4765: [4765 - 211527_x_at (numeric)],\n 4766: [4766 - 211528_x_at (numeric)],\n 4767: [4767 - 211529_x_at (numeric)],\n 4768: [4768 - 211530_x_at (numeric)],\n 4769: [4769 - 211535_s_at (numeric)],\n 4770: [4770 - 211538_s_at (numeric)],\n 4771: [4771 - 211542_x_at (numeric)],\n 4772: [4772 - 211548_s_at (numeric)],\n 4773: [4773 - 211549_s_at (numeric)],\n 4774: [4774 - 211559_s_at (numeric)],\n 4775: [4775 - 211563_s_at (numeric)],\n 4776: [4776 - 211565_at (numeric)],\n 4777: [4777 - 211569_s_at (numeric)],\n 4778: [4778 - 211571_s_at (numeric)],\n 4779: [4779 - 211574_s_at (numeric)],\n 4780: [4780 - 211577_s_at (numeric)],\n 4781: [4781 - 211596_s_at (numeric)],\n 4782: [4782 - 211597_s_at (numeric)],\n 4783: [4783 - 211599_x_at (numeric)],\n 4784: [4784 - 211600_at (numeric)],\n 4785: [4785 - 211609_x_at (numeric)],\n 4786: [4786 - 211612_s_at (numeric)],\n 4787: [4787 - 211615_s_at (numeric)],\n 4788: [4788 - 211619_s_at (numeric)],\n 4789: [4789 - 211623_s_at (numeric)],\n 4790: [4790 - 211628_x_at (numeric)],\n 4791: [4791 - 211630_s_at (numeric)],\n 4792: [4792 - 211633_x_at (numeric)],\n 4793: [4793 - 211634_x_at (numeric)],\n 4794: [4794 - 211635_x_at (numeric)],\n 4795: [4795 - 211637_x_at (numeric)],\n 4796: [4796 - 211639_x_at (numeric)],\n 4797: [4797 - 211641_x_at (numeric)],\n 4798: [4798 - 211643_x_at (numeric)],\n 4799: [4799 - 211644_x_at (numeric)],\n 4800: [4800 - 211645_x_at (numeric)],\n 4801: [4801 - 211650_x_at (numeric)],\n 4802: [4802 - 211651_s_at (numeric)],\n 4803: [4803 - 211652_s_at (numeric)],\n 4804: [4804 - 211653_x_at (numeric)],\n 4805: [4805 - 211654_x_at (numeric)],\n 4806: [4806 - 211656_x_at (numeric)],\n 4807: [4807 - 211657_at (numeric)],\n 4808: [4808 - 211662_s_at (numeric)],\n 4809: [4809 - 211663_x_at (numeric)],\n 4810: [4810 - 211666_x_at (numeric)],\n 4811: [4811 - 211668_s_at (numeric)],\n 4812: [4812 - 211671_s_at (numeric)],\n 4813: [4813 - 211674_x_at (numeric)],\n 4814: [4814 - 211675_s_at (numeric)],\n 4815: [4815 - 211676_s_at (numeric)],\n 4816: [4816 - 211678_s_at (numeric)],\n 4817: [4817 - 211682_x_at (numeric)],\n 4818: [4818 - 211684_s_at (numeric)],\n 4819: [4819 - 211685_s_at (numeric)],\n 4820: [4820 - 211695_x_at (numeric)],\n 4821: [4821 - 211696_x_at (numeric)],\n 4822: [4822 - 211697_x_at (numeric)],\n 4823: [4823 - 211698_at (numeric)],\n 4824: [4824 - 211699_x_at (numeric)],\n 4825: [4825 - 211709_s_at (numeric)],\n 4826: [4826 - 211710_x_at (numeric)],\n 4827: [4827 - 211714_x_at (numeric)],\n 4828: [4828 - 211715_s_at (numeric)],\n 4829: [4829 - 211716_x_at (numeric)],\n 4830: [4830 - 211719_x_at (numeric)],\n 4831: [4831 - 211720_x_at (numeric)],\n 4832: [4832 - 211726_s_at (numeric)],\n 4833: [4833 - 211727_s_at (numeric)],\n 4834: [4834 - 211729_x_at (numeric)],\n 4835: [4835 - 211730_s_at (numeric)],\n 4836: [4836 - 211733_x_at (numeric)],\n 4837: [4837 - 211735_x_at (numeric)],\n 4838: [4838 - 211737_x_at (numeric)],\n 4839: [4839 - 211742_s_at (numeric)],\n 4840: [4840 - 211745_x_at (numeric)],\n 4841: [4841 - 211746_x_at (numeric)],\n 4842: [4842 - 211747_s_at (numeric)],\n 4843: [4843 - 211748_x_at (numeric)],\n 4844: [4844 - 211749_s_at (numeric)],\n 4845: [4845 - 211750_x_at (numeric)],\n 4846: [4846 - 211752_s_at (numeric)],\n 4847: [4847 - 211755_s_at (numeric)],\n 4848: [4848 - 211756_at (numeric)],\n 4849: [4849 - 211758_x_at (numeric)],\n 4850: [4850 - 211759_x_at (numeric)],\n 4851: [4851 - 211761_s_at (numeric)],\n 4852: [4852 - 211762_s_at (numeric)],\n 4853: [4853 - 211763_s_at (numeric)],\n 4854: [4854 - 211764_s_at (numeric)],\n 4855: [4855 - 211765_x_at (numeric)],\n 4856: [4856 - 211769_x_at (numeric)],\n 4857: [4857 - 211779_x_at (numeric)],\n 4858: [4858 - 211783_s_at (numeric)],\n 4859: [4859 - 211784_s_at (numeric)],\n 4860: [4860 - 211787_s_at (numeric)],\n 4861: [4861 - 211795_s_at (numeric)],\n 4862: [4862 - 211796_s_at (numeric)],\n 4863: [4863 - 211798_x_at (numeric)],\n 4864: [4864 - 211799_x_at (numeric)],\n 4865: [4865 - 211806_s_at (numeric)],\n 4866: [4866 - 211813_x_at (numeric)],\n 4867: [4867 - 211828_s_at (numeric)],\n 4868: [4868 - 211848_s_at (numeric)],\n 4869: [4869 - 211858_x_at (numeric)],\n 4870: [4870 - 211862_x_at (numeric)],\n 4871: [4871 - 211864_s_at (numeric)],\n 4872: [4872 - 211868_x_at (numeric)],\n 4873: [4873 - 211881_x_at (numeric)],\n 4874: [4874 - 211883_x_at (numeric)],\n 4875: [4875 - 211896_s_at (numeric)],\n 4876: [4876 - 211908_x_at (numeric)],\n 4877: [4877 - 211911_x_at (numeric)],\n 4878: [4878 - 211919_s_at (numeric)],\n 4879: [4879 - 211921_x_at (numeric)],\n 4880: [4880 - 211922_s_at (numeric)],\n 4881: [4881 - 211924_s_at (numeric)],\n 4882: [4882 - 211926_s_at (numeric)],\n 4883: [4883 - 211927_x_at (numeric)],\n 4884: [4884 - 211928_at (numeric)],\n 4885: [4885 - 211929_at (numeric)],\n 4886: [4886 - 211930_at (numeric)],\n 4887: [4887 - 211931_s_at (numeric)],\n 4888: [4888 - 211932_at (numeric)],\n 4889: [4889 - 211933_s_at (numeric)],\n 4890: [4890 - 211934_x_at (numeric)],\n 4891: [4891 - 211935_at (numeric)],\n 4892: [4892 - 211936_at (numeric)],\n 4893: [4893 - 211937_at (numeric)],\n 4894: [4894 - 211938_at (numeric)],\n 4895: [4895 - 211939_x_at (numeric)],\n 4896: [4896 - 211940_x_at (numeric)],\n 4897: [4897 - 211941_s_at (numeric)],\n 4898: [4898 - 211942_x_at (numeric)],\n 4899: [4899 - 211943_x_at (numeric)],\n 4900: [4900 - 211945_s_at (numeric)],\n 4901: [4901 - 211946_s_at (numeric)],\n 4902: [4902 - 211948_x_at (numeric)],\n 4903: [4903 - 211950_at (numeric)],\n 4904: [4904 - 211951_at (numeric)],\n 4905: [4905 - 211953_s_at (numeric)],\n 4906: [4906 - 211954_s_at (numeric)],\n 4907: [4907 - 211955_at (numeric)],\n 4908: [4908 - 211956_s_at (numeric)],\n 4909: [4909 - 211959_at (numeric)],\n 4910: [4910 - 211960_s_at (numeric)],\n 4911: [4911 - 211961_s_at (numeric)],\n 4912: [4912 - 211962_s_at (numeric)],\n 4913: [4913 - 211963_s_at (numeric)],\n 4914: [4914 - 211964_at (numeric)],\n 4915: [4915 - 211966_at (numeric)],\n 4916: [4916 - 211967_at (numeric)],\n 4917: [4917 - 211968_s_at (numeric)],\n 4918: [4918 - 211969_at (numeric)],\n 4919: [4919 - 211970_x_at (numeric)],\n 4920: [4920 - 211971_s_at (numeric)],\n 4921: [4921 - 211972_x_at (numeric)],\n 4922: [4922 - 211974_x_at (numeric)],\n 4923: [4923 - 211976_at (numeric)],\n 4924: [4924 - 211978_x_at (numeric)],\n 4925: [4925 - 211980_at (numeric)],\n 4926: [4926 - 211981_at (numeric)],\n 4927: [4927 - 211983_x_at (numeric)],\n 4928: [4928 - 211984_at (numeric)],\n 4929: [4929 - 211985_s_at (numeric)],\n 4930: [4930 - 211986_at (numeric)],\n 4931: [4931 - 211987_at (numeric)],\n 4932: [4932 - 211988_at (numeric)],\n 4933: [4933 - 211989_at (numeric)],\n 4934: [4934 - 211990_at (numeric)],\n 4935: [4935 - 211991_s_at (numeric)],\n 4936: [4936 - 211992_at (numeric)],\n 4937: [4937 - 211994_at (numeric)],\n 4938: [4938 - 211995_x_at (numeric)],\n 4939: [4939 - 211996_s_at (numeric)],\n 4940: [4940 - 211997_x_at (numeric)],\n 4941: [4941 - 211998_at (numeric)],\n 4942: [4942 - 211999_at (numeric)],\n 4943: [4943 - 212006_at (numeric)],\n 4944: [4944 - 212007_at (numeric)],\n 4945: [4945 - 212008_at (numeric)],\n 4946: [4946 - 212010_s_at (numeric)],\n 4947: [4947 - 212012_at (numeric)],\n 4948: [4948 - 212013_at (numeric)],\n 4949: [4949 - 212014_x_at (numeric)],\n 4950: [4950 - 212015_x_at (numeric)],\n 4951: [4951 - 212017_at (numeric)],\n 4952: [4952 - 212018_s_at (numeric)],\n 4953: [4953 - 212021_s_at (numeric)],\n 4954: [4954 - 212022_s_at (numeric)],\n 4955: [4955 - 212024_x_at (numeric)],\n 4956: [4956 - 212027_at (numeric)],\n 4957: [4957 - 212032_s_at (numeric)],\n 4958: [4958 - 212033_at (numeric)],\n 4959: [4959 - 212034_s_at (numeric)],\n 4960: [4960 - 212036_s_at (numeric)],\n 4961: [4961 - 212037_at (numeric)],\n 4962: [4962 - 212038_s_at (numeric)],\n 4963: [4963 - 212039_x_at (numeric)],\n 4964: [4964 - 212040_at (numeric)],\n 4965: [4965 - 212041_at (numeric)],\n 4966: [4966 - 212042_x_at (numeric)],\n 4967: [4967 - 212043_at (numeric)],\n 4968: [4968 - 212044_s_at (numeric)],\n 4969: [4969 - 212047_s_at (numeric)],\n 4970: [4970 - 212048_s_at (numeric)],\n 4971: [4971 - 212050_at (numeric)],\n 4972: [4972 - 212052_s_at (numeric)],\n 4973: [4973 - 212053_at (numeric)],\n 4974: [4974 - 212055_at (numeric)],\n 4975: [4975 - 212056_at (numeric)],\n 4976: [4976 - 212057_at (numeric)],\n 4977: [4977 - 212058_at (numeric)],\n 4978: [4978 - 212062_at (numeric)],\n 4979: [4979 - 212063_at (numeric)],\n 4980: [4980 - 212064_x_at (numeric)],\n 4981: [4981 - 212066_s_at (numeric)],\n 4982: [4982 - 212067_s_at (numeric)],\n 4983: [4983 - 212068_s_at (numeric)],\n 4984: [4984 - 212070_at (numeric)],\n 4985: [4985 - 212071_s_at (numeric)],\n 4986: [4986 - 212072_s_at (numeric)],\n 4987: [4987 - 212074_at (numeric)],\n 4988: [4988 - 212077_at (numeric)],\n 4989: [4989 - 212081_x_at (numeric)],\n 4990: [4990 - 212082_s_at (numeric)],\n 4991: [4991 - 212085_at (numeric)],\n 4992: [4992 - 212086_x_at (numeric)],\n 4993: [4993 - 212089_at (numeric)],\n 4994: [4994 - 212090_at (numeric)],\n 4995: [4995 - 212091_s_at (numeric)],\n 4996: [4996 - 212092_at (numeric)],\n 4997: [4997 - 212094_at (numeric)],\n 4998: [4998 - 212096_s_at (numeric)],\n 4999: [4999 - 212097_at (numeric)],\n 5000: [5000 - 212098_at (numeric)],\n 5001: [5001 - 212099_at (numeric)],\n 5002: [5002 - 212104_s_at (numeric)],\n 5003: [5003 - 212108_at (numeric)],\n 5004: [5004 - 212110_at (numeric)],\n 5005: [5005 - 212112_s_at (numeric)],\n 5006: [5006 - 212114_at (numeric)],\n 5007: [5007 - 212115_at (numeric)],\n 5008: [5008 - 212116_at (numeric)],\n 5009: [5009 - 212117_at (numeric)],\n 5010: [5010 - 212118_at (numeric)],\n 5011: [5011 - 212119_at (numeric)],\n 5012: [5012 - 212120_at (numeric)],\n 5013: [5013 - 212121_at (numeric)],\n 5014: [5014 - 212124_at (numeric)],\n 5015: [5015 - 212126_at (numeric)],\n 5016: [5016 - 212129_at (numeric)],\n 5017: [5017 - 212130_x_at (numeric)],\n 5018: [5018 - 212131_at (numeric)],\n 5019: [5019 - 212132_at (numeric)],\n 5020: [5020 - 212135_s_at (numeric)],\n 5021: [5021 - 212136_at (numeric)],\n 5022: [5022 - 212137_at (numeric)],\n 5023: [5023 - 212138_at (numeric)],\n 5024: [5024 - 212139_at (numeric)],\n 5025: [5025 - 212141_at (numeric)],\n 5026: [5026 - 212143_s_at (numeric)],\n 5027: [5027 - 212148_at (numeric)],\n 5028: [5028 - 212149_at (numeric)],\n 5029: [5029 - 212150_at (numeric)],\n 5030: [5030 - 212151_at (numeric)],\n 5031: [5031 - 212152_x_at (numeric)],\n 5032: [5032 - 212153_at (numeric)],\n 5033: [5033 - 212154_at (numeric)],\n 5034: [5034 - 212155_at (numeric)],\n 5035: [5035 - 212157_at (numeric)],\n 5036: [5036 - 212158_at (numeric)],\n 5037: [5037 - 212159_x_at (numeric)],\n 5038: [5038 - 212160_at (numeric)],\n 5039: [5039 - 212163_at (numeric)],\n 5040: [5040 - 212165_at (numeric)],\n 5041: [5041 - 212166_at (numeric)],\n 5042: [5042 - 212167_s_at (numeric)],\n 5043: [5043 - 212168_at (numeric)],\n 5044: [5044 - 212169_at (numeric)],\n 5045: [5045 - 212171_x_at (numeric)],\n 5046: [5046 - 212175_s_at (numeric)],\n 5047: [5047 - 212176_at (numeric)],\n 5048: [5048 - 212177_at (numeric)],\n 5049: [5049 - 212179_at (numeric)],\n 5050: [5050 - 212180_at (numeric)],\n 5051: [5051 - 212181_s_at (numeric)],\n 5052: [5052 - 212183_at (numeric)],\n 5053: [5053 - 212184_s_at (numeric)],\n 5054: [5054 - 212185_x_at (numeric)],\n 5055: [5055 - 212186_at (numeric)],\n 5056: [5056 - 212187_x_at (numeric)],\n 5057: [5057 - 212188_at (numeric)],\n 5058: [5058 - 212190_at (numeric)],\n 5059: [5059 - 212191_x_at (numeric)],\n 5060: [5060 - 212192_at (numeric)],\n 5061: [5061 - 212195_at (numeric)],\n 5062: [5062 - 212196_at (numeric)],\n 5063: [5063 - 212197_x_at (numeric)],\n 5064: [5064 - 212198_s_at (numeric)],\n 5065: [5065 - 212199_at (numeric)],\n 5066: [5066 - 212202_s_at (numeric)],\n 5067: [5067 - 212203_x_at (numeric)],\n 5068: [5068 - 212204_at (numeric)],\n 5069: [5069 - 212205_at (numeric)],\n 5070: [5070 - 212206_s_at (numeric)],\n 5071: [5071 - 212209_at (numeric)],\n 5072: [5072 - 212215_at (numeric)],\n 5073: [5073 - 212217_at (numeric)],\n 5074: [5074 - 212218_s_at (numeric)],\n 5075: [5075 - 212219_at (numeric)],\n 5076: [5076 - 212221_x_at (numeric)],\n 5077: [5077 - 212223_at (numeric)],\n 5078: [5078 - 212224_at (numeric)],\n 5079: [5079 - 212226_s_at (numeric)],\n 5080: [5080 - 212227_x_at (numeric)],\n 5081: [5081 - 212228_s_at (numeric)],\n 5082: [5082 - 212229_s_at (numeric)],\n 5083: [5083 - 212230_at (numeric)],\n 5084: [5084 - 212231_at (numeric)],\n 5085: [5085 - 212232_at (numeric)],\n 5086: [5086 - 212233_at (numeric)],\n 5087: [5087 - 212235_at (numeric)],\n 5088: [5088 - 212236_x_at (numeric)],\n 5089: [5089 - 212239_at (numeric)],\n 5090: [5090 - 212240_s_at (numeric)],\n 5091: [5091 - 212242_at (numeric)],\n 5092: [5092 - 212245_at (numeric)],\n 5093: [5093 - 212247_at (numeric)],\n 5094: [5094 - 212248_at (numeric)],\n 5095: [5095 - 212250_at (numeric)],\n 5096: [5096 - 212251_at (numeric)],\n 5097: [5097 - 212254_s_at (numeric)],\n 5098: [5098 - 212255_s_at (numeric)],\n 5099: [5099 - 212263_at (numeric)],\n 5100: [5100 - 212265_at (numeric)],\n 5101: [5101 - 212266_s_at (numeric)],\n 5102: [5102 - 212268_at (numeric)],\n 5103: [5103 - 212270_x_at (numeric)],\n 5104: [5104 - 212271_at (numeric)],\n 5105: [5105 - 212273_x_at (numeric)],\n 5106: [5106 - 212274_at (numeric)],\n 5107: [5107 - 212276_at (numeric)],\n 5108: [5108 - 212279_at (numeric)],\n 5109: [5109 - 212281_s_at (numeric)],\n 5110: [5110 - 212282_at (numeric)],\n 5111: [5111 - 212284_x_at (numeric)],\n 5112: [5112 - 212285_s_at (numeric)],\n 5113: [5113 - 212287_at (numeric)],\n 5114: [5114 - 212288_at (numeric)],\n 5115: [5115 - 212290_at (numeric)],\n 5116: [5116 - 212293_at (numeric)],\n 5117: [5117 - 212294_at (numeric)],\n 5118: [5118 - 212295_s_at (numeric)],\n 5119: [5119 - 212296_at (numeric)],\n 5120: [5120 - 212297_at (numeric)],\n 5121: [5121 - 212298_at (numeric)],\n 5122: [5122 - 212307_s_at (numeric)],\n 5123: [5123 - 212311_at (numeric)],\n 5124: [5124 - 212312_at (numeric)],\n 5125: [5125 - 212314_at (numeric)],\n 5126: [5126 - 212318_at (numeric)],\n 5127: [5127 - 212320_at (numeric)],\n 5128: [5128 - 212321_at (numeric)],\n 5129: [5129 - 212322_at (numeric)],\n 5130: [5130 - 212327_at (numeric)],\n 5131: [5131 - 212328_at (numeric)],\n 5132: [5132 - 212329_at (numeric)],\n 5133: [5133 - 212330_at (numeric)],\n 5134: [5134 - 212331_at (numeric)],\n 5135: [5135 - 212333_at (numeric)],\n 5136: [5136 - 212334_at (numeric)],\n 5137: [5137 - 212335_at (numeric)],\n 5138: [5138 - 212337_at (numeric)],\n 5139: [5139 - 212338_at (numeric)],\n 5140: [5140 - 212341_at (numeric)],\n 5141: [5141 - 212344_at (numeric)],\n 5142: [5142 - 212345_s_at (numeric)],\n 5143: [5143 - 212348_s_at (numeric)],\n 5144: [5144 - 212350_at (numeric)],\n 5145: [5145 - 212352_s_at (numeric)],\n 5146: [5146 - 212353_at (numeric)],\n 5147: [5147 - 212354_at (numeric)],\n 5148: [5148 - 212361_s_at (numeric)],\n 5149: [5149 - 212363_x_at (numeric)],\n 5150: [5150 - 212364_at (numeric)],\n 5151: [5151 - 212365_at (numeric)],\n 5152: [5152 - 212368_at (numeric)],\n 5153: [5153 - 212370_x_at (numeric)],\n 5154: [5154 - 212371_at (numeric)],\n 5155: [5155 - 212372_at (numeric)],\n 5156: [5156 - 212377_s_at (numeric)],\n 5157: [5157 - 212378_at (numeric)],\n 5158: [5158 - 212382_at (numeric)],\n 5159: [5159 - 212383_at (numeric)],\n 5160: [5160 - 212385_at (numeric)],\n 5161: [5161 - 212386_at (numeric)],\n 5162: [5162 - 212387_at (numeric)],\n 5163: [5163 - 212391_x_at (numeric)],\n 5164: [5164 - 212397_at (numeric)],\n 5165: [5165 - 212398_at (numeric)],\n 5166: [5166 - 212399_s_at (numeric)],\n 5167: [5167 - 212400_at (numeric)],\n 5168: [5168 - 212401_s_at (numeric)],\n 5169: [5169 - 212405_s_at (numeric)],\n 5170: [5170 - 212406_s_at (numeric)],\n 5171: [5171 - 212408_at (numeric)],\n 5172: [5172 - 212410_at (numeric)],\n 5173: [5173 - 212411_at (numeric)],\n 5174: [5174 - 212412_at (numeric)],\n 5175: [5175 - 212416_at (numeric)],\n 5176: [5176 - 212417_at (numeric)],\n 5177: [5177 - 212418_at (numeric)],\n 5178: [5178 - 212420_at (numeric)],\n 5179: [5179 - 212423_at (numeric)],\n 5180: [5180 - 212426_s_at (numeric)],\n 5181: [5181 - 212428_at (numeric)],\n 5182: [5182 - 212429_s_at (numeric)],\n 5183: [5183 - 212430_at (numeric)],\n 5184: [5184 - 212433_x_at (numeric)],\n 5185: [5185 - 212435_at (numeric)],\n 5186: [5186 - 212441_at (numeric)],\n 5187: [5187 - 212442_s_at (numeric)],\n 5188: [5188 - 212444_at (numeric)],\n 5189: [5189 - 212445_s_at (numeric)],\n 5190: [5190 - 212446_s_at (numeric)],\n 5191: [5191 - 212447_at (numeric)],\n 5192: [5192 - 212448_at (numeric)],\n 5193: [5193 - 212449_s_at (numeric)],\n 5194: [5194 - 212450_at (numeric)],\n 5195: [5195 - 212451_at (numeric)],\n 5196: [5196 - 212454_x_at (numeric)],\n 5197: [5197 - 212455_at (numeric)],\n 5198: [5198 - 212456_at (numeric)],\n 5199: [5199 - 212458_at (numeric)],\n 5200: [5200 - 212459_x_at (numeric)],\n 5201: [5201 - 212460_at (numeric)],\n 5202: [5202 - 212461_at (numeric)],\n 5203: [5203 - 212462_at (numeric)],\n 5204: [5204 - 212463_at (numeric)],\n 5205: [5205 - 212464_s_at (numeric)],\n 5206: [5206 - 212465_at (numeric)],\n 5207: [5207 - 212470_at (numeric)],\n 5208: [5208 - 212473_s_at (numeric)],\n 5209: [5209 - 212474_at (numeric)],\n 5210: [5210 - 212476_at (numeric)],\n 5211: [5211 - 212481_s_at (numeric)],\n 5212: [5212 - 212482_at (numeric)],\n 5213: [5213 - 212488_at (numeric)],\n 5214: [5214 - 212489_at (numeric)],\n 5215: [5215 - 212492_s_at (numeric)],\n 5216: [5216 - 212493_s_at (numeric)],\n 5217: [5217 - 212494_at (numeric)],\n 5218: [5218 - 212496_s_at (numeric)],\n 5219: [5219 - 212498_at (numeric)],\n 5220: [5220 - 212501_at (numeric)],\n 5221: [5221 - 212503_s_at (numeric)],\n 5222: [5222 - 212504_at (numeric)],\n 5223: [5223 - 212506_at (numeric)],\n 5224: [5224 - 212507_at (numeric)],\n 5225: [5225 - 212508_at (numeric)],\n 5226: [5226 - 212509_s_at (numeric)],\n 5227: [5227 - 212510_at (numeric)],\n 5228: [5228 - 212511_at (numeric)],\n 5229: [5229 - 212513_s_at (numeric)],\n 5230: [5230 - 212514_x_at (numeric)],\n 5231: [5231 - 212515_s_at (numeric)],\n 5232: [5232 - 212519_at (numeric)],\n 5233: [5233 - 212520_s_at (numeric)],\n 5234: [5234 - 212522_at (numeric)],\n 5235: [5235 - 212526_at (numeric)],\n 5236: [5236 - 212530_at (numeric)],\n 5237: [5237 - 212531_at (numeric)],\n 5238: [5238 - 212532_s_at (numeric)],\n 5239: [5239 - 212533_at (numeric)],\n 5240: [5240 - 212534_at (numeric)],\n 5241: [5241 - 212535_at (numeric)],\n 5242: [5242 - 212537_x_at (numeric)],\n 5243: [5243 - 212538_at (numeric)],\n 5244: [5244 - 212539_at (numeric)],\n 5245: [5245 - 212542_s_at (numeric)],\n 5246: [5246 - 212543_at (numeric)],\n 5247: [5247 - 212544_at (numeric)],\n 5248: [5248 - 212546_s_at (numeric)],\n 5249: [5249 - 212548_s_at (numeric)],\n 5250: [5250 - 212551_at (numeric)],\n 5251: [5251 - 212552_at (numeric)],\n 5252: [5252 - 212553_at (numeric)],\n 5253: [5253 - 212554_at (numeric)],\n 5254: [5254 - 212556_at (numeric)],\n 5255: [5255 - 212558_at (numeric)],\n 5256: [5256 - 212560_at (numeric)],\n 5257: [5257 - 212561_at (numeric)],\n 5258: [5258 - 212566_at (numeric)],\n 5259: [5259 - 212568_s_at (numeric)],\n 5260: [5260 - 212572_at (numeric)],\n 5261: [5261 - 212573_at (numeric)],\n 5262: [5262 - 212578_x_at (numeric)],\n 5263: [5263 - 212581_x_at (numeric)],\n 5264: [5264 - 212582_at (numeric)],\n 5265: [5265 - 212585_at (numeric)],\n 5266: [5266 - 212586_at (numeric)],\n 5267: [5267 - 212587_s_at (numeric)],\n 5268: [5268 - 212588_at (numeric)],\n 5269: [5269 - 212589_at (numeric)],\n 5270: [5270 - 212591_at (numeric)],\n 5271: [5271 - 212592_at (numeric)],\n 5272: [5272 - 212593_s_at (numeric)],\n 5273: [5273 - 212594_at (numeric)],\n 5274: [5274 - 212595_s_at (numeric)],\n 5275: [5275 - 212599_at (numeric)],\n 5276: [5276 - 212600_s_at (numeric)],\n 5277: [5277 - 212603_at (numeric)],\n 5278: [5278 - 212606_at (numeric)],\n 5279: [5279 - 212607_at (numeric)],\n 5280: [5280 - 212608_s_at (numeric)],\n 5281: [5281 - 212609_s_at (numeric)],\n 5282: [5282 - 212610_at (numeric)],\n 5283: [5283 - 212611_at (numeric)],\n 5284: [5284 - 212612_at (numeric)],\n 5285: [5285 - 212613_at (numeric)],\n 5286: [5286 - 212614_at (numeric)],\n 5287: [5287 - 212615_at (numeric)],\n 5288: [5288 - 212616_at (numeric)],\n 5289: [5289 - 212622_at (numeric)],\n 5290: [5290 - 212624_s_at (numeric)],\n 5291: [5291 - 212626_x_at (numeric)],\n 5292: [5292 - 212627_s_at (numeric)],\n 5293: [5293 - 212628_at (numeric)],\n 5294: [5294 - 212629_s_at (numeric)],\n 5295: [5295 - 212630_at (numeric)],\n 5296: [5296 - 212631_at (numeric)],\n 5297: [5297 - 212632_at (numeric)],\n 5298: [5298 - 212633_at (numeric)],\n 5299: [5299 - 212634_at (numeric)],\n 5300: [5300 - 212636_at (numeric)],\n 5301: [5301 - 212638_s_at (numeric)],\n 5302: [5302 - 212639_x_at (numeric)],\n 5303: [5303 - 212640_at (numeric)],\n 5304: [5304 - 212643_at (numeric)],\n 5305: [5305 - 212644_s_at (numeric)],\n 5306: [5306 - 212646_at (numeric)],\n 5307: [5307 - 212647_at (numeric)],\n 5308: [5308 - 212651_at (numeric)],\n 5309: [5309 - 212652_s_at (numeric)],\n 5310: [5310 - 212653_s_at (numeric)],\n 5311: [5311 - 212655_at (numeric)],\n 5312: [5312 - 212656_at (numeric)],\n 5313: [5313 - 212657_s_at (numeric)],\n 5314: [5314 - 212658_at (numeric)],\n 5315: [5315 - 212660_at (numeric)],\n 5316: [5316 - 212661_x_at (numeric)],\n 5317: [5317 - 212665_at (numeric)],\n 5318: [5318 - 212666_at (numeric)],\n 5319: [5319 - 212667_at (numeric)],\n 5320: [5320 - 212671_s_at (numeric)],\n 5321: [5321 - 212672_at (numeric)],\n 5322: [5322 - 212673_at (numeric)],\n 5323: [5323 - 212674_s_at (numeric)],\n 5324: [5324 - 212675_s_at (numeric)],\n 5325: [5325 - 212677_s_at (numeric)],\n 5326: [5326 - 212680_x_at (numeric)],\n 5327: [5327 - 212685_s_at (numeric)],\n 5328: [5328 - 212686_at (numeric)],\n 5329: [5329 - 212687_at (numeric)],\n 5330: [5330 - 212688_at (numeric)],\n 5331: [5331 - 212689_s_at (numeric)],\n 5332: [5332 - 212690_at (numeric)],\n 5333: [5333 - 212692_s_at (numeric)],\n 5334: [5334 - 212694_s_at (numeric)],\n 5335: [5335 - 212696_s_at (numeric)],\n 5336: [5336 - 212697_at (numeric)],\n 5337: [5337 - 212698_s_at (numeric)],\n 5338: [5338 - 212708_at (numeric)],\n 5339: [5339 - 212713_at (numeric)],\n 5340: [5340 - 212714_at (numeric)],\n 5341: [5341 - 212716_s_at (numeric)],\n 5342: [5342 - 212718_at (numeric)],\n 5343: [5343 - 212721_at (numeric)],\n 5344: [5344 - 212724_at (numeric)],\n 5345: [5345 - 212725_s_at (numeric)],\n 5346: [5346 - 212730_at (numeric)],\n 5347: [5347 - 212731_at (numeric)],\n 5348: [5348 - 212734_x_at (numeric)],\n 5349: [5349 - 212737_at (numeric)],\n 5350: [5350 - 212739_s_at (numeric)],\n 5351: [5351 - 212741_at (numeric)],\n 5352: [5352 - 212742_at (numeric)],\n 5353: [5353 - 212747_at (numeric)],\n 5354: [5354 - 212749_s_at (numeric)],\n 5355: [5355 - 212753_at (numeric)],\n 5356: [5356 - 212761_at (numeric)],\n 5357: [5357 - 212762_s_at (numeric)],\n 5358: [5358 - 212764_at (numeric)],\n 5359: [5359 - 212765_at (numeric)],\n 5360: [5360 - 212768_s_at (numeric)],\n 5361: [5361 - 212771_at (numeric)],\n 5362: [5362 - 212773_s_at (numeric)],\n 5363: [5363 - 212774_at (numeric)],\n 5364: [5364 - 212775_at (numeric)],\n 5365: [5365 - 212776_s_at (numeric)],\n 5366: [5366 - 212779_at (numeric)],\n 5367: [5367 - 212782_x_at (numeric)],\n 5368: [5368 - 212784_at (numeric)],\n 5369: [5369 - 212788_x_at (numeric)],\n 5370: [5370 - 212790_x_at (numeric)],\n 5371: [5371 - 212794_s_at (numeric)],\n 5372: [5372 - 212795_at (numeric)],\n 5373: [5373 - 212798_s_at (numeric)],\n 5374: [5374 - 212801_at (numeric)],\n 5375: [5375 - 212805_at (numeric)],\n 5376: [5376 - 212806_at (numeric)],\n 5377: [5377 - 212807_s_at (numeric)],\n 5378: [5378 - 212811_x_at (numeric)],\n 5379: [5379 - 212812_at (numeric)],\n 5380: [5380 - 212813_at (numeric)],\n 5381: [5381 - 212814_at (numeric)],\n 5382: [5382 - 212816_s_at (numeric)],\n 5383: [5383 - 212820_at (numeric)],\n 5384: [5384 - 212824_at (numeric)],\n 5385: [5385 - 212826_s_at (numeric)],\n 5386: [5386 - 212829_at (numeric)],\n 5387: [5387 - 212830_at (numeric)],\n 5388: [5388 - 212833_at (numeric)],\n 5389: [5389 - 212840_at (numeric)],\n 5390: [5390 - 212841_s_at (numeric)],\n 5391: [5391 - 212842_x_at (numeric)],\n 5392: [5392 - 212843_at (numeric)],\n 5393: [5393 - 212845_at (numeric)],\n 5394: [5394 - 212846_at (numeric)],\n 5395: [5395 - 212848_s_at (numeric)],\n 5396: [5396 - 212850_s_at (numeric)],\n 5397: [5397 - 212852_s_at (numeric)],\n 5398: [5398 - 212854_x_at (numeric)],\n 5399: [5399 - 212855_at (numeric)],\n 5400: [5400 - 212856_at (numeric)],\n 5401: [5401 - 212857_x_at (numeric)],\n 5402: [5402 - 212859_x_at (numeric)],\n 5403: [5403 - 212863_x_at (numeric)],\n 5404: [5404 - 212865_s_at (numeric)],\n 5405: [5405 - 212867_at (numeric)],\n 5406: [5406 - 212869_x_at (numeric)],\n 5407: [5407 - 212870_at (numeric)],\n 5408: [5408 - 212875_s_at (numeric)],\n 5409: [5409 - 212878_s_at (numeric)],\n 5410: [5410 - 212884_x_at (numeric)],\n 5411: [5411 - 212887_at (numeric)],\n 5412: [5412 - 212888_at (numeric)],\n 5413: [5413 - 212895_s_at (numeric)],\n 5414: [5414 - 212896_at (numeric)],\n 5415: [5415 - 212899_at (numeric)],\n 5416: [5416 - 212900_at (numeric)],\n 5417: [5417 - 212904_at (numeric)],\n 5418: [5418 - 212907_at (numeric)],\n 5419: [5419 - 212909_at (numeric)],\n 5420: [5420 - 212912_at (numeric)],\n 5421: [5421 - 212914_at (numeric)],\n 5422: [5422 - 212915_at (numeric)],\n 5423: [5423 - 212917_x_at (numeric)],\n 5424: [5424 - 212918_at (numeric)],\n 5425: [5425 - 212919_at (numeric)],\n 5426: [5426 - 212920_at (numeric)],\n 5427: [5427 - 212922_s_at (numeric)],\n 5428: [5428 - 212923_s_at (numeric)],\n 5429: [5429 - 212925_at (numeric)],\n 5430: [5430 - 212928_at (numeric)],\n 5431: [5431 - 212929_s_at (numeric)],\n 5432: [5432 - 212933_x_at (numeric)],\n 5433: [5433 - 212935_at (numeric)],\n 5434: [5434 - 212936_at (numeric)],\n 5435: [5435 - 212942_s_at (numeric)],\n 5436: [5436 - 212943_at (numeric)],\n 5437: [5437 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5463: [5463 - 212999_x_at (numeric)],\n 5464: [5464 - 213001_at (numeric)],\n 5465: [5465 - 213002_at (numeric)],\n 5466: [5466 - 213004_at (numeric)],\n 5467: [5467 - 213005_s_at (numeric)],\n 5468: [5468 - 213009_s_at (numeric)],\n 5469: [5469 - 213010_at (numeric)],\n 5470: [5470 - 213011_s_at (numeric)],\n 5471: [5471 - 213015_at (numeric)],\n 5472: [5472 - 213016_at (numeric)],\n 5473: [5473 - 213017_at (numeric)],\n 5474: [5474 - 213018_at (numeric)],\n 5475: [5475 - 213021_at (numeric)],\n 5476: [5476 - 213025_at (numeric)],\n 5477: [5477 - 213026_at (numeric)],\n 5478: [5478 - 213027_at (numeric)],\n 5479: [5479 - 213029_at (numeric)],\n 5480: [5480 - 213037_x_at (numeric)],\n 5481: [5481 - 213039_at (numeric)],\n 5482: [5482 - 213041_s_at (numeric)],\n 5483: [5483 - 213044_at (numeric)],\n 5484: [5484 - 213047_x_at (numeric)],\n 5485: [5485 - 213048_s_at (numeric)],\n 5486: [5486 - 213049_at (numeric)],\n 5487: [5487 - 213050_at (numeric)],\n 5488: [5488 - 213051_at (numeric)],\n 5489: [5489 - 213052_at (numeric)],\n 5490: [5490 - 213056_at (numeric)],\n 5491: [5491 - 213058_at (numeric)],\n 5492: [5492 - 213059_at (numeric)],\n 5493: [5493 - 213060_s_at (numeric)],\n 5494: [5494 - 213064_at (numeric)],\n 5495: [5495 - 213067_at (numeric)],\n 5496: [5496 - 213068_at (numeric)],\n 5497: [5497 - 213069_at (numeric)],\n 5498: [5498 - 213070_at (numeric)],\n 5499: [5499 - 213074_at (numeric)],\n 5500: [5500 - 213075_at (numeric)],\n 5501: [5501 - 213077_at (numeric)],\n 5502: [5502 - 213080_x_at (numeric)],\n 5503: [5503 - 213084_x_at (numeric)],\n 5504: [5504 - 213085_s_at (numeric)],\n 5505: [5505 - 213086_s_at (numeric)],\n 5506: [5506 - 213088_s_at (numeric)],\n 5507: [5507 - 213089_at (numeric)],\n 5508: [5508 - 213090_s_at (numeric)],\n 5509: [5509 - 213093_at (numeric)],\n 5510: [5510 - 213094_at (numeric)],\n 5511: [5511 - 213095_x_at (numeric)],\n 5512: [5512 - 213097_s_at (numeric)],\n 5513: [5513 - 213101_s_at (numeric)],\n 5514: [5514 - 213102_at (numeric)],\n 5515: [5515 - 213106_at (numeric)],\n 5516: [5516 - 213110_s_at (numeric)],\n 5517: [5517 - 213113_s_at (numeric)],\n 5518: [5518 - 213116_at (numeric)],\n 5519: [5519 - 213122_at (numeric)],\n 5520: [5520 - 213125_at (numeric)],\n 5521: [5521 - 213129_s_at (numeric)],\n 5522: [5522 - 213131_at (numeric)],\n 5523: [5523 - 213133_s_at (numeric)],\n 5524: [5524 - 213134_x_at (numeric)],\n 5525: [5525 - 213135_at (numeric)],\n 5526: [5526 - 213136_at (numeric)],\n 5527: [5527 - 213139_at (numeric)],\n 5528: [5528 - 213142_x_at (numeric)],\n 5529: [5529 - 213145_at (numeric)],\n 5530: [5530 - 213149_at (numeric)],\n 5531: [5531 - 213150_at (numeric)],\n 5532: [5532 - 213151_s_at (numeric)],\n 5533: [5533 - 213153_at (numeric)],\n 5534: [5534 - 213156_at (numeric)],\n 5535: [5535 - 213158_at (numeric)],\n 5536: [5536 - 213164_at (numeric)],\n 5537: [5537 - 213166_x_at (numeric)],\n 5538: [5538 - 213168_at (numeric)],\n 5539: [5539 - 213175_s_at (numeric)],\n 5540: [5540 - 213186_at (numeric)],\n 5541: [5541 - 213187_x_at (numeric)],\n 5542: [5542 - 213188_s_at (numeric)],\n 5543: [5543 - 213189_at (numeric)],\n 5544: [5544 - 213193_x_at (numeric)],\n 5545: [5545 - 213194_at (numeric)],\n 5546: [5546 - 213198_at (numeric)],\n 5547: [5547 - 213201_s_at (numeric)],\n 5548: [5548 - 213203_at (numeric)],\n 5549: [5549 - 213212_x_at (numeric)],\n 5550: [5550 - 213214_x_at (numeric)],\n 5551: [5551 - 213222_at (numeric)],\n 5552: [5552 - 213225_at (numeric)],\n 5553: [5553 - 213227_at (numeric)],\n 5554: [5554 - 213228_at (numeric)],\n 5555: [5555 - 213229_at (numeric)],\n 5556: [5556 - 213233_s_at (numeric)],\n 5557: [5557 - 213236_at (numeric)],\n 5558: [5558 - 213238_at (numeric)],\n 5559: [5559 - 213240_s_at (numeric)],\n 5560: [5560 - 213241_at (numeric)],\n 5561: [5561 - 213245_at (numeric)],\n 5562: [5562 - 213246_at (numeric)],\n 5563: [5563 - 213247_at (numeric)],\n 5564: [5564 - 213251_at (numeric)],\n 5565: [5565 - 213258_at (numeric)],\n 5566: [5566 - 213260_at (numeric)],\n 5567: [5567 - 213273_at (numeric)],\n 5568: [5568 - 213274_s_at (numeric)],\n 5569: [5569 - 213275_x_at (numeric)],\n 5570: [5570 - 213285_at (numeric)],\n 5571: [5571 - 213287_s_at (numeric)],\n 5572: [5572 - 213293_s_at (numeric)],\n 5573: [5573 - 213294_at (numeric)],\n 5574: [5574 - 213301_x_at (numeric)],\n 5575: [5575 - 213306_at (numeric)],\n 5576: [5576 - 213308_at (numeric)],\n 5577: [5577 - 213309_at (numeric)],\n 5578: [5578 - 213311_s_at (numeric)],\n 5579: [5579 - 213313_at (numeric)],\n 5580: [5580 - 213315_x_at (numeric)],\n 5581: [5581 - 213317_at (numeric)],\n 5582: [5582 - 213318_s_at (numeric)],\n 5583: [5583 - 213322_at (numeric)],\n 5584: [5584 - 213325_at (numeric)],\n 5585: [5585 - 213330_s_at (numeric)],\n 5586: [5586 - 213332_at (numeric)],\n 5587: [5587 - 213334_x_at (numeric)],\n 5588: [5588 - 213338_at (numeric)],\n 5589: [5589 - 213342_at (numeric)],\n 5590: [5590 - 213347_x_at (numeric)],\n 5591: [5591 - 213348_at (numeric)],\n 5592: [5592 - 213349_at (numeric)],\n 5593: [5593 - 213350_at (numeric)],\n 5594: [5594 - 213351_s_at (numeric)],\n 5595: [5595 - 213352_at (numeric)],\n 5596: [5596 - 213353_at (numeric)],\n 5597: [5597 - 213355_at (numeric)],\n 5598: [5598 - 213356_x_at (numeric)],\n 5599: [5599 - 213357_at (numeric)],\n 5600: [5600 - 213358_at (numeric)],\n 5601: [5601 - 213359_at (numeric)],\n 5602: [5602 - 213360_s_at (numeric)],\n 5603: [5603 - 213362_at (numeric)],\n 5604: [5604 - 213366_x_at (numeric)],\n 5605: [5605 - 213369_at (numeric)],\n 5606: [5606 - 213373_s_at (numeric)],\n 5607: [5607 - 213374_x_at (numeric)],\n 5608: [5608 - 213375_s_at (numeric)],\n 5609: [5609 - 213376_at (numeric)],\n 5610: [5610 - 213377_x_at (numeric)],\n 5611: [5611 - 213380_x_at (numeric)],\n 5612: [5612 - 213385_at (numeric)],\n 5613: [5613 - 213392_at (numeric)],\n 5614: [5614 - 213397_x_at (numeric)],\n 5615: [5615 - 213398_s_at (numeric)],\n 5616: [5616 - 213399_x_at (numeric)],\n 5617: [5617 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(numeric)],\n 5643: [5643 - 213477_x_at (numeric)],\n 5644: [5644 - 213478_at (numeric)],\n 5645: [5645 - 213479_at (numeric)],\n 5646: [5646 - 213491_x_at (numeric)],\n 5647: [5647 - 213492_at (numeric)],\n 5648: [5648 - 213502_x_at (numeric)],\n 5649: [5649 - 213503_x_at (numeric)],\n 5650: [5650 - 213506_at (numeric)],\n 5651: [5651 - 213507_s_at (numeric)],\n 5652: [5652 - 213509_x_at (numeric)],\n 5653: [5653 - 213510_x_at (numeric)],\n 5654: [5654 - 213511_s_at (numeric)],\n 5655: [5655 - 213513_x_at (numeric)],\n 5656: [5656 - 213515_x_at (numeric)],\n 5657: [5657 - 213517_at (numeric)],\n 5658: [5658 - 213518_at (numeric)],\n 5659: [5659 - 213523_at (numeric)],\n 5660: [5660 - 213524_s_at (numeric)],\n 5661: [5661 - 213531_s_at (numeric)],\n 5662: [5662 - 213532_at (numeric)],\n 5663: [5663 - 213535_s_at (numeric)],\n 5664: [5664 - 213537_at (numeric)],\n 5665: [5665 - 213539_at (numeric)],\n 5666: [5666 - 213545_x_at (numeric)],\n 5667: [5667 - 213546_at (numeric)],\n 5668: [5668 - 213549_at (numeric)],\n 5669: [5669 - 213553_x_at (numeric)],\n 5670: [5670 - 213564_x_at (numeric)],\n 5671: [5671 - 213566_at (numeric)],\n 5672: [5672 - 213571_s_at (numeric)],\n 5673: [5673 - 213572_s_at (numeric)],\n 5674: [5674 - 213574_s_at (numeric)],\n 5675: [5675 - 213577_at (numeric)],\n 5676: [5676 - 213578_at (numeric)],\n 5677: [5677 - 213583_x_at (numeric)],\n 5678: [5678 - 213587_s_at (numeric)],\n 5679: [5679 - 213588_x_at (numeric)],\n 5680: [5680 - 213593_s_at (numeric)],\n 5681: [5681 - 213594_x_at (numeric)],\n 5682: [5682 - 213603_s_at (numeric)],\n 5683: [5683 - 213605_s_at (numeric)],\n 5684: [5684 - 213610_s_at (numeric)],\n 5685: [5685 - 213612_x_at (numeric)],\n 5686: [5686 - 213614_x_at (numeric)],\n 5687: [5687 - 213617_s_at (numeric)],\n 5688: [5688 - 213619_at (numeric)],\n 5689: [5689 - 213620_s_at (numeric)],\n 5690: [5690 - 213622_at (numeric)],\n 5691: [5691 - 213624_at (numeric)],\n 5692: [5692 - 213627_at (numeric)],\n 5693: [5693 - 213629_x_at (numeric)],\n 5694: [5694 - 213642_at (numeric)],\n 5695: [5695 - 213645_at (numeric)],\n 5696: [5696 - 213646_x_at (numeric)],\n 5697: [5697 - 213649_at (numeric)],\n 5698: [5698 - 213651_at (numeric)],\n 5699: [5699 - 213655_at (numeric)],\n 5700: [5700 - 213656_s_at (numeric)],\n 5701: [5701 - 213657_s_at (numeric)],\n 5702: [5702 - 213658_at (numeric)],\n 5703: [5703 - 213664_at (numeric)],\n 5704: [5704 - 213668_s_at (numeric)],\n 5705: [5705 - 213670_x_at (numeric)],\n 5706: [5706 - 213671_s_at (numeric)],\n 5707: [5707 - 213674_x_at (numeric)],\n 5708: [5708 - 213677_s_at (numeric)],\n 5709: [5709 - 213680_at (numeric)],\n 5710: [5710 - 213682_at (numeric)],\n 5711: [5711 - 213687_s_at (numeric)],\n 5712: [5712 - 213693_s_at (numeric)],\n 5713: [5713 - 213695_at (numeric)],\n 5714: [5714 - 213698_at (numeric)],\n 5715: [5715 - 213699_s_at (numeric)],\n 5716: [5716 - 213702_x_at (numeric)],\n 5717: [5717 - 213704_at (numeric)],\n 5718: [5718 - 213706_at (numeric)],\n 5719: [5719 - 213707_s_at (numeric)],\n 5720: [5720 - 213708_s_at (numeric)],\n 5721: [5721 - 213711_at (numeric)],\n 5722: [5722 - 213720_s_at (numeric)],\n 5723: [5723 - 213726_x_at (numeric)],\n 5724: [5724 - 213729_at (numeric)],\n 5725: [5725 - 213734_at (numeric)],\n 5726: [5726 - 213735_s_at (numeric)],\n 5727: [5727 - 213737_x_at (numeric)],\n 5728: [5728 - 213738_s_at (numeric)],\n 5729: [5729 - 213742_at (numeric)],\n 5730: [5730 - 213746_s_at (numeric)],\n 5731: [5731 - 213747_at (numeric)],\n 5732: [5732 - 213753_x_at (numeric)],\n 5733: [5733 - 213754_s_at (numeric)],\n 5734: [5734 - 213757_at (numeric)],\n 5735: [5735 - 213761_at (numeric)],\n 5736: [5736 - 213762_x_at (numeric)],\n 5737: [5737 - 213764_s_at (numeric)],\n 5738: [5738 - 213765_at (numeric)],\n 5739: [5739 - 213766_x_at (numeric)],\n 5740: [5740 - 213773_x_at (numeric)],\n 5741: [5741 - 213775_x_at (numeric)],\n 5742: [5742 - 213787_s_at (numeric)],\n 5743: [5743 - 213791_at (numeric)],\n 5744: [5744 - 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215464_s_at (numeric)],\n 6123: [6123 - 215467_x_at (numeric)],\n 6124: [6124 - 215472_at (numeric)],\n 6125: [6125 - 215483_at (numeric)],\n 6126: [6126 - 215504_x_at (numeric)],\n 6127: [6127 - 215506_s_at (numeric)],\n 6128: [6128 - 215529_x_at (numeric)],\n 6129: [6129 - 215537_x_at (numeric)],\n 6130: [6130 - 215553_x_at (numeric)],\n 6131: [6131 - 215559_at (numeric)],\n 6132: [6132 - 215574_at (numeric)],\n 6133: [6133 - 215582_x_at (numeric)],\n 6134: [6134 - 215588_x_at (numeric)],\n 6135: [6135 - 215596_s_at (numeric)],\n 6136: [6136 - 215599_at (numeric)],\n 6137: [6137 - 215600_x_at (numeric)],\n 6138: [6138 - 215603_x_at (numeric)],\n 6139: [6139 - 215604_x_at (numeric)],\n 6140: [6140 - 215628_x_at (numeric)],\n 6141: [6141 - 215646_s_at (numeric)],\n 6142: [6142 - 215690_x_at (numeric)],\n 6143: [6143 - 215691_x_at (numeric)],\n 6144: [6144 - 215692_s_at (numeric)],\n 6145: [6145 - 215693_x_at (numeric)],\n 6146: [6146 - 215695_s_at (numeric)],\n 6147: [6147 - 215696_s_at (numeric)],\n 6148: [6148 - 215704_at (numeric)],\n 6149: [6149 - 215706_x_at (numeric)],\n 6150: [6150 - 215714_s_at (numeric)],\n 6151: [6151 - 215716_s_at (numeric)],\n 6152: [6152 - 215719_x_at (numeric)],\n 6153: [6153 - 215726_s_at (numeric)],\n 6154: [6154 - 215729_s_at (numeric)],\n 6155: [6155 - 215735_s_at (numeric)],\n 6156: [6156 - 215772_x_at (numeric)],\n 6157: [6157 - 215773_x_at (numeric)],\n 6158: [6158 - 215780_s_at (numeric)],\n 6159: [6159 - 215785_s_at (numeric)],\n 6160: [6160 - 215807_s_at (numeric)],\n 6161: [6161 - 215812_s_at (numeric)],\n 6162: [6162 - 215813_s_at (numeric)],\n 6163: [6163 - 215823_x_at (numeric)],\n 6164: [6164 - 215836_s_at (numeric)],\n 6165: [6165 - 215856_at (numeric)],\n 6166: [6166 - 215867_x_at (numeric)],\n 6167: [6167 - 215884_s_at (numeric)],\n 6168: [6168 - 215913_s_at (numeric)],\n 6169: [6169 - 215936_s_at (numeric)],\n 6170: [6170 - 215946_x_at (numeric)],\n 6171: [6171 - 215947_s_at (numeric)],\n 6172: [6172 - 215949_x_at (numeric)],\n 6173: [6173 - 215952_s_at (numeric)],\n 6174: [6174 - 215963_x_at (numeric)],\n 6175: [6175 - 215978_x_at (numeric)],\n 6176: [6176 - 215983_s_at (numeric)],\n 6177: [6177 - 216012_at (numeric)],\n 6178: [6178 - 216025_x_at (numeric)],\n 6179: [6179 - 216032_s_at (numeric)],\n 6180: [6180 - 216033_s_at (numeric)],\n 6181: [6181 - 216035_x_at (numeric)],\n 6182: [6182 - 216037_x_at (numeric)],\n 6183: [6183 - 216041_x_at (numeric)],\n 6184: [6184 - 216051_x_at (numeric)],\n 6185: [6185 - 216054_x_at (numeric)],\n 6186: [6186 - 216060_s_at (numeric)],\n 6187: [6187 - 216088_s_at (numeric)],\n 6188: [6188 - 216095_x_at (numeric)],\n 6189: [6189 - 216153_x_at (numeric)],\n 6190: [6190 - 216187_x_at (numeric)],\n 6191: [6191 - 216194_s_at (numeric)],\n 6192: [6192 - 216199_s_at (numeric)],\n 6193: [6193 - 216207_x_at (numeric)],\n 6194: [6194 - 216210_x_at (numeric)],\n 6195: [6195 - 216215_s_at (numeric)],\n 6196: [6196 - 216219_at (numeric)],\n 6197: [6197 - 216221_s_at (numeric)],\n 6198: [6198 - 216229_x_at (numeric)],\n 6199: [6199 - 216231_s_at (numeric)],\n 6200: [6200 - 216237_s_at (numeric)],\n 6201: [6201 - 216238_s_at (numeric)],\n 6202: [6202 - 216241_s_at (numeric)],\n 6203: [6203 - 216242_x_at (numeric)],\n 6204: [6204 - 216246_at (numeric)],\n 6205: [6205 - 216248_s_at (numeric)],\n 6206: [6206 - 216250_s_at (numeric)],\n 6207: [6207 - 216264_s_at (numeric)],\n 6208: [6208 - 216268_s_at (numeric)],\n 6209: [6209 - 216274_s_at (numeric)],\n 6210: [6210 - 216295_s_at (numeric)],\n 6211: [6211 - 216304_x_at (numeric)],\n 6212: [6212 - 216306_x_at (numeric)],\n 6213: [6213 - 216308_x_at (numeric)],\n 6214: [6214 - 216320_x_at (numeric)],\n 6215: [6215 - 216321_s_at (numeric)],\n 6216: [6216 - 216323_x_at (numeric)],\n 6217: [6217 - 216331_at (numeric)],\n 6218: [6218 - 216333_x_at (numeric)],\n 6219: [6219 - 216336_x_at (numeric)],\n 6220: [6220 - 216338_s_at (numeric)],\n 6221: [6221 - 216342_x_at (numeric)],\n 6222: [6222 - 216348_at (numeric)],\n 6223: [6223 - 216377_x_at (numeric)],\n 6224: [6224 - 216379_x_at (numeric)],\n 6225: [6225 - 216380_x_at (numeric)],\n 6226: [6226 - 216381_x_at (numeric)],\n 6227: [6227 - 216383_at (numeric)],\n 6228: [6228 - 216384_x_at (numeric)],\n 6229: [6229 - 216396_s_at (numeric)],\n 6230: [6230 - 216401_x_at (numeric)],\n 6231: [6231 - 216412_x_at (numeric)],\n 6232: [6232 - 216430_x_at (numeric)],\n 6233: [6233 - 216438_s_at (numeric)],\n 6234: [6234 - 216442_x_at (numeric)],\n 6235: [6235 - 216449_x_at (numeric)],\n 6236: [6236 - 216457_s_at (numeric)],\n 6237: [6237 - 216474_x_at (numeric)],\n 6238: [6238 - 216483_s_at (numeric)],\n 6239: [6239 - 216484_x_at (numeric)],\n 6240: [6240 - 216491_x_at (numeric)],\n 6241: [6241 - 216505_x_at (numeric)],\n 6242: [6242 - 216508_x_at (numeric)],\n 6243: [6243 - 216510_x_at (numeric)],\n 6244: [6244 - 216511_s_at (numeric)],\n 6245: [6245 - 216515_x_at (numeric)],\n 6246: [6246 - 216520_s_at (numeric)],\n 6247: [6247 - 216524_x_at (numeric)],\n 6248: [6248 - 216525_x_at (numeric)],\n 6249: [6249 - 216526_x_at (numeric)],\n 6250: [6250 - 216542_x_at (numeric)],\n 6251: [6251 - 216557_x_at (numeric)],\n 6252: [6252 - 216559_x_at (numeric)],\n 6253: [6253 - 216560_x_at (numeric)],\n 6254: [6254 - 216565_x_at (numeric)],\n 6255: [6255 - 216570_x_at (numeric)],\n 6256: [6256 - 216576_x_at (numeric)],\n 6257: [6257 - 216591_s_at (numeric)],\n 6258: [6258 - 216594_x_at (numeric)],\n 6259: [6259 - 216598_s_at (numeric)],\n 6260: [6260 - 216607_s_at (numeric)],\n 6261: [6261 - 216615_s_at (numeric)],\n 6262: [6262 - 216620_s_at (numeric)],\n 6263: [6263 - 216623_x_at (numeric)],\n 6264: [6264 - 216635_at (numeric)],\n 6265: [6265 - 216640_s_at (numeric)],\n 6266: [6266 - 216699_s_at (numeric)],\n 6267: [6267 - 216713_at (numeric)],\n 6268: [6268 - 216733_s_at (numeric)],\n 6269: [6269 - 216745_x_at (numeric)],\n 6270: [6270 - 216782_at (numeric)],\n 6271: [6271 - 216804_s_at (numeric)],\n 6272: [6272 - 216834_at (numeric)],\n 6273: [6273 - 216836_s_at (numeric)],\n 6274: [6274 - 216841_s_at (numeric)],\n 6275: [6275 - 216853_x_at (numeric)],\n 6276: [6276 - 216858_x_at (numeric)],\n 6277: [6277 - 216859_x_at (numeric)],\n 6278: [6278 - 216862_s_at (numeric)],\n 6279: [6279 - 216903_s_at (numeric)],\n 6280: [6280 - 216905_s_at (numeric)],\n 6281: [6281 - 216944_s_at (numeric)],\n 6282: [6282 - 216950_s_at (numeric)],\n 6283: [6283 - 216953_s_at (numeric)],\n 6284: [6284 - 216954_x_at (numeric)],\n 6285: [6285 - 216971_s_at (numeric)],\n 6286: [6286 - 216973_s_at (numeric)],\n 6287: [6287 - 216977_x_at (numeric)],\n 6288: [6288 - 216984_x_at (numeric)],\n 6289: [6289 - 216988_s_at (numeric)],\n 6290: [6290 - 217022_s_at (numeric)],\n 6291: [6291 - 217023_x_at (numeric)],\n 6292: [6292 - 217028_at (numeric)],\n 6293: [6293 - 217043_s_at (numeric)],\n 6294: [6294 - 217047_s_at (numeric)],\n 6295: [6295 - 217052_x_at (numeric)],\n 6296: [6296 - 217073_x_at (numeric)],\n 6297: [6297 - 217080_s_at (numeric)],\n 6298: [6298 - 217092_x_at (numeric)],\n 6299: [6299 - 217106_x_at (numeric)],\n 6300: [6300 - 217109_at (numeric)],\n 6301: [6301 - 217118_s_at (numeric)],\n 6302: [6302 - 217122_s_at (numeric)],\n 6303: [6303 - 217137_x_at (numeric)],\n 6304: [6304 - 217140_s_at (numeric)],\n 6305: [6305 - 217144_at (numeric)],\n 6306: [6306 - 217148_x_at (numeric)],\n 6307: [6307 - 217157_x_at (numeric)],\n 6308: [6308 - 217165_x_at (numeric)],\n 6309: [6309 - 217168_s_at (numeric)],\n 6310: [6310 - 217179_x_at (numeric)],\n 6311: [6311 - 217200_x_at (numeric)],\n 6312: [6312 - 217202_s_at (numeric)],\n 6313: [6313 - 217225_x_at (numeric)],\n 6314: [6314 - 217226_s_at (numeric)],\n 6315: [6315 - 217227_x_at (numeric)],\n 6316: [6316 - 217232_x_at (numeric)],\n 6317: [6317 - 217234_s_at (numeric)],\n 6318: [6318 - 217235_x_at (numeric)],\n 6319: [6319 - 217238_s_at (numeric)],\n 6320: [6320 - 217249_x_at (numeric)],\n 6321: [6321 - 217256_x_at (numeric)],\n 6322: [6322 - 217258_x_at (numeric)],\n 6323: [6323 - 217266_at (numeric)],\n 6324: [6324 - 217281_x_at (numeric)],\n 6325: [6325 - 217286_s_at (numeric)],\n 6326: [6326 - 217289_s_at (numeric)],\n 6327: [6327 - 217294_s_at (numeric)],\n 6328: [6328 - 217301_x_at (numeric)],\n 6329: [6329 - 217317_s_at (numeric)],\n 6330: [6330 - 217329_x_at (numeric)],\n 6331: [6331 - 217356_s_at (numeric)],\n 6332: [6332 - 217362_x_at (numeric)],\n 6333: [6333 - 217378_x_at (numeric)],\n 6334: [6334 - 217379_at (numeric)],\n 6335: [6335 - 217388_s_at (numeric)],\n 6336: [6336 - 217398_x_at (numeric)],\n 6337: [6337 - 217404_s_at (numeric)],\n 6338: [6338 - 217408_at (numeric)],\n 6339: [6339 - 217414_x_at (numeric)],\n 6340: [6340 - 217418_x_at (numeric)],\n 6341: [6341 - 217419_x_at (numeric)],\n 6342: [6342 - 217428_s_at (numeric)],\n 6343: [6343 - 217430_x_at (numeric)],\n 6344: [6344 - 217436_x_at (numeric)],\n 6345: [6345 - 217437_s_at (numeric)],\n 6346: [6346 - 217446_x_at (numeric)],\n 6347: [6347 - 217456_x_at (numeric)],\n 6348: [6348 - 217466_x_at (numeric)],\n 6349: [6349 - 217478_s_at (numeric)],\n 6350: [6350 - 217480_x_at (numeric)],\n 6351: [6351 - 217491_x_at (numeric)],\n 6352: [6352 - 217492_s_at (numeric)],\n 6353: [6353 - 217499_x_at (numeric)],\n 6354: [6354 - 217523_at (numeric)],\n 6355: [6355 - 217527_s_at (numeric)],\n 6356: [6356 - 217546_at (numeric)],\n 6357: [6357 - 217579_x_at (numeric)],\n 6358: [6358 - 217586_x_at (numeric)],\n 6359: [6359 - 217591_at (numeric)],\n 6360: [6360 - 217617_at (numeric)],\n 6361: [6361 - 217653_x_at (numeric)],\n 6362: [6362 - 217673_x_at (numeric)],\n 6363: [6363 - 217678_at (numeric)],\n 6364: [6364 - 217679_x_at (numeric)],\n 6365: [6365 - 217691_x_at (numeric)],\n 6366: [6366 - 217703_x_at (numeric)],\n 6367: [6367 - 217707_x_at (numeric)],\n 6368: [6368 - 217713_x_at (numeric)],\n 6369: [6369 - 217715_x_at (numeric)],\n 6370: [6370 - 217716_s_at (numeric)],\n 6371: [6371 - 217717_s_at (numeric)],\n 6372: [6372 - 217718_s_at (numeric)],\n 6373: [6373 - 217719_at (numeric)],\n 6374: [6374 - 217720_at (numeric)],\n 6375: [6375 - 217722_s_at (numeric)],\n 6376: [6376 - 217724_at (numeric)],\n 6377: [6377 - 217725_x_at (numeric)],\n 6378: [6378 - 217726_at (numeric)],\n 6379: [6379 - 217727_x_at (numeric)],\n 6380: [6380 - 217728_at (numeric)],\n 6381: [6381 - 217729_s_at (numeric)],\n 6382: [6382 - 217730_at (numeric)],\n 6383: [6383 - 217731_s_at (numeric)],\n 6384: [6384 - 217732_s_at (numeric)],\n 6385: [6385 - 217733_s_at (numeric)],\n 6386: [6386 - 217734_s_at (numeric)],\n 6387: [6387 - 217736_s_at (numeric)],\n 6388: [6388 - 217738_at (numeric)],\n 6389: [6389 - 217739_s_at (numeric)],\n 6390: [6390 - 217740_x_at (numeric)],\n 6391: [6391 - 217741_s_at (numeric)],\n 6392: [6392 - 217742_s_at (numeric)],\n 6393: [6393 - 217743_s_at (numeric)],\n 6394: [6394 - 217744_s_at (numeric)],\n 6395: [6395 - 217745_s_at (numeric)],\n 6396: [6396 - 217746_s_at (numeric)],\n 6397: [6397 - 217747_s_at (numeric)],\n 6398: [6398 - 217748_at (numeric)],\n 6399: [6399 - 217749_at (numeric)],\n 6400: [6400 - 217750_s_at (numeric)],\n 6401: [6401 - 217751_at (numeric)],\n 6402: [6402 - 217752_s_at (numeric)],\n 6403: [6403 - 217753_s_at (numeric)],\n 6404: [6404 - 217755_at (numeric)],\n 6405: [6405 - 217756_x_at (numeric)],\n 6406: [6406 - 217757_at (numeric)],\n 6407: [6407 - 217758_s_at (numeric)],\n 6408: [6408 - 217759_at (numeric)],\n 6409: [6409 - 217760_at (numeric)],\n 6410: [6410 - 217761_at (numeric)],\n 6411: [6411 - 217762_s_at (numeric)],\n 6412: [6412 - 217763_s_at (numeric)],\n 6413: [6413 - 217764_s_at (numeric)],\n 6414: [6414 - 217766_s_at (numeric)],\n 6415: [6415 - 217767_at (numeric)],\n 6416: [6416 - 217768_at (numeric)],\n 6417: [6417 - 217769_s_at (numeric)],\n 6418: [6418 - 217770_at (numeric)],\n 6419: [6419 - 217771_at (numeric)],\n 6420: [6420 - 217772_s_at (numeric)],\n 6421: [6421 - 217773_s_at (numeric)],\n 6422: [6422 - 217774_s_at (numeric)],\n 6423: [6423 - 217775_s_at (numeric)],\n 6424: [6424 - 217776_at (numeric)],\n 6425: [6425 - 217777_s_at (numeric)],\n 6426: [6426 - 217779_s_at (numeric)],\n 6427: [6427 - 217780_at (numeric)],\n 6428: [6428 - 217783_s_at (numeric)],\n 6429: [6429 - 217786_at (numeric)],\n 6430: [6430 - 217788_s_at (numeric)],\n 6431: [6431 - 217789_at (numeric)],\n 6432: [6432 - 217791_s_at (numeric)],\n 6433: [6433 - 217792_at (numeric)],\n 6434: [6434 - 217794_at (numeric)],\n 6435: [6435 - 217795_s_at (numeric)],\n 6436: [6436 - 217797_at (numeric)],\n 6437: [6437 - 217798_at (numeric)],\n 6438: [6438 - 217799_x_at (numeric)],\n 6439: [6439 - 217800_s_at (numeric)],\n 6440: [6440 - 217801_at (numeric)],\n 6441: [6441 - 217802_s_at (numeric)],\n 6442: [6442 - 217803_at (numeric)],\n 6443: [6443 - 217805_at (numeric)],\n 6444: [6444 - 217806_s_at (numeric)],\n 6445: [6445 - 217807_s_at (numeric)],\n 6446: [6446 - 217809_at (numeric)],\n 6447: [6447 - 217810_x_at (numeric)],\n 6448: [6448 - 217811_at (numeric)],\n 6449: [6449 - 217812_at (numeric)],\n 6450: [6450 - 217813_s_at (numeric)],\n 6451: [6451 - 217814_at (numeric)],\n 6452: [6452 - 217815_at (numeric)],\n 6453: [6453 - 217816_s_at (numeric)],\n 6454: [6454 - 217817_at (numeric)],\n 6455: [6455 - 217819_at (numeric)],\n 6456: [6456 - 217820_s_at (numeric)],\n 6457: [6457 - 217821_s_at (numeric)],\n 6458: [6458 - 217822_at (numeric)],\n 6459: [6459 - 217823_s_at (numeric)],\n 6460: [6460 - 217827_s_at (numeric)],\n 6461: [6461 - 217828_at (numeric)],\n 6462: [6462 - 217832_at (numeric)],\n 6463: [6463 - 217833_at (numeric)],\n 6464: [6464 - 217834_s_at (numeric)],\n 6465: [6465 - 217835_x_at (numeric)],\n 6466: [6466 - 217836_s_at (numeric)],\n 6467: [6467 - 217837_s_at (numeric)],\n 6468: [6468 - 217838_s_at (numeric)],\n 6469: [6469 - 217839_at (numeric)],\n 6470: [6470 - 217840_at (numeric)],\n 6471: [6471 - 217844_at (numeric)],\n 6472: [6472 - 217845_x_at (numeric)],\n 6473: [6473 - 217846_at (numeric)],\n 6474: [6474 - 217848_s_at (numeric)],\n 6475: [6475 - 217850_at (numeric)],\n 6476: [6476 - 217851_s_at (numeric)],\n 6477: [6477 - 217852_s_at (numeric)],\n 6478: [6478 - 217853_at (numeric)],\n 6479: [6479 - 217854_s_at (numeric)],\n 6480: [6480 - 217855_x_at (numeric)],\n 6481: [6481 - 217858_s_at (numeric)],\n 6482: [6482 - 217860_at (numeric)],\n 6483: [6483 - 217865_at (numeric)],\n 6484: [6484 - 217867_x_at (numeric)],\n 6485: [6485 - 217868_s_at (numeric)],\n 6486: [6486 - 217869_at (numeric)],\n 6487: [6487 - 217870_s_at (numeric)],\n 6488: [6488 - 217871_s_at (numeric)],\n 6489: [6489 - 217872_at (numeric)],\n 6490: [6490 - 217873_at (numeric)],\n 6491: [6491 - 217874_at (numeric)],\n 6492: [6492 - 217877_s_at (numeric)],\n 6493: [6493 - 217882_at (numeric)],\n 6494: [6494 - 217883_at (numeric)],\n 6495: [6495 - 217885_at (numeric)],\n 6496: [6496 - 217887_s_at (numeric)],\n 6497: [6497 - 217890_s_at (numeric)],\n 6498: [6498 - 217892_s_at (numeric)],\n 6499: [6499 - 217893_s_at (numeric)],\n 6500: [6500 - 217894_at (numeric)],\n 6501: [6501 - 217895_at (numeric)],\n 6502: [6502 - 217897_at (numeric)],\n 6503: [6503 - 217898_at (numeric)],\n 6504: [6504 - 217900_at (numeric)],\n 6505: [6505 - 217901_at (numeric)],\n 6506: [6506 - 217906_at (numeric)],\n 6507: [6507 - 217907_at (numeric)],\n 6508: [6508 - 217908_s_at (numeric)],\n 6509: [6509 - 217911_s_at (numeric)],\n 6510: [6510 - 217914_at (numeric)],\n 6511: [6511 - 217915_s_at (numeric)],\n 6512: [6512 - 217916_s_at (numeric)],\n 6513: [6513 - 217917_s_at (numeric)],\n 6514: [6514 - 217918_at (numeric)],\n 6515: [6515 - 217919_s_at (numeric)],\n 6516: [6516 - 217922_at (numeric)],\n 6517: [6517 - 217926_at (numeric)],\n 6518: [6518 - 217927_at (numeric)],\n 6519: [6519 - 217932_at (numeric)],\n 6520: [6520 - 217933_s_at (numeric)],\n 6521: [6521 - 217934_x_at (numeric)],\n 6522: [6522 - 217938_s_at (numeric)],\n 6523: [6523 - 217939_s_at (numeric)],\n 6524: [6524 - 217940_s_at (numeric)],\n 6525: [6525 - 217941_s_at (numeric)],\n 6526: [6526 - 217942_at (numeric)],\n 6527: [6527 - 217945_at (numeric)],\n 6528: [6528 - 217946_s_at (numeric)],\n 6529: [6529 - 217947_at (numeric)],\n 6530: [6530 - 217949_s_at (numeric)],\n 6531: [6531 - 217954_s_at (numeric)],\n 6532: [6532 - 217956_s_at (numeric)],\n 6533: [6533 - 217957_at (numeric)],\n 6534: [6534 - 217958_at (numeric)],\n 6535: [6535 - 217959_s_at (numeric)],\n 6536: [6536 - 217961_at (numeric)],\n 6537: [6537 - 217962_at (numeric)],\n 6538: [6538 - 217963_s_at (numeric)],\n 6539: [6539 - 217964_at (numeric)],\n 6540: [6540 - 217966_s_at (numeric)],\n 6541: [6541 - 217967_s_at (numeric)],\n 6542: [6542 - 217969_at (numeric)],\n 6543: [6543 - 217971_at (numeric)],\n 6544: [6544 - 217972_at (numeric)],\n 6545: [6545 - 217973_at (numeric)],\n 6546: [6546 - 217975_at (numeric)],\n 6547: [6547 - 217977_at (numeric)],\n 6548: [6548 - 217978_s_at (numeric)],\n 6549: [6549 - 217979_at (numeric)],\n 6550: [6550 - 217980_s_at (numeric)],\n 6551: [6551 - 217982_s_at (numeric)],\n 6552: [6552 - 217983_s_at (numeric)],\n 6553: [6553 - 217984_at (numeric)],\n 6554: [6554 - 217986_s_at (numeric)],\n 6555: [6555 - 217987_at (numeric)],\n 6556: [6556 - 217988_at (numeric)],\n 6557: [6557 - 217989_at (numeric)],\n 6558: [6558 - 217992_s_at (numeric)],\n 6559: [6559 - 217993_s_at (numeric)],\n 6560: [6560 - 217995_at (numeric)],\n 6561: [6561 - 217996_at (numeric)],\n 6562: [6562 - 217997_at (numeric)],\n 6563: [6563 - 218002_s_at (numeric)],\n 6564: [6564 - 218003_s_at (numeric)],\n 6565: [6565 - 218005_at (numeric)],\n 6566: [6566 - 218007_s_at (numeric)],\n 6567: [6567 - 218008_at (numeric)],\n 6568: [6568 - 218009_s_at (numeric)],\n 6569: [6569 - 218010_x_at (numeric)],\n 6570: [6570 - 218011_at (numeric)],\n 6571: [6571 - 218014_at (numeric)],\n 6572: [6572 - 218016_s_at (numeric)],\n 6573: [6573 - 218017_s_at (numeric)],\n 6574: [6574 - 218019_s_at (numeric)],\n 6575: [6575 - 218020_s_at (numeric)],\n 6576: [6576 - 218024_at (numeric)],\n 6577: [6577 - 218025_s_at (numeric)],\n 6578: [6578 - 218026_at (numeric)],\n 6579: [6579 - 218027_at (numeric)],\n 6580: [6580 - 218028_at (numeric)],\n 6581: [6581 - 218031_s_at (numeric)],\n 6582: [6582 - 218032_at (numeric)],\n 6583: [6583 - 218034_at (numeric)],\n 6584: [6584 - 218035_s_at (numeric)],\n 6585: [6585 - 218036_x_at (numeric)],\n 6586: [6586 - 218039_at (numeric)],\n 6587: [6587 - 218041_x_at (numeric)],\n 6588: [6588 - 218042_at (numeric)],\n 6589: [6589 - 218046_s_at (numeric)],\n 6590: [6590 - 218047_at (numeric)],\n 6591: [6591 - 218048_at (numeric)],\n 6592: [6592 - 218049_s_at (numeric)],\n 6593: [6593 - 218050_at (numeric)],\n 6594: [6594 - 218051_s_at (numeric)],\n 6595: [6595 - 218053_at (numeric)],\n 6596: [6596 - 218057_x_at (numeric)],\n 6597: [6597 - 218059_at (numeric)],\n 6598: [6598 - 218061_at (numeric)],\n 6599: [6599 - 218062_x_at (numeric)],\n 6600: [6600 - 218065_s_at (numeric)],\n 6601: [6601 - 218066_at (numeric)],\n 6602: [6602 - 218067_s_at (numeric)],\n 6603: [6603 - 218069_at (numeric)],\n 6604: [6604 - 218074_at (numeric)],\n 6605: [6605 - 218078_s_at (numeric)],\n 6606: [6606 - 218079_s_at (numeric)],\n 6607: [6607 - 218082_s_at (numeric)],\n 6608: [6608 - 218084_x_at (numeric)],\n 6609: [6609 - 218085_at (numeric)],\n 6610: [6610 - 218086_at (numeric)],\n 6611: [6611 - 218087_s_at (numeric)],\n 6612: [6612 - 218088_s_at (numeric)],\n 6613: [6613 - 218090_s_at (numeric)],\n 6614: [6614 - 218093_s_at (numeric)],\n 6615: [6615 - 218094_s_at (numeric)],\n 6616: [6616 - 218095_s_at (numeric)],\n 6617: [6617 - 218096_at (numeric)],\n 6618: [6618 - 218097_s_at (numeric)],\n 6619: [6619 - 218098_at (numeric)],\n 6620: [6620 - 218100_s_at (numeric)],\n 6621: [6621 - 218101_s_at (numeric)],\n 6622: [6622 - 218102_at (numeric)],\n 6623: [6623 - 218105_s_at (numeric)],\n 6624: [6624 - 218106_s_at (numeric)],\n 6625: [6625 - 218107_at (numeric)],\n 6626: [6626 - 218109_s_at (numeric)],\n 6627: [6627 - 218113_at (numeric)],\n 6628: [6628 - 218117_at (numeric)],\n 6629: [6629 - 218118_s_at (numeric)],\n 6630: [6630 - 218123_at (numeric)],\n 6631: [6631 - 218124_at (numeric)],\n 6632: [6632 - 218132_s_at (numeric)],\n 6633: [6633 - 218133_s_at (numeric)],\n 6634: [6634 - 218134_s_at (numeric)],\n 6635: [6635 - 218135_at (numeric)],\n 6636: [6636 - 218137_s_at (numeric)],\n 6637: [6637 - 218138_at (numeric)],\n 6638: [6638 - 218139_s_at (numeric)],\n 6639: [6639 - 218140_x_at (numeric)],\n 6640: [6640 - 218145_at (numeric)],\n 6641: [6641 - 218146_at (numeric)],\n 6642: [6642 - 218147_s_at (numeric)],\n 6643: [6643 - 218149_s_at (numeric)],\n 6644: [6644 - 218150_at (numeric)],\n 6645: [6645 - 218151_x_at (numeric)],\n 6646: [6646 - 218153_at (numeric)],\n 6647: [6647 - 218155_x_at (numeric)],\n 6648: [6648 - 218157_x_at (numeric)],\n 6649: [6649 - 218159_at (numeric)],\n 6650: [6650 - 218160_at (numeric)],\n 6651: [6651 - 218162_at (numeric)],\n 6652: [6652 - 218163_at (numeric)],\n 6653: [6653 - 218164_at (numeric)],\n 6654: [6654 - 218165_at (numeric)],\n 6655: [6655 - 218167_at (numeric)],\n 6656: [6656 - 218168_s_at (numeric)],\n 6657: [6657 - 218170_at (numeric)],\n 6658: [6658 - 218171_at (numeric)],\n 6659: [6659 - 218172_s_at (numeric)],\n 6660: [6660 - 218175_at (numeric)],\n 6661: [6661 - 218176_at (numeric)],\n 6662: [6662 - 218178_s_at (numeric)],\n 6663: [6663 - 218180_s_at (numeric)],\n 6664: [6664 - 218181_s_at (numeric)],\n 6665: [6665 - 218182_s_at (numeric)],\n 6666: [6666 - 218185_s_at (numeric)],\n 6667: [6667 - 218186_at (numeric)],\n 6668: [6668 - 218187_s_at (numeric)],\n 6669: [6669 - 218188_s_at (numeric)],\n 6670: [6670 - 218189_s_at (numeric)],\n 6671: [6671 - 218190_s_at (numeric)],\n 6672: [6672 - 218191_s_at (numeric)],\n 6673: [6673 - 218192_at (numeric)],\n 6674: [6674 - 218194_at (numeric)],\n 6675: [6675 - 218195_at (numeric)],\n 6676: [6676 - 218196_at (numeric)],\n 6677: [6677 - 218197_s_at (numeric)],\n 6678: [6678 - 218198_at (numeric)],\n 6679: [6679 - 218200_s_at (numeric)],\n 6680: [6680 - 218201_at (numeric)],\n 6681: [6681 - 218203_at (numeric)],\n 6682: [6682 - 218204_s_at (numeric)],\n 6683: [6683 - 218205_s_at (numeric)],\n 6684: [6684 - 218206_x_at (numeric)],\n 6685: [6685 - 218211_s_at (numeric)],\n 6686: [6686 - 218213_s_at (numeric)],\n 6687: [6687 - 218214_at (numeric)],\n 6688: [6688 - 218216_x_at (numeric)],\n 6689: [6689 - 218217_at (numeric)],\n 6690: [6690 - 218224_at (numeric)],\n 6691: [6691 - 218226_s_at (numeric)],\n 6692: [6692 - 218228_s_at (numeric)],\n 6693: [6693 - 218229_s_at (numeric)],\n 6694: [6694 - 218231_at (numeric)],\n 6695: [6695 - 218232_at (numeric)],\n 6696: [6696 - 218233_s_at (numeric)],\n 6697: [6697 - 218236_s_at (numeric)],\n 6698: [6698 - 218237_s_at (numeric)],\n 6699: [6699 - 218238_at (numeric)],\n 6700: [6700 - 218239_s_at (numeric)],\n 6701: [6701 - 218241_at (numeric)],\n 6702: [6702 - 218242_s_at (numeric)],\n 6703: [6703 - 218243_at (numeric)],\n 6704: [6704 - 218245_at (numeric)],\n 6705: [6705 - 218247_s_at (numeric)],\n 6706: [6706 - 218248_at (numeric)],\n 6707: [6707 - 218249_at (numeric)],\n 6708: [6708 - 218250_s_at (numeric)],\n 6709: [6709 - 218252_at (numeric)],\n 6710: [6710 - 218253_s_at (numeric)],\n 6711: [6711 - 218254_s_at (numeric)],\n 6712: [6712 - 218256_s_at (numeric)],\n 6713: [6713 - 218258_at (numeric)],\n 6714: [6714 - 218259_at (numeric)],\n 6715: [6715 - 218261_at (numeric)],\n 6716: [6716 - 218263_s_at (numeric)],\n 6717: [6717 - 218269_at (numeric)],\n 6718: [6718 - 218270_at (numeric)],\n 6719: [6719 - 218271_s_at (numeric)],\n 6720: [6720 - 218273_s_at (numeric)],\n 6721: [6721 - 218277_s_at (numeric)],\n 6722: [6722 - 218280_x_at (numeric)],\n 6723: [6723 - 218281_at (numeric)],\n 6724: [6724 - 218283_at (numeric)],\n 6725: [6725 - 218285_s_at (numeric)],\n 6726: [6726 - 218286_s_at (numeric)],\n 6727: [6727 - 218289_s_at (numeric)],\n 6728: [6728 - 218291_at (numeric)],\n 6729: [6729 - 218298_s_at (numeric)],\n 6730: [6730 - 218302_at (numeric)],\n 6731: [6731 - 218303_x_at (numeric)],\n 6732: [6732 - 218304_s_at (numeric)],\n 6733: [6733 - 218308_at (numeric)],\n 6734: [6734 - 218309_at (numeric)],\n 6735: [6735 - 218310_at (numeric)],\n 6736: [6736 - 218311_at (numeric)],\n 6737: [6737 - 218312_s_at (numeric)],\n 6738: [6738 - 218313_s_at (numeric)],\n 6739: [6739 - 218319_at (numeric)],\n 6740: [6740 - 218320_s_at (numeric)],\n 6741: [6741 - 218322_s_at (numeric)],\n 6742: [6742 - 218323_at (numeric)],\n 6743: [6743 - 218326_s_at (numeric)],\n 6744: [6744 - 218329_at (numeric)],\n 6745: [6745 - 218330_s_at (numeric)],\n 6746: [6746 - 218331_s_at (numeric)],\n 6747: [6747 - 218332_at (numeric)],\n 6748: [6748 - 218333_at (numeric)],\n 6749: [6749 - 218334_at (numeric)],\n 6750: [6750 - 218336_at (numeric)],\n 6751: [6751 - 218338_at (numeric)],\n 6752: [6752 - 218339_at (numeric)],\n 6753: [6753 - 218341_at (numeric)],\n 6754: [6754 - 218342_s_at (numeric)],\n 6755: [6755 - 218345_at (numeric)],\n 6756: [6756 - 218350_s_at (numeric)],\n 6757: [6757 - 218351_at (numeric)],\n 6758: [6758 - 218353_at (numeric)],\n 6759: [6759 - 218354_at (numeric)],\n 6760: [6760 - 218357_s_at (numeric)],\n 6761: [6761 - 218358_at (numeric)],\n 6762: [6762 - 218361_at (numeric)],\n 6763: [6763 - 218366_x_at (numeric)],\n 6764: [6764 - 218368_s_at (numeric)],\n 6765: [6765 - 218375_at (numeric)],\n 6766: [6766 - 218379_at (numeric)],\n 6767: [6767 - 218380_at (numeric)],\n 6768: [6768 - 218381_s_at (numeric)],\n 6769: [6769 - 218384_at (numeric)],\n 6770: [6770 - 218387_s_at (numeric)],\n 6771: [6771 - 218388_at (numeric)],\n 6772: [6772 - 218389_s_at (numeric)],\n 6773: [6773 - 218391_at (numeric)],\n 6774: [6774 - 218395_at (numeric)],\n 6775: [6775 - 218396_at (numeric)],\n 6776: [6776 - 218397_at (numeric)],\n 6777: [6777 - 218398_at (numeric)],\n 6778: [6778 - 218400_at (numeric)],\n 6779: [6779 - 218403_at (numeric)],\n 6780: [6780 - 218404_at (numeric)],\n 6781: [6781 - 218407_x_at (numeric)],\n 6782: [6782 - 218412_s_at (numeric)],\n 6783: [6783 - 218418_s_at (numeric)],\n 6784: [6784 - 218422_s_at (numeric)],\n 6785: [6785 - 218424_s_at (numeric)],\n 6786: [6786 - 218432_at (numeric)],\n 6787: [6787 - 218434_s_at (numeric)],\n 6788: [6788 - 218435_at (numeric)],\n 6789: [6789 - 218437_s_at (numeric)],\n 6790: [6790 - 218440_at (numeric)],\n 6791: [6791 - 218445_at (numeric)],\n 6792: [6792 - 218446_s_at (numeric)],\n 6793: [6793 - 218447_at (numeric)],\n 6794: [6794 - 218448_at (numeric)],\n 6795: [6795 - 218449_at (numeric)],\n 6796: [6796 - 218450_at (numeric)],\n 6797: [6797 - 218451_at (numeric)],\n 6798: [6798 - 218452_at (numeric)],\n 6799: [6799 - 218454_at (numeric)],\n 6800: [6800 - 218456_at (numeric)],\n 6801: [6801 - 218460_at (numeric)],\n 6802: [6802 - 218462_at (numeric)],\n 6803: [6803 - 218465_at (numeric)],\n 6804: [6804 - 218467_at (numeric)],\n 6805: [6805 - 218468_s_at (numeric)],\n 6806: [6806 - 218469_at (numeric)],\n 6807: [6807 - 218471_s_at (numeric)],\n 6808: [6808 - 218477_at (numeric)],\n 6809: [6809 - 218478_s_at (numeric)],\n 6810: [6810 - 218482_at (numeric)],\n 6811: [6811 - 218484_at (numeric)],\n 6812: [6812 - 218490_s_at (numeric)],\n 6813: [6813 - 218491_s_at (numeric)],\n 6814: [6814 - 218493_at (numeric)],\n 6815: [6815 - 218494_s_at (numeric)],\n 6816: [6816 - 218495_at (numeric)],\n 6817: [6817 - 218498_s_at (numeric)],\n 6818: [6818 - 218499_at (numeric)],\n 6819: [6819 - 218501_at (numeric)],\n 6820: [6820 - 218502_s_at (numeric)],\n 6821: [6821 - 218507_at (numeric)],\n 6822: [6822 - 218510_x_at (numeric)],\n 6823: [6823 - 218512_at (numeric)],\n 6824: [6824 - 218515_at (numeric)],\n 6825: [6825 - 218517_at (numeric)],\n 6826: [6826 - 218526_s_at (numeric)],\n 6827: [6827 - 218528_s_at (numeric)],\n 6828: [6828 - 218532_s_at (numeric)],\n 6829: [6829 - 218533_s_at (numeric)],\n 6830: [6830 - 218539_at (numeric)],\n 6831: [6831 - 218541_s_at (numeric)],\n 6832: [6832 - 218542_at (numeric)],\n 6833: [6833 - 218543_s_at (numeric)],\n 6834: [6834 - 218545_at (numeric)],\n 6835: [6835 - 218546_at (numeric)],\n 6836: [6836 - 218552_at (numeric)],\n 6837: [6837 - 218556_at (numeric)],\n 6838: [6838 - 218557_at (numeric)],\n 6839: [6839 - 218559_s_at (numeric)],\n 6840: [6840 - 218561_s_at (numeric)],\n 6841: [6841 - 218563_at (numeric)],\n 6842: [6842 - 218566_s_at (numeric)],\n 6843: [6843 - 218571_s_at (numeric)],\n 6844: [6844 - 218573_at (numeric)],\n 6845: [6845 - 218574_s_at (numeric)],\n 6846: [6846 - 218577_at (numeric)],\n 6847: [6847 - 218580_x_at (numeric)],\n 6848: [6848 - 218584_at (numeric)],\n 6849: [6849 - 218585_s_at (numeric)],\n 6850: [6850 - 218589_at (numeric)],\n 6851: [6851 - 218594_at (numeric)],\n 6852: [6852 - 218595_s_at (numeric)],\n 6853: [6853 - 218597_s_at (numeric)],\n 6854: [6854 - 218599_at (numeric)],\n 6855: [6855 - 218603_at (numeric)],\n 6856: [6856 - 218604_at (numeric)],\n 6857: [6857 - 218605_at (numeric)],\n 6858: [6858 - 218606_at (numeric)],\n 6859: [6859 - 218611_at (numeric)],\n 6860: [6860 - 218613_at (numeric)],\n 6861: [6861 - 218614_at (numeric)],\n 6862: [6862 - 218617_at (numeric)],\n 6863: [6863 - 218618_s_at (numeric)],\n 6864: [6864 - 218622_at (numeric)],\n 6865: [6865 - 218623_at (numeric)],\n 6866: [6866 - 218625_at (numeric)],\n 6867: [6867 - 218627_at (numeric)],\n 6868: [6868 - 218628_at (numeric)],\n 6869: [6869 - 218633_x_at (numeric)],\n 6870: [6870 - 218638_s_at (numeric)],\n 6871: [6871 - 218640_s_at (numeric)],\n 6872: [6872 - 218641_at (numeric)],\n 6873: [6873 - 218642_s_at (numeric)],\n 6874: [6874 - 218644_at (numeric)],\n 6875: [6875 - 218648_at (numeric)],\n 6876: [6876 - 218649_x_at (numeric)],\n 6877: [6877 - 218652_s_at (numeric)],\n 6878: [6878 - 218654_s_at (numeric)],\n 6879: [6879 - 218656_s_at (numeric)],\n 6880: [6880 - 218659_at (numeric)],\n 6881: [6881 - 218660_at (numeric)],\n 6882: [6882 - 218665_at (numeric)],\n 6883: [6883 - 218667_at (numeric)],\n 6884: [6884 - 218669_at (numeric)],\n 6885: [6885 - 218671_s_at (numeric)],\n 6886: [6886 - 218672_at (numeric)],\n 6887: [6887 - 218676_s_at (numeric)],\n 6888: [6888 - 218677_at (numeric)],\n 6889: [6889 - 218678_at (numeric)],\n 6890: [6890 - 218679_s_at (numeric)],\n 6891: [6891 - 218680_x_at (numeric)],\n 6892: [6892 - 218681_s_at (numeric)],\n 6893: [6893 - 218684_at (numeric)],\n 6894: [6894 - 218687_s_at (numeric)],\n 6895: [6895 - 218692_at (numeric)],\n 6896: [6896 - 218694_at (numeric)],\n 6897: [6897 - 218695_at (numeric)],\n 6898: [6898 - 218696_at (numeric)],\n 6899: [6899 - 218698_at (numeric)],\n 6900: [6900 - 218699_at (numeric)],\n 6901: [6901 - 218701_at (numeric)],\n 6902: [6902 - 218704_at (numeric)],\n 6903: [6903 - 218706_s_at (numeric)],\n 6904: [6904 - 218709_s_at (numeric)],\n 6905: [6905 - 218715_at (numeric)],\n 6906: [6906 - 218717_s_at (numeric)],\n 6907: [6907 - 218718_at (numeric)],\n 6908: [6908 - 218720_x_at (numeric)],\n 6909: [6909 - 218721_s_at (numeric)],\n 6910: [6910 - 218723_s_at (numeric)],\n 6911: [6911 - 218728_s_at (numeric)],\n 6912: [6912 - 218729_at (numeric)],\n 6913: [6913 - 218730_s_at (numeric)],\n 6914: [6914 - 218735_s_at (numeric)],\n 6915: [6915 - 218736_s_at (numeric)],\n 6916: [6916 - 218738_s_at (numeric)],\n 6917: [6917 - 218740_s_at (numeric)],\n 6918: [6918 - 218750_at (numeric)],\n 6919: [6919 - 218763_at (numeric)],\n 6920: [6920 - 218764_at (numeric)],\n 6921: [6921 - 218768_at (numeric)],\n 6922: [6922 - 218772_x_at (numeric)],\n 6923: [6923 - 218773_s_at (numeric)],\n 6924: [6924 - 218774_at (numeric)],\n 6925: [6925 - 218775_s_at (numeric)],\n 6926: [6926 - 218779_x_at (numeric)],\n 6927: [6927 - 218780_at (numeric)],\n 6928: [6928 - 218782_s_at (numeric)],\n 6929: [6929 - 218788_s_at (numeric)],\n 6930: [6930 - 218793_s_at (numeric)],\n 6931: [6931 - 218795_at (numeric)],\n 6932: [6932 - 218796_at (numeric)],\n 6933: [6933 - 218802_at (numeric)],\n 6934: [6934 - 218804_at (numeric)],\n 6935: [6935 - 218805_at (numeric)],\n 6936: [6936 - 218806_s_at (numeric)],\n 6937: [6937 - 218807_at (numeric)],\n 6938: [6938 - 218809_at (numeric)],\n 6939: [6939 - 218815_s_at (numeric)],\n 6940: [6940 - 218816_at (numeric)],\n 6941: [6941 - 218819_at (numeric)],\n 6942: [6942 - 218820_at (numeric)],\n 6943: [6943 - 218824_at (numeric)],\n 6944: [6944 - 218830_at (numeric)],\n 6945: [6945 - 218831_s_at (numeric)],\n 6946: [6946 - 218835_at (numeric)],\n 6947: [6947 - 218839_at (numeric)],\n 6948: [6948 - 218845_at (numeric)],\n 6949: [6949 - 218847_at (numeric)],\n 6950: [6950 - 218854_at (numeric)],\n 6951: [6951 - 218856_at (numeric)],\n 6952: [6952 - 218857_s_at (numeric)],\n 6953: [6953 - 218858_at (numeric)],\n 6954: [6954 - 218862_at (numeric)],\n 6955: [6955 - 218870_at (numeric)],\n 6956: [6956 - 218871_x_at (numeric)],\n 6957: [6957 - 218872_at (numeric)],\n 6958: [6958 - 218876_at (numeric)],\n 6959: [6959 - 218882_s_at (numeric)],\n 6960: [6960 - 218883_s_at (numeric)],\n 6961: [6961 - 218885_s_at (numeric)],\n 6962: [6962 - 218888_s_at (numeric)],\n 6963: [6963 - 218899_s_at (numeric)],\n 6964: [6964 - 218901_at (numeric)],\n 6965: [6965 - 218905_at (numeric)],\n 6966: [6966 - 218917_s_at (numeric)],\n 6967: [6967 - 218918_at (numeric)],\n 6968: [6968 - 218930_s_at (numeric)],\n 6969: [6969 - 218942_at (numeric)],\n 6970: [6970 - 218943_s_at (numeric)],\n 6971: [6971 - 218946_at (numeric)],\n 6972: [6972 - 218952_at (numeric)],\n 6973: [6973 - 218959_at (numeric)],\n 6974: [6974 - 218960_at (numeric)],\n 6975: [6975 - 218962_s_at (numeric)],\n 6976: [6976 - 218963_s_at (numeric)],\n 6977: [6977 - 218966_at (numeric)],\n 6978: [6978 - 218967_s_at (numeric)],\n 6979: [6979 - 218971_s_at (numeric)],\n 6980: [6980 - 218974_at (numeric)],\n 6981: [6981 - 218976_at (numeric)],\n 6982: [6982 - 218980_at (numeric)],\n 6983: [6983 - 218982_s_at (numeric)],\n 6984: [6984 - 218983_at (numeric)],\n 6985: [6985 - 218984_at (numeric)],\n 6986: [6986 - 218986_s_at (numeric)],\n 6987: [6987 - 218988_at (numeric)],\n 6988: [6988 - 218990_s_at (numeric)],\n 6989: [6989 - 218995_s_at (numeric)],\n 6990: [6990 - 218999_at (numeric)],\n 6991: [6991 - 219002_at (numeric)],\n 6992: [6992 - 219006_at (numeric)],\n 6993: [6993 - 219007_at (numeric)],\n 6994: [6994 - 219010_at (numeric)],\n 6995: [6995 - 219013_at (numeric)],\n 6996: [6996 - 219014_at (numeric)],\n 6997: [6997 - 219017_at (numeric)],\n 6998: [6998 - 219020_at (numeric)],\n 6999: [6999 - 219024_at (numeric)],\n 7000: [7000 - 219030_at (numeric)],\n 7001: [7001 - 219032_x_at (numeric)],\n 7002: [7002 - 219035_s_at (numeric)],\n 7003: [7003 - 219041_s_at (numeric)],\n 7004: [7004 - 219043_s_at (numeric)],\n 7005: [7005 - 219045_at (numeric)],\n 7006: [7006 - 219049_at (numeric)],\n 7007: [7007 - 219054_at (numeric)],\n 7008: [7008 - 219059_s_at (numeric)],\n 7009: [7009 - 219061_s_at (numeric)],\n 7010: [7010 - 219064_at (numeric)],\n 7011: [7011 - 219065_s_at (numeric)],\n 7012: [7012 - 219067_s_at (numeric)],\n 7013: [7013 - 219073_s_at (numeric)],\n 7014: [7014 - 219076_s_at (numeric)],\n 7015: [7015 - 219087_at (numeric)],\n 7016: [7016 - 219091_s_at (numeric)],\n 7017: [7017 - 219097_x_at (numeric)],\n 7018: [7018 - 219105_x_at (numeric)],\n 7019: [7019 - 219106_s_at (numeric)],\n 7020: [7020 - 219108_x_at (numeric)],\n 7021: [7021 - 219109_at (numeric)],\n 7022: [7022 - 219117_s_at (numeric)],\n 7023: [7023 - 219118_at (numeric)],\n 7024: [7024 - 219119_at (numeric)],\n 7025: [7025 - 219121_s_at (numeric)],\n 7026: [7026 - 219123_at (numeric)],\n 7027: [7027 - 219126_at (numeric)],\n 7028: [7028 - 219127_at (numeric)],\n 7029: [7029 - 219134_at (numeric)],\n 7030: [7030 - 219137_s_at (numeric)],\n 7031: [7031 - 219140_s_at (numeric)],\n 7032: [7032 - 219142_at (numeric)],\n 7033: [7033 - 219147_s_at (numeric)],\n 7034: [7034 - 219148_at (numeric)],\n 7035: [7035 - 219155_at (numeric)],\n 7036: [7036 - 219156_at (numeric)],\n 7037: [7037 - 219157_at (numeric)],\n 7038: [7038 - 219161_s_at (numeric)],\n 7039: [7039 - 219162_s_at (numeric)],\n 7040: [7040 - 219173_at (numeric)],\n 7041: [7041 - 219176_at (numeric)],\n 7042: [7042 - 219179_at (numeric)],\n 7043: [7043 - 219182_at (numeric)],\n 7044: [7044 - 219192_at (numeric)],\n 7045: [7045 - 219195_at (numeric)],\n 7046: [7046 - 219197_s_at (numeric)],\n 7047: [7047 - 219206_x_at (numeric)],\n 7048: [7048 - 219209_at (numeric)],\n 7049: [7049 - 219215_s_at (numeric)],\n 7050: [7050 - 219217_at (numeric)],\n 7051: [7051 - 219220_x_at (numeric)],\n 7052: [7052 - 219221_at (numeric)],\n 7053: [7053 - 219222_at (numeric)],\n 7054: [7054 - 219228_at (numeric)],\n 7055: [7055 - 219230_at (numeric)],\n 7056: [7056 - 219232_s_at (numeric)],\n 7057: [7057 - 219233_s_at (numeric)],\n 7058: [7058 - 219237_s_at (numeric)],\n 7059: [7059 - 219243_at (numeric)],\n 7060: [7060 - 219250_s_at (numeric)],\n 7061: [7061 - 219255_x_at (numeric)],\n 7062: [7062 - 219256_s_at (numeric)],\n 7063: [7063 - 219263_at (numeric)],\n 7064: [7064 - 219271_at (numeric)],\n 7065: [7065 - 219274_at (numeric)],\n 7066: [7066 - 219275_at (numeric)],\n 7067: [7067 - 219276_x_at (numeric)],\n 7068: [7068 - 219277_s_at (numeric)],\n 7069: [7069 - 219281_at (numeric)],\n 7070: [7070 - 219283_at (numeric)],\n 7071: [7071 - 219288_at (numeric)],\n 7072: [7072 - 219290_x_at (numeric)],\n 7073: [7073 - 219293_s_at (numeric)],\n 7074: [7074 - 219295_s_at (numeric)],\n 7075: [7075 - 219298_at (numeric)],\n 7076: [7076 - 219304_s_at (numeric)],\n 7077: [7077 - 219312_s_at (numeric)],\n 7078: [7078 - 219313_at (numeric)],\n 7079: [7079 - 219315_s_at (numeric)],\n 7080: [7080 - 219327_s_at (numeric)],\n 7081: [7081 - 219329_s_at (numeric)],\n 7082: [7082 - 219347_at (numeric)],\n 7083: [7083 - 219350_s_at (numeric)],\n 7084: [7084 - 219352_at (numeric)],\n 7085: [7085 - 219356_s_at (numeric)],\n 7086: [7086 - 219359_at (numeric)],\n 7087: [7087 - 219363_s_at (numeric)],\n 7088: [7088 - 219370_at (numeric)],\n 7089: [7089 - 219371_s_at (numeric)],\n 7090: [7090 - 219373_at (numeric)],\n 7091: [7091 - 219377_at (numeric)],\n 7092: [7092 - 219388_at (numeric)],\n 7093: [7093 - 219392_x_at (numeric)],\n 7094: [7094 - 219395_at (numeric)],\n 7095: [7095 - 219398_at (numeric)],\n 7096: [7096 - 219402_s_at (numeric)],\n 7097: [7097 - 219403_s_at (numeric)],\n 7098: [7098 - 219404_at (numeric)],\n 7099: [7099 - 219410_at (numeric)],\n 7100: [7100 - 219427_at (numeric)],\n 7101: [7101 - 219432_at (numeric)],\n 7102: [7102 - 219436_s_at (numeric)],\n 7103: [7103 - 219440_at (numeric)],\n 7104: [7104 - 219449_s_at (numeric)],\n 7105: [7105 - 219454_at (numeric)],\n 7106: [7106 - 219463_at (numeric)],\n 7107: [7107 - 219465_at (numeric)],\n 7108: [7108 - 219466_s_at (numeric)],\n 7109: [7109 - 219478_at (numeric)],\n 7110: [7110 - 219479_at (numeric)],\n 7111: [7111 - 219485_s_at (numeric)],\n 7112: [7112 - 219487_at (numeric)],\n 7113: [7113 - 219489_s_at (numeric)],\n 7114: [7114 - 219497_s_at (numeric)],\n 7115: [7115 - 219498_s_at (numeric)],\n 7116: [7116 - 219505_at (numeric)],\n 7117: [7117 - 219506_at (numeric)],\n 7118: [7118 - 219508_at (numeric)],\n 7119: [7119 - 219525_at (numeric)],\n 7120: [7120 - 219529_at (numeric)],\n 7121: [7121 - 219543_at (numeric)],\n 7122: [7122 - 219545_at (numeric)],\n 7123: [7123 - 219549_s_at (numeric)],\n 7124: [7124 - 219553_at (numeric)],\n 7125: [7125 - 219554_at (numeric)],\n 7126: [7126 - 219564_at (numeric)],\n 7127: [7127 - 219571_s_at (numeric)],\n 7128: [7128 - 219572_at (numeric)],\n 7129: [7129 - 219575_s_at (numeric)],\n 7130: [7130 - 219580_s_at (numeric)],\n 7131: [7131 - 219584_at (numeric)],\n 7132: [7132 - 219590_x_at (numeric)],\n 7133: [7133 - 219598_s_at (numeric)],\n 7134: [7134 - 219599_at (numeric)],\n 7135: [7135 - 219600_s_at (numeric)],\n 7136: [7136 - 219602_s_at (numeric)],\n 7137: [7137 - 219607_s_at (numeric)],\n 7138: [7138 - 219610_at (numeric)],\n 7139: [7139 - 219612_s_at (numeric)],\n 7140: [7140 - 219616_at (numeric)],\n 7141: [7141 - 219619_at (numeric)],\n 7142: [7142 - 219622_at (numeric)],\n 7143: [7143 - 219625_s_at (numeric)],\n 7144: [7144 - 219628_at (numeric)],\n 7145: [7145 - 219630_at (numeric)],\n 7146: [7146 - 219639_x_at (numeric)],\n 7147: [7147 - 219640_at (numeric)],\n 7148: [7148 - 219648_at (numeric)],\n 7149: [7149 - 219664_s_at (numeric)],\n 7150: [7150 - 219666_at (numeric)],\n 7151: [7151 - 219675_s_at (numeric)],\n 7152: [7152 - 219678_x_at (numeric)],\n 7153: [7153 - 219681_s_at (numeric)],\n 7154: [7154 - 219683_at (numeric)],\n 7155: [7155 - 219684_at (numeric)],\n 7156: [7156 - 219691_at (numeric)],\n 7157: [7157 - 219697_at (numeric)],\n 7158: [7158 - 219700_at (numeric)],\n 7159: [7159 - 219725_at (numeric)],\n 7160: [7160 - 219727_at (numeric)],\n 7161: [7161 - 219735_s_at (numeric)],\n 7162: [7162 - 219739_at (numeric)],\n 7163: [7163 - 219747_at (numeric)],\n 7164: [7164 - 219757_s_at (numeric)],\n 7165: [7165 - 219759_at (numeric)],\n 7166: [7166 - 219762_s_at (numeric)],\n 7167: [7167 - 219764_at (numeric)],\n 7168: [7168 - 219768_at (numeric)],\n 7169: [7169 - 219773_at (numeric)],\n 7170: [7170 - 219777_at (numeric)],\n 7171: [7171 - 219778_at (numeric)],\n 7172: [7172 - 219787_s_at (numeric)],\n 7173: [7173 - 219789_at (numeric)],\n 7174: [7174 - 219795_at (numeric)],\n 7175: [7175 - 219796_s_at (numeric)],\n 7176: [7176 - 219799_s_at (numeric)],\n 7177: [7177 - 219803_at (numeric)],\n 7178: [7178 - 219806_s_at (numeric)],\n 7179: [7179 - 219816_s_at (numeric)],\n 7180: [7180 - 219819_s_at (numeric)],\n 7181: [7181 - 219821_s_at (numeric)],\n 7182: [7182 - 219823_at (numeric)],\n 7183: [7183 - 219833_s_at (numeric)],\n 7184: [7184 - 219836_at (numeric)],\n 7185: [7185 - 219837_s_at (numeric)],\n 7186: [7186 - 219848_s_at (numeric)],\n 7187: [7187 - 219850_s_at (numeric)],\n 7188: [7188 - 219862_s_at (numeric)],\n 7189: [7189 - 219863_at (numeric)],\n 7190: [7190 - 219866_at (numeric)],\n 7191: [7191 - 219869_s_at (numeric)],\n 7192: [7192 - 219871_at (numeric)],\n 7193: [7193 - 219873_at (numeric)],\n 7194: [7194 - 219888_at (numeric)],\n 7195: [7195 - 219895_at (numeric)],\n 7196: [7196 - 219902_at (numeric)],\n 7197: [7197 - 219911_s_at (numeric)],\n 7198: [7198 - 219918_s_at (numeric)],\n 7199: [7199 - 219922_s_at (numeric)],\n 7200: [7200 - 219935_at (numeric)],\n 7201: [7201 - 219937_at (numeric)],\n 7202: [7202 - 219939_s_at (numeric)],\n 7203: [7203 - 219940_s_at (numeric)],\n 7204: [7204 - 219948_x_at (numeric)],\n 7205: [7205 - 219954_s_at (numeric)],\n 7206: [7206 - 219955_at (numeric)],\n 7207: [7207 - 219956_at (numeric)],\n 7208: [7208 - 219961_s_at (numeric)],\n 7209: [7209 - 219962_at (numeric)],\n 7210: [7210 - 219970_at (numeric)],\n 7211: [7211 - 219972_s_at (numeric)],\n 7212: [7212 - 219981_x_at (numeric)],\n 7213: [7213 - 219983_at (numeric)],\n 7214: [7214 - 219985_at (numeric)],\n 7215: [7215 - 219987_at (numeric)],\n 7216: [7216 - 219993_at (numeric)],\n 7217: [7217 - 220017_x_at (numeric)],\n 7218: [7218 - 220026_at (numeric)],\n 7219: [7219 - 220044_x_at (numeric)],\n 7220: [7220 - 220046_s_at (numeric)],\n 7221: [7221 - 220051_at (numeric)],\n 7222: [7222 - 220057_at (numeric)],\n 7223: [7223 - 220059_at (numeric)],\n 7224: [7224 - 220061_at (numeric)],\n 7225: [7225 - 220071_x_at (numeric)],\n 7226: [7226 - 220079_s_at (numeric)],\n 7227: [7227 - 220081_x_at (numeric)],\n 7228: [7228 - 220084_at (numeric)],\n 7229: [7229 - 220088_at (numeric)],\n 7230: [7230 - 220094_s_at (numeric)],\n 7231: [7231 - 220099_s_at (numeric)],\n 7232: [7232 - 220102_at (numeric)],\n 7233: [7233 - 220113_x_at (numeric)],\n 7234: [7234 - 220127_s_at (numeric)],\n 7235: [7235 - 220135_s_at (numeric)],\n 7236: [7236 - 220147_s_at (numeric)],\n 7237: [7237 - 220148_at (numeric)],\n 7238: [7238 - 220161_s_at (numeric)],\n 7239: [7239 - 220175_s_at (numeric)],\n 7240: [7240 - 220177_s_at (numeric)],\n 7241: [7241 - 220179_at (numeric)],\n 7242: [7242 - 220184_at (numeric)],\n 7243: [7243 - 220189_s_at (numeric)],\n 7244: [7244 - 220191_at (numeric)],\n 7245: [7245 - 220192_x_at (numeric)],\n 7246: [7246 - 220196_at (numeric)],\n 7247: [7247 - 220197_at (numeric)],\n 7248: [7248 - 220199_s_at (numeric)],\n 7249: [7249 - 220231_at (numeric)],\n 7250: [7250 - 220232_at (numeric)],\n 7251: [7251 - 220248_x_at (numeric)],\n 7252: [7252 - 220261_s_at (numeric)],\n 7253: [7253 - 220272_at (numeric)],\n 7254: [7254 - 220276_at (numeric)],\n 7255: [7255 - 220281_at (numeric)],\n 7256: [7256 - 220316_at (numeric)],\n 7257: [7257 - 220319_s_at (numeric)],\n 7258: [7258 - 220330_s_at (numeric)],\n 7259: [7259 - 220332_at (numeric)],\n 7260: [7260 - 220335_x_at (numeric)],\n 7261: [7261 - 220355_s_at (numeric)],\n 7262: [7262 - 220417_s_at (numeric)],\n 7263: [7263 - 220419_s_at (numeric)],\n 7264: [7264 - 220452_x_at (numeric)],\n 7265: [7265 - 220467_at (numeric)],\n 7266: [7266 - 220468_at (numeric)],\n 7267: [7267 - 220477_s_at (numeric)],\n 7268: [7268 - 220495_s_at (numeric)],\n 7269: [7269 - 220502_s_at (numeric)],\n 7270: [7270 - 220503_at (numeric)],\n 7271: [7271 - 220507_s_at (numeric)],\n 7272: [7272 - 220510_at (numeric)],\n 7273: [7273 - 220518_at (numeric)],\n 7274: [7274 - 220525_s_at (numeric)],\n 7275: [7275 - 220526_s_at (numeric)],\n 7276: [7276 - 220532_s_at (numeric)],\n 7277: [7277 - 220547_s_at (numeric)],\n 7278: [7278 - 220607_x_at (numeric)],\n 7279: [7279 - 220617_s_at (numeric)],\n 7280: [7280 - 220625_s_at (numeric)],\n 7281: [7281 - 220639_at (numeric)],\n 7282: [7282 - 220642_x_at (numeric)],\n 7283: [7283 - 220643_s_at (numeric)],\n 7284: [7284 - 220647_s_at (numeric)],\n 7285: [7285 - 220690_s_at (numeric)],\n 7286: [7286 - 220720_x_at (numeric)],\n 7287: [7287 - 220723_s_at (numeric)],\n 7288: [7288 - 220724_at (numeric)],\n 7289: [7289 - 220725_x_at (numeric)],\n 7290: [7290 - 220741_s_at (numeric)],\n 7291: [7291 - 220742_s_at (numeric)],\n 7292: [7292 - 220753_s_at (numeric)],\n 7293: [7293 - 220755_s_at (numeric)],\n 7294: [7294 - 220760_x_at (numeric)],\n 7295: [7295 - 220761_s_at (numeric)],\n 7296: [7296 - 220773_s_at (numeric)],\n 7297: [7297 - 220796_x_at (numeric)],\n 7298: [7298 - 220801_s_at (numeric)],\n 7299: [7299 - 220816_at (numeric)],\n 7300: [7300 - 220856_x_at (numeric)],\n 7301: [7301 - 220864_s_at (numeric)],\n 7302: [7302 - 220890_s_at (numeric)],\n 7303: [7303 - 220892_s_at (numeric)],\n 7304: [7304 - 220917_s_at (numeric)],\n 7305: [7305 - 220924_s_at (numeric)],\n 7306: [7306 - 220939_s_at (numeric)],\n 7307: [7307 - 220940_at (numeric)],\n 7308: [7308 - 220942_x_at (numeric)],\n 7309: [7309 - 220945_x_at (numeric)],\n 7310: [7310 - 220948_s_at (numeric)],\n 7311: [7311 - 220949_s_at (numeric)],\n 7312: [7312 - 220951_s_at (numeric)],\n 7313: [7313 - 220952_s_at (numeric)],\n 7314: [7314 - 220954_s_at (numeric)],\n 7315: [7315 - 220959_s_at (numeric)],\n 7316: [7316 - 220960_x_at (numeric)],\n 7317: [7317 - 220964_s_at (numeric)],\n 7318: [7318 - 220966_x_at (numeric)],\n 7319: [7319 - 220974_x_at (numeric)],\n 7320: [7320 - 220979_s_at (numeric)],\n 7321: [7321 - 220987_s_at (numeric)],\n 7322: [7322 - 220988_s_at (numeric)],\n 7323: [7323 - 220990_s_at (numeric)],\n 7324: [7324 - 220994_s_at (numeric)],\n 7325: [7325 - 220999_s_at (numeric)],\n 7326: [7326 - 221004_s_at (numeric)],\n 7327: [7327 - 221009_s_at (numeric)],\n 7328: [7328 - 221011_s_at (numeric)],\n 7329: [7329 - 221012_s_at (numeric)],\n 7330: [7330 - 221016_s_at (numeric)],\n 7331: [7331 - 221019_s_at (numeric)],\n 7332: [7332 - 221024_s_at (numeric)],\n 7333: [7333 - 221031_s_at (numeric)],\n 7334: [7334 - 221051_s_at (numeric)],\n 7335: [7335 - 221059_s_at (numeric)],\n 7336: [7336 - 221081_s_at (numeric)],\n 7337: [7337 - 221094_s_at (numeric)],\n 7338: [7338 - 221123_x_at (numeric)],\n 7339: [7339 - 221132_at (numeric)],\n 7340: [7340 - 221133_s_at (numeric)],\n 7341: [7341 - 221150_at (numeric)],\n 7342: [7342 - 221173_at (numeric)],\n 7343: [7343 - 221185_s_at (numeric)],\n 7344: [7344 - 221188_s_at (numeric)],\n 7345: [7345 - 221196_x_at (numeric)],\n 7346: [7346 - 221203_s_at (numeric)],\n 7347: [7347 - 221204_s_at (numeric)],\n 7348: [7348 - 221207_s_at (numeric)],\n 7349: [7349 - 221208_s_at (numeric)],\n 7350: [7350 - 221210_s_at (numeric)],\n 7351: [7351 - 221211_s_at (numeric)],\n 7352: [7352 - 221215_s_at (numeric)],\n 7353: [7353 - 221218_s_at (numeric)],\n 7354: [7354 - 221221_s_at (numeric)],\n 7355: [7355 - 221230_s_at (numeric)],\n 7356: [7356 - 221245_s_at (numeric)],\n 7357: [7357 - 221246_x_at (numeric)],\n 7358: [7358 - 221253_s_at (numeric)],\n 7359: [7359 - 221261_x_at (numeric)],\n 7360: [7360 - 221263_s_at (numeric)],\n 7361: [7361 - 221267_s_at (numeric)],\n 7362: [7362 - 221269_s_at (numeric)],\n 7363: [7363 - 221275_s_at (numeric)],\n 7364: [7364 - 221286_s_at (numeric)],\n 7365: [7365 - 221295_at (numeric)],\n 7366: [7366 - 221305_s_at (numeric)],\n 7367: [7367 - 221381_s_at (numeric)],\n 7368: [7368 - 221419_s_at (numeric)],\n 7369: [7369 - 221427_s_at (numeric)],\n 7370: [7370 - 221428_s_at (numeric)],\n 7371: [7371 - 221434_s_at (numeric)],\n 7372: [7372 - 221447_s_at (numeric)],\n 7373: [7373 - 221449_s_at (numeric)],\n 7374: [7374 - 221452_s_at (numeric)],\n 7375: [7375 - 221471_at (numeric)],\n 7376: [7376 - 221472_at (numeric)],\n 7377: [7377 - 221473_x_at (numeric)],\n 7378: [7378 - 221474_at (numeric)],\n 7379: [7379 - 221475_s_at (numeric)],\n 7380: [7380 - 221476_s_at (numeric)],\n 7381: [7381 - 221477_s_at (numeric)],\n 7382: [7382 - 221478_at (numeric)],\n 7383: [7383 - 221479_s_at (numeric)],\n 7384: [7384 - 221481_x_at (numeric)],\n 7385: [7385 - 221482_s_at (numeric)],\n 7386: [7386 - 221483_s_at (numeric)],\n 7387: [7387 - 221484_at (numeric)],\n 7388: [7388 - 221485_at (numeric)],\n 7389: [7389 - 221486_at (numeric)],\n 7390: [7390 - 221488_s_at (numeric)],\n 7391: [7391 - 221489_s_at (numeric)],\n 7392: [7392 - 221491_x_at (numeric)],\n 7393: [7393 - 221492_s_at (numeric)],\n 7394: [7394 - 221493_at (numeric)],\n 7395: [7395 - 221494_x_at (numeric)],\n 7396: [7396 - 221495_s_at (numeric)],\n 7397: [7397 - 221499_s_at (numeric)],\n 7398: [7398 - 221500_s_at (numeric)],\n 7399: [7399 - 221501_x_at (numeric)],\n 7400: [7400 - 221502_at (numeric)],\n 7401: [7401 - 221504_s_at (numeric)],\n 7402: [7402 - 221505_at (numeric)],\n 7403: [7403 - 221506_s_at (numeric)],\n 7404: [7404 - 221509_at (numeric)],\n 7405: [7405 - 221510_s_at (numeric)],\n 7406: [7406 - 221511_x_at (numeric)],\n 7407: [7407 - 221515_s_at (numeric)],\n 7408: [7408 - 221517_s_at (numeric)],\n 7409: [7409 - 221518_s_at (numeric)],\n 7410: [7410 - 221522_at (numeric)],\n 7411: [7411 - 221523_s_at (numeric)],\n 7412: [7412 - 221524_s_at (numeric)],\n 7413: [7413 - 221529_s_at (numeric)],\n 7414: [7414 - 221530_s_at (numeric)],\n 7415: [7415 - 221531_at (numeric)],\n 7416: [7416 - 221532_s_at (numeric)],\n 7417: [7417 - 221535_at (numeric)],\n 7418: [7418 - 221538_s_at (numeric)],\n 7419: [7419 - 221539_at (numeric)],\n 7420: [7420 - 221540_x_at (numeric)],\n 7421: [7421 - 221541_at (numeric)],\n 7422: [7422 - 221543_s_at (numeric)],\n 7423: [7423 - 221553_at (numeric)],\n 7424: [7424 - 221558_s_at (numeric)],\n 7425: [7425 - 221561_at (numeric)],\n 7426: [7426 - 221566_s_at (numeric)],\n 7427: [7427 - 221567_at (numeric)],\n 7428: [7428 - 221568_s_at (numeric)],\n 7429: [7429 - 221570_s_at (numeric)],\n 7430: [7430 - 221577_x_at (numeric)],\n 7431: [7431 - 221580_s_at (numeric)],\n 7432: [7432 - 221582_at (numeric)],\n 7433: [7433 - 221584_s_at (numeric)],\n 7434: [7434 - 221588_x_at (numeric)],\n 7435: [7435 - 221589_s_at (numeric)],\n 7436: [7436 - 221593_s_at (numeric)],\n 7437: [7437 - 221597_s_at (numeric)],\n 7438: [7438 - 221605_s_at (numeric)],\n 7439: [7439 - 221607_x_at (numeric)],\n 7440: [7440 - 221613_s_at (numeric)],\n 7441: [7441 - 221619_s_at (numeric)],\n 7442: [7442 - 221620_s_at (numeric)],\n 7443: [7443 - 221622_s_at (numeric)],\n 7444: [7444 - 221636_s_at (numeric)],\n 7445: [7445 - 221637_s_at (numeric)],\n 7446: [7446 - 221645_s_at (numeric)],\n 7447: [7447 - 221646_s_at (numeric)],\n 7448: [7448 - 221648_s_at (numeric)],\n 7449: [7449 - 221651_x_at (numeric)],\n 7450: [7450 - 221652_s_at (numeric)],\n 7451: [7451 - 221654_s_at (numeric)],\n 7452: [7452 - 221655_x_at (numeric)],\n 7453: [7453 - 221664_s_at (numeric)],\n 7454: [7454 - 221665_s_at (numeric)],\n 7455: [7455 - 221666_s_at (numeric)],\n 7456: [7456 - 221667_s_at (numeric)],\n 7457: [7457 - 221671_x_at (numeric)],\n 7458: [7458 - 221675_s_at (numeric)],\n 7459: [7459 - 221676_s_at (numeric)],\n 7460: [7460 - 221688_s_at (numeric)],\n 7461: [7461 - 221689_s_at (numeric)],\n 7462: [7462 - 221690_s_at (numeric)],\n 7463: [7463 - 221691_x_at (numeric)],\n 7464: [7464 - 221692_s_at (numeric)],\n 7465: [7465 - 221698_s_at (numeric)],\n 7466: [7466 - 221699_s_at (numeric)],\n 7467: [7467 - 221700_s_at (numeric)],\n 7468: [7468 - 221702_s_at (numeric)],\n 7469: [7469 - 221711_s_at (numeric)],\n 7470: [7470 - 221718_s_at (numeric)],\n 7471: [7471 - 221725_at (numeric)],\n 7472: [7472 - 221726_at (numeric)],\n 7473: [7473 - 221727_at (numeric)],\n 7474: [7474 - 221728_x_at (numeric)],\n 7475: [7475 - 221729_at (numeric)],\n 7476: [7476 - 221730_at (numeric)],\n 7477: [7477 - 221731_x_at (numeric)],\n 7478: [7478 - 221732_at (numeric)],\n 7479: [7479 - 221739_at (numeric)],\n 7480: [7480 - 221741_s_at (numeric)],\n 7481: [7481 - 221743_at (numeric)],\n 7482: [7482 - 221747_at (numeric)],\n 7483: [7483 - 221748_s_at (numeric)],\n 7484: [7484 - 221749_at (numeric)],\n 7485: [7485 - 221750_at (numeric)],\n 7486: [7486 - 221751_at (numeric)],\n 7487: [7487 - 221756_at (numeric)],\n 7488: [7488 - 221760_at (numeric)],\n 7489: [7489 - 221761_at (numeric)],\n 7490: [7490 - 221763_at (numeric)],\n 7491: [7491 - 221766_s_at (numeric)],\n 7492: [7492 - 221767_x_at (numeric)],\n 7493: [7493 - 221768_at (numeric)],\n 7494: [7494 - 221773_at (numeric)],\n 7495: [7495 - 221775_x_at (numeric)],\n 7496: [7496 - 221776_s_at (numeric)],\n 7497: [7497 - 221778_at (numeric)],\n 7498: [7498 - 221781_s_at (numeric)],\n 7499: [7499 - 221782_at (numeric)],\n 7500: [7500 - 221786_at (numeric)],\n 7501: [7501 - 221791_s_at (numeric)],\n 7502: [7502 - 221795_at (numeric)],\n 7503: [7503 - 221798_x_at (numeric)],\n 7504: [7504 - 221802_s_at (numeric)],\n 7505: [7505 - 221804_s_at (numeric)],\n 7506: [7506 - 221805_at (numeric)],\n 7507: [7507 - 221806_s_at (numeric)],\n 7508: [7508 - 221808_at (numeric)],\n 7509: [7509 - 221811_at (numeric)],\n 7510: [7510 - 221815_at (numeric)],\n 7511: [7511 - 221816_s_at (numeric)],\n 7512: [7512 - 221827_at (numeric)],\n 7513: [7513 - 221829_s_at (numeric)],\n 7514: [7514 - 221830_at (numeric)],\n 7515: [7515 - 221834_at (numeric)],\n 7516: [7516 - 221840_at (numeric)],\n 7517: [7517 - 221841_s_at (numeric)],\n 7518: [7518 - 221842_s_at (numeric)],\n 7519: [7519 - 221844_x_at (numeric)],\n 7520: [7520 - 221847_at (numeric)],\n 7521: [7521 - 221853_s_at (numeric)],\n 7522: [7522 - 221864_at (numeric)],\n 7523: [7523 - 221870_at (numeric)],\n 7524: [7524 - 221872_at (numeric)],\n 7525: [7525 - 221874_at (numeric)],\n 7526: [7526 - 221875_x_at (numeric)],\n 7527: [7527 - 221879_at (numeric)],\n 7528: [7528 - 221882_s_at (numeric)],\n 7529: [7529 - 221884_at (numeric)],\n 7530: [7530 - 221891_x_at (numeric)],\n 7531: [7531 - 221896_s_at (numeric)],\n 7532: [7532 - 221898_at (numeric)],\n 7533: [7533 - 221899_at (numeric)],\n 7534: [7534 - 221911_at (numeric)],\n 7535: [7535 - 221916_at (numeric)],\n 7536: [7536 - 221918_at (numeric)],\n 7537: [7537 - 221922_at (numeric)],\n 7538: [7538 - 221923_s_at (numeric)],\n 7539: [7539 - 221927_s_at (numeric)],\n 7540: [7540 - 221928_at (numeric)],\n 7541: [7541 - 221931_s_at (numeric)],\n 7542: [7542 - 221932_s_at (numeric)],\n 7543: [7543 - 221933_at (numeric)],\n 7544: [7544 - 221934_s_at (numeric)],\n 7545: [7545 - 221937_at (numeric)],\n 7546: [7546 - 221942_s_at (numeric)],\n 7547: [7547 - 221943_x_at (numeric)],\n 7548: [7548 - 221950_at (numeric)],\n 7549: [7549 - 221952_x_at (numeric)],\n 7550: [7550 - 221958_s_at (numeric)],\n 7551: [7551 - 221962_s_at (numeric)],\n 7552: [7552 - 221970_s_at (numeric)],\n 7553: [7553 - 221971_x_at (numeric)],\n 7554: [7554 - 221972_s_at (numeric)],\n 7555: [7555 - 221984_s_at (numeric)],\n 7556: [7556 - 221989_at (numeric)],\n 7557: [7557 - 222000_at (numeric)],\n 7558: [7558 - 222001_x_at (numeric)],\n 7559: [7559 - 222021_x_at (numeric)],\n 7560: [7560 - 222033_s_at (numeric)],\n 7561: [7561 - 222035_s_at (numeric)],\n 7562: [7562 - 222036_s_at (numeric)],\n 7563: [7563 - 222043_at (numeric)],\n 7564: [7564 - 222047_s_at (numeric)],\n 7565: [7565 - 222065_s_at (numeric)],\n 7566: [7566 - 222067_x_at (numeric)],\n 7567: [7567 - 222071_s_at (numeric)],\n 7568: [7568 - 222073_at (numeric)],\n 7569: [7569 - 222077_s_at (numeric)],\n 7570: [7570 - 222083_at (numeric)],\n 7571: [7571 - 222088_s_at (numeric)],\n 7572: [7572 - 222101_s_at (numeric)],\n 7573: [7573 - 222103_at (numeric)],\n 7574: [7574 - 222104_x_at (numeric)],\n 7575: [7575 - 222108_at (numeric)],\n 7576: [7576 - 222111_at (numeric)],\n 7577: [7577 - 222116_s_at (numeric)],\n 7578: [7578 - 222119_s_at (numeric)],\n 7579: [7579 - 222125_s_at (numeric)],\n 7580: [7580 - 222127_s_at (numeric)],\n 7581: [7581 - 222138_s_at (numeric)],\n 7582: [7582 - 222139_at (numeric)],\n 7583: [7583 - 222140_s_at (numeric)],\n 7584: [7584 - 222145_at (numeric)],\n 7585: [7585 - 222146_s_at (numeric)],\n 7586: [7586 - 222150_s_at (numeric)],\n 7587: [7587 - 222154_s_at (numeric)],\n 7588: [7588 - 222158_s_at (numeric)],\n 7589: [7589 - 222162_s_at (numeric)],\n 7590: [7590 - 222164_at (numeric)],\n 7591: [7591 - 222182_s_at (numeric)],\n 7592: [7592 - 222187_x_at (numeric)],\n 7593: [7593 - 222203_s_at (numeric)],\n 7594: [7594 - 222207_x_at (numeric)],\n 7595: [7595 - 222209_s_at (numeric)],\n 7596: [7596 - 222212_s_at (numeric)],\n 7597: [7597 - 222216_s_at (numeric)],\n 7598: [7598 - 222229_x_at (numeric)],\n 7599: [7599 - 222230_s_at (numeric)],\n 7600: [7600 - 222231_s_at (numeric)],\n 7601: [7601 - 222235_s_at (numeric)],\n 7602: [7602 - 222240_s_at (numeric)],\n 7603: [7603 - 222242_s_at (numeric)],\n 7604: [7604 - 222243_s_at (numeric)],\n 7605: [7605 - 222244_s_at (numeric)],\n 7606: [7606 - 222252_x_at (numeric)],\n 7607: [7607 - 222257_s_at (numeric)],\n 7608: [7608 - 222258_s_at (numeric)],\n 7609: [7609 - 222266_at (numeric)],\n 7610: [7610 - 222271_at (numeric)],\n 7611: [7611 - 222281_s_at (numeric)],\n 7612: [7612 - 222288_at (numeric)],\n 7613: [7613 - 222291_at (numeric)],\n 7614: [7614 - 222303_at (numeric)],\n 7615: [7615 - 222314_x_at (numeric)],\n 7616: [7616 - 222329_x_at (numeric)],\n 7617: [7617 - 222334_at (numeric)],\n 7618: [7618 - 222357_at (numeric)],\n 7619: [7619 - 222368_at (numeric)],\n 7620: [7620 - 222372_at (numeric)],\n 7621: [7621 - 222380_s_at (numeric)],\n 7622: [7622 - 222385_x_at (numeric)],\n 7623: [7623 - 222386_s_at (numeric)],\n 7624: [7624 - 222388_s_at (numeric)],\n 7625: [7625 - 222389_s_at (numeric)],\n 7626: [7626 - 222390_at (numeric)],\n 7627: [7627 - 222391_at (numeric)],\n 7628: [7628 - 222392_x_at (numeric)],\n 7629: [7629 - 222393_s_at (numeric)],\n 7630: [7630 - 222394_at (numeric)],\n 7631: [7631 - 222395_s_at (numeric)],\n 7632: [7632 - 222396_at (numeric)],\n 7633: [7633 - 222398_s_at (numeric)],\n 7634: [7634 - 222399_s_at (numeric)],\n 7635: [7635 - 222400_s_at (numeric)],\n 7636: [7636 - 222401_s_at (numeric)],\n 7637: [7637 - 222404_x_at (numeric)],\n 7638: [7638 - 222405_at (numeric)],\n 7639: [7639 - 222408_s_at (numeric)],\n 7640: [7640 - 222409_at (numeric)],\n 7641: [7641 - 222410_s_at (numeric)],\n 7642: [7642 - 222411_s_at (numeric)],\n 7643: [7643 - 222412_s_at (numeric)],\n 7644: [7644 - 222413_s_at (numeric)],\n 7645: [7645 - 222414_at (numeric)],\n 7646: [7646 - 222415_at (numeric)],\n 7647: [7647 - 222416_at (numeric)],\n 7648: [7648 - 222417_s_at (numeric)],\n 7649: [7649 - 222420_s_at (numeric)],\n 7650: [7650 - 222421_at (numeric)],\n 7651: [7651 - 222423_at (numeric)],\n 7652: [7652 - 222427_s_at (numeric)],\n 7653: [7653 - 222428_s_at (numeric)],\n 7654: [7654 - 222430_s_at (numeric)],\n 7655: [7655 - 222431_at (numeric)],\n 7656: [7656 - 222432_s_at (numeric)],\n 7657: [7657 - 222433_at (numeric)],\n 7658: [7658 - 222436_s_at (numeric)],\n 7659: [7659 - 222437_s_at (numeric)],\n 7660: [7660 - 222441_x_at (numeric)],\n 7661: [7661 - 222443_s_at (numeric)],\n 7662: [7662 - 222444_at (numeric)],\n 7663: [7663 - 222445_at (numeric)],\n 7664: [7664 - 222446_s_at (numeric)],\n 7665: [7665 - 222447_at (numeric)],\n 7666: [7666 - 222448_s_at (numeric)],\n 7667: [7667 - 222449_at (numeric)],\n 7668: [7668 - 222450_at (numeric)],\n 7669: [7669 - 222451_s_at (numeric)],\n 7670: [7670 - 222452_s_at (numeric)],\n 7671: [7671 - 222453_at (numeric)],\n 7672: [7672 - 222457_s_at (numeric)],\n 7673: [7673 - 222459_at (numeric)],\n 7674: [7674 - 222460_s_at (numeric)],\n 7675: [7675 - 222464_s_at (numeric)],\n 7676: [7676 - 222465_at (numeric)],\n 7677: [7677 - 222467_s_at (numeric)],\n 7678: [7678 - 222474_s_at (numeric)],\n 7679: [7679 - 222477_s_at (numeric)],\n 7680: [7680 - 222478_at (numeric)],\n 7681: [7681 - 222482_at (numeric)],\n 7682: [7682 - 222484_s_at (numeric)],\n 7683: [7683 - 222486_s_at (numeric)],\n 7684: [7684 - 222488_s_at (numeric)],\n 7685: [7685 - 222489_s_at (numeric)],\n 7686: [7686 - 222494_at (numeric)],\n 7687: [7687 - 222495_at (numeric)],\n 7688: [7688 - 222496_s_at (numeric)],\n 7689: [7689 - 222497_x_at (numeric)],\n 7690: [7690 - 222500_at (numeric)],\n 7691: [7691 - 222512_at (numeric)],\n 7692: [7692 - 222513_s_at (numeric)],\n 7693: [7693 - 222514_at (numeric)],\n 7694: [7694 - 222516_at (numeric)],\n 7695: [7695 - 222518_at (numeric)],\n 7696: [7696 - 222521_x_at (numeric)],\n 7697: [7697 - 222529_at (numeric)],\n 7698: [7698 - 222530_s_at (numeric)],\n 7699: [7699 - 222531_s_at (numeric)],\n 7700: [7700 - 222533_at (numeric)],\n 7701: [7701 - 222536_s_at (numeric)],\n 7702: [7702 - 222543_at (numeric)],\n 7703: [7703 - 222544_s_at (numeric)],\n 7704: [7704 - 222549_at (numeric)],\n 7705: [7705 - 222550_at (numeric)],\n 7706: [7706 - 222552_at (numeric)],\n 7707: [7707 - 222553_x_at (numeric)],\n 7708: [7708 - 222557_at (numeric)],\n 7709: [7709 - 222559_s_at (numeric)],\n 7710: [7710 - 222572_at (numeric)],\n 7711: [7711 - 222574_s_at (numeric)],\n 7712: [7712 - 222578_s_at (numeric)],\n 7713: [7713 - 222579_at (numeric)],\n 7714: [7714 - 222580_at (numeric)],\n 7715: [7715 - 222581_at (numeric)],\n 7716: [7716 - 222587_s_at (numeric)],\n 7717: [7717 - 222592_s_at (numeric)],\n 7718: [7718 - 222602_at (numeric)],\n 7719: [7719 - 222603_at (numeric)],\n 7720: [7720 - 222605_at (numeric)],\n 7721: [7721 - 222606_at (numeric)],\n 7722: [7722 - 222608_s_at (numeric)],\n 7723: [7723 - 222620_s_at (numeric)],\n 7724: [7724 - 222621_at (numeric)],\n 7725: [7725 - 222623_s_at (numeric)],\n 7726: [7726 - 222633_at (numeric)],\n 7727: [7727 - 222637_at (numeric)],\n 7728: [7728 - 222640_at (numeric)],\n 7729: [7729 - 222646_s_at (numeric)],\n 7730: [7730 - 222651_s_at (numeric)],\n 7731: [7731 - 222654_at (numeric)],\n 7732: [7732 - 222662_at (numeric)],\n 7733: [7733 - 222667_s_at (numeric)],\n 7734: [7734 - 222669_s_at (numeric)],\n 7735: [7735 - 222670_s_at (numeric)],\n 7736: [7736 - 222673_x_at (numeric)],\n 7737: [7737 - 222675_s_at (numeric)],\n 7738: [7738 - 222679_s_at (numeric)],\n 7739: [7739 - 222683_at (numeric)],\n 7740: [7740 - 222691_at (numeric)],\n 7741: [7741 - 222692_s_at (numeric)],\n 7742: [7742 - 222696_at (numeric)],\n 7743: [7743 - 222700_at (numeric)],\n 7744: [7744 - 222701_s_at (numeric)],\n 7745: [7745 - 222712_s_at (numeric)],\n 7746: [7746 - 222714_s_at (numeric)],\n 7747: [7747 - 222717_at (numeric)],\n 7748: [7748 - 222722_at (numeric)],\n 7749: [7749 - 222728_s_at (numeric)],\n 7750: [7750 - 222731_at (numeric)],\n 7751: [7751 - 222735_at (numeric)],\n 7752: [7752 - 222740_at (numeric)],\n 7753: [7753 - 222742_s_at (numeric)],\n 7754: [7754 - 222750_s_at (numeric)],\n 7755: [7755 - 222751_at (numeric)],\n 7756: [7756 - 222753_s_at (numeric)],\n 7757: [7757 - 222754_at (numeric)],\n 7758: [7758 - 222759_at (numeric)],\n 7759: [7759 - 222760_at (numeric)],\n 7760: [7760 - 222761_at (numeric)],\n 7761: [7761 - 222763_s_at (numeric)],\n 7762: [7762 - 222764_at (numeric)],\n 7763: [7763 - 222771_s_at (numeric)],\n 7764: [7764 - 222774_s_at (numeric)],\n 7765: [7765 - 222784_at (numeric)],\n 7766: [7766 - 222785_x_at (numeric)],\n 7767: [7767 - 222787_s_at (numeric)],\n 7768: [7768 - 222791_at (numeric)],\n 7769: [7769 - 222794_x_at (numeric)],\n 7770: [7770 - 222798_at (numeric)],\n 7771: [7771 - 222802_at (numeric)],\n 7772: [7772 - 222803_at (numeric)],\n 7773: [7773 - 222808_at (numeric)],\n 7774: [7774 - 222810_s_at 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[7851 - 223037_at (numeric)],\n 7852: [7852 - 223038_s_at (numeric)],\n 7853: [7853 - 223039_at (numeric)],\n 7854: [7854 - 223040_at (numeric)],\n 7855: [7855 - 223042_s_at (numeric)],\n 7856: [7856 - 223043_at (numeric)],\n 7857: [7857 - 223044_at (numeric)],\n 7858: [7858 - 223046_at (numeric)],\n 7859: [7859 - 223047_at (numeric)],\n 7860: [7860 - 223049_at (numeric)],\n 7861: [7861 - 223051_at (numeric)],\n 7862: [7862 - 223053_x_at (numeric)],\n 7863: [7863 - 223054_at (numeric)],\n 7864: [7864 - 223056_s_at (numeric)],\n 7865: [7865 - 223058_at (numeric)],\n 7866: [7866 - 223059_s_at (numeric)],\n 7867: [7867 - 223060_at (numeric)],\n 7868: [7868 - 223062_s_at (numeric)],\n 7869: [7869 - 223063_at (numeric)],\n 7870: [7870 - 223064_at (numeric)],\n 7871: [7871 - 223065_s_at (numeric)],\n 7872: [7872 - 223066_at (numeric)],\n 7873: [7873 - 223067_at (numeric)],\n 7874: [7874 - 223068_at (numeric)],\n 7875: [7875 - 223070_at (numeric)],\n 7876: [7876 - 223071_at (numeric)],\n 7877: [7877 - 223073_at (numeric)],\n 7878: [7878 - 223075_s_at (numeric)],\n 7879: [7879 - 223077_at (numeric)],\n 7880: [7880 - 223081_at (numeric)],\n 7881: [7881 - 223082_at (numeric)],\n 7882: [7882 - 223084_s_at (numeric)],\n 7883: [7883 - 223085_at (numeric)],\n 7884: [7884 - 223086_x_at (numeric)],\n 7885: [7885 - 223087_at (numeric)],\n 7886: [7886 - 223088_x_at (numeric)],\n 7887: [7887 - 223090_x_at (numeric)],\n 7888: [7888 - 223091_x_at (numeric)],\n 7889: [7889 - 223092_at (numeric)],\n 7890: [7890 - 223096_at (numeric)],\n 7891: [7891 - 223097_at (numeric)],\n 7892: [7892 - 223098_s_at (numeric)],\n 7893: [7893 - 223099_s_at (numeric)],\n 7894: [7894 - 223100_s_at (numeric)],\n 7895: [7895 - 223103_at (numeric)],\n 7896: [7896 - 223104_at (numeric)],\n 7897: [7897 - 223105_s_at (numeric)],\n 7898: [7898 - 223106_at (numeric)],\n 7899: [7899 - 223110_at (numeric)],\n 7900: [7900 - 223112_s_at (numeric)],\n 7901: [7901 - 223113_at (numeric)],\n 7902: [7902 - 223120_at (numeric)],\n 7903: [7903 - 223121_s_at (numeric)],\n 7904: [7904 - 223122_s_at (numeric)],\n 7905: [7905 - 223125_s_at (numeric)],\n 7906: [7906 - 223129_x_at (numeric)],\n 7907: [7907 - 223130_s_at (numeric)],\n 7908: [7908 - 223132_s_at (numeric)],\n 7909: [7909 - 223133_at (numeric)],\n 7910: [7910 - 223134_at (numeric)],\n 7911: [7911 - 223136_at (numeric)],\n 7912: [7912 - 223139_s_at (numeric)],\n 7913: [7913 - 223151_at (numeric)],\n 7914: [7914 - 223154_at (numeric)],\n 7915: [7915 - 223155_at (numeric)],\n 7916: [7916 - 223156_at (numeric)],\n 7917: [7917 - 223157_at (numeric)],\n 7918: [7918 - 223158_s_at (numeric)],\n 7919: [7919 - 223162_s_at (numeric)],\n 7920: [7920 - 223165_s_at (numeric)],\n 7921: [7921 - 223168_at (numeric)],\n 7922: [7922 - 223170_at (numeric)],\n 7923: [7923 - 223172_s_at (numeric)],\n 7924: [7924 - 223177_at (numeric)],\n 7925: [7925 - 223180_s_at (numeric)],\n 7926: [7926 - 223187_s_at (numeric)],\n 7927: [7927 - 223189_x_at (numeric)],\n 7928: [7928 - 223190_s_at (numeric)],\n 7929: [7929 - 223191_at (numeric)],\n 7930: [7930 - 223193_x_at (numeric)],\n 7931: [7931 - 223203_at (numeric)],\n 7932: [7932 - 223204_at (numeric)],\n 7933: [7933 - 223207_x_at (numeric)],\n 7934: [7934 - 223209_s_at (numeric)],\n 7935: [7935 - 223210_at (numeric)],\n 7936: [7936 - 223213_s_at (numeric)],\n 7937: [7937 - 223215_s_at (numeric)],\n 7938: [7938 - 223216_x_at (numeric)],\n 7939: [7939 - 223217_s_at (numeric)],\n 7940: [7940 - 223218_s_at (numeric)],\n 7941: [7941 - 223220_s_at (numeric)],\n 7942: [7942 - 223223_at (numeric)],\n 7943: [7943 - 223227_at (numeric)],\n 7944: [7944 - 223228_at (numeric)],\n 7945: [7945 - 223229_at (numeric)],\n 7946: [7946 - 223231_at (numeric)],\n 7947: [7947 - 223232_s_at (numeric)],\n 7948: [7948 - 223233_s_at (numeric)],\n 7949: [7949 - 223235_s_at (numeric)],\n 7950: [7950 - 223239_at (numeric)],\n 7951: [7951 - 223240_at (numeric)],\n 7952: [7952 - 223243_s_at (numeric)],\n 7953: [7953 - 223244_s_at 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223310_x_at (numeric)],\n 7980: [7980 - 223313_s_at (numeric)],\n 7981: [7981 - 223315_at (numeric)],\n 7982: [7982 - 223316_at (numeric)],\n 7983: [7983 - 223318_s_at (numeric)],\n 7984: [7984 - 223319_at (numeric)],\n 7985: [7985 - 223328_at (numeric)],\n 7986: [7986 - 223329_x_at (numeric)],\n 7987: [7987 - 223333_s_at (numeric)],\n 7988: [7988 - 223334_at (numeric)],\n 7989: [7989 - 223335_at (numeric)],\n 7990: [7990 - 223336_s_at (numeric)],\n 7991: [7991 - 223338_s_at (numeric)],\n 7992: [7992 - 223339_at (numeric)],\n 7993: [7993 - 223341_s_at (numeric)],\n 7994: [7994 - 223342_at (numeric)],\n 7995: [7995 - 223343_at (numeric)],\n 7996: [7996 - 223344_s_at (numeric)],\n 7997: [7997 - 223350_x_at (numeric)],\n 7998: [7998 - 223354_x_at (numeric)],\n 7999: [7999 - 223356_s_at (numeric)],\n 8000: [8000 - 223358_s_at (numeric)],\n 8001: [8001 - 223361_at (numeric)],\n 8002: [8002 - 223366_at (numeric)],\n 8003: [8003 - 223370_at (numeric)],\n 8004: [8004 - 223374_s_at 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223475_at (numeric)],\n 8031: [8031 - 223480_s_at (numeric)],\n 8032: [8032 - 223484_at (numeric)],\n 8033: [8033 - 223492_s_at (numeric)],\n 8034: [8034 - 223494_at (numeric)],\n 8035: [8035 - 223495_at (numeric)],\n 8036: [8036 - 223497_at (numeric)],\n 8037: [8037 - 223501_at (numeric)],\n 8038: [8038 - 223502_s_at (numeric)],\n 8039: [8039 - 223509_at (numeric)],\n 8040: [8040 - 223516_s_at (numeric)],\n 8041: [8041 - 223519_at (numeric)],\n 8042: [8042 - 223528_s_at (numeric)],\n 8043: [8043 - 223531_x_at (numeric)],\n 8044: [8044 - 223538_at (numeric)],\n 8045: [8045 - 223541_at (numeric)],\n 8046: [8046 - 223551_at (numeric)],\n 8047: [8047 - 223559_s_at (numeric)],\n 8048: [8048 - 223572_at (numeric)],\n 8049: [8049 - 223577_x_at (numeric)],\n 8050: [8050 - 223578_x_at (numeric)],\n 8051: [8051 - 223591_at (numeric)],\n 8052: [8052 - 223592_s_at (numeric)],\n 8053: [8053 - 223595_at (numeric)],\n 8054: [8054 - 223597_at (numeric)],\n 8055: [8055 - 223599_at (numeric)],\n 8056: 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(numeric)],\n 8082: [8082 - 223754_at (numeric)],\n 8083: [8083 - 223773_s_at (numeric)],\n 8084: [8084 - 223781_x_at (numeric)],\n 8085: [8085 - 223782_s_at (numeric)],\n 8086: [8086 - 223784_at (numeric)],\n 8087: [8087 - 223800_s_at (numeric)],\n 8088: [8088 - 223806_s_at (numeric)],\n 8089: [8089 - 223820_at (numeric)],\n 8090: [8090 - 223824_at (numeric)],\n 8091: [8091 - 223839_s_at (numeric)],\n 8092: [8092 - 223842_s_at (numeric)],\n 8093: [8093 - 223843_at (numeric)],\n 8094: [8094 - 223857_x_at (numeric)],\n 8095: [8095 - 223880_x_at (numeric)],\n 8096: [8096 - 223886_s_at (numeric)],\n 8097: [8097 - 223892_s_at (numeric)],\n 8098: [8098 - 223922_x_at (numeric)],\n 8099: [8099 - 223939_at (numeric)],\n 8100: [8100 - 223940_x_at (numeric)],\n 8101: [8101 - 223948_s_at (numeric)],\n 8102: [8102 - 223949_at (numeric)],\n 8103: [8103 - 223952_x_at (numeric)],\n 8104: [8104 - 223978_s_at (numeric)],\n 8105: [8105 - 223982_s_at (numeric)],\n 8106: [8106 - 223983_s_at (numeric)],\n 8107: [8107 - 223986_x_at (numeric)],\n 8108: [8108 - 223988_x_at (numeric)],\n 8109: [8109 - 223993_s_at (numeric)],\n 8110: [8110 - 224009_x_at (numeric)],\n 8111: [8111 - 224012_at (numeric)],\n 8112: [8112 - 224027_at (numeric)],\n 8113: [8113 - 224068_x_at (numeric)],\n 8114: [8114 - 224076_s_at (numeric)],\n 8115: [8115 - 224097_s_at (numeric)],\n 8116: [8116 - 224129_s_at (numeric)],\n 8117: [8117 - 224151_s_at (numeric)],\n 8118: [8118 - 224156_x_at (numeric)],\n 8119: [8119 - 224159_x_at (numeric)],\n 8120: [8120 - 224179_s_at (numeric)],\n 8121: [8121 - 224184_s_at (numeric)],\n 8122: [8122 - 224187_x_at (numeric)],\n 8123: [8123 - 224189_x_at (numeric)],\n 8124: [8124 - 224196_x_at (numeric)],\n 8125: [8125 - 224206_x_at (numeric)],\n 8126: [8126 - 224209_s_at (numeric)],\n 8127: [8127 - 224215_s_at (numeric)],\n 8128: [8128 - 224217_s_at (numeric)],\n 8129: [8129 - 224250_s_at (numeric)],\n 8130: [8130 - 224252_s_at (numeric)],\n 8131: [8131 - 224254_x_at (numeric)],\n 8132: [8132 - 224281_s_at (numeric)],\n 8133: [8133 - 224284_x_at (numeric)],\n 8134: [8134 - 224288_x_at (numeric)],\n 8135: [8135 - 224298_s_at (numeric)],\n 8136: [8136 - 224301_x_at (numeric)],\n 8137: [8137 - 224302_s_at (numeric)],\n 8138: [8138 - 224309_s_at (numeric)],\n 8139: [8139 - 224318_s_at (numeric)],\n 8140: [8140 - 224321_at (numeric)],\n 8141: [8141 - 224330_s_at (numeric)],\n 8142: [8142 - 224331_s_at (numeric)],\n 8143: [8143 - 224333_s_at (numeric)],\n 8144: [8144 - 224342_x_at (numeric)],\n 8145: [8145 - 224345_x_at (numeric)],\n 8146: [8146 - 224352_s_at (numeric)],\n 8147: [8147 - 224355_s_at (numeric)],\n 8148: [8148 - 224356_x_at (numeric)],\n 8149: [8149 - 224358_s_at (numeric)],\n 8150: [8150 - 224361_s_at (numeric)],\n 8151: [8151 - 224364_s_at (numeric)],\n 8152: [8152 - 224366_s_at (numeric)],\n 8153: [8153 - 224367_at (numeric)],\n 8154: [8154 - 224372_at (numeric)],\n 8155: [8155 - 224373_s_at (numeric)],\n 8156: [8156 - 224375_at (numeric)],\n 8157: 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224732_at (numeric)],\n 8312: [8312 - 224734_at (numeric)],\n 8313: [8313 - 224735_at (numeric)],\n 8314: [8314 - 224736_at (numeric)],\n 8315: [8315 - 224738_x_at (numeric)],\n 8316: [8316 - 224739_at (numeric)],\n 8317: [8317 - 224740_at (numeric)],\n 8318: [8318 - 224741_x_at (numeric)],\n 8319: [8319 - 224743_at (numeric)],\n 8320: [8320 - 224745_x_at (numeric)],\n 8321: [8321 - 224746_at (numeric)],\n 8322: [8322 - 224747_at (numeric)],\n 8323: [8323 - 224748_at (numeric)],\n 8324: [8324 - 224751_at (numeric)],\n 8325: [8325 - 224752_at (numeric)],\n 8326: [8326 - 224754_at (numeric)],\n 8327: [8327 - 224755_at (numeric)],\n 8328: [8328 - 224756_s_at (numeric)],\n 8329: [8329 - 224759_s_at (numeric)],\n 8330: [8330 - 224761_at (numeric)],\n 8331: [8331 - 224762_at (numeric)],\n 8332: [8332 - 224764_at (numeric)],\n 8333: [8333 - 224765_at (numeric)],\n 8334: [8334 - 224768_at (numeric)],\n 8335: [8335 - 224769_at (numeric)],\n 8336: [8336 - 224772_at (numeric)],\n 8337: [8337 - 224776_at (numeric)],\n 8338: [8338 - 224777_s_at (numeric)],\n 8339: [8339 - 224778_s_at (numeric)],\n 8340: [8340 - 224779_s_at (numeric)],\n 8341: [8341 - 224781_s_at (numeric)],\n 8342: [8342 - 224782_at (numeric)],\n 8343: [8343 - 224784_at (numeric)],\n 8344: [8344 - 224786_at (numeric)],\n 8345: [8345 - 224787_s_at (numeric)],\n 8346: [8346 - 224788_at (numeric)],\n 8347: [8347 - 224789_at (numeric)],\n 8348: [8348 - 224791_at (numeric)],\n 8349: [8349 - 224793_s_at (numeric)],\n 8350: [8350 - 224795_x_at (numeric)],\n 8351: [8351 - 224796_at (numeric)],\n 8352: [8352 - 224797_at (numeric)],\n 8353: [8353 - 224799_at (numeric)],\n 8354: [8354 - 224800_at (numeric)],\n 8355: [8355 - 224802_at (numeric)],\n 8356: [8356 - 224806_at (numeric)],\n 8357: [8357 - 224810_s_at (numeric)],\n 8358: [8358 - 224811_at (numeric)],\n 8359: [8359 - 224812_at (numeric)],\n 8360: [8360 - 224813_at (numeric)],\n 8361: [8361 - 224815_at (numeric)],\n 8362: [8362 - 224817_at (numeric)],\n 8363: 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224851_at (numeric)],\n 8390: [8390 - 224856_at (numeric)],\n 8391: [8391 - 224858_at (numeric)],\n 8392: [8392 - 224861_at (numeric)],\n 8393: [8393 - 224862_at (numeric)],\n 8394: [8394 - 224867_at (numeric)],\n 8395: [8395 - 224869_s_at (numeric)],\n 8396: [8396 - 224870_at (numeric)],\n 8397: [8397 - 224871_at (numeric)],\n 8398: [8398 - 224874_at (numeric)],\n 8399: [8399 - 224876_at (numeric)],\n 8400: [8400 - 224879_at (numeric)],\n 8401: [8401 - 224880_at (numeric)],\n 8402: [8402 - 224885_s_at (numeric)],\n 8403: [8403 - 224889_at (numeric)],\n 8404: [8404 - 224890_s_at (numeric)],\n 8405: [8405 - 224891_at (numeric)],\n 8406: [8406 - 224892_at (numeric)],\n 8407: [8407 - 224893_at (numeric)],\n 8408: [8408 - 224894_at (numeric)],\n 8409: [8409 - 224895_at (numeric)],\n 8410: [8410 - 224899_s_at (numeric)],\n 8411: [8411 - 224900_at (numeric)],\n 8412: [8412 - 224901_at (numeric)],\n 8413: [8413 - 224902_at (numeric)],\n 8414: [8414 - 224903_at (numeric)],\n 8415: [8415 - 224906_at (numeric)],\n 8416: [8416 - 224911_s_at (numeric)],\n 8417: [8417 - 224913_s_at (numeric)],\n 8418: [8418 - 224914_s_at (numeric)],\n 8419: [8419 - 224915_x_at (numeric)],\n 8420: [8420 - 224916_at (numeric)],\n 8421: [8421 - 224917_at (numeric)],\n 8422: [8422 - 224918_x_at (numeric)],\n 8423: [8423 - 224919_at (numeric)],\n 8424: [8424 - 224927_at (numeric)],\n 8425: [8425 - 224928_at (numeric)],\n 8426: [8426 - 224929_at (numeric)],\n 8427: [8427 - 224930_x_at (numeric)],\n 8428: [8428 - 224932_at (numeric)],\n 8429: [8429 - 224933_s_at (numeric)],\n 8430: [8430 - 224934_at (numeric)],\n 8431: [8431 - 224935_at (numeric)],\n 8432: [8432 - 224936_at (numeric)],\n 8433: [8433 - 224937_at (numeric)],\n 8434: [8434 - 224938_at (numeric)],\n 8435: [8435 - 224939_at (numeric)],\n 8436: [8436 - 224940_s_at (numeric)],\n 8437: [8437 - 224944_at (numeric)],\n 8438: [8438 - 224945_at (numeric)],\n 8439: [8439 - 224948_at (numeric)],\n 8440: [8440 - 224949_at (numeric)],\n 8441: 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224997_x_at (numeric)],\n 8468: [8468 - 224998_at (numeric)],\n 8469: [8469 - 224999_at (numeric)],\n 8470: [8470 - 225000_at (numeric)],\n 8471: [8471 - 225001_at (numeric)],\n 8472: [8472 - 225002_s_at (numeric)],\n 8473: [8473 - 225003_at (numeric)],\n 8474: [8474 - 225004_at (numeric)],\n 8475: [8475 - 225005_at (numeric)],\n 8476: [8476 - 225006_x_at (numeric)],\n 8477: [8477 - 225007_at (numeric)],\n 8478: [8478 - 225008_at (numeric)],\n 8479: [8479 - 225009_at (numeric)],\n 8480: [8480 - 225010_at (numeric)],\n 8481: [8481 - 225011_at (numeric)],\n 8482: [8482 - 225014_at (numeric)],\n 8483: [8483 - 225016_at (numeric)],\n 8484: [8484 - 225017_at (numeric)],\n 8485: [8485 - 225018_at (numeric)],\n 8486: [8486 - 225019_at (numeric)],\n 8487: [8487 - 225029_at (numeric)],\n 8488: [8488 - 225030_at (numeric)],\n 8489: [8489 - 225032_at (numeric)],\n 8490: [8490 - 225033_at (numeric)],\n 8491: [8491 - 225035_x_at (numeric)],\n 8492: [8492 - 225036_at (numeric)],\n 8493: [8493 - 225037_at (numeric)],\n 8494: [8494 - 225040_s_at (numeric)],\n 8495: [8495 - 225043_at (numeric)],\n 8496: [8496 - 225044_at (numeric)],\n 8497: [8497 - 225046_at (numeric)],\n 8498: [8498 - 225049_at (numeric)],\n 8499: [8499 - 225050_at (numeric)],\n 8500: [8500 - 225051_at (numeric)],\n 8501: [8501 - 225052_at (numeric)],\n 8502: [8502 - 225053_at (numeric)],\n 8503: [8503 - 225056_at (numeric)],\n 8504: [8504 - 225057_at (numeric)],\n 8505: [8505 - 225058_at (numeric)],\n 8506: [8506 - 225060_at (numeric)],\n 8507: [8507 - 225061_at (numeric)],\n 8508: [8508 - 225062_at (numeric)],\n 8509: [8509 - 225065_x_at (numeric)],\n 8510: [8510 - 225068_at (numeric)],\n 8511: [8511 - 225070_at (numeric)],\n 8512: [8512 - 225071_at (numeric)],\n 8513: [8513 - 225073_at (numeric)],\n 8514: [8514 - 225078_at (numeric)],\n 8515: [8515 - 225079_at (numeric)],\n 8516: [8516 - 225080_at (numeric)],\n 8517: [8517 - 225081_s_at (numeric)],\n 8518: [8518 - 225082_at (numeric)],\n 8519: [8519 - 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225318_at (numeric)],\n 8650: [8650 - 225321_s_at (numeric)],\n 8651: [8651 - 225324_at (numeric)],\n 8652: [8652 - 225325_at (numeric)],\n 8653: [8653 - 225327_at (numeric)],\n 8654: [8654 - 225328_at (numeric)],\n 8655: [8655 - 225330_at (numeric)],\n 8656: [8656 - 225331_at (numeric)],\n 8657: [8657 - 225334_at (numeric)],\n 8658: [8658 - 225336_at (numeric)],\n 8659: [8659 - 225337_at (numeric)],\n 8660: [8660 - 225339_at (numeric)],\n 8661: [8661 - 225340_s_at (numeric)],\n 8662: [8662 - 225342_at (numeric)],\n 8663: [8663 - 225343_at (numeric)],\n 8664: [8664 - 225344_at (numeric)],\n 8665: [8665 - 225350_s_at (numeric)],\n 8666: [8666 - 225352_at (numeric)],\n 8667: [8667 - 225353_s_at (numeric)],\n 8668: [8668 - 225354_s_at (numeric)],\n 8669: [8669 - 225355_at (numeric)],\n 8670: [8670 - 225356_at (numeric)],\n 8671: [8671 - 225359_at (numeric)],\n 8672: [8672 - 225361_x_at (numeric)],\n 8673: [8673 - 225363_at (numeric)],\n 8674: [8674 - 225364_at (numeric)],\n 8675: [8675 - 225365_at (numeric)],\n 8676: [8676 - 225367_at (numeric)],\n 8677: [8677 - 225368_at (numeric)],\n 8678: [8678 - 225369_at (numeric)],\n 8679: [8679 - 225376_at (numeric)],\n 8680: [8680 - 225380_at (numeric)],\n 8681: [8681 - 225381_at (numeric)],\n 8682: [8682 - 225382_at (numeric)],\n 8683: [8683 - 225383_at (numeric)],\n 8684: [8684 - 225384_at (numeric)],\n 8685: [8685 - 225386_s_at (numeric)],\n 8686: [8686 - 225387_at (numeric)],\n 8687: [8687 - 225388_at (numeric)],\n 8688: [8688 - 225389_at (numeric)],\n 8689: [8689 - 225390_s_at (numeric)],\n 8690: [8690 - 225394_s_at (numeric)],\n 8691: [8691 - 225395_s_at (numeric)],\n 8692: [8692 - 225399_at (numeric)],\n 8693: [8693 - 225401_at (numeric)],\n 8694: [8694 - 225405_at (numeric)],\n 8695: [8695 - 225406_at (numeric)],\n 8696: [8696 - 225407_at (numeric)],\n 8697: [8697 - 225410_at (numeric)],\n 8698: [8698 - 225411_at (numeric)],\n 8699: [8699 - 225412_at (numeric)],\n 8700: [8700 - 225413_at (numeric)],\n 8701: [8701 - 225414_at (numeric)],\n 8702: [8702 - 225415_at (numeric)],\n 8703: [8703 - 225418_at (numeric)],\n 8704: [8704 - 225419_at (numeric)],\n 8705: [8705 - 225421_at (numeric)],\n 8706: [8706 - 225422_at (numeric)],\n 8707: [8707 - 225424_at (numeric)],\n 8708: [8708 - 225427_s_at (numeric)],\n 8709: [8709 - 225431_x_at (numeric)],\n 8710: [8710 - 225433_at (numeric)],\n 8711: [8711 - 225435_at (numeric)],\n 8712: [8712 - 225436_at (numeric)],\n 8713: [8713 - 225439_at (numeric)],\n 8714: [8714 - 225440_at (numeric)],\n 8715: [8715 - 225441_x_at (numeric)],\n 8716: [8716 - 225442_at (numeric)],\n 8717: [8717 - 225445_at (numeric)],\n 8718: [8718 - 225447_at (numeric)],\n 8719: [8719 - 225450_at (numeric)],\n 8720: [8720 - 225457_s_at (numeric)],\n 8721: [8721 - 225458_at (numeric)],\n 8722: [8722 - 225462_at (numeric)],\n 8723: [8723 - 225463_x_at (numeric)],\n 8724: [8724 - 225464_at (numeric)],\n 8725: [8725 - 225467_s_at (numeric)],\n 8726: [8726 - 225469_at (numeric)],\n 8727: [8727 - 225470_at (numeric)],\n 8728: [8728 - 225472_at (numeric)],\n 8729: [8729 - 225474_at (numeric)],\n 8730: [8730 - 225477_s_at (numeric)],\n 8731: [8731 - 225478_at (numeric)],\n 8732: [8732 - 225479_at (numeric)],\n 8733: [8733 - 225480_at (numeric)],\n 8734: [8734 - 225481_at (numeric)],\n 8735: [8735 - 225492_at (numeric)],\n 8736: [8736 - 225494_at (numeric)],\n 8737: [8737 - 225496_s_at (numeric)],\n 8738: [8738 - 225497_at (numeric)],\n 8739: [8739 - 225498_at (numeric)],\n 8740: [8740 - 225499_at (numeric)],\n 8741: [8741 - 225501_at (numeric)],\n 8742: [8742 - 225502_at (numeric)],\n 8743: [8743 - 225505_s_at (numeric)],\n 8744: [8744 - 225507_at (numeric)],\n 8745: [8745 - 225510_at (numeric)],\n 8746: [8746 - 225512_at (numeric)],\n 8747: [8747 - 225515_s_at (numeric)],\n 8748: [8748 - 225516_at (numeric)],\n 8749: [8749 - 225517_at (numeric)],\n 8750: [8750 - 225519_at (numeric)],\n 8751: [8751 - 225520_at (numeric)],\n 8752: [8752 - 225522_at (numeric)],\n 8753: [8753 - 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225576_at (numeric)],\n 8780: [8780 - 225578_at (numeric)],\n 8781: [8781 - 225579_at (numeric)],\n 8782: [8782 - 225583_at (numeric)],\n 8783: [8783 - 225585_at (numeric)],\n 8784: [8784 - 225589_at (numeric)],\n 8785: [8785 - 225591_at (numeric)],\n 8786: [8786 - 225597_at (numeric)],\n 8787: [8787 - 225606_at (numeric)],\n 8788: [8788 - 225609_at (numeric)],\n 8789: [8789 - 225610_at (numeric)],\n 8790: [8790 - 225611_at (numeric)],\n 8791: [8791 - 225612_s_at (numeric)],\n 8792: [8792 - 225613_at (numeric)],\n 8793: [8793 - 225619_at (numeric)],\n 8794: [8794 - 225621_at (numeric)],\n 8795: [8795 - 225623_at (numeric)],\n 8796: [8796 - 225624_at (numeric)],\n 8797: [8797 - 225626_at (numeric)],\n 8798: [8798 - 225627_s_at (numeric)],\n 8799: [8799 - 225629_s_at (numeric)],\n 8800: [8800 - 225633_at (numeric)],\n 8801: [8801 - 225636_at (numeric)],\n 8802: [8802 - 225637_at (numeric)],\n 8803: [8803 - 225638_at (numeric)],\n 8804: [8804 - 225639_at (numeric)],\n 8805: [8805 - 225640_at (numeric)],\n 8806: [8806 - 225643_at (numeric)],\n 8807: [8807 - 225645_at (numeric)],\n 8808: [8808 - 225646_at (numeric)],\n 8809: [8809 - 225647_s_at (numeric)],\n 8810: [8810 - 225649_s_at (numeric)],\n 8811: [8811 - 225651_at (numeric)],\n 8812: [8812 - 225655_at (numeric)],\n 8813: [8813 - 225656_at (numeric)],\n 8814: [8814 - 225657_at (numeric)],\n 8815: [8815 - 225658_at (numeric)],\n 8816: [8816 - 225660_at (numeric)],\n 8817: [8817 - 225662_at (numeric)],\n 8818: [8818 - 225663_at (numeric)],\n 8819: [8819 - 225664_at (numeric)],\n 8820: [8820 - 225665_at (numeric)],\n 8821: [8821 - 225666_at (numeric)],\n 8822: [8822 - 225667_s_at (numeric)],\n 8823: [8823 - 225673_at (numeric)],\n 8824: [8824 - 225674_at (numeric)],\n 8825: [8825 - 225676_s_at (numeric)],\n 8826: [8826 - 225681_at (numeric)],\n 8827: [8827 - 225684_at (numeric)],\n 8828: [8828 - 225685_at (numeric)],\n 8829: [8829 - 225687_at (numeric)],\n 8830: [8830 - 225688_s_at (numeric)],\n 8831: [8831 - 225692_at (numeric)],\n 8832: [8832 - 225693_s_at (numeric)],\n 8833: [8833 - 225695_at (numeric)],\n 8834: [8834 - 225698_at (numeric)],\n 8835: [8835 - 225703_at (numeric)],\n 8836: [8836 - 225706_at (numeric)],\n 8837: [8837 - 225707_at (numeric)],\n 8838: [8838 - 225710_at (numeric)],\n 8839: [8839 - 225716_at (numeric)],\n 8840: [8840 - 225720_at (numeric)],\n 8841: [8841 - 225721_at (numeric)],\n 8842: [8842 - 225723_at (numeric)],\n 8843: [8843 - 225725_at (numeric)],\n 8844: [8844 - 225726_s_at (numeric)],\n 8845: [8845 - 225728_at (numeric)],\n 8846: [8846 - 225731_at (numeric)],\n 8847: [8847 - 225732_at (numeric)],\n 8848: [8848 - 225740_x_at (numeric)],\n 8849: [8849 - 225745_at (numeric)],\n 8850: [8850 - 225750_at (numeric)],\n 8851: [8851 - 225765_at (numeric)],\n 8852: [8852 - 225768_at (numeric)],\n 8853: [8853 - 225770_at (numeric)],\n 8854: [8854 - 225772_s_at (numeric)],\n 8855: [8855 - 225775_at (numeric)],\n 8856: [8856 - 225782_at (numeric)],\n 8857: [8857 - 225785_at (numeric)],\n 8858: [8858 - 225786_at (numeric)],\n 8859: [8859 - 225788_at (numeric)],\n 8860: [8860 - 225792_at (numeric)],\n 8861: [8861 - 225793_at (numeric)],\n 8862: [8862 - 225794_s_at (numeric)],\n 8863: [8863 - 225795_at (numeric)],\n 8864: [8864 - 225796_at (numeric)],\n 8865: [8865 - 225797_at (numeric)],\n 8866: [8866 - 225798_at (numeric)],\n 8867: [8867 - 225799_at (numeric)],\n 8868: [8868 - 225803_at (numeric)],\n 8869: [8869 - 225806_at (numeric)],\n 8870: [8870 - 225808_at (numeric)],\n 8871: [8871 - 225809_at (numeric)],\n 8872: [8872 - 225810_at (numeric)],\n 8873: [8873 - 225811_at (numeric)],\n 8874: [8874 - 225813_at (numeric)],\n 8875: [8875 - 225814_at (numeric)],\n 8876: [8876 - 225816_at (numeric)],\n 8877: [8877 - 225817_at (numeric)],\n 8878: [8878 - 225822_at (numeric)],\n 8879: [8879 - 225823_at (numeric)],\n 8880: [8880 - 225824_at (numeric)],\n 8881: [8881 - 225827_at (numeric)],\n 8882: [8882 - 225834_at (numeric)],\n 8883: [8883 - 225835_at 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226804_at (numeric)],\n 9248: [9248 - 226806_s_at (numeric)],\n 9249: [9249 - 226809_at (numeric)],\n 9250: [9250 - 226810_at (numeric)],\n 9251: [9251 - 226811_at (numeric)],\n 9252: [9252 - 226814_at (numeric)],\n 9253: [9253 - 226816_s_at (numeric)],\n 9254: [9254 - 226817_at (numeric)],\n 9255: [9255 - 226818_at (numeric)],\n 9256: [9256 - 226820_at (numeric)],\n 9257: [9257 - 226823_at (numeric)],\n 9258: [9258 - 226824_at (numeric)],\n 9259: [9259 - 226825_s_at (numeric)],\n 9260: [9260 - 226828_s_at (numeric)],\n 9261: [9261 - 226831_at (numeric)],\n 9262: [9262 - 226832_at (numeric)],\n 9263: [9263 - 226834_at (numeric)],\n 9264: [9264 - 226835_s_at (numeric)],\n 9265: [9265 - 226837_at (numeric)],\n 9266: [9266 - 226841_at (numeric)],\n 9267: [9267 - 226844_at (numeric)],\n 9268: [9268 - 226845_s_at (numeric)],\n 9269: [9269 - 226847_at (numeric)],\n 9270: [9270 - 226850_at (numeric)],\n 9271: [9271 - 226851_at (numeric)],\n 9272: [9272 - 226863_at (numeric)],\n 9273: [9273 - 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(numeric)],\n 10864: [10864 - 58916_at (numeric)],\n 10865: [10865 - 59375_at (numeric)],\n 10866: [10866 - 59437_at (numeric)],\n 10867: [10867 - 59625_at (numeric)],\n 10868: [10868 - 60474_at (numeric)],\n 10869: [10869 - 63825_at (numeric)],\n 10870: [10870 - 64064_at (numeric)],\n 10871: [10871 - 64408_s_at (numeric)],\n 10872: [10872 - 64486_at (numeric)],\n 10873: [10873 - 64900_at (numeric)],\n 10874: [10874 - 65635_at (numeric)],\n 10875: [10875 - 78047_s_at (numeric)],\n 10876: [10876 - 823_at (numeric)],\n 10877: [10877 - 89977_at (numeric)],\n 10878: [10878 - 90265_at (numeric)],\n 10879: [10879 - 91826_at (numeric)],\n 10880: [10880 - AFFX-BioB-3_at (numeric)],\n 10881: [10881 - AFFX-BioB-5_at (numeric)],\n 10882: [10882 - AFFX-BioB-M_at (numeric)],\n 10883: [10883 - AFFX-BioC-3_at (numeric)],\n 10884: [10884 - AFFX-BioC-5_at (numeric)],\n 10885: [10885 - AFFX-BioDn-3_at (numeric)],\n 10886: [10886 - AFFX-BioDn-5_at (numeric)],\n 10887: [10887 - AFFX-CreX-3_at (numeric)],\n 10888: [10888 - AFFX-CreX-5_at (numeric)],\n 10889: [10889 - AFFX-DapX-3_at (numeric)],\n 10890: [10890 - AFFX-DapX-5_at (numeric)],\n 10891: [10891 - AFFX-DapX-M_at (numeric)],\n 10892: [10892 - AFFX-HSAC07/X00351_3_at (numeric)],\n 10893: [10893 - AFFX-HSAC07/X00351_5_at (numeric)],\n 10894: [10894 - AFFX-HSAC07/X00351_M_at (numeric)],\n 10895: [10895 - AFFX-hum_alu_at (numeric)],\n 10896: [10896 - AFFX-HUMGAPDH/M33197_3_at (numeric)],\n 10897: [10897 - AFFX-HUMGAPDH/M33197_5_at (numeric)],\n 10898: [10898 - AFFX-HUMGAPDH/M33197_M_at (numeric)],\n 10899: [10899 - AFFX-HUMISGF3A/M97935_3_at (numeric)],\n 10900: [10900 - AFFX-HUMISGF3A/M97935_MA_at (numeric)],\n 10901: [10901 - AFFX-HUMISGF3A/M97935_MB_at (numeric)],\n 10902: [10902 - AFFX-HUMRGE/M10098_3_at (numeric)],\n 10903: [10903 - AFFX-HUMRGE/M10098_5_at (numeric)],\n 10904: [10904 - AFFX-HUMRGE/M10098_M_at (numeric)],\n 10905: [10905 - AFFX-LysX-3_at (numeric)],\n 10906: [10906 - AFFX-LysX-M_at (numeric)],\n 10907: [10907 - AFFX-M27830_3_at (numeric)],\n 10908: [10908 - AFFX-M27830_5_at (numeric)],\n 10909: [10909 - AFFX-M27830_M_at (numeric)],\n 10910: [10910 - AFFX-PheX-3_at (numeric)],\n 10911: [10911 - AFFX-PheX-5_at (numeric)],\n 10912: [10912 - AFFX-PheX-M_at (numeric)],\n 10913: [10913 - AFFX-r2-Bs-dap-3_at (numeric)],\n 10914: [10914 - AFFX-r2-Bs-dap-5_at (numeric)],\n 10915: [10915 - AFFX-r2-Bs-dap-M_at (numeric)],\n 10916: [10916 - AFFX-r2-Bs-lys-3_at (numeric)],\n 10917: [10917 - AFFX-r2-Bs-lys-M_at (numeric)],\n 10918: [10918 - AFFX-r2-Bs-phe-3_at (numeric)],\n 10919: [10919 - AFFX-r2-Bs-phe-5_at (numeric)],\n 10920: [10920 - AFFX-r2-Bs-phe-M_at (numeric)],\n 10921: [10921 - AFFX-r2-Bs-thr-3_s_at (numeric)],\n 10922: [10922 - AFFX-r2-Bs-thr-5_s_at (numeric)],\n 10923: [10923 - AFFX-r2-Bs-thr-M_s_at (numeric)],\n 10924: [10924 - AFFX-r2-Ec-bioB-3_at (numeric)],\n 10925: [10925 - AFFX-r2-Ec-bioB-5_at (numeric)],\n 10926: [10926 - AFFX-r2-Ec-bioB-M_at (numeric)],\n 10927: [10927 - AFFX-r2-Ec-bioC-3_at (numeric)],\n 10928: [10928 - AFFX-r2-Ec-bioC-5_at (numeric)],\n 10929: [10929 - AFFX-r2-Ec-bioD-3_at (numeric)],\n 10930: [10930 - AFFX-r2-Ec-bioD-5_at (numeric)],\n 10931: [10931 - AFFX-r2-P1-cre-3_at (numeric)],\n 10932: [10932 - AFFX-r2-P1-cre-5_at (numeric)],\n 10933: [10933 - AFFX-ThrX-3_at (numeric)],\n 10934: [10934 - AFFX-ThrX-5_at (numeric)],\n 10935: [10935 - AFFX-ThrX-M_at (numeric)],\n 10936: [10936 - Tissue (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 260.0,\n 'MaxNominalAttDistinctValues': 2.0,\n 'MinorityClassSize': 198.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 10936.0,\n 'NumberOfInstances': 458.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 10935.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 1149,\n 'estimation_procedure': '10 times 10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 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false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2871,"string":"2,871"}}},{"rowIdx":2739,"cells":{"uid":{"kind":"number","value":361166,"string":"361,166"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_ACT_410_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album MPII Human Pose Dataset Dataset (Micro)**\n***\nThe MPII Human Pose dataset (http://human-pose.mpi-inf.mpg.de/#download) is a state of the art benchmark for evaluation of articulated human pose estimation. It includes around 25 000 images containing over 40 000 people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video. Like other Meta-Album datasets, this dataset is preprocessed and all images are resized into 128x128 pixels. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.ACT_410 \n**Meta Album URL**: [https://meta-album.github.io/datasets/ACT_410.html](https://meta-album.github.io/datasets/ACT_410.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: ACT_410 \n**Dataset Name**: MPII Human Pose Dataset \n**Short Description**: MPII Human Pose Dataset with images of humans performing 410 activities. \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: human actions, sports \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Simplified BSD License \n**License URL(original data release)**: http://human-pose.mpi-inf.mpg.de/#download\nhttp://human-pose.mpi-inf.mpg.de/bsd.txt\n \n**License (Meta-Album data release)**: Simplified BSD License \n**License URL (Meta-Album data release)**: [http://human-pose.mpi-inf.mpg.de/bsd.txt](http://human-pose.mpi-inf.mpg.de/bsd.txt) \n\n**Source**: MPII Human Pose Dataset \n**Source URL**: http://human-pose.mpi-inf.mpg.de/#download \n \n**Original Author**: Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt \n**Original contact**: leonid@mpi-inf.mpg.de \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@INPROCEEDINGS{6909866, \n author={Andriluka, Mykhaylo and Pishchulin, Leonid and Gehler, Peter and Schiele, Bernt}, booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition}, \n title={2D Human Pose Estimation: New Benchmark and State of the Art Analysis}, \n year={2014},\n pages={3686-3693}, \n doi={10.1109/CVPR.2014.471}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44301) [[Extended]](https://www.openml.org/d/44334)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44271,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_ACT_410_Micro',\n 'source_data': '44271',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361166,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_ACT_410_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2872,"string":"2,872"}}},{"rowIdx":2740,"cells":{"uid":{"kind":"number","value":361154,"string":"361,154"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_VIL_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Plant Village Dataset (Micro)**\n***\nThe Plant Village dataset(https://data.mendeley.com/datasets/tywbtsjrjv/1) contains camera photos of 17 crop leaves. The original image resolution is 256x256 px. This collection covers 26 plant diseases and 12 healthy plants. The leaves are removed from the plant and placed on gray or black background, in various lighting conditions. All images are captured on a variety of gray backgrounds, except Corn Common rust which has a black background. For the curated version, we exclude the irrelevant Background and Corn Common Rust classes from the original collection. Plant Village has a 2-level label hierarchy, the supercategory is the crop type and the category is the disease type. We have preprocessed Plant Village for Meta-Album by resizing a subset from the original dataset to 128x128 image size. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.PLT_VIL \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_VIL.html](https://meta-album.github.io/datasets/PLT_VIL.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: PLT_VIL \n**Dataset Name**: Plant Village \n**Short Description**: Plant disease dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: plants, plant diseases \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC0 1.0 \n**License URL(original data release)**: https://data.mendeley.com/datasets/tywbtsjrjv/1\nhttps://creativecommons.org/publicdomain/zero/1.0/\n \n**License (Meta-Album data release)**: CC0 1.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/) \n\n**Source**: Plant Village Dataset \n**Source URL**: https://github.com/spMohanty/PlantVillage-Dataset \n \n**Original Author**: Sharada Mohanty, David Hughes, and Marcel Salathe \n**Original contact**: arunpandian@mamcet.com \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{G2019323,\n title = {Identification of plant leaf diseases using a nine-layer deep convolutional neural network},\n journal = {Computers & Electrical Engineering},\n volume = {76},\n pages = {323-338},\n year = {2019},\n issn = {0045-7906},\n doi = {https://doi.org/10.1016/j.compeleceng.2019.04.011},\n url = {https://www.sciencedirect.com/science/article/pii/S0045790619300023},\n author = {Geetharamani G. and Arun Pandian J.},\n keywords = {Artificial intelligence, Deep convolutional neural networks, Deep learning, Dropout, Image augmentation, Leaf diseases identification, Machine learning, Mini batch, Training epoch, Transfer learning},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44286) [[Extended]](https://www.openml.org/d/44321)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44242,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_VIL_Micro',\n 'source_data': '44242',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361154,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_VIL_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2873,"string":"2,873"}}},{"rowIdx":2741,"cells":{"uid":{"kind":"number","value":361053,"string":"361,053"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"eye_movements"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"regression on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/\n\nCompetition 1 (preprocessed data)\nA straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.\n\nThe dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.\n\n\n* Features are in columns, feature vectors in rows.\n* Each assignment is a time sequence of 22-dimensional feature vectors.\n* The first column is the line number, second the assignment number and the next 22 columns (3 to 24) are the different features. Columns 25 to 27 contain extra information about the example. The training data set contains the classification label in the 28th column: \"0\" for irrelevant, \"1\" for relevant and \"2\" for the correct answer.\n* Each example (row) represents a single word. You are asked to return the classification of each read sentence.\n* The 22 features provided are commonly used in psychological studies on eye movement. All of them are not necessarily relevant in this context.\n\nThe objective of the Challenge is to predict the classification labels (I, R, C).\n\n\n\nPlease see the technical report for information of eye movements, experimental setup, baseline methods and references:\n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. [PDF]\n\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nFEATURES\n\nThe values in columns marked with an asterisk (*) are same for all occurances of the word.\n\nCOL\tNAME\t\tDESCRIPTION\n1\t#line\t\tLine number\n2\t#assg\t\tAssignment Number\n3\tfixcount\tNumber of fixations to the word\n4*\tfirstPassCnt\tNumber of fixations to the word when it is first encountered\n5*\tP1stFixation\t'1' if fixation occured when the sentence the word was in was encountered the first time\n6*\tP2stFixation\t'1' if fixation occured when the sentence the word was in was encountered the second time\n7*\tprevFixDur\tDuration of previous fixation\n8*\tfirstfixDur\tDuration of the first fixation when the word is first encountered\n9*\tfirstPassFixDur\tSum of durations of fixations when the word is first encountered\n10*\tnextFixDur\tDuration of the next fixation when gaze initially moves from the word\n11\tfirstSaccLen\tLength of the first saccade\n12\tlastSaccLen\tDistance between fixation on the word and the next fixation\n13\tprevFixPos\tDistance between the first fixation preceding the word and the beginning ot the word\n14\tlandingPos\tDistance between the first fixation on the word and the beginning of the word\n15\tleavingPos\tDistance between the last fixation on the word and the beginning of the word\n16\ttotalFixDur\tSum of all durations of fixations to the word\n17\tmeanFixDur\tMean duration of the fixations to the word\n18*\tnRegressFrom\tNumber of regressions leaving from the word\n19*\tregressLen\tSum of durations of regressions initiating from this word\n20*\tnextWordRegress\t'1' if a regression initiated from the following word\n21*\tregressDur\tSum of durations of the fixations on the word during regression\n22\tpupilDiamMax\tMaximum pupil diameter\n23\tpupilDiamLag\tMaximum pupil diameter 0.5 - 1.5 seconds after the beginning of fixation\n24\ttimePrtctg\tFirst fixation duration divided by the total number of fixations\n25\tnWordsInTitle\tNumber of word in the sentence (title) this word is in\n26\ttitleNo\t\tTitle number\n27\twordNo\t\tWord number (ordinal) in this title\n28\tlabel\t\tClassification for training data ('0'=irrelevant, '1'=relevant, '2'=correct)"},"dataset_features":{"kind":"string","value":"{0: [0 - lineNo (numeric)],\n 1: [1 - assgNo (numeric)],\n 2: [2 - prevFixDur (numeric)],\n 3: [3 - firstfixDur (numeric)],\n 4: [4 - firstPassFixDur (nominal)],\n 5: [5 - nextFixDur (nominal)],\n 6: [6 - firstSaccLen (numeric)],\n 7: [7 - lastSaccLen (numeric)],\n 8: [8 - prevFixPos (numeric)],\n 9: [9 - landingPos (numeric)],\n 10: [10 - leavingPos (numeric)],\n 11: [11 - totalFixDur (numeric)],\n 12: [12 - meanFixDur (numeric)],\n 13: [13 - regressLen (numeric)],\n 14: [14 - regressDur (numeric)],\n 15: [15 - pupilDiamMax (numeric)],\n 16: [16 - pupilDiamLag (numeric)],\n 17: [17 - timePrtctg (numeric)],\n 18: [18 - titleNo (numeric)],\n 19: [19 - wordNo (nominal)],\n 20: [20 - label (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 3804.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 3804.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 21.0,\n 'NumberOfInstances': 7608.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 17.0,\n 'NumberOfSymbolicFeatures': 4.0,\n 'cost_matrix': nan,\n 'did': 44093,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'eye_movements',\n 'source_data': '44093',\n 'status': 'active',\n 'target_feature': 'label',\n 'task_type': 'Supervised Classification',\n 'tid': 361053,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"eye_movements"},"attribute_names":{"kind":"list like","value":["lineNo","assgNo","prevFixDur","firstfixDur","firstPassFixDur","nextFixDur","firstSaccLen","lastSaccLen","prevFixPos","landingPos","leavingPos","totalFixDur","meanFixDur","regressLen","regressDur","pupilDiamMax","pupilDiamLag","timePrtctg","titleNo","wordNo"],"string":"[\n \"lineNo\",\n \"assgNo\",\n \"prevFixDur\",\n \"firstfixDur\",\n \"firstPassFixDur\",\n \"nextFixDur\",\n \"firstSaccLen\",\n \"lastSaccLen\",\n \"prevFixPos\",\n \"landingPos\",\n \"leavingPos\",\n \"totalFixDur\",\n \"meanFixDur\",\n \"regressLen\",\n \"regressDur\",\n \"pupilDiamMax\",\n \"pupilDiamLag\",\n \"timePrtctg\",\n \"titleNo\",\n \"wordNo\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,true,true,false,false,false,false,false,false,false,false,false,false,false,false,false,true],"string":"[\n false,\n false,\n false,\n false,\n true,\n true,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2874,"string":"2,874"}}},{"rowIdx":2742,"cells":{"uid":{"kind":"number","value":361186,"string":"361,186"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_RSICB_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album RSICB Dataset (Mini)**\n***\nRSICB128 dataset (https://github.com/lehaifeng/RSI-CB) covers 45 scene categories, assembling in total 36 000 images of resolution 128x128 px. The data authors select various locations around the world, and follow China's landuse classification standard. This collection has 2-level label hierarchy with 6 super-categories: agricultural land, construction land and facilities, transportation and facilities, water and water conservancy facilities, woodland, and other lands. The preprocessed version of RSICB is created by resizing the images into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: REM_SEN.RSICB \n**Meta Album URL**: [https://meta-album.github.io/datasets/RSICB.html](https://meta-album.github.io/datasets/RSICB.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RSICB \n**Dataset Name**: RSICB \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 45 \n**\\# Images**: 1800 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data \n**Source URL**: https://github.com/lehaifeng/RSI-CB \n \n**Original Author**: Haifeng Li, Xin Dou, Chao Tao, Zhixiang Hou, Jie Chen, Jian Peng, Min Deng, Ling Zhao \n**Original contact**: lihaifeng@csu.edu.cn \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{li2020RSI-CB,\n title={RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data},\n author={Li, Haifeng and Dou, Xin and Tao, Chao and Wu, Zhixiang and Chen, Jie and Peng, Jian and Deng, Min and Zhao, Ling},\n journal={Sensors},\n DOI = {doi.org/10.3390/s20061594},\n year={2020},\n volume = {20},\n number = {6},\n pages = {1594},\n type = {Journal Article}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44315) [[Extended]](https://www.openml.org/d/44333)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 45.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44300,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_RSICB_Mini',\n 'source_data': '44300',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361186,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_RSICB_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2875,"string":"2,875"}}},{"rowIdx":2743,"cells":{"uid":{"kind":"number","value":361179,"string":"361,179"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_RESISC_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album RESISC Dataset (Mini)**\n***\nRESISC45 dataset(https://gcheng-nwpu.github.io/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating many image variations. Even though RESISC45 does not propose a label hierarchy, it can be created from other common aerial image label organization scheme. We have preprocessed RESISC for Meta-Album by resizing the dataset to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: REM_SEN.RESISC \n**Meta Album URL**: [https://meta-album.github.io/datasets/RESISC.html](https://meta-album.github.io/datasets/RESISC.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RESISC \n**Dataset Name**: RESISC \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 45 \n**\\# Images**: 1800 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC-BY-NC 4.0 \n**License URL(original data release)**: https://creativecommons.org/licenses/by-nc/4.0/\n \n**License (Meta-Album data release)**: CC-BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: NWPU-RESISC45 Dataset \n**Source URL**: https://gcheng-nwpu.github.io/ \n \n**Original Author**: Gong Cheng, Junwei Han, and Xiaoqiang Lu \n**Original contact**: chenggong1119@gmail.com \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{DBLP:journals/corr/ChengHL17,\n author = {Gong Cheng and Junwei Han and Xiaoqiang Lu},\n title = {Remote Sensing Image Scene Classification: Benchmark and State of the Art},\n journal = {CoRR},\n volume = {abs/1703.00121},\n year = {2017},\n url = {http://arxiv.org/abs/1703.00121},\n eprinttype = {arXiv},\n eprint = {1703.00121},\n timestamp = {Mon, 02 Dec 2019 09:32:19 +0100},\n biburl = {https://dblp.org/rec/journals/corr/ChengHL17.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44246) [[Extended]](https://www.openml.org/d/44324)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 45.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1800.0,\n 'NumberOfInstancesWithMissingValues': 1800.0,\n 'NumberOfMissingValues': 1800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44290,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_RESISC_Mini',\n 'source_data': '44290',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361179,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_RESISC_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2876,"string":"2,876"}}},{"rowIdx":2744,"cells":{"uid":{"kind":"number","value":361185,"string":"361,185"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PNU_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album PanNuke Dataset (Mini)**\n***\nThe PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was designed as a segmentation task, we were able to transform it into a tissue classification task since we had the tissue type for each sample in the dataset. We also resized the images to 128x128 pixels and applied stain normalization to avoid bias and remove some spurious features. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.PNU \n**Meta Album URL**: [https://meta-album.github.io/datasets/PNU.html](https://meta-album.github.io/datasets/PNU.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopic \n**Dataset ID**: PNU \n**Dataset Name**: PanNuke \n**Short Description**: 19 Human Tissues Dataset \n**\\# Classes**: 19 \n**\\# Images**: 760 \n**Keywords**: microscopic, human tissues \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Attribution-NonCommercial-ShareAlike 4.0 International \n**License URL(original data release)**: https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/\n \n**License (Meta-Album data release)**: Attribution-NonCommercial-ShareAlike 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) \n\n**Source**: PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification \n**Source URL**: https://jgamper.github.io/PanNukeDataset/ \n \n**Original Author**: Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir \n**Original contact**: j.gamper@warwick.ac.uk \n\n**Meta Album author**: Romain Mussard \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{gamper2019pannuke,\n title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},\n author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},\n booktitle={European Congress on Digital Pathology},\n pages={11--19},\n year={2019},\n organization={Springer}\n}\n\n@article{gamper2020pannuke,\n title={PanNuke Dataset Extension, Insights and Baselines},\n author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},\n journal={arXiv preprint arXiv:2003.10778},\n year={2020}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44312) [[Extended]](https://www.openml.org/d/44330)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 19.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 760.0,\n 'NumberOfInstancesWithMissingValues': 760.0,\n 'NumberOfMissingValues': 760.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44297,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PNU_Mini',\n 'source_data': '44297',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361185,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PNU_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2877,"string":"2,877"}}},{"rowIdx":2745,"cells":{"uid":{"kind":"number","value":361174,"string":"361,174"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_RSICB_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album RSICB Dataset (Micro)**\n***\nRSICB128 dataset (https://github.com/lehaifeng/RSI-CB) covers 45 scene categories, assembling in total 36 000 images of resolution 128x128 px. The data authors select various locations around the world, and follow China's landuse classification standard. This collection has 2-level label hierarchy with 6 super-categories: agricultural land, construction land and facilities, transportation and facilities, water and water conservancy facilities, woodland, and other lands. The preprocessed version of RSICB is created by resizing the images into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: REM_SEN.RSICB \n**Meta Album URL**: [https://meta-album.github.io/datasets/RSICB.html](https://meta-album.github.io/datasets/RSICB.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RSICB \n**Dataset Name**: RSICB \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data \n**Source URL**: https://github.com/lehaifeng/RSI-CB \n \n**Original Author**: Haifeng Li, Xin Dou, Chao Tao, Zhixiang Hou, Jie Chen, Jian Peng, Min Deng, Ling Zhao \n**Original contact**: lihaifeng@csu.edu.cn \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{li2020RSI-CB,\n title={RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data},\n author={Li, Haifeng and Dou, Xin and Tao, Chao and Wu, Zhixiang and Chen, Jie and Peng, Jian and Deng, Min and Zhao, Ling},\n journal={Sensors},\n DOI = {doi.org/10.3390/s20061594},\n year={2020},\n volume = {20},\n number = {6},\n pages = {1594},\n type = {Journal Article}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44300) [[Extended]](https://www.openml.org/d/44333)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44315,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_RSICB_Micro',\n 'source_data': '44315',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361174,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_RSICB_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2878,"string":"2,878"}}},{"rowIdx":2746,"cells":{"uid":{"kind":"number","value":361175,"string":"361,175"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PNU_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album PanNuke Dataset (Micro)**\n***\nThe PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was designed as a segmentation task, we were able to transform it into a tissue classification task since we had the tissue type for each sample in the dataset. We also resized the images to 128x128 pixels and applied stain normalization to avoid bias and remove some spurious features. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.PNU \n**Meta Album URL**: [https://meta-album.github.io/datasets/PNU.html](https://meta-album.github.io/datasets/PNU.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopic \n**Dataset ID**: PNU \n**Dataset Name**: PanNuke \n**Short Description**: 19 Human Tissues Dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: microscopic, human tissues \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Attribution-NonCommercial-ShareAlike 4.0 International \n**License URL(original data release)**: https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/\n \n**License (Meta-Album data release)**: Attribution-NonCommercial-ShareAlike 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) \n\n**Source**: PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification \n**Source URL**: https://jgamper.github.io/PanNukeDataset/ \n \n**Original Author**: Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir \n**Original contact**: j.gamper@warwick.ac.uk \n\n**Meta Album author**: Romain Mussard \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{gamper2019pannuke,\n title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},\n author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},\n booktitle={European Congress on Digital Pathology},\n pages={11--19},\n year={2019},\n organization={Springer}\n}\n\n@article{gamper2020pannuke,\n title={PanNuke Dataset Extension, Insights and Baselines},\n author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},\n journal={arXiv preprint arXiv:2003.10778},\n year={2020}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44297) [[Extended]](https://www.openml.org/d/44330)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 19.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 760.0,\n 'NumberOfInstancesWithMissingValues': 760.0,\n 'NumberOfMissingValues': 760.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44312,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PNU_Micro',\n 'source_data': '44312',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361175,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PNU_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2879,"string":"2,879"}}},{"rowIdx":2747,"cells":{"uid":{"kind":"number","value":361183,"string":"361,183"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_APL_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Airplanes Dataset (Mini)**\n***\nThe original Airplanes dataset (https://zenodo.org/record/3464319) comprises more than 9 000 remote sensing images acquired from Google Earth satellite imagery, including 21 different types of aircraft from around 36 airports. All the images were carefully labeled by seven specialists in the field of remote sensing image interpretation. Each class can have 230 to 846 images, where each image contains only one complete aircraft, and they have variable resolutions. To preprocess this dataset, we cropped the images from either side to make them square, and then we resized them into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.APL \n**Meta Album URL**: [https://meta-album.github.io/datasets/APL.html](https://meta-album.github.io/datasets/APL.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: APL \n**Dataset Name**: Airplanes \n**Short Description**: Airplanes dataset with different aiplane models \n**\\# Classes**: 21 \n**\\# Images**: 840 \n**Keywords**: vehicles, airplanes \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://zenodo.org/record/3464319\nhttps://creativecommons.org/licenses/by/4.0/legalcode\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) \n\n**Source**: Muti-type Aircraft of Remote Sensing Images: MTARSI \n**Source URL**: https://zenodo.org/record/3464319 \n \n**Original Author**: Wu, Zhize \n**Original contact**: \n\n**Meta Album author**: Philip Boser \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@dataset{wu_zhize_2019_3464319,\n author = {Wu, Zhize},\n title = {{Muti-type Aircraft of Remote Sensing Images: MTARSI}},\n month = may,\n year = 2019,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.3464319},\n url = {https://doi.org/10.5281/zenodo.3464319}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44251) [[Extended]](https://www.openml.org/d/44329)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 21.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 840.0,\n 'NumberOfInstancesWithMissingValues': 840.0,\n 'NumberOfMissingValues': 840.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44295,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_APL_Mini',\n 'source_data': '44295',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361183,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_APL_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2880,"string":"2,880"}}},{"rowIdx":2748,"cells":{"uid":{"kind":"number","value":361317,"string":"361,317"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"churn"},"dataset_description":{"kind":"string","value":"A dataset relating characteristics of telephony account features and usage and whether or not the customer churned. Originally used in Discovering Knowledge in Data: An Introduction to Data Mining.Source: https://www.secs.ac.in/wp-content/CSE_PORTAL/DataMining_Daniel.pdf"},"dataset_features":{"kind":"string","value":"{0: [0 - state (numeric)],\n 1: [1 - account_length (numeric)],\n 2: [2 - phone_number (numeric)],\n 3: [3 - number_vmail_messages (numeric)],\n 4: [4 - total_day_minutes (numeric)],\n 5: [5 - total_day_calls (numeric)],\n 6: [6 - total_day_charge (numeric)],\n 7: [7 - total_eve_minutes (numeric)],\n 8: [8 - total_eve_calls (numeric)],\n 9: [9 - total_eve_charge (numeric)],\n 10: [10 - total_night_minutes (numeric)],\n 11: [11 - total_night_calls (numeric)],\n 12: [12 - total_night_charge (numeric)],\n 13: [13 - total_intl_minutes (numeric)],\n 14: [14 - total_intl_calls (numeric)],\n 15: [15 - total_intl_charge (numeric)],\n 16: [16 - area_code (nominal)],\n 17: [17 - international_plan (nominal)],\n 18: [18 - voice_mail_plan (nominal)],\n 19: [19 - number_customer_service_calls (nominal)],\n 20: [20 - class (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': nan,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': nan,\n 'NumberOfClasses': 0.0,\n 'NumberOfFeatures': 21.0,\n 'NumberOfInstances': 5000.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 17.0,\n 'NumberOfSymbolicFeatures': 4.0,\n 'cost_matrix': nan,\n 'did': 45076,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'churn',\n 'source_data': '45076',\n 'status': 'active',\n 'target_feature': 'class',\n 'task_type': 'Supervised Classification',\n 'tid': 361317,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"churn"},"attribute_names":{"kind":"list like","value":["state","account_length","phone_number","number_vmail_messages","total_day_minutes","total_day_calls","total_day_charge","total_eve_minutes","total_eve_calls","total_eve_charge","total_night_minutes","total_night_calls","total_night_charge","total_intl_minutes","total_intl_calls","total_intl_charge","area_code","international_plan","voice_mail_plan","number_customer_service_calls"],"string":"[\n \"state\",\n \"account_length\",\n \"phone_number\",\n \"number_vmail_messages\",\n \"total_day_minutes\",\n \"total_day_calls\",\n \"total_day_charge\",\n \"total_eve_minutes\",\n \"total_eve_calls\",\n \"total_eve_charge\",\n \"total_night_minutes\",\n \"total_night_calls\",\n \"total_night_charge\",\n \"total_intl_minutes\",\n \"total_intl_calls\",\n \"total_intl_charge\",\n \"area_code\",\n \"international_plan\",\n \"voice_mail_plan\",\n \"number_customer_service_calls\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,true,true,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n true,\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2881,"string":"2,881"}}},{"rowIdx":2749,"cells":{"uid":{"kind":"number","value":361173,"string":"361,173"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_DOC_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Plant Doc Dataset (Micro)**\n***\nThe PlantDoc dataset(https://github.com/pratikkayal/PlantDoc-Dataset) is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one image. The original, uncropped images are generally different in scale, light conditions, and pose. However, within one category, images of leaves that came from the same original image can be found. The images correspond to 27 classes, including plant disease names and plant species names, e.g.: Corn Leaf Blight and Cherry Leaf respectively. The dataset was created for a benchmarking classification model work, published in 2020 by Singh et al. The PlantDoc dataset in the Meta-Album benchmark is extracted from a preprocessed version of the original PlantDoc dataset. First, to get i.i.d. samples, only one leaf image per each original image is randomly picked. Then, leaves images are cropped and made into squared images which are then resized into 128x128 with anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.PLT_DOC \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_DOC.html](https://meta-album.github.io/datasets/PLT_DOC.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: PLT_DOC \n**Dataset Name**: Plant Doc \n**Short Description**: Plant disease dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: plants, plant diseases, \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/blob/master/LICENSE.txt\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: PlantDoc: A Dataset for Visual Plant Disease Detection \n**Source URL**: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset \n \n**Original Author**: Sharada Mohanty, David Hughes, and Marcel Salathe \n**Original contact**: \n\n**Meta Album author**: Maria Belen Guaranda Cabezas \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{10.1145/3371158.3371196,\n author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},\n title = {PlantDoc: A Dataset for Visual Plant Disease Detection},\n year = {2020},\n isbn = {9781450377386},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3371158.3371196},\n doi = {10.1145/3371158.3371196},\n booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},\n pages = {249-253},\n numpages = {5},\n keywords = {Object Detection, Image Classification, Deep Learning},\n location = {Hyderabad, India},\n series = {CoDS COMAD 2020}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44303) [[Extended]](https://www.openml.org/d/44336)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44273,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_DOC_Micro',\n 'source_data': '44273',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361173,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_DOC_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2882,"string":"2,882"}}},{"rowIdx":2750,"cells":{"uid":{"kind":"number","value":361189,"string":"361,189"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_BTS_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Boats Dataset (Mini)**\n***\nThe original version of the Meta-Album boats dataset is called MARVEL dataset (https://github.com/avaapm/marveldataset2016). It has more than 138 000 images of 26 different maritime vessels in their natural background. Each class can have 1 802 to 8 930 images of variable resolutions. To preprocess this dataset, we either duplicate the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image to create square images. No cropping was applied because the boats occupy most of the image, and applying this technique will lead to incomplete images. Finally, the square images were resized into 128x128 px using an anti-aliasing filter \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.BTS \n**Meta Album URL**: [https://meta-album.github.io/datasets/BTS.html](https://meta-album.github.io/datasets/BTS.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: BTS \n**Dataset Name**: Boats \n**Short Description**: Dataset with images of different boats \n**\\# Classes**: 26 \n**\\# Images**: 1040 \n**Keywords**: vehicles, boats \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Cite paper to use dataset \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: MARVEL: A LARGE-SCALE IMAGE DATASET FOR MARITIME VESSELS \n**Source URL**: https://github.com/avaapm/marveldataset2016 \n \n**Original Author**: Gundogdu E., Solmaz B, Yucesoy V., Koc A. \n**Original contact**: \n\n**Meta Album author**: Dustin Carrion \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@InProceedings{MARVEL,\n author=\"Gundogdu, Erhan and Solmaz, Berkan and Yucesoy, Veysel and Koc, Aykut\",\n editor=\"Lai, Shang-Hong and Lepetit, Vincent and Nishino, Ko and Sato, Yoichi\",\n title=\"MARVEL: A Large-Scale Image Dataset for Maritime Vessels\",\n booktitle=\"Computer Vision -- ACCV 2016\",\n year=\"2017\",\n publisher=\"Springer International Publishing\",\n address=\"Cham\",\n pages=\"165--180\",\n isbn=\"978-3-319-54193-8\"\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44279) [[Extended]](https://www.openml.org/d/44343)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 26.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1040.0,\n 'NumberOfInstancesWithMissingValues': 1040.0,\n 'NumberOfMissingValues': 1040.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44309,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_BTS_Mini',\n 'source_data': '44309',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361189,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_BTS_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2883,"string":"2,883"}}},{"rowIdx":2751,"cells":{"uid":{"kind":"number","value":361480,"string":"361,480"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"PLK_Mini_Copy"},"dataset_description":{"kind":"string","value":"A copy of PLK_Mini dataset from Meta album Set0"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (nominal)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 86.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 3440.0,\n 'NumberOfInstancesWithMissingValues': 3440.0,\n 'NumberOfMissingValues': 3440.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 45104,\n 'estimation_procedure': '5 times 2-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'PLK_Mini_Copy',\n 'source_data': '45104',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361480,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"PLK_Mini_Copy"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2884,"string":"2,884"}}},{"rowIdx":2752,"cells":{"uid":{"kind":"number","value":361171,"string":"361,171"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_FNG_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Fungi Dataset (Micro)**\n***\nMeta-Album Fungi dataset is created by sampling the Danish Fungi 2020 dataset(https://arxiv.org/abs/2103.10107), itself a sampling of the Atlas of Danish Fungi repository. The images and labels which enter this database are sourced by a group consisting of 3 300 citizen botanists, then verified by their peers using a ranking of each person reliability, then finally verified by experts working at the Atlas. Of the 128 classes in the original Danish Fungi 2020 dataset, FNG retains the 25 most populous classes, belonging to six genera, for a total of 15 122 images total, with min 372, and max 1 221 images per class. Each image contains a colored 128x128 image of a fungus or a piece of a fungus from the corresponding class. Because the initial data were of widely varying sizes, we needed to crop a significant portion of the images, which we implemented by taking the largest possible square with center at the middle of the initial image. We then scaled each squared image to the 128x128 standard using the INTER_AREA anti-aliasing filter from Open-CV. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT.FNG \n**Meta Album URL**: [https://meta-album.github.io/datasets/FNG.html](https://meta-album.github.io/datasets/FNG.html) \n**Domain ID**: PLT \n**Domain Name**: Plants \n**Dataset ID**: FNG \n**Dataset Name**: Fungi \n**Short Description**: Fungi dataset from Denmark \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: fungi, ecology, plants \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: BSD-3-Clause License \n**License URL(original data release)**: https://github.com/picekl/DanishFungiDataset/blob/main/LICENSE\n \n**License (Meta-Album data release)**: BSD-3-Clause License \n**License URL (Meta-Album data release)**: [https://github.com/picekl/DanishFungiDataset/blob/main/LICENSE](https://github.com/picekl/DanishFungiDataset/blob/main/LICENSE) \n\n**Source**: Danish Fungi Dataset \n**Source URL**: https://sites.google.com/view/danish-fungi-dataset \n \n**Original Author**: Lukas Picek, Milan Sulc, Jiri Matas, Jacob Heilmann-Clausen, Thomas S. Jeppesen, Thomas Laessoe, Tobias Froslev \n**Original contact**: lukaspicek@gmail.com \n\n**Meta Album author**: Felix Herron \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{picek2021danish,\n title={Danish Fungi 2020 - Not Just Another Image Recognition Dataset},\n author={Lukas Picek and Milan Sulc and Jiri Matas and Jacob Heilmann-Clausen and Thomas S. Jeppesen and Thomas Laessoe and Tobias Froslev},\n year={2021},\n eprint={2103.10107},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44302) [[Extended]](https://www.openml.org/d/44335)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44272,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_FNG_Micro',\n 'source_data': '44272',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361171,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_FNG_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2885,"string":"2,885"}}},{"rowIdx":2753,"cells":{"uid":{"kind":"number","value":361197,"string":"361,197"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_VIL_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Plant Village Dataset (Mini)**\n***\nThe Plant Village dataset(https://data.mendeley.com/datasets/tywbtsjrjv/1) contains camera photos of 17 crop leaves. The original image resolution is 256x256 px. This collection covers 26 plant diseases and 12 healthy plants. The leaves are removed from the plant and placed on gray or black background, in various lighting conditions. All images are captured on a variety of gray backgrounds, except Corn Common rust which has a black background. For the curated version, we exclude the irrelevant Background and Corn Common Rust classes from the original collection. Plant Village has a 2-level label hierarchy, the supercategory is the crop type and the category is the disease type. We have preprocessed Plant Village for Meta-Album by resizing a subset from the original dataset to 128x128 image size. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.PLT_VIL \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_VIL.html](https://meta-album.github.io/datasets/PLT_VIL.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: PLT_VIL \n**Dataset Name**: Plant Village \n**Short Description**: Plant disease dataset \n**\\# Classes**: 38 \n**\\# Images**: 1520 \n**Keywords**: plants, plant diseases \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC0 1.0 \n**License URL(original data release)**: https://data.mendeley.com/datasets/tywbtsjrjv/1\nhttps://creativecommons.org/publicdomain/zero/1.0/\n \n**License (Meta-Album data release)**: CC0 1.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/) \n\n**Source**: Plant Village Dataset \n**Source URL**: https://github.com/spMohanty/PlantVillage-Dataset \n \n**Original Author**: Sharada Mohanty, David Hughes, and Marcel Salathe \n**Original contact**: arunpandian@mamcet.com \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{G2019323,\n title = {Identification of plant leaf diseases using a nine-layer deep convolutional neural network},\n journal = {Computers & Electrical Engineering},\n volume = {76},\n pages = {323-338},\n year = {2019},\n issn = {0045-7906},\n doi = {https://doi.org/10.1016/j.compeleceng.2019.04.011},\n url = {https://www.sciencedirect.com/science/article/pii/S0045790619300023},\n author = {Geetharamani G. and Arun Pandian J.},\n keywords = {Artificial intelligence, Deep convolutional neural networks, Deep learning, Dropout, Image augmentation, Leaf diseases identification, Machine learning, Mini batch, Training epoch, Transfer learning},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44242) [[Extended]](https://www.openml.org/d/44321)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 38.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1520.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44286,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_VIL_Mini',\n 'source_data': '44286',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361197,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_VIL_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2886,"string":"2,886"}}},{"rowIdx":2754,"cells":{"uid":{"kind":"number","value":361181,"string":"361,181"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_INS_2_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Insects2 Dataset (Mini)**\n***\nThe pest insects dataset was originally created as a large scale benchmark dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102). It contains more than 75 000 images belongs to 102 categories. It also has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upper-level category. The preprocessed version is made from the original dataset by cropping the images in perfect squares and then resizing them into the required images size of 128x128. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: SM_AM.INS2 \n**Meta Album URL**: [https://meta-album.github.io/datasets/INS_2.html](https://meta-album.github.io/datasets/INS_2.html) \n**Domain ID**: SM_AM \n**Domain Name**: Small Aninamls \n**Dataset ID**: INS_2 \n**Dataset Name**: Insects2 \n**Short Description**: Insects dataset for Insect Pest Recognition \n**\\# Classes**: 102 \n**\\# Images**: 4080 \n**Keywords**: insects, ecology \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Free for academic usage, cite to use dataset \n**License URL(original data release)**: https://github.com/xpwu95/IP102\n \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition \n**Source URL**: https://github.com/xpwu95/IP102 \n \n**Original Author**: Xiaoping Wu, Chi Zhan, Yukun Lai, Ming-Ming Cheng, Jufeng Yang \n**Original contact**: xpwu95@163.com \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{Wu2019Insect,\n title={IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition},\n author={Xiaoping Wu and Chi Zhan and Yukun Lai and Ming-Ming Cheng and Jufeng Yang},\n booktitle={IEEE CVPR},\n pages={8787--8796},\n year={2019},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44248) [[Extended]](https://www.openml.org/d/44326)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 102.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 4080.0,\n 'NumberOfInstancesWithMissingValues': 4080.0,\n 'NumberOfMissingValues': 4080.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44292,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_INS_2_Mini',\n 'source_data': '44292',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361181,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_INS_2_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2887,"string":"2,887"}}},{"rowIdx":2755,"cells":{"uid":{"kind":"number","value":361180,"string":"361,180"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_ACT_40_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Stanford 40 Actions Dataset (Mini)**\n***\nThe Stanford 40 Actions dataset(http://vision.stanford.edu/Datasets/40actions.html) contains images of humans performing 40 actions. There are 9 532 images in total with 180-300 images per action class. The dataset is designed for understanding human actions in still images. For Meta-Album, the dataset is preprocessed and images are resized into 128x128 pixels using Open-CV. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.ACT_40 \n**Meta Album URL**: [https://meta-album.github.io/datasets/ACT_40.html](https://meta-album.github.io/datasets/ACT_40.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: ACT_40 \n**Dataset Name**: Stanford 40 Actions \n**Short Description**: Stanford 40 Actions dataset with images of humans performing 40 actions. \n**\\# Classes**: 39 \n**\\# Images**: 1560 \n**Keywords**: human actions \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Cite paper to use dataset \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Stanford 40 Actions \n**Source URL**: http://vision.stanford.edu/Datasets/40actions.html \n \n**Original Author**: B. Yao, X. Jiang, A. Khosla, A.L. Lin, L.J. Guibas, and L. Fei-Fei. \n**Original contact**: bangpeng@cs.stanford.edu \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@INPROCEEDINGS{6126386,\n author={Yao, Bangpeng and Jiang, Xiaoye and Khosla, Aditya and Lin, Andy Lai and Guibas, Leonidas and Fei-Fei, Li},\n booktitle={2011 International Conference on Computer Vision}, \n title={Human action recognition by learning bases of action attributes and parts}, \n year={2011},\n pages={1331-1338},\n doi={10.1109/ICCV.2011.6126386}\n}\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44247) [[Extended]](https://www.openml.org/d/44325)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 39.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1560.0,\n 'NumberOfInstancesWithMissingValues': 1560.0,\n 'NumberOfMissingValues': 1560.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44291,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_ACT_40_Mini',\n 'source_data': '44291',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361180,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_ACT_40_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2888,"string":"2,888"}}},{"rowIdx":2756,"cells":{"uid":{"kind":"number","value":361199,"string":"361,199"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_MED_LF_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Medicinal Leaf Dataset (Mini)**\n***\nThe Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 800 images in total, captured with a mobile phone camera. The original resolution is 1 600x1 200 px. We create Medleaf for Meta-Album by cropping them at the center and resize to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.MED_LF \n**Meta Album URL**: [https://meta-album.github.io/datasets/MED_LF.html](https://meta-album.github.io/datasets/MED_LF.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: MED_LF \n**Dataset Name**: Medicinal Leaf \n**Short Description**: Healthy Medicinal Leaf \n**\\# Classes**: 25 \n**\\# Images**: 1000 \n**Keywords**: medicinal leaf, plants, plant diseases \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY 4.0 \n**License URL(original data release)**: https://data.mendeley.com/datasets/nnytj2v3n5/1\nhttps://creativecommons.org/licenses/by/4.0/\n \n**License (Meta-Album data release)**: CC BY 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: Medicinal Leaf Dataset \n**Source URL**: https://data.mendeley.com/datasets/nnytj2v3n5/1 \n \n**Original Author**: S Roopashree, J Anitha \n**Original contact**: \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n @article{s_j_2020, \n title={Medicinal Leaf Dataset}, \n url={https://data.mendeley.com/datasets/nnytj2v3n5/1}, \n author={S, Roopashree and J, Anitha}, \n year={2020}, \n month={Oct},\n doi={10.17632/nnytj2v3n5.1},\n version={1},\n publisher={Mendeley Data} \n} \n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44314) [[Extended]](https://www.openml.org/d/44332)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 25.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1000.0,\n 'NumberOfInstancesWithMissingValues': 1000.0,\n 'NumberOfMissingValues': 1000.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44299,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_MED_LF_Mini',\n 'source_data': '44299',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361199,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_MED_LF_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2889,"string":"2,889"}}},{"rowIdx":2757,"cells":{"uid":{"kind":"number","value":361176,"string":"361,176"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_INS_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Insects Dataset (Micro)**\n***\nThe original Insects dataset is created by the National Museum of Natural History, Paris (https://www.mnhn.fr/fr). It has more than 290 000 images in different sizes and orientations. The dataset has hierarchical classes which are listed from top to bottom as Order, Super-Family, Family, and Texa. Each image contains an insect in its natural environment or habitat, i.e, either on a flower or near to vegetation. The images are collected by the researchers and hundreds of volunteers from SPIPOLL Science project(https://www.spipoll.org/). The images are uploaded to a centralized server either by using the SPIPOLL website, Android application or IOS application. The preprocessed insect dataset is prepared from the original Insects dataset by carefully preprocessing the images, i.e., cropping the images from either side to make squared images. These cropped images are then resized into 128x128 using Open-CV with an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: SM_AM.INS \n**Meta Album URL**: [https://meta-album.github.io/datasets/INS.html](https://meta-album.github.io/datasets/INS.html) \n**Domain ID**: SM_AM \n**Domain Name**: Small Aninamls \n**Dataset ID**: INS \n**Dataset Name**: Insects \n**Short Description**: Insects dataset from Science Project SPIPOLL \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: insects, ecology \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY-NC 2.0 \n**License URL(original data release)**: https://www.spipoll.org/mentions-legales\n \n**License (Meta-Album data release)**: CC BY-NC 2.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/2.0/](https://creativecommons.org/licenses/by-nc/2.0/) \n\n**Source**: SPIPOLL; National Museum of Natural History, Paris \n**Source URL**: https://www.spipoll.org/ \n \n**Original Author**: Gregoire Lois, Colin Fontaine, Jean-Francois Julien \n**Original contact**: contact@spipoll.org \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{insects, \n title={Data quality and participant engagement in citizen science: comparing two approaches for monitoring pollinators in France and South Korea}, \n author={Serret, Hortense and Deguines, Nicolas and Jang, Yikweon and Lois, Gregoire and Julliard, Romain}, \n journal={Citizen Science: Theory and Practice}, \n volume={4}, \n number={1}, \n pages={22}, \n year={2019} \n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44306) [[Extended]](https://www.openml.org/d/44340)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44276,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_INS_Micro',\n 'source_data': '44276',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361176,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_INS_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2890,"string":"2,890"}}},{"rowIdx":2758,"cells":{"uid":{"kind":"number","value":361196,"string":"361,196"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_BCT_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album DIBaS Dataset (Mini)**\n***\nThe Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each class, we decided to split each image into several smaller images before resizing them to 128x128. We then obtained a preprocessed dataset of 4 060 images with at least 108 images for each class. This dataset was also preprocessed with blob normalization techniques, which is quite unusual for this type of image. The goal of this transformation was to reduce the importance of color in decision-making for a bias-aware challenge. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.BCT \n**Meta Album URL**: [https://meta-album.github.io/datasets/BCT.html](https://meta-album.github.io/datasets/BCT.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopic \n**Dataset ID**: BCT \n**Dataset Name**: DIBaS \n**Short Description**: Digital Image of Bacterial Species (DIBaS) \n**\\# Classes**: 33 \n**\\# Images**: 1320 \n**Keywords**: microscopic, bacteria \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Public for researchers \n**License URL(original data release)**: \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Digital Image of Bacterial Species (DIBaS) \n**Source URL**: http://misztal.edu.pl/software/databases/dibas/ \nhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184554 \nhttps://github.com/gallardorafael/DIBaS-Dataset \n \n**Original Author**: Bartosz Zielinski, Anna Plichta, Krzysztof Misztal, Przemyslaw Spurek, Monika Brzychczy-Wloch, Dorota Ochonska \n**Original contact**: krzysztof.misztal@uj.edu.pl \n\n**Meta Album author**: Romain Mussard \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{10.1371/journal.pone.0184554,\n doi = {10.1371/journal.pone.0184554},\n author = {Zielinski, Bartosz AND Plichta, Anna AND Misztal, Krzysztof AND Spurek, Przemyslaw AND Brzychczy-Wloch, Monika AND Ochonska, Dorota},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Deep learning approach to bacterial colony classification},\n year = {2017},\n month = {09},\n volume = {12},\n url = {https://doi.org/10.1371/journal.pone.0184554},\n pages = {1-14},\n number = {9}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44237) [[Extended]](https://www.openml.org/d/44316)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 33.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1320.0,\n 'NumberOfInstancesWithMissingValues': 1320.0,\n 'NumberOfMissingValues': 1320.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44281,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_BCT_Mini',\n 'source_data': '44281',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361196,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_BCT_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2891,"string":"2,891"}}},{"rowIdx":2759,"cells":{"uid":{"kind":"number","value":361187,"string":"361,187"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_FNG_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Fungi Dataset (Mini)**\n***\nMeta-Album Fungi dataset is created by sampling the Danish Fungi 2020 dataset(https://arxiv.org/abs/2103.10107), itself a sampling of the Atlas of Danish Fungi repository. The images and labels which enter this database are sourced by a group consisting of 3 300 citizen botanists, then verified by their peers using a ranking of each person reliability, then finally verified by experts working at the Atlas. Of the 128 classes in the original Danish Fungi 2020 dataset, FNG retains the 25 most populous classes, belonging to six genera, for a total of 15 122 images total, with min 372, and max 1 221 images per class. Each image contains a colored 128x128 image of a fungus or a piece of a fungus from the corresponding class. Because the initial data were of widely varying sizes, we needed to crop a significant portion of the images, which we implemented by taking the largest possible square with center at the middle of the initial image. We then scaled each squared image to the 128x128 standard using the INTER_AREA anti-aliasing filter from Open-CV. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT.FNG \n**Meta Album URL**: [https://meta-album.github.io/datasets/FNG.html](https://meta-album.github.io/datasets/FNG.html) \n**Domain ID**: PLT \n**Domain Name**: Plants \n**Dataset ID**: FNG \n**Dataset Name**: Fungi \n**Short Description**: Fungi dataset from Denmark \n**\\# Classes**: 25 \n**\\# Images**: 1000 \n**Keywords**: fungi, ecology, plants \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: BSD-3-Clause License \n**License URL(original data release)**: https://github.com/picekl/DanishFungiDataset/blob/main/LICENSE\n \n**License (Meta-Album data release)**: BSD-3-Clause License \n**License URL (Meta-Album data release)**: [https://github.com/picekl/DanishFungiDataset/blob/main/LICENSE](https://github.com/picekl/DanishFungiDataset/blob/main/LICENSE) \n\n**Source**: Danish Fungi Dataset \n**Source URL**: https://sites.google.com/view/danish-fungi-dataset \n \n**Original Author**: Lukas Picek, Milan Sulc, Jiri Matas, Jacob Heilmann-Clausen, Thomas S. Jeppesen, Thomas Laessoe, Tobias Froslev \n**Original contact**: lukaspicek@gmail.com \n\n**Meta Album author**: Felix Herron \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{picek2021danish,\n title={Danish Fungi 2020 - Not Just Another Image Recognition Dataset},\n author={Lukas Picek and Milan Sulc and Jiri Matas and Jacob Heilmann-Clausen and Thomas S. Jeppesen and Thomas Laessoe and Tobias Froslev},\n year={2021},\n eprint={2103.10107},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44272) [[Extended]](https://www.openml.org/d/44335)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 25.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1000.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44302,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_FNG_Mini',\n 'source_data': '44302',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361187,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_FNG_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2892,"string":"2,892"}}},{"rowIdx":2760,"cells":{"uid":{"kind":"number","value":361204,"string":"361,204"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_AWA_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Animals with Attributes Dataset (Mini)**\n***\nThe original Animals with Attributes 2 (AWA) dataset (https://cvml.ist.ac.at/AwA2/) was designed to benchmark transfer-learning algorithms, in particular attribute base classification and zero-shot learning. It has more than 37 000 images from 50 animals, where each animal corresponds to a class. The images of this dataset were collected from public sources, such as Flickr, in 2016, considering only images licensed for free use and redistribution. Each class can have 100 to 1 645 images with a resolution from 100x100 to 1 893x1 920 px. To preprocess this dataset, we cropped the images from either side to make them square. In case an image has a resolution lower than 128 px, the squared images are done by either duplicating the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image. Lastly, the square images are resized into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: LR_AM.AWA \n**Meta Album URL**: [https://meta-album.github.io/datasets/AWA.html](https://meta-album.github.io/datasets/AWA.html) \n**Domain ID**: LR_AM \n**Domain Name**: Large Aninamls \n**Dataset ID**: AWA \n**Dataset Name**: Animals with Attributes \n**Short Description**: Mamals dataset for image classification \n**\\# Classes**: 50 \n**\\# Images**: 2000 \n**Keywords**: mammals, animals, \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons \n**License URL(original data release)**: https://cvml.ist.ac.at/AwA2/\n \n**License (Meta-Album data release)**: Creative Commons \n**License URL (Meta-Album data release)**: [https://cvml.ist.ac.at/AwA2/](https://cvml.ist.ac.at/AwA2/) \n\n**Source**: Animals with attributes 2 \n**Source URL**: https://cvml.ist.ac.at/AwA2/ \n \n**Original Author**: Christoph H. Lampert, Bernt Schiele, Zeynep Akata \n**Original contact**: chl@ist.ac.at \n\n**Meta Album author**: Dustin Carrion \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@ARTICLE{8413121,\n author={Xian, Yongqin and Lampert, Christoph H. and Schiele, Bernt and Akata, Zeynep},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, \n title={Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly}, \n year={2019},\n volume={41},\n number={9},\n pages={2251-2265},\n doi={10.1109/TPAMI.2018.2857768}\n}\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44275) [[Extended]](https://www.openml.org/d/44338)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 50.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 2000.0,\n 'NumberOfInstancesWithMissingValues': 2000.0,\n 'NumberOfMissingValues': 2000.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44305,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_AWA_Mini',\n 'source_data': '44305',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361204,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_AWA_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2893,"string":"2,893"}}},{"rowIdx":2761,"cells":{"uid":{"kind":"number","value":361198,"string":"361,198"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_NET_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album PlantNet Dataset (Mini)**\n***\nMeta-Album PlantNet dataset is created by sampling the Pl@ntNet-300k dataset (https://openreview.net/forum?id=eLYinD0TtIt), itself a sampling of the Pl@ntNet Project's repository. The images and labels which enter this database are sourced by citizen botanists from around the world, then confirmed using a weighted reliability score from others users, such that each image has been reviewed by 2.03 citizen botanists on average. Of the 1 081 classes in the original Pl@ntNet-300k dataset, PLT_NET retains the 25 most populous classes, belonging to 21 genera, for a total of 120 688 images total, with min 2 914, max 9 011 image distribution per class. Each image contains a colored 128x128 image of a plant or a piece or a plant from the corresponding class (or in some cases sketches of plants or plant cells on microscope slides), scaled from the initial variable width using the INTER_AREA anti-aliasing filter from Open-CV. Almost all images were initially square; cropping by taking the largest possible square with center at the middle of the initial image was applied otherwise. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT.PLT_NET \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_NET.html](https://meta-album.github.io/datasets/PLT_NET.html) \n**Domain ID**: PLT \n**Domain Name**: Plants \n**Dataset ID**: PLT_NET \n**Dataset Name**: PlantNet \n**Short Description**: Plants Dataset with different species of plants \n**\\# Classes**: 25 \n**\\# Images**: 1000 \n**Keywords**: ecology, plants, plant species \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://zenodo.org/record/4726653\nhttps://creativecommons.org/licenses/by/4.0/legalcode\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) \n\n**Source**: PlantNet \n**Source URL**: https://plantnet.org/en/2021/03/30/a-plntnet-dataset-for-machine-learning-researchers/ \n \n**Original Author**: Garcin, Camille and Joly, Alexis and Bonnet, Pierre and Lombardo, Jean-Christophe and Affouard, Antoine and Chouet, Mathias and Servajean, Maximilien and Salmon, Joseph and Lorieul, Titouan \n**Original contact**: camille.garcin@inria.fr \n\n**Meta Album author**: Felix Herron \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{garcin2021plntnetk,\n title={Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution},\n author={Camille Garcin and alexis joly and Pierre Bonnet and Antoine Affouard and Jean-Christophe Lombardo and Mathias Chouet and Maximilien Servajean and Titouan Lorieul and Joseph Salmon},\n booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},\n year={2021},\n url={https://openreview.net/forum?id=eLYinD0TtIt}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44249) [[Extended]](https://www.openml.org/d/44327)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 25.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1000.0,\n 'NumberOfInstancesWithMissingValues': 1000.0,\n 'NumberOfMissingValues': 1000.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44293,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_NET_Mini',\n 'source_data': '44293',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361198,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_NET_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2894,"string":"2,894"}}},{"rowIdx":2762,"cells":{"uid":{"kind":"number","value":361070,"string":"361,070"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"eye_movements"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/\n\nCompetition 1 (preprocessed data)\nA straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.\n\nThe dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.\n\n\n* Features are in columns, feature vectors in rows.\n* Each assignment is a time sequence of 22-dimensional feature vectors.\n* The first column is the line number, second the assignment number and the next 22 columns (3 to 24) are the different features. Columns 25 to 27 contain extra information about the example. The training data set contains the classification label in the 28th column: \"0\" for irrelevant, \"1\" for relevant and \"2\" for the correct answer.\n* Each example (row) represents a single word. You are asked to return the classification of each read sentence.\n* The 22 features provided are commonly used in psychological studies on eye movement. All of them are not necessarily relevant in this context.\n\nThe objective of the Challenge is to predict the classification labels (I, R, C).\n\n\n\nPlease see the technical report for information of eye movements, experimental setup, baseline methods and references:\n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. [PDF]\n\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nFEATURES\n\nThe values in columns marked with an asterisk (*) are same for all occurances of the word.\n\nCOL\tNAME\t\tDESCRIPTION\n1\t#line\t\tLine number\n2\t#assg\t\tAssignment Number\n3\tfixcount\tNumber of fixations to the word\n4*\tfirstPassCnt\tNumber of fixations to the word when it is first encountered\n5*\tP1stFixation\t'1' if fixation occured when the sentence the word was in was encountered the first time\n6*\tP2stFixation\t'1' if fixation occured when the sentence the word was in was encountered the second time\n7*\tprevFixDur\tDuration of previous fixation\n8*\tfirstfixDur\tDuration of the first fixation when the word is first encountered\n9*\tfirstPassFixDur\tSum of durations of fixations when the word is first encountered\n10*\tnextFixDur\tDuration of the next fixation when gaze initially moves from the word\n11\tfirstSaccLen\tLength of the first saccade\n12\tlastSaccLen\tDistance between fixation on the word and the next fixation\n13\tprevFixPos\tDistance between the first fixation preceding the word and the beginning ot the word\n14\tlandingPos\tDistance between the first fixation on the word and the beginning of the word\n15\tleavingPos\tDistance between the last fixation on the word and the beginning of the word\n16\ttotalFixDur\tSum of all durations of fixations to the word\n17\tmeanFixDur\tMean duration of the fixations to the word\n18*\tnRegressFrom\tNumber of regressions leaving from the word\n19*\tregressLen\tSum of durations of regressions initiating from this word\n20*\tnextWordRegress\t'1' if a regression initiated from the following word\n21*\tregressDur\tSum of durations of the fixations on the word during regression\n22\tpupilDiamMax\tMaximum pupil diameter\n23\tpupilDiamLag\tMaximum pupil diameter 0.5 - 1.5 seconds after the beginning of fixation\n24\ttimePrtctg\tFirst fixation duration divided by the total number of fixations\n25\tnWordsInTitle\tNumber of word in the sentence (title) this word is in\n26\ttitleNo\t\tTitle number\n27\twordNo\t\tWord number (ordinal) in this title\n28\tlabel\t\tClassification for training data ('0'=irrelevant, '1'=relevant, '2'=correct)"},"dataset_features":{"kind":"string","value":"{0: [0 - lineNo (numeric)],\n 1: [1 - assgNo (numeric)],\n 2: [2 - prevFixDur (numeric)],\n 3: [3 - firstfixDur (numeric)],\n 4: [4 - firstPassFixDur (numeric)],\n 5: [5 - nextFixDur (numeric)],\n 6: [6 - firstSaccLen (numeric)],\n 7: [7 - lastSaccLen (numeric)],\n 8: [8 - prevFixPos (numeric)],\n 9: [9 - landingPos (numeric)],\n 10: [10 - leavingPos (numeric)],\n 11: [11 - totalFixDur (numeric)],\n 12: [12 - meanFixDur (numeric)],\n 13: [13 - regressLen (numeric)],\n 14: [14 - regressDur (numeric)],\n 15: [15 - pupilDiamMax (numeric)],\n 16: [16 - pupilDiamLag (numeric)],\n 17: [17 - timePrtctg (numeric)],\n 18: [18 - titleNo (numeric)],\n 19: [19 - wordNo (numeric)],\n 20: [20 - label (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 3804.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 3804.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 21.0,\n 'NumberOfInstances': 7608.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 20.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 44130,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'eye_movements',\n 'source_data': '44130',\n 'status': 'active',\n 'target_feature': 'label',\n 'task_type': 'Supervised Classification',\n 'tid': 361070,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"eye_movements"},"attribute_names":{"kind":"list like","value":["lineNo","assgNo","prevFixDur","firstfixDur","firstPassFixDur","nextFixDur","firstSaccLen","lastSaccLen","prevFixPos","landingPos","leavingPos","totalFixDur","meanFixDur","regressLen","regressDur","pupilDiamMax","pupilDiamLag","timePrtctg","titleNo","wordNo"],"string":"[\n \"lineNo\",\n \"assgNo\",\n \"prevFixDur\",\n \"firstfixDur\",\n \"firstPassFixDur\",\n \"nextFixDur\",\n \"firstSaccLen\",\n \"lastSaccLen\",\n \"prevFixPos\",\n \"landingPos\",\n \"leavingPos\",\n \"totalFixDur\",\n \"meanFixDur\",\n \"regressLen\",\n \"regressDur\",\n \"pupilDiamMax\",\n \"pupilDiamLag\",\n \"timePrtctg\",\n \"titleNo\",\n \"wordNo\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2895,"string":"2,895"}}},{"rowIdx":2763,"cells":{"uid":{"kind":"number","value":361111,"string":"361,111"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"eye_movements"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, \n transformed in the same way. This dataset belongs to the \"classification on categorical and\n numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/\n\nCompetition 1 (preprocessed data)\nA straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.\n\nThe dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.\n\n\n* Features are in columns, feature vectors in rows.\n* Each assignment is a time sequence of 22-dimensional feature vectors.\n* The first column is the line number, second the assignment number and the next 22 columns (3 to 24) are the different features. Columns 25 to 27 contain extra information about the example. The training data set contains the classification label in the 28th column: \"0\" for irrelevant, \"1\" for relevant and \"2\" for the correct answer.\n* Each example (row) represents a single word. You are asked to return the classification of each read sentence.\n* The 22 features provided are commonly used in psychological studies on eye movement. All of them are not necessarily relevant in this context.\n\nThe objective of the Challenge is to predict the classification labels (I, R, C).\n\n\n\nPlease see the technical report for information of eye movements, experimental setup, baseline methods and references:\n\nJarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. [PDF]\n\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nFEATURES\n\nThe values in columns marked with an asterisk (*) are same for all occurances of the word.\n\nCOL\tNAME\t\tDESCRIPTION\n1\t#line\t\tLine number\n2\t#assg\t\tAssignment Number\n3\tfixcount\tNumber of fixations to the word\n4*\tfirstPassCnt\tNumber of fixations to the word when it is first encountered\n5*\tP1stFixation\t'1' if fixation occured when the sentence the word was in was encountered the first time\n6*\tP2stFixation\t'1' if fixation occured when the sentence the word was in was encountered the second time\n7*\tprevFixDur\tDuration of previous fixation\n8*\tfirstfixDur\tDuration of the first fixation when the word is first encountered\n9*\tfirstPassFixDur\tSum of durations of fixations when the word is first encountered\n10*\tnextFixDur\tDuration of the next fixation when gaze initially moves from the word\n11\tfirstSaccLen\tLength of the first saccade\n12\tlastSaccLen\tDistance between fixation on the word and the next fixation\n13\tprevFixPos\tDistance between the first fixation preceding the word and the beginning ot the word\n14\tlandingPos\tDistance between the first fixation on the word and the beginning of the word\n15\tleavingPos\tDistance between the last fixation on the word and the beginning of the word\n16\ttotalFixDur\tSum of all durations of fixations to the word\n17\tmeanFixDur\tMean duration of the fixations to the word\n18*\tnRegressFrom\tNumber of regressions leaving from the word\n19*\tregressLen\tSum of durations of regressions initiating from this word\n20*\tnextWordRegress\t'1' if a regression initiated from the following word\n21*\tregressDur\tSum of durations of the fixations on the word during regression\n22\tpupilDiamMax\tMaximum pupil diameter\n23\tpupilDiamLag\tMaximum pupil diameter 0.5 - 1.5 seconds after the beginning of fixation\n24\ttimePrtctg\tFirst fixation duration divided by the total number of fixations\n25\tnWordsInTitle\tNumber of word in the sentence (title) this word is in\n26\ttitleNo\t\tTitle number\n27\twordNo\t\tWord number (ordinal) in this title\n28\tlabel\t\tClassification for training data ('0'=irrelevant, '1'=relevant, '2'=correct)"},"dataset_features":{"kind":"string","value":"{0: [0 - lineNo (numeric)],\n 1: [1 - assgNo (numeric)],\n 2: [2 - P1stFixation (nominal)],\n 3: [3 - P2stFixation (nominal)],\n 4: [4 - prevFixDur (numeric)],\n 5: [5 - firstfixDur (numeric)],\n 6: [6 - firstPassFixDur (numeric)],\n 7: [7 - nextFixDur (numeric)],\n 8: [8 - firstSaccLen (numeric)],\n 9: [9 - lastSaccLen (numeric)],\n 10: [10 - prevFixPos (numeric)],\n 11: [11 - landingPos (numeric)],\n 12: [12 - leavingPos (numeric)],\n 13: [13 - totalFixDur (numeric)],\n 14: [14 - meanFixDur (numeric)],\n 15: [15 - regressLen (numeric)],\n 16: [16 - nextWordRegress (nominal)],\n 17: [17 - regressDur (numeric)],\n 18: [18 - pupilDiamMax (numeric)],\n 19: [19 - pupilDiamLag (numeric)],\n 20: [20 - timePrtctg (numeric)],\n 21: [21 - titleNo (numeric)],\n 22: [22 - wordNo (numeric)],\n 23: [23 - label (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 3804.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 3804.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 24.0,\n 'NumberOfInstances': 7608.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 20.0,\n 'NumberOfSymbolicFeatures': 4.0,\n 'cost_matrix': nan,\n 'did': 44157,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'eye_movements',\n 'source_data': '44157',\n 'status': 'active',\n 'target_feature': 'label',\n 'task_type': 'Supervised Classification',\n 'tid': 361111,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"eye_movements"},"attribute_names":{"kind":"list like","value":["lineNo","assgNo","P1stFixation","P2stFixation","prevFixDur","firstfixDur","firstPassFixDur","nextFixDur","firstSaccLen","lastSaccLen","prevFixPos","landingPos","leavingPos","totalFixDur","meanFixDur","regressLen","nextWordRegress","regressDur","pupilDiamMax","pupilDiamLag","timePrtctg","titleNo","wordNo"],"string":"[\n \"lineNo\",\n \"assgNo\",\n \"P1stFixation\",\n \"P2stFixation\",\n \"prevFixDur\",\n \"firstfixDur\",\n \"firstPassFixDur\",\n \"nextFixDur\",\n \"firstSaccLen\",\n \"lastSaccLen\",\n \"prevFixPos\",\n \"landingPos\",\n \"leavingPos\",\n \"totalFixDur\",\n \"meanFixDur\",\n \"regressLen\",\n \"nextWordRegress\",\n \"regressDur\",\n \"pupilDiamMax\",\n \"pupilDiamLag\",\n \"timePrtctg\",\n \"titleNo\",\n \"wordNo\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,true,true,false,false,false,false,false,false,false,false,false,false,false,false,true,false,false,false,false,false,false],"string":"[\n false,\n false,\n true,\n true,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n false,\n false,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2896,"string":"2,896"}}},{"rowIdx":2764,"cells":{"uid":{"kind":"number","value":361206,"string":"361,206"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_RSD_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album RSD Dataset (Mini)**\n***\nRSD46 dataset (https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU) is collected from Google Earth and Tianditu. The collection contains 46 scene categories, with a total of 117 000 images. Each scene category has between 500 - 3000 images. The original resolution are 256x256 px or 512x512 px. We have created preprocessed version of RSD for Meta-Album by resizing the original dataset to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: REM_SEN.RSD \n**Meta Album URL**: [https://meta-album.github.io/datasets/RSD.html](https://meta-album.github.io/datasets/RSD.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RSD \n**Dataset Name**: RSD \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 38 \n**\\# Images**: 1520 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research and non-profit purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: RSD46-WHU \n**Source URL**: https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU \n \n**Original Author**: Yang Long, Yiping Gong, Zhifeng Xiao, and Qing Liu, Deren Li, Chunshan Wei, Gefu Tang and Junyi Liu \n**Original contact**: longyang@whu.edu.cn \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@ARTICLE{7827088,\n author={Long, Yang and Gong, Yiping and Xiao, Zhifeng and Liu, Qing},\n journal={IEEE Transactions on Geoscience and Remote Sensing}, \n title={Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks}, \n year={2017},\n volume={55},\n number={5},\n pages={2486-2498},\n doi={10.1109/TGRS.2016.2645610}}\n\n@article{xiao2017high,\n title={High-resolution remote sensing image retrieval based on CNNs from a dimensional perspective},\n author={Xiao, Zhifeng and Long, Yang and Li, Deren and Wei, Chunshan and Tang, Gefu and Liu, Junyi},\n journal={Remote Sensing},\n volume={9},\n number={7},\n pages={725},\n year={2017},\n publisher={Multidisciplinary Digital Publishing Institute}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44277) [[Extended]](https://www.openml.org/d/44341)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 38.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1520.0,\n 'NumberOfInstancesWithMissingValues': 1520.0,\n 'NumberOfMissingValues': 1520.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44307,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_RSD_Mini',\n 'source_data': '44307',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361206,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_RSD_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2897,"string":"2,897"}}},{"rowIdx":2765,"cells":{"uid":{"kind":"number","value":361202,"string":"361,202"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_ACT_410_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album MPII Human Pose Dataset Dataset (Mini)**\n***\nThe MPII Human Pose dataset (http://human-pose.mpi-inf.mpg.de/#download) is a state of the art benchmark for evaluation of articulated human pose estimation. It includes around 25 000 images containing over 40 000 people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video. Like other Meta-Album datasets, this dataset is preprocessed and all images are resized into 128x128 pixels. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.ACT_410 \n**Meta Album URL**: [https://meta-album.github.io/datasets/ACT_410.html](https://meta-album.github.io/datasets/ACT_410.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: ACT_410 \n**Dataset Name**: MPII Human Pose Dataset \n**Short Description**: MPII Human Pose Dataset with images of humans performing 410 activities. \n**\\# Classes**: 29 \n**\\# Images**: 1160 \n**Keywords**: human actions, sports \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Simplified BSD License \n**License URL(original data release)**: http://human-pose.mpi-inf.mpg.de/#download\nhttp://human-pose.mpi-inf.mpg.de/bsd.txt\n \n**License (Meta-Album data release)**: Simplified BSD License \n**License URL (Meta-Album data release)**: [http://human-pose.mpi-inf.mpg.de/bsd.txt](http://human-pose.mpi-inf.mpg.de/bsd.txt) \n\n**Source**: MPII Human Pose Dataset \n**Source URL**: http://human-pose.mpi-inf.mpg.de/#download \n \n**Original Author**: Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt \n**Original contact**: leonid@mpi-inf.mpg.de \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@INPROCEEDINGS{6909866, \n author={Andriluka, Mykhaylo and Pishchulin, Leonid and Gehler, Peter and Schiele, Bernt}, booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition}, \n title={2D Human Pose Estimation: New Benchmark and State of the Art Analysis}, \n year={2014},\n pages={3686-3693}, \n doi={10.1109/CVPR.2014.471}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44271) [[Extended]](https://www.openml.org/d/44334)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 29.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1160.0,\n 'NumberOfInstancesWithMissingValues': 1160.0,\n 'NumberOfMissingValues': 1160.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44301,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_ACT_410_Mini',\n 'source_data': '44301',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361202,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_ACT_410_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2898,"string":"2,898"}}},{"rowIdx":2766,"cells":{"uid":{"kind":"number","value":361190,"string":"361,190"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PRT_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Subcellular Human Protein Dataset (Mini)**\n***\nThis dataset is a subset of the Subcellular dataset in the Protein Atlas project(https://www.proteinatlas.org/). The original dataset, which stems from the Human Protein Atlas Image Classification Kaggle competition(https://www.kaggle.com/competitions/human-protein-atlas-image-classification), comprises 31 072 RGBY images of size 512x512 px, each of which belongs to one or more out of 28 classes. The labels correspond to protein organelle localizations. For Meta-Album, we performed two modifications: (1), to turn the dataset into a multi-class dataset, we dropped all images belonging to more than a single class and also those images that belong to classes with less than 40 members; (2) we converted the remaining images into RGB simply by dropping the yellow channel; this was also a common practice in the competition. Finally, and as for all datasets in Meta-Album, the images from the original dataset were resized to 128x128 image size. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.PRT \n**Meta Album URL**: [https://meta-album.github.io/datasets/PRT.html](https://meta-album.github.io/datasets/PRT.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopy \n**Dataset ID**: PRT \n**Dataset Name**: Subcellular Human Protein \n**Short Description**: Subcellular protein patterns in human cells \n**\\# Classes**: 21 \n**\\# Images**: 840 \n**Keywords**: human protein, subcellular \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY-SA 3.0 \n**License URL(original data release)**: https://www.proteinatlas.org/about/licence\n \n**License (Meta-Album data release)**: CC BY-SA 3.0 \n**License URL (Meta-Album data release)**: [https://www.proteinatlas.org/about/licence](https://www.proteinatlas.org/about/licence) \n\n**Source**: The Human Protein Atlas \n**Source URL**: https://proteinatlas.org \nhttps://www.kaggle.com/c/human-protein-atlas-image-classification \n \n**Original Author**: Peter J Thul, Lovisa Akesson, Mikaela Wiking, Diana Mahdessian, Aikaterini Geladaki, Hammou Ait Blal, Tove Alm, Anna Asplund, Lars Bjork, Lisa Breckels, and others \n**Original contact**: contact@proteinatlas.org \n\n**Meta Album author**: Felix Mohr \n**Created Date**: 01 June 2022 \n**Contact Name**: Felix Mohr \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{thul2017subcellular, \n title={A subcellular map of the human proteome},\n author={Thul, Peter J and Akesson, Lovisa and Wiking, Mikaela and Mahdessian, Diana and Geladaki, Aikaterini and Ait Blal, Hammou and Alm, Tove and Asplund, Anna and Bjork, Lars and Breckels, Lisa M},\n journal={Science},\n volume={356},\n number={6340},\n year={2017},\n publisher={American Association for the Advancement of Science}\n}\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44278) [[Extended]](https://www.openml.org/d/44342)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 21.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 840.0,\n 'NumberOfInstancesWithMissingValues': 840.0,\n 'NumberOfMissingValues': 840.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44308,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PRT_Mini',\n 'source_data': '44308',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361190,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PRT_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2899,"string":"2,899"}}},{"rowIdx":2767,"cells":{"uid":{"kind":"number","value":361182,"string":"361,182"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_TEX_DTD_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Textures-DTD Dataset (Mini)**\n***\nThe Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data was annotated using Amazon Mechanical Turk. The data collection process is mentioned on the dataset overview page. For Meta-Album meta-dataset, this dataset is preprocessed by cropping the images to square images and then resizing them to 128x128 using Open-CV with an anti-aliasing filter. This dataset has 47 class labels. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX_DTD \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX_DTD.html](https://meta-album.github.io/datasets/TEX_DTD.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX_DTD \n**Dataset Name**: Textures-DTD \n**Short Description**: Textures dataset from Describable Textures Dataset \n**\\# Classes**: 47 \n**\\# Images**: 1880 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Describable Textures Dataset (DTD), University of Oxford, England \n**Source URL**: https://www.robots.ox.ac.uk/~vgg/data/dtd/ \n \n**Original Author**: Mircea Cimpoi, Subhransu354Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi \n**Original contact**: {mircea, vedaldi}@robots.ox.ac.uk \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@InProceedings{cimpoi14describing,\n\tAuthor = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi},\n\tTitle = {Describing Textures in the Wild},\n\tBooktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},\n\tYear = {2014}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44250) [[Extended]](https://www.openml.org/d/44328)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 47.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1880.0,\n 'NumberOfInstancesWithMissingValues': 1880.0,\n 'NumberOfMissingValues': 1880.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44294,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_TEX_DTD_Mini',\n 'source_data': '44294',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361182,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_TEX_DTD_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2900,"string":"2,900"}}},{"rowIdx":2768,"cells":{"uid":{"kind":"number","value":190143,"string":"190,143"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"isolet"},"dataset_description":{"kind":"string","value":"Binarized version of the isolet dataset (see version 1). 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Dataset (Mini)**\n***\nThe Flowers dataset(https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) consists of a variety of flowers gathered from different websites and some are photographed by the original creators. These flowers are commonly found in the UK. The images generally have large scale, pose and light variations. Some categories of flowers in the dataset has large variations of flowers while other have similar flowers in a category. The dataset was created back in 2008 at Oxford University by Nilsback, M-E. and Zisserman, A.[21]. The Flowers dataset in the Meta-Album meta-dataset is a preprocessed version of the original flowers dataset. The images are first cropped and made into squared images which are then resized into 128x128 with anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT.FLW \n**Meta Album URL**: [https://meta-album.github.io/datasets/FLW.html](https://meta-album.github.io/datasets/FLW.html) \n**Domain ID**: PLT \n**Domain Name**: Plants \n**Dataset ID**: FLW \n**Dataset Name**: Flowers \n**Short Description**: Flowers dataset from Visual Geometry Group, University of Oxford \n**\\# Classes**: 102 \n**\\# Images**: 4080 \n**Keywords**: flowers, ecology, plants \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: GNU General Public License Version 2 \n**License URL(original data release)**: https://peltarion.com/knowledge-center/documentation/terms/dataset-licenses/oxford-102-category-flowers\nhttps://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html\n \n**License (Meta-Album data release)**: GNU General Public License Version 2 \n**License URL (Meta-Album data release)**: [https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) \n\n**Source**: Visual Geometry Group, University of Oxford, England \n**Source URL**: https://www.robots.ox.ac.uk/~vgg/data/flowers/ \n \n**Original Author**: Maria-Elena Nilsback, Andrew Zisserman \n**Original contact**: {men,az}@robots.ox.ac.uk \n\n**Meta Album author**: Felix Mohr \n**Created Date**: 01 March 2022 \n**Contact Name**: Felix Mohr \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@InProceedings{Nilsback08,\n author = \"Maria-Elena Nilsback and Andrew Zisserman\",\n title = \"Automated Flower Classification over a Large Number of Classes\",\n booktitle = \"Indian Conference on Computer Vision, Graphics and Image Processing\",\n month = \"Dec\",\n year = \"2008\",\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44239) [[Extended]](https://www.openml.org/d/44318)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 102.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 4080.0,\n 'NumberOfInstancesWithMissingValues': 4080.0,\n 'NumberOfMissingValues': 4080.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44283,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_FLW_Mini',\n 'source_data': '44283',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361191,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_FLW_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2902,"string":"2,902"}}},{"rowIdx":2770,"cells":{"uid":{"kind":"number","value":361203,"string":"361,203"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_DOC_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Plant Doc Dataset (Mini)**\n***\nThe PlantDoc dataset(https://github.com/pratikkayal/PlantDoc-Dataset) is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one image. The original, uncropped images are generally different in scale, light conditions, and pose. However, within one category, images of leaves that came from the same original image can be found. The images correspond to 27 classes, including plant disease names and plant species names, e.g.: Corn Leaf Blight and Cherry Leaf respectively. The dataset was created for a benchmarking classification model work, published in 2020 by Singh et al. The PlantDoc dataset in the Meta-Album benchmark is extracted from a preprocessed version of the original PlantDoc dataset. First, to get i.i.d. samples, only one leaf image per each original image is randomly picked. Then, leaves images are cropped and made into squared images which are then resized into 128x128 with anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.PLT_DOC \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_DOC.html](https://meta-album.github.io/datasets/PLT_DOC.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: PLT_DOC \n**Dataset Name**: Plant Doc \n**Short Description**: Plant disease dataset \n**\\# Classes**: 27 \n**\\# Images**: 1080 \n**Keywords**: plants, plant diseases, \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/blob/master/LICENSE.txt\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: PlantDoc: A Dataset for Visual Plant Disease Detection \n**Source URL**: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset \n \n**Original Author**: Sharada Mohanty, David Hughes, and Marcel Salathe \n**Original contact**: \n\n**Meta Album author**: Maria Belen Guaranda Cabezas \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{10.1145/3371158.3371196,\n author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},\n title = {PlantDoc: A Dataset for Visual Plant Disease Detection},\n year = {2020},\n isbn = {9781450377386},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3371158.3371196},\n doi = {10.1145/3371158.3371196},\n booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},\n pages = {249-253},\n numpages = {5},\n keywords = {Object Detection, Image Classification, Deep Learning},\n location = {Hyderabad, India},\n series = {CoDS COMAD 2020}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44273) [[Extended]](https://www.openml.org/d/44336)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 27.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1080.0,\n 'NumberOfInstancesWithMissingValues': 1080.0,\n 'NumberOfMissingValues': 1080.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44303,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_DOC_Mini',\n 'source_data': '44303',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361203,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_DOC_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2903,"string":"2,903"}}},{"rowIdx":2771,"cells":{"uid":{"kind":"number","value":361194,"string":"361,194"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLK_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Plankton Dataset (Mini)**\n***\nThe Plankton dataset is created by researchers at the Woods Hole Oceanographic Institution (https://www.whoi.edu/). Imaging FlowCytobot (IFCB) was used for the data collection. The Complete process and mechanism are described in the paper [31]. Each image in the dataset contains one or multiple planktons. The images are captured in a controlled environment and have different orientations based on the flow of the fluid in which the images are captured and the size and shape of the planktons. The preprocessed plankton dataset is prepared from the original WHOI Plankton dataset. The preprocessing of the images is done by creating a background squared image by either duplicating the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image to match the width or height of the image respectively. A Gaussian kernal of size 29x29 is applied to the background image to blur the image. Finally, the original plankton image is pasted on the background image at the center of the image. The squared background image with the original plankton image on top of it as one image is then resized into 128x128 with anti-aliasing. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: SM_AM.PLK \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLK.html](https://meta-album.github.io/datasets/PLK.html) \n**Domain ID**: SM_AM \n**Domain Name**: Small Animals \n**Dataset ID**: PLK \n**Dataset Name**: Plankton \n**Short Description**: Plankton dataset from WHOI \n**\\# Classes**: 86 \n**\\# Images**: 3440 \n**Keywords**: plankton, ecology \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: MIT License \n**License URL(original data release)**: https://github.com/hsosik/WHOI-Plankton/blob/master/LICENSE\n \n**License (Meta-Album data release)**: MIT License \n**License URL (Meta-Album data release)**: [https://github.com/hsosik/WHOI-Plankton/blob/master/LICENSE](https://github.com/hsosik/WHOI-Plankton/blob/master/LICENSE) \n\n**Source**: Woods Hole Oceanographic Institution \n**Source URL**: https://github.com/hsosik/WHOI-Plankton \n \n**Original Author**: Heidi M. Sosik, Emily E. Peacock, Emily F. Brownlee, Eric Orenstein \n**Original contact**: hsosik@whoi.edu \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@misc{whoiplankton,\n title={Annotated Plankton Images - Data Set for Developing and Evaluating Classification Methods.}, \n author={Heidi M. Sosik, Emily E. Peacock, Emily F. Brownlee},\n year={2015},\n DOI = {10.1575/1912/7341},\n url={https://hdl.handle.net/10.1575/1912/7341}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44238) [[Extended]](https://www.openml.org/d/44317)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 86.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 3440.0,\n 'NumberOfInstancesWithMissingValues': 3440.0,\n 'NumberOfMissingValues': 3440.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44282,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLK_Mini',\n 'source_data': '44282',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361194,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLK_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2904,"string":"2,904"}}},{"rowIdx":2772,"cells":{"uid":{"kind":"number","value":361227,"string":"361,227"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_ACT_40_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album Stanford 40 Actions Dataset (Extended)**\n***\nThe Stanford 40 Actions dataset(http://vision.stanford.edu/Datasets/40actions.html) contains images of humans performing 40 actions. There are 9 532 images in total with 180-300 images per action class. The dataset is designed for understanding human actions in still images. For Meta-Album, the dataset is preprocessed and images are resized into 128x128 pixels using Open-CV. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.ACT_40 \n**Meta Album URL**: [https://meta-album.github.io/datasets/ACT_40.html](https://meta-album.github.io/datasets/ACT_40.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: ACT_40 \n**Dataset Name**: Stanford 40 Actions \n**Short Description**: Stanford 40 Actions dataset with images of humans performing 40 actions. \n**\\# Classes**: 40 \n**\\# Images**: 3389 \n**Keywords**: human actions \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Cite paper to use dataset \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Stanford 40 Actions \n**Source URL**: http://vision.stanford.edu/Datasets/40actions.html \n \n**Original Author**: B. Yao, X. Jiang, A. Khosla, A.L. Lin, L.J. Guibas, and L. Fei-Fei. \n**Original contact**: bangpeng@cs.stanford.edu \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@INPROCEEDINGS{6126386,\n author={Yao, Bangpeng and Jiang, Xiaoye and Khosla, Aditya and Lin, Andy Lai and Guibas, Leonidas and Fei-Fei, Li},\n booktitle={2011 International Conference on Computer Vision}, \n title={Human action recognition by learning bases of action attributes and parts}, \n year={2011},\n pages={1331-1338},\n doi={10.1109/ICCV.2011.6126386}\n}\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44247) [[Mini]](https://www.openml.org/d/44291)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 110.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 11.0,\n 'NumberOfClasses': 40.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 3389.0,\n 'NumberOfInstancesWithMissingValues': 3389.0,\n 'NumberOfMissingValues': 3389.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44325,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_ACT_40_Extended',\n 'source_data': '44325',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361227,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_ACT_40_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2905,"string":"2,905"}}},{"rowIdx":2773,"cells":{"uid":{"kind":"number","value":167132,"string":"167,132"},"orig_metric":{"kind":"string","value":"area_under_roc_curve"},"sklearn_metric":{"kind":"string","value":"roc_auc_score"},"dataset_name":{"kind":"string","value":"Speech"},"dataset_description":{"kind":"string","value":""The speech dataset was also provided by (see citation request) and contains real world data from recorded English language. The normal class contains data from persons having an American accent whereas the outliers are represented from seven other speakers, having a different accent. The feature vector is the i-vector of the speech segment, which is a state-of-the- art feature in speaker recognition. The dataset has 400 dimensions and is thus the task in our evaluation with the largest number of dimensions. It has 3,686 instances including 1.65% anomalies." (cite from Goldstein, Markus, and Seiichi Uchida. "A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data." PloS one 11.4 (2016): e0152173.). This dataset is not the original dataset. The target variable "Target" is relabeled into "Normal" and "Anomaly"."},"dataset_features":{"kind":"string","value":"{0: [0 - V1 (numeric)],\n 1: [1 - V2 (numeric)],\n 2: [2 - V3 (numeric)],\n 3: [3 - V4 (numeric)],\n 4: [4 - V5 (numeric)],\n 5: [5 - V6 (numeric)],\n 6: [6 - V7 (numeric)],\n 7: [7 - V8 (numeric)],\n 8: [8 - V9 (numeric)],\n 9: [9 - V10 (numeric)],\n 10: [10 - V11 (numeric)],\n 11: [11 - V12 (numeric)],\n 12: [12 - V13 (numeric)],\n 13: [13 - V14 (numeric)],\n 14: [14 - V15 (numeric)],\n 15: [15 - V16 (numeric)],\n 16: [16 - V17 (numeric)],\n 17: [17 - V18 (numeric)],\n 18: [18 - V19 (numeric)],\n 19: [19 - V20 (numeric)],\n 20: [20 - V21 (numeric)],\n 21: [21 - V22 (numeric)],\n 22: [22 - V23 (numeric)],\n 23: [23 - V24 (numeric)],\n 24: [24 - V25 (numeric)],\n 25: [25 - V26 (numeric)],\n 26: [26 - V27 (numeric)],\n 27: [27 - V28 (numeric)],\n 28: [28 - V29 (numeric)],\n 29: [29 - V30 (numeric)],\n 30: [30 - V31 (numeric)],\n 31: [31 - V32 (numeric)],\n 32: [32 - V33 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false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2906,"string":"2,906"}}},{"rowIdx":2774,"cells":{"uid":{"kind":"number","value":361226,"string":"361,226"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_MED_LF_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album Medicinal Leaf Dataset (Extended)**\n***\nThe Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 800 images in total, captured with a mobile phone camera. The original resolution is 1 600x1 200 px. We create Medleaf for Meta-Album by cropping them at the center and resize to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.MED_LF \n**Meta Album URL**: [https://meta-album.github.io/datasets/MED_LF.html](https://meta-album.github.io/datasets/MED_LF.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: MED_LF \n**Dataset Name**: Medicinal Leaf \n**Short Description**: Healthy Medicinal Leaf \n**\\# Classes**: 26 \n**\\# Images**: 1596 \n**Keywords**: medicinal leaf, plants, plant diseases \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY 4.0 \n**License URL(original data release)**: https://data.mendeley.com/datasets/nnytj2v3n5/1\nhttps://creativecommons.org/licenses/by/4.0/\n \n**License (Meta-Album data release)**: CC BY 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: Medicinal Leaf Dataset \n**Source URL**: https://data.mendeley.com/datasets/nnytj2v3n5/1 \n \n**Original Author**: S Roopashree, J Anitha \n**Original contact**: \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n @article{s_j_2020, \n title={Medicinal Leaf Dataset}, \n url={https://data.mendeley.com/datasets/nnytj2v3n5/1}, \n author={S, Roopashree and J, Anitha}, \n year={2020}, \n month={Oct},\n doi={10.17632/nnytj2v3n5.1},\n version={1},\n publisher={Mendeley Data} \n} \n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44314) [[Mini]](https://www.openml.org/d/44299)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 122.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 14.0,\n 'NumberOfClasses': 26.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 1596.0,\n 'NumberOfInstancesWithMissingValues': 1596.0,\n 'NumberOfMissingValues': 1596.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44332,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_MED_LF_Extended',\n 'source_data': '44332',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361226,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_MED_LF_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2909,"string":"2,909"}}},{"rowIdx":2775,"cells":{"uid":{"kind":"number","value":361208,"string":"361,208"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PLT_DOC_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album Plant Doc Dataset (Extended)**\n***\nThe PlantDoc dataset(https://github.com/pratikkayal/PlantDoc-Dataset) is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one image. The original, uncropped images are generally different in scale, light conditions, and pose. However, within one category, images of leaves that came from the same original image can be found. The images correspond to 27 classes, including plant disease names and plant species names, e.g.: Corn Leaf Blight and Cherry Leaf respectively. The dataset was created for a benchmarking classification model work, published in 2020 by Singh et al. The PlantDoc dataset in the Meta-Album benchmark is extracted from a preprocessed version of the original PlantDoc dataset. First, to get i.i.d. samples, only one leaf image per each original image is randomly picked. Then, leaves images are cropped and made into squared images which are then resized into 128x128 with anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: PLT_DIS.PLT_DOC \n**Meta Album URL**: [https://meta-album.github.io/datasets/PLT_DOC.html](https://meta-album.github.io/datasets/PLT_DOC.html) \n**Domain ID**: PLT_DIS \n**Domain Name**: Plant Diseases \n**Dataset ID**: PLT_DOC \n**Dataset Name**: Plant Doc \n**Short Description**: Plant disease dataset \n**\\# Classes**: 27 \n**\\# Images**: 2549 \n**Keywords**: plants, plant diseases, \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/blob/master/LICENSE.txt\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) \n\n**Source**: PlantDoc: A Dataset for Visual Plant Disease Detection \n**Source URL**: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset \n \n**Original Author**: Sharada Mohanty, David Hughes, and Marcel Salathe \n**Original contact**: \n\n**Meta Album author**: Maria Belen Guaranda Cabezas \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{10.1145/3371158.3371196,\n author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},\n title = {PlantDoc: A Dataset for Visual Plant Disease Detection},\n year = {2020},\n isbn = {9781450377386},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3371158.3371196},\n doi = {10.1145/3371158.3371196},\n booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},\n pages = {249-253},\n numpages = {5},\n keywords = {Object Detection, Image Classification, Deep Learning},\n location = {Hyderabad, India},\n series = {CoDS COMAD 2020}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44273) [[Mini]](https://www.openml.org/d/44303)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 189.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 54.0,\n 'NumberOfClasses': 27.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 2549.0,\n 'NumberOfInstancesWithMissingValues': 2549.0,\n 'NumberOfMissingValues': 2549.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44336,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PLT_DOC_Extended',\n 'source_data': '44336',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361208,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PLT_DOC_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2910,"string":"2,910"}}},{"rowIdx":2776,"cells":{"uid":{"kind":"number","value":361219,"string":"361,219"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_BCT_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album DIBaS Dataset (Extended)**\n***\nThe Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each class, we decided to split each image into several smaller images before resizing them to 128x128. We then obtained a preprocessed dataset of 4 060 images with at least 108 images for each class. This dataset was also preprocessed with blob normalization techniques, which is quite unusual for this type of image. The goal of this transformation was to reduce the importance of color in decision-making for a bias-aware challenge. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.BCT \n**Meta Album URL**: [https://meta-album.github.io/datasets/BCT.html](https://meta-album.github.io/datasets/BCT.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopic \n**Dataset ID**: BCT \n**Dataset Name**: DIBaS \n**Short Description**: Digital Image of Bacterial Species (DIBaS) \n**\\# Classes**: 33 \n**\\# Images**: 4060 \n**Keywords**: microscopic, bacteria \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Public for researchers \n**License URL(original data release)**: \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Digital Image of Bacterial Species (DIBaS) \n**Source URL**: http://misztal.edu.pl/software/databases/dibas/ \nhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184554 \nhttps://github.com/gallardorafael/DIBaS-Dataset \n \n**Original Author**: Bartosz Zielinski, Anna Plichta, Krzysztof Misztal, Przemyslaw Spurek, Monika Brzychczy-Wloch, Dorota Ochonska \n**Original contact**: krzysztof.misztal@uj.edu.pl \n\n**Meta Album author**: Romain Mussard \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{10.1371/journal.pone.0184554,\n doi = {10.1371/journal.pone.0184554},\n author = {Zielinski, Bartosz AND Plichta, Anna AND Misztal, Krzysztof AND Spurek, Przemyslaw AND Brzychczy-Wloch, Monika AND Ochonska, Dorota},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Deep learning approach to bacterial colony classification},\n year = {2017},\n month = {09},\n volume = {12},\n url = {https://doi.org/10.1371/journal.pone.0184554},\n pages = {1-14},\n number = {9}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44237) [[Mini]](https://www.openml.org/d/44281)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 138.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 108.0,\n 'NumberOfClasses': 33.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 4060.0,\n 'NumberOfInstancesWithMissingValues': 4060.0,\n 'NumberOfMissingValues': 4060.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44316,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_BCT_Extended',\n 'source_data': '44316',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361219,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_BCT_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2911,"string":"2,911"}}},{"rowIdx":2777,"cells":{"uid":{"kind":"number","value":361207,"string":"361,207"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_ACT_410_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album MPII Human Pose Dataset Dataset (Extended)**\n***\nThe MPII Human Pose dataset (http://human-pose.mpi-inf.mpg.de/#download) is a state of the art benchmark for evaluation of articulated human pose estimation. It includes around 25 000 images containing over 40 000 people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video. Like other Meta-Album datasets, this dataset is preprocessed and all images are resized into 128x128 pixels. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: HUM_ACT.ACT_410 \n**Meta Album URL**: [https://meta-album.github.io/datasets/ACT_410.html](https://meta-album.github.io/datasets/ACT_410.html) \n**Domain ID**: HUM_ACT \n**Domain Name**: Human Actions \n**Dataset ID**: ACT_410 \n**Dataset Name**: MPII Human Pose Dataset \n**Short Description**: MPII Human Pose Dataset with images of humans performing 410 activities. \n**\\# Classes**: 29 \n**\\# Images**: 2402 \n**Keywords**: human actions, sports \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Simplified BSD License \n**License URL(original data release)**: http://human-pose.mpi-inf.mpg.de/#download\nhttp://human-pose.mpi-inf.mpg.de/bsd.txt\n \n**License (Meta-Album data release)**: Simplified BSD License \n**License URL (Meta-Album data release)**: [http://human-pose.mpi-inf.mpg.de/bsd.txt](http://human-pose.mpi-inf.mpg.de/bsd.txt) \n\n**Source**: MPII Human Pose Dataset \n**Source URL**: http://human-pose.mpi-inf.mpg.de/#download \n \n**Original Author**: Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt \n**Original contact**: leonid@mpi-inf.mpg.de \n\n**Meta Album author**: Jilin He \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@INPROCEEDINGS{6909866, \n author={Andriluka, Mykhaylo and Pishchulin, Leonid and Gehler, Peter and Schiele, Bernt}, booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition}, \n title={2D Human Pose Estimation: New Benchmark and State of the Art Analysis}, \n year={2014},\n pages={3686-3693}, \n doi={10.1109/CVPR.2014.471}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44271) [[Mini]](https://www.openml.org/d/44301)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 176.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 44.0,\n 'NumberOfClasses': 29.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 2402.0,\n 'NumberOfInstancesWithMissingValues': 2402.0,\n 'NumberOfMissingValues': 2402.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44334,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_ACT_410_Extended',\n 'source_data': '44334',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361207,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_ACT_410_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2912,"string":"2,912"}}},{"rowIdx":2778,"cells":{"uid":{"kind":"number","value":362301,"string":"362,301"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"olindda_outliers"},"dataset_description":{"kind":"string","value":"Outliers data set extracted from the Illustration (Fig. 3) in "Novelty detection with application to data\nstreams""},"dataset_features":{"kind":"string","value":"{0: [0 - x_0 (numeric)], 1: [1 - x_1 (numeric)], 2: [2 - label (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 30.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 12.0,\n 'NumberOfClasses': 4.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 75.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 2.0,\n 'NumberOfSymbolicFeatures': 1.0,\n 'cost_matrix': nan,\n 'did': 42793,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'olindda_outliers',\n 'source_data': '42793',\n 'status': 'active',\n 'target_feature': 'label',\n 'task_type': 'Supervised Classification',\n 'tid': 362301,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"olindda_outliers"},"attribute_names":{"kind":"list like","value":["x_0","x_1"],"string":"[\n \"x_0\",\n \"x_1\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2913,"string":"2,913"}}},{"rowIdx":2779,"cells":{"uid":{"kind":"number","value":361228,"string":"361,228"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PNU_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album PanNuke Dataset (Extended)**\n***\nThe PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was designed as a segmentation task, we were able to transform it into a tissue classification task since we had the tissue type for each sample in the dataset. We also resized the images to 128x128 pixels and applied stain normalization to avoid bias and remove some spurious features. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.PNU \n**Meta Album URL**: [https://meta-album.github.io/datasets/PNU.html](https://meta-album.github.io/datasets/PNU.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopic \n**Dataset ID**: PNU \n**Dataset Name**: PanNuke \n**Short Description**: 19 Human Tissues Dataset \n**\\# Classes**: 19 \n**\\# Images**: 5530 \n**Keywords**: microscopic, human tissues \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Attribution-NonCommercial-ShareAlike 4.0 International \n**License URL(original data release)**: https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/\n \n**License (Meta-Album data release)**: Attribution-NonCommercial-ShareAlike 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) \n\n**Source**: PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification \n**Source URL**: https://jgamper.github.io/PanNukeDataset/ \n \n**Original Author**: Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir \n**Original contact**: j.gamper@warwick.ac.uk \n\n**Meta Album author**: Romain Mussard \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{gamper2019pannuke,\n title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},\n author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},\n booktitle={European Congress on Digital Pathology},\n pages={11--19},\n year={2019},\n organization={Springer}\n}\n\n@article{gamper2020pannuke,\n title={PanNuke Dataset Extension, Insights and Baselines},\n author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},\n journal={arXiv preprint arXiv:2003.10778},\n year={2020}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44312) [[Mini]](https://www.openml.org/d/44297)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 967.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 101.0,\n 'NumberOfClasses': 19.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 5530.0,\n 'NumberOfInstancesWithMissingValues': 5530.0,\n 'NumberOfMissingValues': 5530.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44330,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PNU_Extended',\n 'source_data': '44330',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361228,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PNU_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2914,"string":"2,914"}}},{"rowIdx":2780,"cells":{"uid":{"kind":"number","value":361148,"string":"361,148"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_BRD_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Birds Dataset (Micro)**\n***\nWhen Meta-Album was created, the Birds dataset(https://www.kaggle.com/datasets/gpiosenka/100-bird-species) contained images of 315 bird species, but now it has increased the number of species to 450. It has more than 49 000 images, each with a resolution of 224x224 px. All the images have their natural background, which can lead to bias since, for example, some birds are frequently found in water backgrounds. Additionally, the dataset is imbalanced regarding the ratio of male species images to female species images. The preprocessed version distributed in Meta-Album is made from the original dataset by resizing all the images to a resolution of 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: LR_AM.BRD \n**Meta Album URL**: [https://meta-album.github.io/datasets/BRD.html](https://meta-album.github.io/datasets/BRD.html) \n**Domain ID**: LR_AM \n**Domain Name**: Large Animals \n**Dataset ID**: BRD \n**Dataset Name**: Birds \n**Short Description**: Birds dataset for image classification \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: birds, animals \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC0 Public Domain \n**License URL(original data release)**: https://www.kaggle.com/gpiosenka/100-bird-species\nhttps://creativecommons.org/publicdomain/zero/1.0/\n \n**License (Meta-Album data release)**: CC0 Public Domain \n**License URL (Meta-Album data release)**: [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/) \n\n**Source**: BIRDS 400 - SPECIES IMAGE CLASSIFICATION \n**Source URL**: https://www.kaggle.com/gpiosenka/100-bird-species \n \n**Original Author**: Gerald Piosenka \n**Original contact**: https://www.kaggle.com/gpiosenka/contact \n\n**Meta Album author**: Dustin Carrion \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{birds,\n title={BIRDS 400 - SPECIES IMAGE CLASSIFICATION},\n author={Gerald Piosenka},\n url={https://www.kaggle.com/datasets/gpiosenka/100-bird-species},\n publisher= {Kaggle}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44285) [[Extended]](https://www.openml.org/d/44320)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44241,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_BRD_Micro',\n 'source_data': '44241',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361148,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_BRD_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2915,"string":"2,915"}}},{"rowIdx":2781,"cells":{"uid":{"kind":"number","value":361230,"string":"361,230"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_APL_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album Airplanes Dataset (Extended)**\n***\nThe original Airplanes dataset (https://zenodo.org/record/3464319) comprises more than 9 000 remote sensing images acquired from Google Earth satellite imagery, including 21 different types of aircraft from around 36 airports. All the images were carefully labeled by seven specialists in the field of remote sensing image interpretation. Each class can have 230 to 846 images, where each image contains only one complete aircraft, and they have variable resolutions. To preprocess this dataset, we cropped the images from either side to make them square, and then we resized them into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.APL \n**Meta Album URL**: [https://meta-album.github.io/datasets/APL.html](https://meta-album.github.io/datasets/APL.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: APL \n**Dataset Name**: Airplanes \n**Short Description**: Airplanes dataset with different aiplane models \n**\\# Classes**: 21 \n**\\# Images**: 9625 \n**Keywords**: vehicles, airplanes \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Creative Commons Attribution 4.0 International \n**License URL(original data release)**: https://zenodo.org/record/3464319\nhttps://creativecommons.org/licenses/by/4.0/legalcode\n \n**License (Meta-Album data release)**: Creative Commons Attribution 4.0 International \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) \n\n**Source**: Muti-type Aircraft of Remote Sensing Images: MTARSI \n**Source URL**: https://zenodo.org/record/3464319 \n \n**Original Author**: Wu, Zhize \n**Original contact**: \n\n**Meta Album author**: Philip Boser \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@dataset{wu_zhize_2019_3464319,\n author = {Wu, Zhize},\n title = {{Muti-type Aircraft of Remote Sensing Images: MTARSI}},\n month = may,\n year = 2019,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.3464319},\n url = {https://doi.org/10.5281/zenodo.3464319}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44251) [[Mini]](https://www.openml.org/d/44295)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 846.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 230.0,\n 'NumberOfClasses': 21.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 9625.0,\n 'NumberOfInstancesWithMissingValues': 9625.0,\n 'NumberOfMissingValues': 9625.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44329,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_APL_Extended',\n 'source_data': '44329',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361230,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_APL_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2916,"string":"2,916"}}},{"rowIdx":2782,"cells":{"uid":{"kind":"number","value":361164,"string":"361,164"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_TEX_ALOT_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Textures-ALOT Dataset (Micro)**\n***\nTextures ALOT dataset (https://aloi.science.uva.nl/public_alot/) consists of 27 500 images from 250 categories. The images in the dataset are captured in controlled environment by the creators of the dataset. The images have different viewing angle, illumination angle, and illumination color for each material of texture. A preprocessed version of Textures-ALOT is used in the Meta-Album meta-dataset. The images are first cropped into square images and then resized to 128x128 with anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX_ALOT \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX_ALOT.html](https://meta-album.github.io/datasets/TEX_ALOT.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX_ALOT \n**Dataset Name**: Textures-ALOT \n**Short Description**: Textures dataset from Amsterdam Library of Textures (ALOT) \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research, cite paper to use dataset \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Amsterdam Library of Textures (ALOT), University of Amsterdam, Netherlands \n**Source URL**: https://aloi.science.uva.nl/public_alot/ \n \n**Original Author**: Gertjan Burghouts, Jan-Mark Geusebroek \n**Original contact**: geusebroek@uva.nl \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{BURGHOUTS2009306,\n title = {Material-specific adaptation of color invariant features},\n journal = {Pattern Recognition Letters},\n volume = {30},\n number = {3},\n pages = {306-313},\n year = {2009},\n issn = {0167-8655},\n doi = {https://doi.org/10.1016/j.patrec.2008.10.005},\n url = {https://www.sciencedirect.com/science/article/pii/S0167865508003073},\n author = {Gertjan J. Burghouts and Jan-Mark Geusebroek},\n keywords = {Image modeling, Color, Codebook representation, Texture, Textons},\n abstract = {For the modeling of materials, the mapping of image features onto a codebook of feature representatives receives extensive treatment. For reason of their generality and simplicity, filterbank outputs are commonly used as features. The MR8 filterbank of Varma and Zisserman is performing well in a recent evaluation. In this paper, we construct color invariant filter sets from the original MR8 filterbank. We evaluate several color invariant alternatives over more than 250 real-world materials recorded under a variety of imaging conditions including clutter. Our contribution is a material recognition framework that learns automatically for each material specifically the most discriminative filterbank combination and corresponding degree of color invariance. For a large set of materials each with different physical properties, we demonstrate the material-specific filterbank models to be preferred over models with fixed filterbanks.}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44304) [[Extended]](https://www.openml.org/d/44337)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44274,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_TEX_ALOT_Micro',\n 'source_data': '44274',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361164,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_TEX_ALOT_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2917,"string":"2,917"}}},{"rowIdx":2783,"cells":{"uid":{"kind":"number","value":361126,"string":"361,126"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"KDDCup09_upselling"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, \n transformed in the same way. This dataset belongs to the \"classification on categorical and\n numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nDatasets from ACM KDD Cup (http://www.sigkdd.org/kddcup/index.php)\n\nKDD Cup 2009\nhttp://www.kddcup-orange.com\n\nConverted to ARFF format by TunedIT\nCustomer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).\nThe most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is an evaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.\nUp-selling (wikipedia definition): Up-selling is a sales technique whereby a salesman attempts to have the customer purchase more expensive\nitems, upgrades, or other add-ons in an attempt to make a more profitable sale.\nUp-selling usually involves marketing more profitable services or products, but up-selling can also be simply exposing the customer\nto other options he or she may not have considered previously.\nUp-selling can imply selling something additional, or selling something that is more profitable or otherwise preferable for the seller instead of the original sale.\nThe training set contains 50,000 examples.\nThe first predictive 190 variables are numerical and the last 40 predictive variables are categorical.\nThe last target variable is binary {-1,1}."},"dataset_features":{"kind":"string","value":"{0: [0 - Var6 (numeric)],\n 1: [1 - Var13 (numeric)],\n 2: [2 - Var21 (numeric)],\n 3: [3 - Var22 (numeric)],\n 4: [4 - Var24 (numeric)],\n 5: [5 - Var25 (numeric)],\n 6: [6 - Var28 (numeric)],\n 7: [7 - Var35 (numeric)],\n 8: [8 - Var38 (numeric)],\n 9: [9 - Var57 (numeric)],\n 10: [10 - Var65 (numeric)],\n 11: [11 - Var73 (numeric)],\n 12: [12 - Var74 (numeric)],\n 13: [13 - Var76 (numeric)],\n 14: [14 - Var78 (numeric)],\n 15: [15 - Var81 (numeric)],\n 16: [16 - Var83 (numeric)],\n 17: [17 - Var85 (numeric)],\n 18: [18 - Var109 (numeric)],\n 19: [19 - Var112 (numeric)],\n 20: [20 - Var113 (numeric)],\n 21: [21 - Var119 (numeric)],\n 22: [22 - Var123 (numeric)],\n 23: [23 - Var125 (numeric)],\n 24: [24 - Var126 (numeric)],\n 25: [25 - Var132 (numeric)],\n 26: [26 - Var133 (numeric)],\n 27: [27 - Var134 (numeric)],\n 28: [28 - Var140 (numeric)],\n 29: [29 - Var144 (numeric)],\n 30: [30 - Var149 (numeric)],\n 31: [31 - Var153 (numeric)],\n 32: [32 - Var160 (numeric)],\n 33: [33 - Var163 (numeric)],\n 34: [34 - Var196 (nominal)],\n 35: [35 - Var203 (nominal)],\n 36: [36 - Var205 (nominal)],\n 37: [37 - Var207 (nominal)],\n 38: [38 - Var208 (nominal)],\n 39: [39 - Var210 (nominal)],\n 40: [40 - Var211 (nominal)],\n 41: [41 - Var218 (nominal)],\n 42: [42 - Var221 (nominal)],\n 43: [43 - Var223 (nominal)],\n 44: [44 - Var227 (nominal)],\n 45: [45 - UPSELLING (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 2516.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 2516.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 46.0,\n 'NumberOfInstances': 5032.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 34.0,\n 'NumberOfSymbolicFeatures': 12.0,\n 'cost_matrix': nan,\n 'did': 44185,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'KDDCup09_upselling',\n 'source_data': '44185',\n 'status': 'active',\n 'target_feature': 'UPSELLING',\n 'task_type': 'Supervised Classification',\n 'tid': 361126,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"KDDCup09_upselling"},"attribute_names":{"kind":"list like","value":["Var6","Var13","Var21","Var22","Var24","Var25","Var28","Var35","Var38","Var57","Var65","Var73","Var74","Var76","Var78","Var81","Var83","Var85","Var109","Var112","Var113","Var119","Var123","Var125","Var126","Var132","Var133","Var134","Var140","Var144","Var149","Var153","Var160","Var163","Var196","Var203","Var205","Var207","Var208","Var210","Var211","Var218","Var221","Var223","Var227"],"string":"[\n \"Var6\",\n \"Var13\",\n \"Var21\",\n \"Var22\",\n \"Var24\",\n \"Var25\",\n \"Var28\",\n \"Var35\",\n \"Var38\",\n \"Var57\",\n \"Var65\",\n \"Var73\",\n \"Var74\",\n \"Var76\",\n \"Var78\",\n \"Var81\",\n \"Var83\",\n \"Var85\",\n \"Var109\",\n \"Var112\",\n \"Var113\",\n \"Var119\",\n \"Var123\",\n \"Var125\",\n \"Var126\",\n \"Var132\",\n \"Var133\",\n \"Var134\",\n \"Var140\",\n \"Var144\",\n \"Var149\",\n \"Var153\",\n \"Var160\",\n \"Var163\",\n \"Var196\",\n \"Var203\",\n \"Var205\",\n \"Var207\",\n \"Var208\",\n \"Var210\",\n \"Var211\",\n \"Var218\",\n \"Var221\",\n \"Var223\",\n \"Var227\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,true,true,true,true,true,true,true,true,true,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2918,"string":"2,918"}}},{"rowIdx":2784,"cells":{"uid":{"kind":"number","value":361170,"string":"361,170"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_RSD_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album RSD Dataset (Micro)**\n***\nRSD46 dataset (https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU) is collected from Google Earth and Tianditu. The collection contains 46 scene categories, with a total of 117 000 images. Each scene category has between 500 - 3000 images. The original resolution are 256x256 px or 512x512 px. We have created preprocessed version of RSD for Meta-Album by resizing the original dataset to 128x128 px. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: REM_SEN.RSD \n**Meta Album URL**: [https://meta-album.github.io/datasets/RSD.html](https://meta-album.github.io/datasets/RSD.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RSD \n**Dataset Name**: RSD \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research and non-profit purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: RSD46-WHU \n**Source URL**: https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU \n \n**Original Author**: Yang Long, Yiping Gong, Zhifeng Xiao, and Qing Liu, Deren Li, Chunshan Wei, Gefu Tang and Junyi Liu \n**Original contact**: longyang@whu.edu.cn \n\n**Meta Album author**: Phan Anh VU \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@ARTICLE{7827088,\n author={Long, Yang and Gong, Yiping and Xiao, Zhifeng and Liu, Qing},\n journal={IEEE Transactions on Geoscience and Remote Sensing}, \n title={Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks}, \n year={2017},\n volume={55},\n number={5},\n pages={2486-2498},\n doi={10.1109/TGRS.2016.2645610}}\n\n@article{xiao2017high,\n title={High-resolution remote sensing image retrieval based on CNNs from a dimensional perspective},\n author={Xiao, Zhifeng and Long, Yang and Li, Deren and Wei, Chunshan and Tang, Gefu and Liu, Junyi},\n journal={Remote Sensing},\n volume={9},\n number={7},\n pages={725},\n year={2017},\n publisher={Multidisciplinary Digital Publishing Institute}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44307) [[Extended]](https://www.openml.org/d/44341)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44277,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_RSD_Micro',\n 'source_data': '44277',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361170,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_RSD_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2919,"string":"2,919"}}},{"rowIdx":2785,"cells":{"uid":{"kind":"number","value":361225,"string":"361,225"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_TEX_DTD_Extended"},"dataset_description":{"kind":"string","value":"## **Meta-Album Textures-DTD Dataset (Extended)**\n***\nThe Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data was annotated using Amazon Mechanical Turk. The data collection process is mentioned on the dataset overview page. For Meta-Album meta-dataset, this dataset is preprocessed by cropping the images to square images and then resizing them to 128x128 using Open-CV with an anti-aliasing filter. This dataset has 47 class labels. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX_DTD \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX_DTD.html](https://meta-album.github.io/datasets/TEX_DTD.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX_DTD \n**Dataset Name**: Textures-DTD \n**Short Description**: Textures dataset from Describable Textures Dataset \n**\\# Classes**: 47 \n**\\# Images**: 5640 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: Open for research purposes \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: Describable Textures Dataset (DTD), University of Oxford, England \n**Source URL**: https://www.robots.ox.ac.uk/~vgg/data/dtd/ \n \n**Original Author**: Mircea Cimpoi, Subhransu354Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi \n**Original contact**: {mircea, vedaldi}@robots.ox.ac.uk \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@InProceedings{cimpoi14describing,\n\tAuthor = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi},\n\tTitle = {Describing Textures in the Wild},\n\tBooktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},\n\tYear = {2014}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44250) [[Mini]](https://www.openml.org/d/44294)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 120.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 120.0,\n 'NumberOfClasses': 47.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 5640.0,\n 'NumberOfInstancesWithMissingValues': 5640.0,\n 'NumberOfMissingValues': 5640.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44328,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_TEX_DTD_Extended',\n 'source_data': '44328',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361225,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_TEX_DTD_Extended"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2920,"string":"2,920"}}},{"rowIdx":2786,"cells":{"uid":{"kind":"number","value":361305,"string":"361,305"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"credit-approval"},"dataset_description":{"kind":"string","value":"This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect the confidentiality of the data.This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values."},"dataset_features":{"kind":"string","value":"{0: [0 - A2 (numeric)],\n 1: [1 - A3 (numeric)],\n 2: [2 - A8 (numeric)],\n 3: [3 - A11 (numeric)],\n 4: [4 - A14 (numeric)],\n 5: [5 - A15 (numeric)],\n 6: [6 - A1 (nominal)],\n 7: [7 - A4 (nominal)],\n 8: [8 - A5 (nominal)],\n 9: [9 - A6 (nominal)],\n 10: [10 - A7 (nominal)],\n 11: [11 - A9 (nominal)],\n 12: [12 - A10 (nominal)],\n 13: [13 - A12 (nominal)],\n 14: [14 - A13 (nominal)],\n 15: [15 - class (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 357.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 296.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 16.0,\n 'NumberOfInstances': 653.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 6.0,\n 'NumberOfSymbolicFeatures': 9.0,\n 'cost_matrix': nan,\n 'did': 45063,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'credit-approval',\n 'source_data': '45063',\n 'status': 'active',\n 'target_feature': 'class',\n 'task_type': 'Supervised Classification',\n 'tid': 361305,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"credit-approval"},"attribute_names":{"kind":"list like","value":["A2","A3","A8","A11","A14","A15","A1","A4","A5","A6","A7","A9","A10","A12","A13"],"string":"[\n \"A2\",\n \"A3\",\n \"A8\",\n \"A11\",\n \"A14\",\n \"A15\",\n \"A1\",\n \"A4\",\n \"A5\",\n \"A6\",\n \"A7\",\n \"A9\",\n \"A10\",\n \"A12\",\n \"A13\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,true,true,true,true,true,true,true,true,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2921,"string":"2,921"}}},{"rowIdx":2787,"cells":{"uid":{"kind":"number","value":361300,"string":"361,300"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"credit-g"},"dataset_description":{"kind":"string","value":"This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix:Good Bad (predicted) Good 0 1 (actual)Bad 5 0 It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1)."},"dataset_features":{"kind":"string","value":"{0: [0 - duration (numeric)],\n 1: [1 - credit_amount (numeric)],\n 2: [2 - installment_commitment (numeric)],\n 3: [3 - residence_since (numeric)],\n 4: [4 - age (numeric)],\n 5: [5 - existing_credits (numeric)],\n 6: [6 - num_dependents (numeric)],\n 7: [7 - checking_status (nominal)],\n 8: [8 - credit_history (nominal)],\n 9: [9 - purpose (nominal)],\n 10: [10 - savings_status (nominal)],\n 11: [11 - employment (nominal)],\n 12: [12 - personal_status (nominal)],\n 13: [13 - other_parties (nominal)],\n 14: [14 - property_magnitude (nominal)],\n 15: [15 - other_payment_plans (nominal)],\n 16: [16 - housing (nominal)],\n 17: [17 - job (nominal)],\n 18: [18 - own_telephone (nominal)],\n 19: [19 - foreign_worker (nominal)],\n 20: [20 - class (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 700.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 300.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 21.0,\n 'NumberOfInstances': 1000.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 7.0,\n 'NumberOfSymbolicFeatures': 13.0,\n 'cost_matrix': nan,\n 'did': 45058,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'credit-g',\n 'source_data': '45058',\n 'status': 'active',\n 'target_feature': 'class',\n 'task_type': 'Supervised Classification',\n 'tid': 361300,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"credit-g"},"attribute_names":{"kind":"list like","value":["duration","credit_amount","installment_commitment","residence_since","age","existing_credits","num_dependents","checking_status","credit_history","purpose","savings_status","employment","personal_status","other_parties","property_magnitude","other_payment_plans","housing","job","own_telephone","foreign_worker"],"string":"[\n \"duration\",\n \"credit_amount\",\n \"installment_commitment\",\n \"residence_since\",\n \"age\",\n \"existing_credits\",\n \"num_dependents\",\n \"checking_status\",\n \"credit_history\",\n \"purpose\",\n \"savings_status\",\n \"employment\",\n \"personal_status\",\n \"other_parties\",\n \"property_magnitude\",\n \"other_payment_plans\",\n \"housing\",\n \"job\",\n \"own_telephone\",\n \"foreign_worker\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,true,true,true,true,true,true,true,true,true,true,true,true,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2922,"string":"2,922"}}},{"rowIdx":2788,"cells":{"uid":{"kind":"number","value":361112,"string":"361,112"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"KDDCup09_upselling"},"dataset_description":{"kind":"string","value":"Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, \n transformed in the same way. This dataset belongs to the \"classification on categorical and\n numerical features\" benchmark. Original description: \n \n**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nDatasets from ACM KDD Cup (http://www.sigkdd.org/kddcup/index.php)\n\nKDD Cup 2009\nhttp://www.kddcup-orange.com\n\nConverted to ARFF format by TunedIT\nCustomer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).\nThe most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is an evaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.\nUp-selling (wikipedia definition): Up-selling is a sales technique whereby a salesman attempts to have the customer purchase more expensive\nitems, upgrades, or other add-ons in an attempt to make a more profitable sale.\nUp-selling usually involves marketing more profitable services or products, but up-selling can also be simply exposing the customer\nto other options he or she may not have considered previously.\nUp-selling can imply selling something additional, or selling something that is more profitable or otherwise preferable for the seller instead of the original sale.\nThe training set contains 50,000 examples.\nThe first predictive 190 variables are numerical and the last 40 predictive variables are categorical.\nThe last target variable is binary {-1,1}."},"dataset_features":{"kind":"string","value":"{0: [0 - Var6 (numeric)],\n 1: [1 - Var13 (numeric)],\n 2: [2 - Var21 (numeric)],\n 3: [3 - Var22 (numeric)],\n 4: [4 - Var24 (numeric)],\n 5: [5 - Var25 (numeric)],\n 6: [6 - Var28 (numeric)],\n 7: [7 - Var35 (numeric)],\n 8: [8 - Var38 (numeric)],\n 9: [9 - Var57 (numeric)],\n 10: [10 - Var65 (numeric)],\n 11: [11 - Var73 (numeric)],\n 12: [12 - Var74 (numeric)],\n 13: [13 - Var76 (numeric)],\n 14: [14 - Var78 (numeric)],\n 15: [15 - Var81 (numeric)],\n 16: [16 - Var83 (numeric)],\n 17: [17 - Var85 (numeric)],\n 18: [18 - Var109 (numeric)],\n 19: [19 - Var112 (numeric)],\n 20: [20 - Var113 (numeric)],\n 21: [21 - Var119 (numeric)],\n 22: [22 - Var123 (numeric)],\n 23: [23 - Var125 (numeric)],\n 24: [24 - Var126 (numeric)],\n 25: [25 - Var132 (numeric)],\n 26: [26 - Var133 (numeric)],\n 27: [27 - Var134 (numeric)],\n 28: [28 - Var140 (numeric)],\n 29: [29 - Var144 (numeric)],\n 30: [30 - Var149 (numeric)],\n 31: [31 - Var153 (numeric)],\n 32: [32 - Var160 (numeric)],\n 33: [33 - Var163 (numeric)],\n 34: [34 - Var196 (nominal)],\n 35: [35 - Var203 (nominal)],\n 36: [36 - Var205 (nominal)],\n 37: [37 - Var207 (nominal)],\n 38: [38 - Var208 (nominal)],\n 39: [39 - Var210 (nominal)],\n 40: [40 - Var211 (nominal)],\n 41: [41 - Var218 (nominal)],\n 42: [42 - Var221 (nominal)],\n 43: [43 - Var223 (nominal)],\n 44: [44 - Var227 (nominal)],\n 45: [45 - UPSELLING (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 2516.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 2516.0,\n 'NumberOfClasses': 2.0,\n 'NumberOfFeatures': 46.0,\n 'NumberOfInstances': 5032.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 34.0,\n 'NumberOfSymbolicFeatures': 12.0,\n 'cost_matrix': nan,\n 'did': 44158,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'KDDCup09_upselling',\n 'source_data': '44158',\n 'status': 'active',\n 'target_feature': 'UPSELLING',\n 'task_type': 'Supervised Classification',\n 'tid': 361112,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"KDDCup09_upselling"},"attribute_names":{"kind":"list like","value":["Var6","Var13","Var21","Var22","Var24","Var25","Var28","Var35","Var38","Var57","Var65","Var73","Var74","Var76","Var78","Var81","Var83","Var85","Var109","Var112","Var113","Var119","Var123","Var125","Var126","Var132","Var133","Var134","Var140","Var144","Var149","Var153","Var160","Var163","Var196","Var203","Var205","Var207","Var208","Var210","Var211","Var218","Var221","Var223","Var227"],"string":"[\n \"Var6\",\n \"Var13\",\n \"Var21\",\n \"Var22\",\n \"Var24\",\n \"Var25\",\n \"Var28\",\n \"Var35\",\n \"Var38\",\n \"Var57\",\n \"Var65\",\n \"Var73\",\n \"Var74\",\n \"Var76\",\n \"Var78\",\n \"Var81\",\n \"Var83\",\n \"Var85\",\n \"Var109\",\n \"Var112\",\n \"Var113\",\n \"Var119\",\n \"Var123\",\n \"Var125\",\n \"Var126\",\n \"Var132\",\n \"Var133\",\n \"Var134\",\n \"Var140\",\n \"Var144\",\n \"Var149\",\n \"Var153\",\n \"Var160\",\n \"Var163\",\n \"Var196\",\n \"Var203\",\n \"Var205\",\n \"Var207\",\n \"Var208\",\n \"Var210\",\n \"Var211\",\n \"Var218\",\n \"Var221\",\n \"Var223\",\n \"Var227\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,true,true,true,true,true,true,true,true,true,true],"string":"[\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2923,"string":"2,923"}}},{"rowIdx":2789,"cells":{"uid":{"kind":"number","value":361195,"string":"361,195"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_TEX_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Textures Dataset (Mini)**\n***\nThe original Textures dataset is a combination of 4 texture datasets: KTH-TIPS and KTH-TIPS 2 (https://www.csc.kth.se/cvap/databases/kth-tips/index.html), Kylberg Textures Dataset (http://www.cb.uu.se/~gustaf/texture/) and UIUC Textures Dataset. The data in all four datasets is collected in laboratory conditions, i.e., images were captured in a controlled environment with configurable brightness, luminosity, scale and angle. The KTH-TIPS dataset was collected by Mario Fritz and KTH-TIPS 2 dataset was collected by P. Mallikarjuna and Alireza Tavakoli Targhi, created in 2004 and 2006 respectively. Both of these datasets were prepared under the supervision of Eric Hayman and Barbara Caputo. The data for Kylberg Textures Dataset and UIUC Textures Dataset data was collected by the original authors of these datasets in September 2010 and August 2005 respectively. The Meta-Album Textures dataset is a preprocessed version of the original dataset (combination of 4 datasets). All the images are preprocessed by first cropping into perfect squared images and then resized into 128x128 with an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX.html](https://meta-album.github.io/datasets/TEX.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX \n**Dataset Name**: Textures \n**Short Description**: Textures dataset from KTH-TIPS, Kylberg and UIUC \n**\\# Classes**: 64 \n**\\# Images**: 2560 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY NC 4.0, public for research, cite paper to use dataset \n**License URL(original data release)**: https://creativecommons.org/licenses/by-nc/4.0/\nhttps://kylberg.org/wp-content/uploads/2020/05/KylbergTextureDocumentation-1.0.pdf\n \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: KTH-TIPS, Kylberg, UIUC \n**Source URL**: https://www.csc.kth.se/cvap/databases/kth-tips/index.html \nhttps://www.cb.uu.se/~gustaf/texture/ \n \n**Original Author**: Eric Hayman, Barbara Caputo, Mario Fritz, P. Mallikarjuna, Alireza Tavakoli Targhi, Jean Ponce, Gustaf Kylberg, Svetlana Lazebnik, Cordelia Schmid \n**Original contact**: KTH-TIPS: hayman@nada.kth.se, Kylberg: gustaf.kylberg@gmail.com, UIUC: Jean.Ponce@inria.fr \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{Fritz2004THEKD,\n title={THE KTH-TIPS database},\n author={Mario Fritz and E. Hayman and B. Caputo and J. Eklundh},\n year={2004},\n url = {https://www.csc.kth.se/cvap/databases/kth-tips/index.html}\n}\n\n@inproceedings{Mallikarjuna2006THEK2,\n title={THE KTH-TIPS 2 database},\n author={P. Mallikarjuna and Alireza Tavakoli Targhi and Mario Fritz and E. Hayman and B. Caputo and J. Eklundh},\n year={2006},\n url = {https://www.csc.kth.se/cvap/databases/kth-tips/index.html}\n}\n\n@TECHREPORT{Kylberg2011c,\n author = {Gustaf Kylberg},\n title = {The Kylberg Texture Dataset v. 1.0},\n institution = {Centre for Image Analysis, Swedish University of Agricultural Sciences\n\tand Uppsala University, Uppsala, Sweden},\n year = {2011},\n type = {External report (Blue series)},\n number = {35},\n month = {September},\n url = {http://www.cb.uu.se/~gustaf/texture/}\n}\n\n@article{lazebnik:inria-00548530,\n TITLE = {{A sparse texture representation using local affine regions}},\n AUTHOR = {Lazebnik, Svetlana and Schmid, Cordelia and Ponce, Jean},\n URL = {https://hal.inria.fr/inria-00548530},\n JOURNAL = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},\n PUBLISHER = {{Institute of Electrical and Electronics Engineers}},\n VOLUME = {27},\n NUMBER = {8},\n PAGES = {1265--1278},\n YEAR = {2005},\n MONTH = Aug,\n DOI = {10.1109/TPAMI.2005.151},\n KEYWORDS = {Index Terms- Image processing and computer vision ; feature measurement ; pattern recognition ; texture},\n PDF = {https://hal.inria.fr/inria-00548530/file/lana_pami_final.pdf},\n HAL_ID = {inria-00548530},\n HAL_VERSION = {v1},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44244) [[Extended]](https://www.openml.org/d/44322)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 64.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 2560.0,\n 'NumberOfInstancesWithMissingValues': 2560.0,\n 'NumberOfMissingValues': 2560.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44288,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_TEX_Mini',\n 'source_data': '44288',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361195,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_TEX_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2924,"string":"2,924"}}},{"rowIdx":2790,"cells":{"uid":{"kind":"number","value":361167,"string":"361,167"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_PRT_Micro"},"dataset_description":{"kind":"string","value":"## **Meta-Album Subcellular Human Protein Dataset (Micro)**\n***\nThis dataset is a subset of the Subcellular dataset in the Protein Atlas project(https://www.proteinatlas.org/). The original dataset, which stems from the Human Protein Atlas Image Classification Kaggle competition(https://www.kaggle.com/competitions/human-protein-atlas-image-classification), comprises 31 072 RGBY images of size 512x512 px, each of which belongs to one or more out of 28 classes. The labels correspond to protein organelle localizations. For Meta-Album, we performed two modifications: (1), to turn the dataset into a multi-class dataset, we dropped all images belonging to more than a single class and also those images that belong to classes with less than 40 members; (2) we converted the remaining images into RGB simply by dropping the yellow channel; this was also a common practice in the competition. Finally, and as for all datasets in Meta-Album, the images from the original dataset were resized to 128x128 image size. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MCR.PRT \n**Meta Album URL**: [https://meta-album.github.io/datasets/PRT.html](https://meta-album.github.io/datasets/PRT.html) \n**Domain ID**: MCR \n**Domain Name**: Microscopy \n**Dataset ID**: PRT \n**Dataset Name**: Subcellular Human Protein \n**Short Description**: Subcellular protein patterns in human cells \n**\\# Classes**: 20 \n**\\# Images**: 800 \n**Keywords**: human protein, subcellular \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY-SA 3.0 \n**License URL(original data release)**: https://www.proteinatlas.org/about/licence\n \n**License (Meta-Album data release)**: CC BY-SA 3.0 \n**License URL (Meta-Album data release)**: [https://www.proteinatlas.org/about/licence](https://www.proteinatlas.org/about/licence) \n\n**Source**: The Human Protein Atlas \n**Source URL**: https://proteinatlas.org \nhttps://www.kaggle.com/c/human-protein-atlas-image-classification \n \n**Original Author**: Peter J Thul, Lovisa Akesson, Mikaela Wiking, Diana Mahdessian, Aikaterini Geladaki, Hammou Ait Blal, Tove Alm, Anna Asplund, Lars Bjork, Lisa Breckels, and others \n**Original contact**: contact@proteinatlas.org \n\n**Meta Album author**: Felix Mohr \n**Created Date**: 01 June 2022 \n**Contact Name**: Felix Mohr \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{thul2017subcellular, \n title={A subcellular map of the human proteome},\n author={Thul, Peter J and Akesson, Lovisa and Wiking, Mikaela and Mahdessian, Diana and Geladaki, Aikaterini and Ait Blal, Hammou and Alm, Tove and Asplund, Anna and Bjork, Lars and Breckels, Lisa M},\n journal={Science},\n volume={356},\n number={6340},\n year={2017},\n publisher={American Association for the Advancement of Science}\n}\n\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https://www.openml.org/d/44308) [[Extended]](https://www.openml.org/d/44342)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 20.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 800.0,\n 'NumberOfInstancesWithMissingValues': 800.0,\n 'NumberOfMissingValues': 800.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44278,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_PRT_Micro',\n 'source_data': '44278',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361167,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_PRT_Micro"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2925,"string":"2,925"}}},{"rowIdx":2791,"cells":{"uid":{"kind":"number","value":362310,"string":"362,310"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc"},"dataset_description":{"kind":"string","value":"Arbres urbains"},"dataset_features":{"kind":"string","value":"{0: [0 - id_arbre (nominal)],\n 1: [1 - commune (nominal)],\n 2: [2 - quartier (nominal)],\n 3: [3 - site (nominal)],\n 4: [4 - cote_voirie (numeric)],\n 5: [5 - matricule_arbre (numeric)],\n 6: [6 - genre_arbre (nominal)],\n 7: [7 - espece_arbre (nominal)],\n 8: [8 - controle (numeric)],\n 9: [9 - situation (nominal)],\n 10: [10 - type_sol (nominal)],\n 11: [11 - surf_permeable (numeric)],\n 12: [12 - date_plantation (numeric)],\n 13: [13 - classe_age (nominal)],\n 14: [14 - hauteur (numeric)],\n 15: [15 - classe_hauteur (nominal)],\n 16: [16 - diametre (numeric)],\n 17: [17 - circonference-en-cm (numeric)],\n 18: [18 - classe_circonference (nominal)],\n 19: [19 - port_arbre (nominal)],\n 20: [20 - vigueur_pousse (nominal)],\n 21: [21 - champignon_collet (nominal)],\n 22: [22 - insecte_collet (nominal)],\n 23: [23 - plaie_collet (nominal)],\n 24: [24 - observation_collet (numeric)],\n 25: [25 - champignon_tronc (nominal)],\n 26: [26 - insecte_tronc (nominal)],\n 27: [27 - fissure_tronc (nominal)],\n 28: [28 - rejet_tronc (nominal)],\n 29: [29 - tuteurage_arbre (nominal)],\n 30: [30 - canisse_arbre (nominal)],\n 31: [31 - plaie_tronc (nominal)],\n 32: [32 - observation_tronc (numeric)],\n 33: [33 - champignon_houppier (nominal)],\n 34: [34 - insecte_houppier (nominal)],\n 35: [35 - fissure_houppier (nominal)],\n 36: [36 - ecorce_incluse_houppier (nominal)],\n 37: [37 - bois_mort_houppier (nominal)],\n 38: [38 - plaie_houppier (nominal)],\n 39: [39 - observation_houppier (numeric)],\n 40: [40 - esperance_maintien (numeric)],\n 41: [41 - contrainte (nominal)],\n 42: [42 - classification_diagnostic (nominal)],\n 43: [43 - date_diagnostic (nominal)],\n 44: [44 - prescription_1 (nominal)],\n 45: [45 - prescription_2 (nominal)],\n 46: [46 - prescription_3 (numeric)],\n 47: [47 - observation_travaux (numeric)],\n 48: [48 - type_delai_1 (numeric)],\n 49: [49 - delai_annee_programmation (numeric)],\n 50: [50 - type_delai_2 (nominal)],\n 51: [51 - delai_preconisation_2 (numeric)],\n 52: [52 - delai_saison_programmation_2 (nominal)],\n 53: [53 - delai_annee_programmation_2 (numeric)],\n 54: [54 - reference_photo (numeric)],\n 55: [55 - long (numeric)],\n 56: [56 - lat (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 2.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 2.0,\n 'NumberOfClasses': 1.0,\n 'NumberOfFeatures': 57.0,\n 'NumberOfInstances': 2.0,\n 'NumberOfInstancesWithMissingValues': 2.0,\n 'NumberOfMissingValues': 22.0,\n 'NumberOfNumericFeatures': 21.0,\n 'NumberOfSymbolicFeatures': 36.0,\n 'cost_matrix': nan,\n 'did': 43025,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc',\n 'source_data': '43025',\n 'status': 'active',\n 'target_feature': 'classification_diagnostic',\n 'task_type': 'Supervised Classification',\n 'tid': 362310,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc"},"attribute_names":{"kind":"list like","value":["id_arbre","commune","quartier","site","cote_voirie","matricule_arbre","genre_arbre","espece_arbre","controle","situation","type_sol","surf_permeable","date_plantation","classe_age","hauteur","classe_hauteur","diametre","circonference-en-cm","classe_circonference","port_arbre","vigueur_pousse","champignon_collet","insecte_collet","plaie_collet","observation_collet","champignon_tronc","insecte_tronc","fissure_tronc","rejet_tronc","tuteurage_arbre","canisse_arbre","plaie_tronc","observation_tronc","champignon_houppier","insecte_houppier","fissure_houppier","ecorce_incluse_houppier","bois_mort_houppier","plaie_houppier","observation_houppier","esperance_maintien","contrainte","date_diagnostic","prescription_1","prescription_2","prescription_3","observation_travaux","type_delai_1","delai_annee_programmation","type_delai_2","delai_preconisation_2","delai_saison_programmation_2","delai_annee_programmation_2","reference_photo","long","lat"],"string":"[\n \"id_arbre\",\n \"commune\",\n \"quartier\",\n \"site\",\n \"cote_voirie\",\n \"matricule_arbre\",\n \"genre_arbre\",\n \"espece_arbre\",\n \"controle\",\n \"situation\",\n \"type_sol\",\n \"surf_permeable\",\n \"date_plantation\",\n \"classe_age\",\n \"hauteur\",\n \"classe_hauteur\",\n \"diametre\",\n \"circonference-en-cm\",\n \"classe_circonference\",\n \"port_arbre\",\n \"vigueur_pousse\",\n \"champignon_collet\",\n \"insecte_collet\",\n \"plaie_collet\",\n \"observation_collet\",\n \"champignon_tronc\",\n \"insecte_tronc\",\n \"fissure_tronc\",\n \"rejet_tronc\",\n \"tuteurage_arbre\",\n \"canisse_arbre\",\n \"plaie_tronc\",\n \"observation_tronc\",\n \"champignon_houppier\",\n \"insecte_houppier\",\n \"fissure_houppier\",\n \"ecorce_incluse_houppier\",\n \"bois_mort_houppier\",\n \"plaie_houppier\",\n \"observation_houppier\",\n \"esperance_maintien\",\n \"contrainte\",\n \"date_diagnostic\",\n \"prescription_1\",\n \"prescription_2\",\n \"prescription_3\",\n \"observation_travaux\",\n \"type_delai_1\",\n \"delai_annee_programmation\",\n \"type_delai_2\",\n \"delai_preconisation_2\",\n \"delai_saison_programmation_2\",\n \"delai_annee_programmation_2\",\n \"reference_photo\",\n \"long\",\n \"lat\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[true,true,true,true,false,false,true,true,false,true,true,false,false,true,false,true,false,false,true,true,true,true,true,true,false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,false,false,true,true,true,true,false,false,false,false,true,false,true,false,false,false,false],"string":"[\n true,\n true,\n true,\n true,\n false,\n false,\n true,\n true,\n false,\n true,\n true,\n false,\n false,\n true,\n false,\n true,\n false,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n true,\n false,\n true,\n true,\n true,\n true,\n true,\n true,\n false,\n false,\n true,\n true,\n true,\n true,\n false,\n false,\n false,\n false,\n true,\n false,\n true,\n false,\n false,\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2926,"string":"2,926"}}},{"rowIdx":2792,"cells":{"uid":{"kind":"number","value":362312,"string":"362,312"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc"},"dataset_description":{"kind":"string","value":"Arbres urbains"},"dataset_features":{"kind":"string","value":"{0: [0 - id_arbre (nominal)],\n 1: [1 - commune (nominal)],\n 2: [2 - quartier (nominal)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 421.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 421.0,\n 'NumberOfClasses': 1.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 421.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 3.0,\n 'cost_matrix': nan,\n 'did': 43026,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc',\n 'source_data': '43026',\n 'status': 'active',\n 'target_feature': 'commune',\n 'task_type': 'Supervised Classification',\n 'tid': 362312,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc"},"attribute_names":{"kind":"list like","value":["id_arbre","quartier"],"string":"[\n \"id_arbre\",\n \"quartier\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[true,true],"string":"[\n true,\n true\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2927,"string":"2,927"}}},{"rowIdx":2793,"cells":{"uid":{"kind":"number","value":361205,"string":"361,205"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_INS_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Insects Dataset (Mini)**\n***\nThe original Insects dataset is created by the National Museum of Natural History, Paris (https://www.mnhn.fr/fr). It has more than 290 000 images in different sizes and orientations. The dataset has hierarchical classes which are listed from top to bottom as Order, Super-Family, Family, and Texa. Each image contains an insect in its natural environment or habitat, i.e, either on a flower or near to vegetation. The images are collected by the researchers and hundreds of volunteers from SPIPOLL Science project(https://www.spipoll.org/). The images are uploaded to a centralized server either by using the SPIPOLL website, Android application or IOS application. The preprocessed insect dataset is prepared from the original Insects dataset by carefully preprocessing the images, i.e., cropping the images from either side to make squared images. These cropped images are then resized into 128x128 using Open-CV with an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: SM_AM.INS \n**Meta Album URL**: [https://meta-album.github.io/datasets/INS.html](https://meta-album.github.io/datasets/INS.html) \n**Domain ID**: SM_AM \n**Domain Name**: Small Aninamls \n**Dataset ID**: INS \n**Dataset Name**: Insects \n**Short Description**: Insects dataset from Science Project SPIPOLL \n**\\# Classes**: 104 \n**\\# Images**: 4110 \n**Keywords**: insects, ecology \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY-NC 2.0 \n**License URL(original data release)**: https://www.spipoll.org/mentions-legales\n \n**License (Meta-Album data release)**: CC BY-NC 2.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/2.0/](https://creativecommons.org/licenses/by-nc/2.0/) \n\n**Source**: SPIPOLL; National Museum of Natural History, Paris \n**Source URL**: https://www.spipoll.org/ \n \n**Original Author**: Gregoire Lois, Colin Fontaine, Jean-Francois Julien \n**Original contact**: contact@spipoll.org \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@article{insects, \n title={Data quality and participant engagement in citizen science: comparing two approaches for monitoring pollinators in France and South Korea}, \n author={Serret, Hortense and Deguines, Nicolas and Jang, Yikweon and Lois, Gregoire and Julliard, Romain}, \n journal={Citizen Science: Theory and Practice}, \n volume={4}, \n number={1}, \n pages={22}, \n year={2019} \n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44276) [[Extended]](https://www.openml.org/d/44340)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (string)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 104.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 4160.0,\n 'NumberOfInstancesWithMissingValues': 0.0,\n 'NumberOfMissingValues': 0.0,\n 'NumberOfNumericFeatures': 0.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44306,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_INS_Mini',\n 'source_data': '44306',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361205,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_INS_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2928,"string":"2,928"}}},{"rowIdx":2794,"cells":{"uid":{"kind":"number","value":361192,"string":"361,192"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"Meta_Album_CRS_Mini"},"dataset_description":{"kind":"string","value":"## **Meta-Album Cars Dataset (Mini)**\n***\nThe original Cars dataset (https://ai.stanford.edu/~jkrause/cars/car_dataset.html) was collected in 2013, and it contains more than 16 000 images from 196 classes of cars. Most images are on the road, but some have different backgrounds, and each image has only one car. Each class can have 48 to 136 images of variable resolutions. The preprocess version for this dataset was obtained by creating square images either duplicating the top and bottom-most 3 rows or the left and right most 3 columns based on the orientation of the original image. In this case, cropping was not applied to create the square images since following this technique results in losing too much information from the cars. Then, the square images were resized into 128x128 px using an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: VCL.CRS \n**Meta Album URL**: [https://meta-album.github.io/datasets/CRS.html](https://meta-album.github.io/datasets/CRS.html) \n**Domain ID**: VCL \n**Domain Name**: Vehicles \n**Dataset ID**: CRS \n**Dataset Name**: Cars \n**Short Description**: Dataset with images of different car models \n**\\# Classes**: 196 \n**\\# Images**: 7840 \n**Keywords**: vehicles, cars \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: ImageNet License \n**License URL(original data release)**: https://ai.stanford.edu/~jkrause/cars/car_dataset.html\nhttps://www.image-net.org/download.php\n \n**License (Meta-Album data release)**: ImageNet License \n**License URL (Meta-Album data release)**: [https://www.image-net.org/download.php](https://www.image-net.org/download.php) \n\n**Source**: Stanford Cars Dataset \n**Source URL**: https://ai.stanford.edu/~jkrause/cars/car_dataset.html \n \n**Original Author**: Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei \n**Original contact**: jkrause@cs.stanford.edu \n\n**Meta Album author**: Philip Boser \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{KrauseStarkDengFei-Fei_3DRR2013,\n title = {3D Object Representations for Fine-Grained Categorization},\n booktitle = {4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13)},\n year = {2013},\n address = {Sydney, Australia},\n author = {Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44245) [[Extended]](https://www.openml.org/d/44323)"},"dataset_features":{"kind":"string","value":"{0: [0 - FILE_NAME (string)],\n 1: [1 - CATEGORY (string)],\n 2: [2 - SUPER_CATEGORY (numeric)]}"},"task_description":{"kind":"string","value":"{'MajorityClassSize': 40.0,\n 'MaxNominalAttDistinctValues': nan,\n 'MinorityClassSize': 40.0,\n 'NumberOfClasses': 196.0,\n 'NumberOfFeatures': 3.0,\n 'NumberOfInstances': 7840.0,\n 'NumberOfInstancesWithMissingValues': 7840.0,\n 'NumberOfMissingValues': 7840.0,\n 'NumberOfNumericFeatures': 1.0,\n 'NumberOfSymbolicFeatures': 0.0,\n 'cost_matrix': nan,\n 'did': 44289,\n 'estimation_procedure': '10-fold Crossvalidation',\n 'evaluation_measures': 'predictive_accuracy',\n 'name': 'Meta_Album_CRS_Mini',\n 'source_data': '44289',\n 'status': 'active',\n 'target_feature': 'CATEGORY',\n 'task_type': 'Supervised Classification',\n 'tid': 361192,\n 'ttid': <TaskType.SUPERVISED_CLASSIFICATION: 1>}"},"task_name":{"kind":"string","value":"Meta_Album_CRS_Mini"},"attribute_names":{"kind":"list like","value":["FILE_NAME","SUPER_CATEGORY"],"string":"[\n \"FILE_NAME\",\n \"SUPER_CATEGORY\"\n]","isConversational":false},"categorical_indicator":{"kind":"list like","value":[false,false],"string":"[\n false,\n false\n]","isConversational":false},"__index_level_0__":{"kind":"number","value":2929,"string":"2,929"}}},{"rowIdx":2795,"cells":{"uid":{"kind":"number","value":361059,"string":"361,059"},"orig_metric":{"kind":"string","value":"predictive_accuracy"},"sklearn_metric":{"kind":"string","value":"accuracy_score"},"dataset_name":{"kind":"string","value":"spambase_reproduced"},"dataset_description":{"kind":"string","value":"**Author**: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt \n\n**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/spambase) \n\n**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)\n\n\n\nSPAM E-mail Database \n\nThe \"spam\" concept is diverse: advertisements for products/websites, make money fast schemes, chain letters, pornography... Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.\n\n \n\nFor background on spam: \n\nCranor, Lorrie F., LaMacchia, Brian A. Spam! Communications of the ACM, 41(8):74-83, 1998. \n\n\n\n### Attribute Information: \n\nThe last column denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. Most of the attributes indicate whether a particular word or character was frequently occurring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters. \n\n\n\nFor the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes: \n\n\n\n48 continuous real [0,100] attributes of type \n\nword_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail. A \"word\" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.\n\n \n\n6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) / total characters in e-mail\n\n \n\n1 continuous real [1,...] attribute of type capital_run_length_average\n\n = average length of uninterrupted sequences of capital letters\n\n \n\n1 continuous integer [1,...] attribute of type capital_run_length_longest\n\n = length of longest uninterrupted sequence of capital letters\n\n \n\n1 continuous integer [1,...] attribute of type capital_run_length_total\n\n = sum of length of uninterrupted sequences of capital letters\n\n = total number of capital letters in the e-mail\n\n \n\n1 nominal {0,1} class attribute of type spam\n\n = denotes whether the e-mail was considered spam (1) or not (0), \n\n i.e. unsolicited commercial e-mail."},"dataset_features":{"kind":"string","value":"{0: [0 - word_freq_make (numeric)],\n 1: [1 - word_freq_address (numeric)],\n 2: [2 - word_freq_all (numeric)],\n 3: [3 - word_freq_3d (numeric)],\n 4: [4 - word_freq_our (numeric)],\n 5: [5 - word_freq_over (numeric)],\n 6: [6 - word_freq_remove (numeric)],\n 7: [7 - word_freq_internet (numeric)],\n 8: [8 - word_freq_order (numeric)],\n 9: [9 - word_freq_mail (numeric)],\n 10: [10 - word_freq_receive (numeric)],\n 11: [11 - word_freq_will (numeric)],\n 12: [12 - word_freq_people (numeric)],\n 13: [13 - word_freq_report (numeric)],\n 14: [14 - word_freq_addresses (numeric)],\n 15: [15 - word_freq_free (numeric)],\n 16: [16 - word_freq_business (numeric)],\n 17: [17 - word_freq_email (numeric)],\n 18: [18 - word_freq_you (numeric)],\n 19: [19 - word_freq_credit (numeric)],\n 20: [20 - word_freq_your (numeric)],\n 21: [21 - word_freq_font (numeric)],\n 22: [22 - word_freq_000 (numeric)],\n 23: [23 - word_freq_money (numeric)],\n 24: [24 - word_freq_hp (numeric)],\n 25: [25 - word_freq_hpl (numeric)],\n 26: [26 - word_freq_george (numeric)],\n 27: [27 - word_freq_650 (numeric)],\n 28: [28 - word_freq_lab (numeric)],\n 29: [29 - word_freq_labs (numeric)],\n 30: [30 - word_freq_telnet (numeric)],\n 31: [31 - word_freq_857 (numeric)],\n 32: [32 - word_freq_data (numeric)],\n 33: [33 - word_freq_415 (numeric)],\n 34: [34 - word_freq_85 (numeric)],\n 35: [35 - word_freq_technology (numeric)],\n 36: [36 - word_freq_1999 (numeric)],\n 37: [37 - word_freq_parts (numeric)],\n 38: [38 - word_freq_pm (numeric)],\n 39: [39 - word_freq_direct (numeric)],\n 40: [40 - word_freq_cs (numeric)],\n 41: [41 - word_freq_meeting (numeric)],\n 42: [42 - word_freq_original (numeric)],\n 43: [43 - word_freq_project (numeric)],\n 44: [44 - word_freq_re (numeric)],\n 45: [45 - word_freq_edu (numeric)],\n 46: [46 - word_freq_table (numeric)],\n 47: [47 - word_freq_conference (numeric)],\n 48: [48 - char_freq_%3B (numeric)],\n 49: [49 - 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Textures Dataset. The data in all four datasets is collected in laboratory conditions, i.e., images were captured in a controlled environment with configurable brightness, luminosity, scale and angle. The KTH-TIPS dataset was collected by Mario Fritz and KTH-TIPS 2 dataset was collected by P. Mallikarjuna and Alireza Tavakoli Targhi, created in 2004 and 2006 respectively. Both of these datasets were prepared under the supervision of Eric Hayman and Barbara Caputo. The data for Kylberg Textures Dataset and UIUC Textures Dataset data was collected by the original authors of these datasets in September 2010 and August 2005 respectively. The Meta-Album Textures dataset is a preprocessed version of the original dataset (combination of 4 datasets). All the images are preprocessed by first cropping into perfect squared images and then resized into 128x128 with an anti-aliasing filter. \n\n\n\n### **Dataset Details**\n\n\n**Meta Album ID**: MNF.TEX \n**Meta Album URL**: [https://meta-album.github.io/datasets/TEX.html](https://meta-album.github.io/datasets/TEX.html) \n**Domain ID**: MNF \n**Domain Name**: Manufacturing \n**Dataset ID**: TEX \n**Dataset Name**: Textures \n**Short Description**: Textures dataset from KTH-TIPS, Kylberg and UIUC \n**\\# Classes**: 64 \n**\\# Images**: 8675 \n**Keywords**: textures, manufacturing \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC BY NC 4.0, public for research, cite paper to use dataset \n**License URL(original data release)**: https://creativecommons.org/licenses/by-nc/4.0/\nhttps://kylberg.org/wp-content/uploads/2020/05/KylbergTextureDocumentation-1.0.pdf\n \n**License (Meta-Album data release)**: CC BY-NC 4.0 \n**License URL (Meta-Album data release)**: [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) \n\n**Source**: KTH-TIPS, Kylberg, UIUC \n**Source URL**: https://www.csc.kth.se/cvap/databases/kth-tips/index.html \nhttps://www.cb.uu.se/~gustaf/texture/ \n \n**Original Author**: Eric Hayman, Barbara Caputo, Mario Fritz, P. Mallikarjuna, Alireza Tavakoli Targhi, Jean Ponce, Gustaf Kylberg, Svetlana Lazebnik, Cordelia Schmid \n**Original contact**: KTH-TIPS: hayman@nada.kth.se, Kylberg: gustaf.kylberg@gmail.com, UIUC: Jean.Ponce@inria.fr \n\n**Meta Album author**: Ihsan Ullah \n**Created Date**: 01 March 2022 \n**Contact Name**: Ihsan Ullah \n**Contact Email**: meta-album@chalearn.org \n**Contact URL**: [https://meta-album.github.io/](https://meta-album.github.io/) \n\n\n\n### **Cite this dataset**\n```\n@inproceedings{Fritz2004THEKD,\n title={THE KTH-TIPS database},\n author={Mario Fritz and E. Hayman and B. Caputo and J. Eklundh},\n year={2004},\n url = {https://www.csc.kth.se/cvap/databases/kth-tips/index.html}\n}\n\n@inproceedings{Mallikarjuna2006THEK2,\n title={THE KTH-TIPS 2 database},\n author={P. Mallikarjuna and Alireza Tavakoli Targhi and Mario Fritz and E. Hayman and B. Caputo and J. Eklundh},\n year={2006},\n url = {https://www.csc.kth.se/cvap/databases/kth-tips/index.html}\n}\n\n@TECHREPORT{Kylberg2011c,\n author = {Gustaf Kylberg},\n title = {The Kylberg Texture Dataset v. 1.0},\n institution = {Centre for Image Analysis, Swedish University of Agricultural Sciences\n\tand Uppsala University, Uppsala, Sweden},\n year = {2011},\n type = {External report (Blue series)},\n number = {35},\n month = {September},\n url = {http://www.cb.uu.se/~gustaf/texture/}\n}\n\n@article{lazebnik:inria-00548530,\n TITLE = {{A sparse texture representation using local affine regions}},\n AUTHOR = {Lazebnik, Svetlana and Schmid, Cordelia and Ponce, Jean},\n URL = {https://hal.inria.fr/inria-00548530},\n JOURNAL = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},\n PUBLISHER = {{Institute of Electrical and Electronics Engineers}},\n VOLUME = {27},\n NUMBER = {8},\n PAGES = {1265--1278},\n YEAR = {2005},\n MONTH = Aug,\n DOI = {10.1109/TPAMI.2005.151},\n KEYWORDS = {Index Terms- Image processing and computer vision ; feature measurement ; pattern recognition ; texture},\n PDF = {https://hal.inria.fr/inria-00548530/file/lana_pami_final.pdf},\n HAL_ID = {inria-00548530},\n HAL_VERSION = {v1},\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},\n booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n url = {https://meta-album.github.io/},\n year = {2022}\n }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) \nSupporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) \nMeta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) \n\n\n\n### **Other versions of this dataset**\n[[Micro]](https://www.openml.org/d/44244) [[Mini]](https://www.openml.org/d/44288)"},"dataset_features":{"kind":"string","value":"{0: [0 - 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|---|---|---|---|---|---|---|---|---|---|---|
361,135 | predictive_accuracy | accuracy_score | credit-g | **Author**: Dr. Hans Hofmann
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)) - 1994
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**German Credit dataset**
This dataset classifies people described by a set of attributes as good or bad credit... | {0: [0 - checking_status (nominal)],
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4,596 | predictive_accuracy | accuracy_score | gina_agnostic | **Author**: [Isabelle Guyon](isabelle@clopinet.com)
**Source**: [Agnostic Learning vs. Prior Knowledge Challenge](http://www.agnostic.inf.ethz.ch)
**Please cite**: None
Dataset from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLV... | {0: [0 - attr0 (numeric)],
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361,114 | predictive_accuracy | accuracy_score | rl | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
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359,961 | predictive_accuracy | accuracy_score | mfeat-factors | **Author**: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
**Multiple Features Dataset: Factors**
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3,892 | predictive_accuracy | accuracy_score | hiva_agnostic | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch)
Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php
Modified by TunedIT (converted to ARFF format)
HIVA is the HIV infection database
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361,692 | f_measure | f1_score | kr-vs-kp | Author: Alen Shapiro
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Chess+(King-Rook+vs.+King-Pawn))
Please cite: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
1. Title: Chess End-Game -- King+Rook versus King+Pawn on a7
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4,687 | predictive_accuracy | accuracy_score | AP_Breast_Uterus | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
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167,107 | average_cost | accuracy_score | SpeedDating | **Author**: Ray Fisman and Sheena Iyengar
**Source**: [Columbia Business School](http://www.stat.columbia.edu/~gelman/arm/examples/speed.dating/) - 2004
**Please cite**: None
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146,679 | predictive_accuracy | accuracy_score | SpeedDating | **Author**: Ray Fisman and Sheena Iyengar
**Source**: [Columbia Business School](http://www.stat.columbia.edu/~gelman/arm/examples/speed.dating/) - 2004
**Please cite**: None
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3,927 | predictive_accuracy | accuracy_score | rsctc2010_4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar... | {0: [0 - Var1 (numeric)],
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146,173 | precision | precision_score | GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1 | GAMETES_Epistasis_2-Way_1000atts_0.4H_EDM-1_EDM-1_1-pmlb | {0: [0 - N0 (nominal)],
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190,392 | predictive_accuracy | accuracy_score | madeline | The goal of this challenge is to expose the research community to real world datasets of interest to 4Paradigm. All datasets are formatted in a uniform way, though the type of data might differ. The data are provided as preprocessed matrices, so that participants can focus on classification, although participants are w... | {0: [0 - class (nominal)],
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146,079 | precision | precision_score | GCM | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Multiclass cancer diagnosis using 16063 tumor gene expression signatures.
PNAS, VOL 98, no 26, pp. 15149-15154, December 18, 2001.
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361,298 | predictive_accuracy | accuracy_score | cmc | This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) o... | {0: [0 - Wifes_age (numeric)],
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361,303 | predictive_accuracy | accuracy_score | dresses-sales | This dataset contain attributes of dresses and their recommendations according to their sales. Sales are monitor on the basis of alternate days.The attributes present analyzed are: Recommendation, Style, Price, Rating, Size, Season, NeckLine, SleeveLength, waiseline, Material, FabricType, Decoration, Pattern, Type. In ... | {0: [0 - V4 (numeric)],
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3,929 | predictive_accuracy | accuracy_score | rsctc2010_6 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar... | {0: [0 - Var1 (numeric)],
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361,047 | predictive_accuracy | accuracy_score | kdd_ipums_la_97-small | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the origi... | {0: [0 - value (nominal)],
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'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 6.0... | kdd_ipums_la_97-small | [
"value",
"rent",
"ftotinc",
"momloc",
"famsize",
"nchild",
"eldch",
"yngch",
"nsibs",
"age",
"occscore",
"sei",
"inctot",
"incwage",
"incbus",
"incfarm",
"incss",
"incwelfr",
"incother",
"poverty"
] | [
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,847 |
361,286 | predictive_accuracy | accuracy_score | compas-two-years | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical features" benchmark.
Original link: https://openml.org/d/42192
Original description:
nominal features and target... | {0: [0 - sex (nominal)],
1: [1 - age (numeric)],
2: [2 - juv_misd_count (numeric)],
3: [3 - priors_count (numeric)],
4: [4 - age_cat_25-45 (nominal)],
5: [5 - age_cat_Greaterthan45 (nominal)],
6: [6 - age_cat_Lessthan25 (nominal)],
7: [7 - race_African-American (nominal)],
8: [8 - race_Caucasian (nominal)],
9:... | {'MajorityClassSize': 2483.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2483.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 4966.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 9.0,... | compas-two-years | [
"sex",
"age",
"juv_misd_count",
"priors_count",
"age_cat_25-45",
"age_cat_Greaterthan45",
"age_cat_Lessthan25",
"race_African-American",
"race_Caucasian",
"c_charge_degree_F",
"c_charge_degree_M"
] | [
true,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true
] | 2,848 |
361,149 | predictive_accuracy | accuracy_score | Meta_Album_SPT_Micro | ## **Meta-Album Sports Actions Dataset (Micro)**
***
The 100-Sports dataset(https://www.kaggle.com/datasets/gpiosenka/sports-classification) is a collection of sports images covering 73 different sports. Images are 224x224x3 in size and in .jpg format. Images were gathered from internet searches. The images were scanne... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_SPT_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,849 |
361,057 | predictive_accuracy | accuracy_score | wine | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark.
Original source: https://archive.ics.uci.edu/ml/datasets/wine+quality Please give credit to the original source if you u... | {0: [0 - fixed acidity (numeric)],
1: [1 - volatile acidity (numeric)],
2: [2 - citric acid (numeric)],
3: [3 - residual sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free sulfur dioxide (numeric)],
6: [6 - total sulfur dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': 1277.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1277.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2554.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0... | wine | [
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,850 |
146,572 | predictive_accuracy | accuracy_score | scene | **Author**: Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown.
**Source**: [Mulan](http://mulan.sourceforge.net/datasets-mlc.html)
**Please cite**:
### Description
Scene recognition dataset - It contains characteristics about images and their classes.
The original dataset is a multi-label ... | {0: [0 - attr1 (numeric)],
1: [1 - attr2 (numeric)],
2: [2 - attr3 (numeric)],
3: [3 - attr4 (numeric)],
4: [4 - attr5 (numeric)],
5: [5 - attr6 (numeric)],
6: [6 - attr7 (numeric)],
7: [7 - attr8 (numeric)],
8: [8 - attr9 (numeric)],
9: [9 - attr10 (numeric)],
10: [10 - attr11 (numeric)],
11: [11 - attr12 (... | {'MajorityClassSize': 1976.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 431.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 300.0,
'NumberOfInstances': 2407.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 294.0,
'NumberOfSymbolicFeatures': 6.... | scene | [
"attr1",
"attr2",
"attr3",
"attr4",
"attr5",
"attr6",
"attr7",
"attr8",
"attr9",
"attr10",
"attr11",
"attr12",
"attr13",
"attr14",
"attr15",
"attr16",
"attr17",
"attr18",
"attr19",
"attr20",
"attr21",
"attr22",
"attr23",
"attr24",
"attr25",
"attr26",
"attr27",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,851 |
361,158 | predictive_accuracy | accuracy_score | Meta_Album_MD_5_BIS_Micro | ## **Meta-Album OmniPrint-MD-5-bis Dataset (Micro)**
***
OmniPrint-MD-5-bis dataset consists of 28 240 images (128x128, RGB) from 706 categories. The images are synthesized with OmniPrint, and no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, ... | {0: [0 - FILE_NAME (string)],
1: [1 - text (string)],
2: [2 - CATEGORY (numeric)],
3: [3 - font_file (string)],
4: [4 - background (string)],
5: [5 - background_image_crop_x (numeric)],
6: [6 - background_image_crop_x_plus_width (numeric)],
7: [7 - background_image_crop_y (numeric)],
8: [8 - background_image_cr... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 39.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 24.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | Meta_Album_MD_5_BIS_Micro | [
"FILE_NAME",
"text",
"font_file",
"background",
"background_image_crop_x",
"background_image_crop_x_plus_width",
"background_image_crop_y",
"background_image_crop_y_plus_height",
"background_image_name",
"background_image_original_height",
"background_image_original_width",
"background_image_r... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,853 |
361,150 | predictive_accuracy | accuracy_score | Meta_Album_TEX_Micro | ## **Meta-Album Textures Dataset (Micro)**
***
The original Textures dataset is a combination of 4 texture datasets: KTH-TIPS and KTH-TIPS 2 (https://www.csc.kth.se/cvap/databases/kth-tips/index.html), Kylberg Textures Dataset (http://www.cb.uu.se/~gustaf/texture/) and UIUC Textures Dataset. The data in all four datase... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_TEX_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,854 |
361,155 | predictive_accuracy | accuracy_score | Meta_Album_MED_LF_Micro | ## **Meta-Album Medicinal Leaf Dataset (Micro)**
***
The Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 800 ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_MED_LF_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,855 |
361,151 | predictive_accuracy | accuracy_score | Meta_Album_CRS_Micro | ## **Meta-Album Cars Dataset (Micro)**
***
The original Cars dataset (https://ai.stanford.edu/~jkrause/cars/car_dataset.html) was collected in 2013, and it contains more than 16 000 images from 196 classes of cars. Most images are on the road, but some have different backgrounds, and each image has only one car. Each c... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_CRS_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,856 |
361,147 | predictive_accuracy | accuracy_score | Meta_Album_RESISC_Micro | ## **Meta-Album RESISC Dataset (Micro)**
***
RESISC45 dataset(https://gcheng-nwpu.github.io/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_RESISC_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,857 |
361,163 | predictive_accuracy | accuracy_score | Meta_Album_APL_Micro | ## **Meta-Album Airplanes Dataset (Micro)**
***
The original Airplanes dataset (https://zenodo.org/record/3464319) comprises more than 9 000 remote sensing images acquired from Google Earth satellite imagery, including 21 different types of aircraft from around 36 airports. All the images were carefully labeled by seve... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_APL_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,858 |
361,064 | predictive_accuracy | accuracy_score | kdd_ipums_la_97-small | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Binarized version of the o... | {0: [0 - value (numeric)],
1: [1 - rent (numeric)],
2: [2 - ftotinc (numeric)],
3: [3 - momloc (numeric)],
4: [4 - famsize (numeric)],
5: [5 - nchild (numeric)],
6: [6 - eldch (numeric)],
7: [7 - yngch (numeric)],
8: [8 - nsibs (numeric)],
9: [9 - age (numeric)],
10: [10 - occscore (numeric)],
11: [11 - sei ... | {'MajorityClassSize': 2594.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2594.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 5188.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | kdd_ipums_la_97-small | [
"value",
"rent",
"ftotinc",
"momloc",
"famsize",
"nchild",
"eldch",
"yngch",
"nsibs",
"age",
"occscore",
"sei",
"inctot",
"incwage",
"incbus",
"incfarm",
"incss",
"incwelfr",
"incother",
"poverty"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 2,859 |
361,159 | predictive_accuracy | accuracy_score | Meta_Album_TEX_DTD_Micro | ## **Meta-Album Textures-DTD Dataset (Micro)**
***
The Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data was... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_TEX_DTD_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,860 |
361,172 | predictive_accuracy | accuracy_score | Meta_Album_MD_6_Micro | ## **Meta-Album OmniPrint-MD-6 Dataset (Micro)**
***
OmniPrint-MD-6 dataset consists of 28 120 images (128x128, RGB) from 703 categories. The images are synthesized with OmniPrint, no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, the strength... | {0: [0 - FILE_NAME (string)],
1: [1 - text (string)],
2: [2 - CATEGORY (numeric)],
3: [3 - font_file (string)],
4: [4 - background (string)],
5: [5 - background_image_crop_x (numeric)],
6: [6 - background_image_crop_x_plus_width (numeric)],
7: [7 - background_image_crop_y (numeric)],
8: [8 - background_image_cr... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 47.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | Meta_Album_MD_6_Micro | [
"FILE_NAME",
"text",
"font_file",
"background",
"background_image_crop_x",
"background_image_crop_x_plus_width",
"background_image_crop_y",
"background_image_crop_y_plus_height",
"background_image_name",
"background_image_original_height",
"background_image_original_width",
"background_image_r... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,861 |
361,168 | predictive_accuracy | accuracy_score | Meta_Album_BTS_Micro | ## **Meta-Album Boats Dataset (Micro)**
***
The original version of the Meta-Album boats dataset is called MARVEL dataset (https://github.com/avaapm/marveldataset2016). It has more than 138 000 images of 26 different maritime vessels in their natural background. Each class can have 1 802 to 8 930 images of variable res... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_BTS_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,862 |
361,157 | predictive_accuracy | accuracy_score | Meta_Album_BCT_Micro | ## **Meta-Album DIBaS Dataset (Micro)**
***
The Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each cl... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_BCT_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,864 |
361,161 | predictive_accuracy | accuracy_score | Meta_Album_INS_2_Micro | ## **Meta-Album Insects2 Dataset (Micro)**
***
The pest insects dataset was originally created as a large scale benchmark dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102). It contains more than 75 000 images belongs to 102 categories. It also has a hierarchical taxonomy and the insect pests which m... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_INS_2_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,865 |
361,165 | predictive_accuracy | accuracy_score | Meta_Album_DOG_Micro | ## **Meta-Album Dogs Dataset (Micro)**
***
Researchers from Stanford University created the original Dogs dataset (http://vision.stanford.edu/aditya86/ImageNetDogs/). It contains more than 20 000 images belonging to 120 breeds of dogs worldwide. The images and annotations came from ImageNet for the task of fine-grained... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 19.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 760.0,
'NumberOfInstancesWithMissingValues': 760.0,
'NumberOfMissingValues': 760.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_DOG_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,866 |
361,160 | predictive_accuracy | accuracy_score | Meta_Album_PLT_NET_Micro | ## **Meta-Album PlantNet Dataset (Micro)**
***
Meta-Album PlantNet dataset is created by sampling the Pl@ntNet-300k dataset (https://openreview.net/forum?id=eLYinD0TtIt), itself a sampling of the Pl@ntNet Project's repository. The images and labels which enter this database are sourced by citizen botanists from around ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_PLT_NET_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,867 |
361,146 | predictive_accuracy | accuracy_score | Meta_Album_MD_MIX_Micro | ## **Meta-Album OmniPrint-MD-mix Dataset (Micro)**
***
OmniPrint-MD-mix dataset consists of 28 240 images (128x128, RGB) from 706 categories. The images are synthesized with OmniPrint, and no further processing was done. The OmniPrint synthesis parameters are stated as follows: font size is 192, image size is 128, the ... | {0: [0 - FILE_NAME (string)],
1: [1 - text (string)],
2: [2 - CATEGORY (numeric)],
3: [3 - font_file (string)],
4: [4 - background (string)],
5: [5 - background_color (string)],
6: [6 - brightness (numeric)],
7: [7 - color_enhance (numeric)],
8: [8 - contrast (numeric)],
9: [9 - font_size (numeric)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 69.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 18840.0,
'NumberOfNumericFeatures': 46.0,
'NumberOfSymbolicFeatures': 1.0,... | Meta_Album_MD_MIX_Micro | [
"FILE_NAME",
"text",
"font_file",
"background",
"background_color",
"brightness",
"color_enhance",
"contrast",
"font_size",
"font_weight",
"foreground",
"image_blending_method",
"image_height_resolution",
"image_mode",
"image_width_resolution",
"margin_bottom",
"margin_left",
"marg... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
false,
fa... | 2,868 |
361,156 | predictive_accuracy | accuracy_score | Meta_Album_ACT_40_Micro | ## **Meta-Album Stanford 40 Actions Dataset (Micro)**
***
The Stanford 40 Actions dataset(http://vision.stanford.edu/Datasets/40actions.html) contains images of humans performing 40 actions. There are 9 532 images in total with 180-300 images per action class. The dataset is designed for understanding human actions in ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_ACT_40_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,869 |
361,041 | predictive_accuracy | accuracy_score | eye_movements | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Jarkko Salojarvi, Kai Puolamak... | {0: [0 - lineNo (numeric)],
1: [1 - assgNo (numeric)],
2: [2 - prevFixDur (numeric)],
3: [3 - firstfixDur (numeric)],
4: [4 - firstPassFixDur (nominal)],
5: [5 - nextFixDur (nominal)],
6: [6 - firstSaccLen (numeric)],
7: [7 - lastSaccLen (numeric)],
8: [8 - prevFixPos (numeric)],
9: [9 - landingPos (numeric)],... | {'MajorityClassSize': 3804.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3804.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 7608.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 4.0... | eye_movements | [
"lineNo",
"assgNo",
"prevFixDur",
"firstfixDur",
"firstPassFixDur",
"nextFixDur",
"firstSaccLen",
"lastSaccLen",
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] | 2,870 |
4,688 | predictive_accuracy | accuracy_score | AP_Ovary_Kidney | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC... | {0: [0 - ID_REF (numeric)],
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5: [5 - 1552256_a_at (numeric)],
6: [6 - 1552257_a_at (numeric)],
7: [7 - 1552274_at (numeric)],
8: [8 - 1552275_s_at (numeric)],
9: [9 - 1552348_at (numeric)],
10: [1... | {'MajorityClassSize': 260.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 198.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10936.0,
'NumberOfInstances': 458.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10935.0,
'NumberOfSymbolicFeatures': ... | AP_Ovary_Kidney | [
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"121_at",
"1405_i_at",
"1487_at",
"1552256_a_at",
"1552257_a_at",
"1552274_at",
"1552275_s_at",
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"1552349_a_at",
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"1552368_at",
"1552426_a_at",
"1552455_at",
"1552519_at",
"1552566_at",
"1552610_a_at",
"1552... | [
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f... | 2,871 |
361,166 | predictive_accuracy | accuracy_score | Meta_Album_ACT_410_Micro | ## **Meta-Album MPII Human Pose Dataset Dataset (Micro)**
***
The MPII Human Pose dataset (http://human-pose.mpi-inf.mpg.de/#download) is a state of the art benchmark for evaluation of articulated human pose estimation. It includes around 25 000 images containing over 40 000 people with annotated body joints. The image... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_ACT_410_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,872 |
361,154 | predictive_accuracy | accuracy_score | Meta_Album_PLT_VIL_Micro | ## **Meta-Album Plant Village Dataset (Micro)**
***
The Plant Village dataset(https://data.mendeley.com/datasets/tywbtsjrjv/1) contains camera photos of 17 crop leaves. The original image resolution is 256x256 px. This collection covers 26 plant diseases and 12 healthy plants. The leaves are removed from the plant and ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Meta_Album_PLT_VIL_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,873 |
361,053 | predictive_accuracy | accuracy_score | eye_movements | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Jarkko Salojarvi, Kai Puolamak... | {0: [0 - lineNo (numeric)],
1: [1 - assgNo (numeric)],
2: [2 - prevFixDur (numeric)],
3: [3 - firstfixDur (numeric)],
4: [4 - firstPassFixDur (nominal)],
5: [5 - nextFixDur (nominal)],
6: [6 - firstSaccLen (numeric)],
7: [7 - lastSaccLen (numeric)],
8: [8 - prevFixPos (numeric)],
9: [9 - landingPos (numeric)],... | {'MajorityClassSize': 3804.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3804.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 7608.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 4.0... | eye_movements | [
"lineNo",
"assgNo",
"prevFixDur",
"firstfixDur",
"firstPassFixDur",
"nextFixDur",
"firstSaccLen",
"lastSaccLen",
"prevFixPos",
"landingPos",
"leavingPos",
"totalFixDur",
"meanFixDur",
"regressLen",
"regressDur",
"pupilDiamMax",
"pupilDiamLag",
"timePrtctg",
"titleNo",
"wordNo"
... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 2,874 |
361,186 | predictive_accuracy | accuracy_score | Meta_Album_RSICB_Mini | ## **Meta-Album RSICB Dataset (Mini)**
***
RSICB128 dataset (https://github.com/lehaifeng/RSI-CB) covers 45 scene categories, assembling in total 36 000 images of resolution 128x128 px. The data authors select various locations around the world, and follow China's landuse classification standard. This collection has 2-... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 45.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Meta_Album_RSICB_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,875 |
361,179 | predictive_accuracy | accuracy_score | Meta_Album_RESISC_Mini | ## **Meta-Album RESISC Dataset (Mini)**
***
RESISC45 dataset(https://gcheng-nwpu.github.io/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 45.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1800.0,
'NumberOfInstancesWithMissingValues': 1800.0,
'NumberOfMissingValues': 1800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_RESISC_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,876 |
361,185 | predictive_accuracy | accuracy_score | Meta_Album_PNU_Mini | ## **Meta-Album PanNuke Dataset (Mini)**
***
The PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was desi... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 19.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 760.0,
'NumberOfInstancesWithMissingValues': 760.0,
'NumberOfMissingValues': 760.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_PNU_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,877 |
361,174 | predictive_accuracy | accuracy_score | Meta_Album_RSICB_Micro | ## **Meta-Album RSICB Dataset (Micro)**
***
RSICB128 dataset (https://github.com/lehaifeng/RSI-CB) covers 45 scene categories, assembling in total 36 000 images of resolution 128x128 px. The data authors select various locations around the world, and follow China's landuse classification standard. This collection has 2... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Meta_Album_RSICB_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,878 |
361,175 | predictive_accuracy | accuracy_score | Meta_Album_PNU_Micro | ## **Meta-Album PanNuke Dataset (Micro)**
***
The PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was des... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 19.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 760.0,
'NumberOfInstancesWithMissingValues': 760.0,
'NumberOfMissingValues': 760.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_PNU_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,879 |
361,183 | predictive_accuracy | accuracy_score | Meta_Album_APL_Mini | ## **Meta-Album Airplanes Dataset (Mini)**
***
The original Airplanes dataset (https://zenodo.org/record/3464319) comprises more than 9 000 remote sensing images acquired from Google Earth satellite imagery, including 21 different types of aircraft from around 36 airports. All the images were carefully labeled by seven... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 21.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 840.0,
'NumberOfInstancesWithMissingValues': 840.0,
'NumberOfMissingValues': 840.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_APL_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,880 |
361,317 | predictive_accuracy | accuracy_score | churn | A dataset relating characteristics of telephony account features and usage and whether or not the customer churned. Originally used in Discovering Knowledge in Data: An Introduction to Data Mining.Source: https://www.secs.ac.in/wp-content/CSE_PORTAL/DataMining_Daniel.pdf | {0: [0 - state (numeric)],
1: [1 - account_length (numeric)],
2: [2 - phone_number (numeric)],
3: [3 - number_vmail_messages (numeric)],
4: [4 - total_day_minutes (numeric)],
5: [5 - total_day_calls (numeric)],
6: [6 - total_day_charge (numeric)],
7: [7 - total_eve_minutes (numeric)],
8: [8 - total_eve_calls (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 5000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 4.0,
'co... | churn | [
"state",
"account_length",
"phone_number",
"number_vmail_messages",
"total_day_minutes",
"total_day_calls",
"total_day_charge",
"total_eve_minutes",
"total_eve_calls",
"total_eve_charge",
"total_night_minutes",
"total_night_calls",
"total_night_charge",
"total_intl_minutes",
"total_intl_... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true
] | 2,881 |
361,173 | predictive_accuracy | accuracy_score | Meta_Album_PLT_DOC_Micro | ## **Meta-Album Plant Doc Dataset (Micro)**
***
The PlantDoc dataset(https://github.com/pratikkayal/PlantDoc-Dataset) is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one im... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_PLT_DOC_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,882 |
361,189 | predictive_accuracy | accuracy_score | Meta_Album_BTS_Mini | ## **Meta-Album Boats Dataset (Mini)**
***
The original version of the Meta-Album boats dataset is called MARVEL dataset (https://github.com/avaapm/marveldataset2016). It has more than 138 000 images of 26 different maritime vessels in their natural background. Each class can have 1 802 to 8 930 images of variable reso... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 26.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1040.0,
'NumberOfInstancesWithMissingValues': 1040.0,
'NumberOfMissingValues': 1040.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_BTS_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,883 |
361,480 | predictive_accuracy | accuracy_score | PLK_Mini_Copy | A copy of PLK_Mini dataset from Meta album Set0 | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (nominal)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 86.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 3440.0,
'NumberOfInstancesWithMissingValues': 3440.0,
'NumberOfMissingValues': 3440.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 1.... | PLK_Mini_Copy | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,884 |
361,171 | predictive_accuracy | accuracy_score | Meta_Album_FNG_Micro | ## **Meta-Album Fungi Dataset (Micro)**
***
Meta-Album Fungi dataset is created by sampling the Danish Fungi 2020 dataset(https://arxiv.org/abs/2103.10107), itself a sampling of the Atlas of Danish Fungi repository. The images and labels which enter this database are sourced by a group consisting of 3 300 citizen botan... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Meta_Album_FNG_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,885 |
361,197 | predictive_accuracy | accuracy_score | Meta_Album_PLT_VIL_Mini | ## **Meta-Album Plant Village Dataset (Mini)**
***
The Plant Village dataset(https://data.mendeley.com/datasets/tywbtsjrjv/1) contains camera photos of 17 crop leaves. The original image resolution is 256x256 px. This collection covers 26 plant diseases and 12 healthy plants. The leaves are removed from the plant and p... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 38.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1520.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Meta_Album_PLT_VIL_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,886 |
361,181 | predictive_accuracy | accuracy_score | Meta_Album_INS_2_Mini | ## **Meta-Album Insects2 Dataset (Mini)**
***
The pest insects dataset was originally created as a large scale benchmark dataset for Insect Pest Recognition (https://github.com/xpwu95/IP102). It contains more than 75 000 images belongs to 102 categories. It also has a hierarchical taxonomy and the insect pests which ma... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 102.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 4080.0,
'NumberOfInstancesWithMissingValues': 4080.0,
'NumberOfMissingValues': 4080.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_INS_2_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,887 |
361,180 | predictive_accuracy | accuracy_score | Meta_Album_ACT_40_Mini | ## **Meta-Album Stanford 40 Actions Dataset (Mini)**
***
The Stanford 40 Actions dataset(http://vision.stanford.edu/Datasets/40actions.html) contains images of humans performing 40 actions. There are 9 532 images in total with 180-300 images per action class. The dataset is designed for understanding human actions in s... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 39.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1560.0,
'NumberOfInstancesWithMissingValues': 1560.0,
'NumberOfMissingValues': 1560.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_ACT_40_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,888 |
361,199 | predictive_accuracy | accuracy_score | Meta_Album_MED_LF_Mini | ## **Meta-Album Medicinal Leaf Dataset (Mini)**
***
The Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 800 i... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 25.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 1000.0,
'NumberOfMissingValues': 1000.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_MED_LF_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,889 |
361,176 | predictive_accuracy | accuracy_score | Meta_Album_INS_Micro | ## **Meta-Album Insects Dataset (Micro)**
***
The original Insects dataset is created by the National Museum of Natural History, Paris (https://www.mnhn.fr/fr). It has more than 290 000 images in different sizes and orientations. The dataset has hierarchical classes which are listed from top to bottom as Order, Super-F... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Meta_Album_INS_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,890 |
361,196 | predictive_accuracy | accuracy_score | Meta_Album_BCT_Mini | ## **Meta-Album DIBaS Dataset (Mini)**
***
The Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each cla... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 33.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1320.0,
'NumberOfInstancesWithMissingValues': 1320.0,
'NumberOfMissingValues': 1320.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_BCT_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,891 |
361,187 | predictive_accuracy | accuracy_score | Meta_Album_FNG_Mini | ## **Meta-Album Fungi Dataset (Mini)**
***
Meta-Album Fungi dataset is created by sampling the Danish Fungi 2020 dataset(https://arxiv.org/abs/2103.10107), itself a sampling of the Atlas of Danish Fungi repository. The images and labels which enter this database are sourced by a group consisting of 3 300 citizen botani... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 25.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Meta_Album_FNG_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,892 |
361,204 | predictive_accuracy | accuracy_score | Meta_Album_AWA_Mini | ## **Meta-Album Animals with Attributes Dataset (Mini)**
***
The original Animals with Attributes 2 (AWA) dataset (https://cvml.ist.ac.at/AwA2/) was designed to benchmark transfer-learning algorithms, in particular attribute base classification and zero-shot learning. It has more than 37 000 images from 50 animals, whe... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 50.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 2000.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_AWA_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,893 |
361,198 | predictive_accuracy | accuracy_score | Meta_Album_PLT_NET_Mini | ## **Meta-Album PlantNet Dataset (Mini)**
***
Meta-Album PlantNet dataset is created by sampling the Pl@ntNet-300k dataset (https://openreview.net/forum?id=eLYinD0TtIt), itself a sampling of the Pl@ntNet Project's repository. The images and labels which enter this database are sourced by citizen botanists from around t... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 25.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 1000.0,
'NumberOfMissingValues': 1000.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_PLT_NET_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,894 |
361,070 | predictive_accuracy | accuracy_score | eye_movements | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Jarkko Salojarvi, Kai Puol... | {0: [0 - lineNo (numeric)],
1: [1 - assgNo (numeric)],
2: [2 - prevFixDur (numeric)],
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4: [4 - firstPassFixDur (numeric)],
5: [5 - nextFixDur (numeric)],
6: [6 - firstSaccLen (numeric)],
7: [7 - lastSaccLen (numeric)],
8: [8 - prevFixPos (numeric)],
9: [9 - landingPos (numeric)],... | {'MajorityClassSize': 3804.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3804.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 7608.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | eye_movements | [
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"leavingPos",
"totalFixDur",
"meanFixDur",
"regressLen",
"regressDur",
"pupilDiamMax",
"pupilDiamLag",
"timePrtctg",
"titleNo",
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... | [
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361,111 | predictive_accuracy | accuracy_score | eye_movements | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
**Author**:
**Source**: Un... | {0: [0 - lineNo (numeric)],
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6: [6 - firstPassFixDur (numeric)],
7: [7 - nextFixDur (numeric)],
8: [8 - firstSaccLen (numeric)],
9: [9 - lastSaccLen (numeri... | {'MajorityClassSize': 3804.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3804.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 7608.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 4.0... | eye_movements | [
"lineNo",
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] | 2,896 |
361,206 | predictive_accuracy | accuracy_score | Meta_Album_RSD_Mini | ## **Meta-Album RSD Dataset (Mini)**
***
RSD46 dataset (https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU) is collected from Google Earth and Tianditu. The collection contains 46 scene categories, with a total of 117 000 images. Each scene category has between 500 - 3000 images. The original resolution are 256x256 px or 5... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 38.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1520.0,
'NumberOfInstancesWithMissingValues': 1520.0,
'NumberOfMissingValues': 1520.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_RSD_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,897 |
361,202 | predictive_accuracy | accuracy_score | Meta_Album_ACT_410_Mini | ## **Meta-Album MPII Human Pose Dataset Dataset (Mini)**
***
The MPII Human Pose dataset (http://human-pose.mpi-inf.mpg.de/#download) is a state of the art benchmark for evaluation of articulated human pose estimation. It includes around 25 000 images containing over 40 000 people with annotated body joints. The images... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 29.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1160.0,
'NumberOfInstancesWithMissingValues': 1160.0,
'NumberOfMissingValues': 1160.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_ACT_410_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,898 |
361,190 | predictive_accuracy | accuracy_score | Meta_Album_PRT_Mini | ## **Meta-Album Subcellular Human Protein Dataset (Mini)**
***
This dataset is a subset of the Subcellular dataset in the Protein Atlas project(https://www.proteinatlas.org/). The original dataset, which stems from the Human Protein Atlas Image Classification Kaggle competition(https://www.kaggle.com/competitions/human... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 21.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 840.0,
'NumberOfInstancesWithMissingValues': 840.0,
'NumberOfMissingValues': 840.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_PRT_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,899 |
361,182 | predictive_accuracy | accuracy_score | Meta_Album_TEX_DTD_Mini | ## **Meta-Album Textures-DTD Dataset (Mini)**
***
The Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data was ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 47.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1880.0,
'NumberOfInstancesWithMissingValues': 1880.0,
'NumberOfMissingValues': 1880.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_TEX_DTD_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,900 |
190,143 | predictive_accuracy | accuracy_score | isolet | Binarized version of the isolet dataset (see version 1). Only instances with class labels 1 and 2 from the original dataset are considered. | {0: [0 - f1 (numeric)],
1: [1 - f2 (numeric)],
2: [2 - f3 (numeric)],
3: [3 - f4 (numeric)],
4: [4 - f5 (numeric)],
5: [5 - f6 (numeric)],
6: [6 - f7 (numeric)],
7: [7 - f8 (numeric)],
8: [8 - f9 (numeric)],
9: [9 - f10 (numeric)],
10: [10 - f11 (numeric)],
11: [11 - f12 (numeric)],
12: [12 - f13 (numeric)]... | {'MajorityClassSize': 300.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 618.0,
'NumberOfInstances': 600.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 617.0,
'NumberOfSymbolicFeatures': 1.0,... | isolet | [
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"f10",
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"f31",
"f32",
"f33",
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"... | [
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361,191 | predictive_accuracy | accuracy_score | Meta_Album_FLW_Mini | ## **Meta-Album Flowers Dataset (Mini)**
***
The Flowers dataset(https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) consists of a variety of flowers gathered from different websites and some are photographed by the original creators. These flowers are commonly found in the UK. The images generally have large... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 102.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 4080.0,
'NumberOfInstancesWithMissingValues': 4080.0,
'NumberOfMissingValues': 4080.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_FLW_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,902 |
361,203 | predictive_accuracy | accuracy_score | Meta_Album_PLT_DOC_Mini | ## **Meta-Album Plant Doc Dataset (Mini)**
***
The PlantDoc dataset(https://github.com/pratikkayal/PlantDoc-Dataset) is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one ima... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 27.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1080.0,
'NumberOfInstancesWithMissingValues': 1080.0,
'NumberOfMissingValues': 1080.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_PLT_DOC_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,903 |
361,194 | predictive_accuracy | accuracy_score | Meta_Album_PLK_Mini | ## **Meta-Album Plankton Dataset (Mini)**
***
The Plankton dataset is created by researchers at the Woods Hole Oceanographic Institution (https://www.whoi.edu/). Imaging FlowCytobot (IFCB) was used for the data collection. The Complete process and mechanism are described in the paper [31]. Each image in the dataset con... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 86.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 3440.0,
'NumberOfInstancesWithMissingValues': 3440.0,
'NumberOfMissingValues': 3440.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_PLK_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,904 |
361,227 | predictive_accuracy | accuracy_score | Meta_Album_ACT_40_Extended | ## **Meta-Album Stanford 40 Actions Dataset (Extended)**
***
The Stanford 40 Actions dataset(http://vision.stanford.edu/Datasets/40actions.html) contains images of humans performing 40 actions. There are 9 532 images in total with 180-300 images per action class. The dataset is designed for understanding human actions ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 110.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 11.0,
'NumberOfClasses': 40.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 3389.0,
'NumberOfInstancesWithMissingValues': 3389.0,
'NumberOfMissingValues': 3389.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_ACT_40_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,905 |
167,132 | area_under_roc_curve | roc_auc_score | Speech | "The speech dataset was also provided by (see citation request) and contains real world data from recorded English language. The normal class contains data from persons having an American accent whereas the outliers are represented from seven other speakers, having a different accent. The feature vector is the i-v... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 3625.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 61.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 401.0,
'NumberOfInstances': 3686.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 400.0,
'NumberOfSymbolicFeatures': 1.0... | Speech | [
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"V6",
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"V8",
"V9",
"V10",
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"V12",
"V13",
"V14",
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"V17",
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"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
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361,226 | predictive_accuracy | accuracy_score | Meta_Album_MED_LF_Extended | ## **Meta-Album Medicinal Leaf Dataset (Extended)**
***
The Medicinal Leaf Database(https://data.mendeley.com/datasets/nnytj2v3n5/1) gathers 30 species of healthy and mature medicinal herbs. The leaves are plucked from different plants of the same species, then placed on a white uniform background. There are around 1 8... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 122.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 14.0,
'NumberOfClasses': 26.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 1596.0,
'NumberOfInstancesWithMissingValues': 1596.0,
'NumberOfMissingValues': 1596.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_MED_LF_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,909 |
361,208 | predictive_accuracy | accuracy_score | Meta_Album_PLT_DOC_Extended | ## **Meta-Album Plant Doc Dataset (Extended)**
***
The PlantDoc dataset(https://github.com/pratikkayal/PlantDoc-Dataset) is made up of images of leaves of healthy and unhealthy plants. The images were downloaded from Google Images and Ecosia, and later cropped by the authors, so generally, one complete leaf fits in one... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 189.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 54.0,
'NumberOfClasses': 27.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 2549.0,
'NumberOfInstancesWithMissingValues': 2549.0,
'NumberOfMissingValues': 2549.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_PLT_DOC_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,910 |
361,219 | predictive_accuracy | accuracy_score | Meta_Album_BCT_Extended | ## **Meta-Album DIBaS Dataset (Extended)**
***
The Digital Images of Bacteria Species dataset (DIBaS) (https://github.com/gallardorafael/DIBaS-Dataset) is a dataset of 33 bacterial species with around 20 images for each species. For the Meta-Album, since the images were large (2 048x1 532) with very few samples in each... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 138.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 108.0,
'NumberOfClasses': 33.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 4060.0,
'NumberOfInstancesWithMissingValues': 4060.0,
'NumberOfMissingValues': 4060.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': ... | Meta_Album_BCT_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,911 |
361,207 | predictive_accuracy | accuracy_score | Meta_Album_ACT_410_Extended | ## **Meta-Album MPII Human Pose Dataset Dataset (Extended)**
***
The MPII Human Pose dataset (http://human-pose.mpi-inf.mpg.de/#download) is a state of the art benchmark for evaluation of articulated human pose estimation. It includes around 25 000 images containing over 40 000 people with annotated body joints. The im... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 176.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 44.0,
'NumberOfClasses': 29.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 2402.0,
'NumberOfInstancesWithMissingValues': 2402.0,
'NumberOfMissingValues': 2402.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_ACT_410_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,912 |
362,301 | predictive_accuracy | accuracy_score | olindda_outliers | Outliers data set extracted from the Illustration (Fig. 3) in "Novelty detection with application to data
streams" | {0: [0 - x_0 (numeric)], 1: [1 - x_1 (numeric)], 2: [2 - label (nominal)]} | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 12.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | olindda_outliers | [
"x_0",
"x_1"
] | [
false,
false
] | 2,913 |
361,228 | predictive_accuracy | accuracy_score | Meta_Album_PNU_Extended | ## **Meta-Album PanNuke Dataset (Extended)**
***
The PanNuke dataset(https://jgamper.github.io/PanNukeDataset/) is a semi-automatically generated segmentation and classification task of nuclei. The dataset contains 7 753 images of 19 different tissue types. For the Meta-Album meta-dataset, even though this dataset was ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 967.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 101.0,
'NumberOfClasses': 19.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 5530.0,
'NumberOfInstancesWithMissingValues': 5530.0,
'NumberOfMissingValues': 5530.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': ... | Meta_Album_PNU_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,914 |
361,148 | predictive_accuracy | accuracy_score | Meta_Album_BRD_Micro | ## **Meta-Album Birds Dataset (Micro)**
***
When Meta-Album was created, the Birds dataset(https://www.kaggle.com/datasets/gpiosenka/100-bird-species) contained images of 315 bird species, but now it has increased the number of species to 450. It has more than 49 000 images, each with a resolution of 224x224 px. All th... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_BRD_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,915 |
361,230 | predictive_accuracy | accuracy_score | Meta_Album_APL_Extended | ## **Meta-Album Airplanes Dataset (Extended)**
***
The original Airplanes dataset (https://zenodo.org/record/3464319) comprises more than 9 000 remote sensing images acquired from Google Earth satellite imagery, including 21 different types of aircraft from around 36 airports. All the images were carefully labeled by s... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 846.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 230.0,
'NumberOfClasses': 21.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 9625.0,
'NumberOfInstancesWithMissingValues': 9625.0,
'NumberOfMissingValues': 9625.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': ... | Meta_Album_APL_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,916 |
361,164 | predictive_accuracy | accuracy_score | Meta_Album_TEX_ALOT_Micro | ## **Meta-Album Textures-ALOT Dataset (Micro)**
***
Textures ALOT dataset (https://aloi.science.uva.nl/public_alot/) consists of 27 500 images from 250 categories. The images in the dataset are captured in controlled environment by the creators of the dataset. The images have different viewing angle, illumination angle... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_TEX_ALOT_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,917 |
361,126 | predictive_accuracy | accuracy_score | KDDCup09_upselling | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
**Author**:
**Source**: Un... | {0: [0 - Var6 (numeric)],
1: [1 - Var13 (numeric)],
2: [2 - Var21 (numeric)],
3: [3 - Var22 (numeric)],
4: [4 - Var24 (numeric)],
5: [5 - Var25 (numeric)],
6: [6 - Var28 (numeric)],
7: [7 - Var35 (numeric)],
8: [8 - Var38 (numeric)],
9: [9 - Var57 (numeric)],
10: [10 - Var65 (numeric)],
11: [11 - Var73 (nume... | {'MajorityClassSize': 2516.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2516.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 46.0,
'NumberOfInstances': 5032.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 12.... | KDDCup09_upselling | [
"Var6",
"Var13",
"Var21",
"Var22",
"Var24",
"Var25",
"Var28",
"Var35",
"Var38",
"Var57",
"Var65",
"Var73",
"Var74",
"Var76",
"Var78",
"Var81",
"Var83",
"Var85",
"Var109",
"Var112",
"Var113",
"Var119",
"Var123",
"Var125",
"Var126",
"Var132",
"Var133",
"Var134",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
tr... | 2,918 |
361,170 | predictive_accuracy | accuracy_score | Meta_Album_RSD_Micro | ## **Meta-Album RSD Dataset (Micro)**
***
RSD46 dataset (https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU) is collected from Google Earth and Tianditu. The collection contains 46 scene categories, with a total of 117 000 images. Each scene category has between 500 - 3000 images. The original resolution are 256x256 px or ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_RSD_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,919 |
361,225 | predictive_accuracy | accuracy_score | Meta_Album_TEX_DTD_Extended | ## **Meta-Album Textures-DTD Dataset (Extended)**
***
The Textures DTD dataset(https://www.robots.ox.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which consists of 5 640 images. The data is collected from Google and Flicker by the original authors of the paper 'Describing Textures in the Wild'. The data ... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 120.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 120.0,
'NumberOfClasses': 47.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 5640.0,
'NumberOfInstancesWithMissingValues': 5640.0,
'NumberOfMissingValues': 5640.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': ... | Meta_Album_TEX_DTD_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,920 |
361,305 | predictive_accuracy | accuracy_score | credit-approval | This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect the confidentiality of the data.This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of va... | {0: [0 - A2 (numeric)],
1: [1 - A3 (numeric)],
2: [2 - A8 (numeric)],
3: [3 - A11 (numeric)],
4: [4 - A14 (numeric)],
5: [5 - A15 (numeric)],
6: [6 - A1 (nominal)],
7: [7 - A4 (nominal)],
8: [8 - A5 (nominal)],
9: [9 - A6 (nominal)],
10: [10 - A7 (nominal)],
11: [11 - A9 (nominal)],
12: [12 - A10 (nominal)]... | {'MajorityClassSize': 357.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 296.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 653.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9.0,
'... | credit-approval | [
"A2",
"A3",
"A8",
"A11",
"A14",
"A15",
"A1",
"A4",
"A5",
"A6",
"A7",
"A9",
"A10",
"A12",
"A13"
] | [
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,921 |
361,300 | predictive_accuracy | accuracy_score | credit-g | This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix:Good Bad (predicted) Good 0 1 (actual)Bad 5 0 It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1). | {0: [0 - duration (numeric)],
1: [1 - credit_amount (numeric)],
2: [2 - installment_commitment (numeric)],
3: [3 - residence_since (numeric)],
4: [4 - age (numeric)],
5: [5 - existing_credits (numeric)],
6: [6 - num_dependents (numeric)],
7: [7 - checking_status (nominal)],
8: [8 - credit_history (nominal)],
9... | {'MajorityClassSize': 700.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 13.0,
... | credit-g | [
"duration",
"credit_amount",
"installment_commitment",
"residence_since",
"age",
"existing_credits",
"num_dependents",
"checking_status",
"credit_history",
"purpose",
"savings_status",
"employment",
"personal_status",
"other_parties",
"property_magnitude",
"other_payment_plans",
"hou... | [
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,922 |
361,112 | predictive_accuracy | accuracy_score | KDDCup09_upselling | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
**Author**:
**Source**: Un... | {0: [0 - Var6 (numeric)],
1: [1 - Var13 (numeric)],
2: [2 - Var21 (numeric)],
3: [3 - Var22 (numeric)],
4: [4 - Var24 (numeric)],
5: [5 - Var25 (numeric)],
6: [6 - Var28 (numeric)],
7: [7 - Var35 (numeric)],
8: [8 - Var38 (numeric)],
9: [9 - Var57 (numeric)],
10: [10 - Var65 (numeric)],
11: [11 - Var73 (nume... | {'MajorityClassSize': 2516.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2516.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 46.0,
'NumberOfInstances': 5032.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 12.... | KDDCup09_upselling | [
"Var6",
"Var13",
"Var21",
"Var22",
"Var24",
"Var25",
"Var28",
"Var35",
"Var38",
"Var57",
"Var65",
"Var73",
"Var74",
"Var76",
"Var78",
"Var81",
"Var83",
"Var85",
"Var109",
"Var112",
"Var113",
"Var119",
"Var123",
"Var125",
"Var126",
"Var132",
"Var133",
"Var134",
... | [
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false,
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false,
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false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
tr... | 2,923 |
361,195 | predictive_accuracy | accuracy_score | Meta_Album_TEX_Mini | ## **Meta-Album Textures Dataset (Mini)**
***
The original Textures dataset is a combination of 4 texture datasets: KTH-TIPS and KTH-TIPS 2 (https://www.csc.kth.se/cvap/databases/kth-tips/index.html), Kylberg Textures Dataset (http://www.cb.uu.se/~gustaf/texture/) and UIUC Textures Dataset. The data in all four dataset... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 64.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 2560.0,
'NumberOfInstancesWithMissingValues': 2560.0,
'NumberOfMissingValues': 2560.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.... | Meta_Album_TEX_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,924 |
361,167 | predictive_accuracy | accuracy_score | Meta_Album_PRT_Micro | ## **Meta-Album Subcellular Human Protein Dataset (Micro)**
***
This dataset is a subset of the Subcellular dataset in the Protein Atlas project(https://www.proteinatlas.org/). The original dataset, which stems from the Human Protein Atlas Image Classification Kaggle competition(https://www.kaggle.com/competitions/huma... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 800.0,
'NumberOfMissingValues': 800.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0.0,
... | Meta_Album_PRT_Micro | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,925 |
362,310 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | Arbres urbains | {0: [0 - id_arbre (nominal)],
1: [1 - commune (nominal)],
2: [2 - quartier (nominal)],
3: [3 - site (nominal)],
4: [4 - cote_voirie (numeric)],
5: [5 - matricule_arbre (numeric)],
6: [6 - genre_arbre (nominal)],
7: [7 - espece_arbre (nominal)],
8: [8 - controle (numeric)],
9: [9 - situation (nominal)],
10: [1... | {'MajorityClassSize': 2.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 1.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 2.0,
'NumberOfInstancesWithMissingValues': 2.0,
'NumberOfMissingValues': 22.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 36.0,
'cos... | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | [
"id_arbre",
"commune",
"quartier",
"site",
"cote_voirie",
"matricule_arbre",
"genre_arbre",
"espece_arbre",
"controle",
"situation",
"type_sol",
"surf_permeable",
"date_plantation",
"classe_age",
"hauteur",
"classe_hauteur",
"diametre",
"circonference-en-cm",
"classe_circonferenc... | [
true,
true,
true,
true,
false,
false,
true,
true,
false,
true,
true,
false,
false,
true,
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
tr... | 2,926 |
362,312 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | Arbres urbains | {0: [0 - id_arbre (nominal)],
1: [1 - commune (nominal)],
2: [2 - quartier (nominal)]} | {'MajorityClassSize': 421.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 421.0,
'NumberOfClasses': 1.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 421.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 3.0,
'c... | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | [
"id_arbre",
"quartier"
] | [
true,
true
] | 2,927 |
361,205 | predictive_accuracy | accuracy_score | Meta_Album_INS_Mini | ## **Meta-Album Insects Dataset (Mini)**
***
The original Insects dataset is created by the National Museum of Natural History, Paris (https://www.mnhn.fr/fr). It has more than 290 000 images in different sizes and orientations. The dataset has hierarchical classes which are listed from top to bottom as Order, Super-Fa... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 104.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 4160.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'... | Meta_Album_INS_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,928 |
361,192 | predictive_accuracy | accuracy_score | Meta_Album_CRS_Mini | ## **Meta-Album Cars Dataset (Mini)**
***
The original Cars dataset (https://ai.stanford.edu/~jkrause/cars/car_dataset.html) was collected in 2013, and it contains more than 16 000 images from 196 classes of cars. Most images are on the road, but some have different backgrounds, and each image has only one car. Each cl... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 196.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 7840.0,
'NumberOfInstancesWithMissingValues': 7840.0,
'NumberOfMissingValues': 7840.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_CRS_Mini | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,929 |
361,059 | predictive_accuracy | accuracy_score | spambase_reproduced | **Author**: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/spambase)
**Please cite**: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html)
SPAM E-mail Database
The "spam" concept is diverse: advertisements for products/websites, ma... | {0: [0 - word_freq_make (numeric)],
1: [1 - word_freq_address (numeric)],
2: [2 - word_freq_all (numeric)],
3: [3 - word_freq_3d (numeric)],
4: [4 - word_freq_our (numeric)],
5: [5 - word_freq_over (numeric)],
6: [6 - word_freq_remove (numeric)],
7: [7 - word_freq_internet (numeric)],
8: [8 - word_freq_order (n... | {'MajorityClassSize': 2788.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1813.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 58.0,
'NumberOfInstances': 4601.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 57.0,
'NumberOfSymbolicFeatures': 1.0... | spambase_reproduced | [
"word_freq_make",
"word_freq_address",
"word_freq_all",
"word_freq_3d",
"word_freq_our",
"word_freq_over",
"word_freq_remove",
"word_freq_internet",
"word_freq_order",
"word_freq_mail",
"word_freq_receive",
"word_freq_will",
"word_freq_people",
"word_freq_report",
"word_freq_addresses",
... | [
false,
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false,
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false,
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false,
false,
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false,
false,
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false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 2,930 |
361,217 | predictive_accuracy | accuracy_score | Meta_Album_TEX_Extended | ## **Meta-Album Textures Dataset (Extended)**
***
The original Textures dataset is a combination of 4 texture datasets: KTH-TIPS and KTH-TIPS 2 (https://www.csc.kth.se/cvap/databases/kth-tips/index.html), Kylberg Textures Dataset (http://www.cb.uu.se/~gustaf/texture/) and UIUC Textures Dataset. The data in all four dat... | {0: [0 - FILE_NAME (string)],
1: [1 - CATEGORY (string)],
2: [2 - SUPER_CATEGORY (numeric)]} | {'MajorityClassSize': 513.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 64.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 8675.0,
'NumberOfInstancesWithMissingValues': 8675.0,
'NumberOfMissingValues': 8675.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 0... | Meta_Album_TEX_Extended | [
"FILE_NAME",
"SUPER_CATEGORY"
] | [
false,
false
] | 2,931 |
362,311 | predictive_accuracy | accuracy_score | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | Arbres urbains | {0: [0 - id_arbre (nominal)],
1: [1 - commune (nominal)],
2: [2 - quartier (nominal)],
3: [3 - site (nominal)],
4: [4 - cote_voirie (numeric)],
5: [5 - matricule_arbre (numeric)],
6: [6 - genre_arbre (nominal)],
7: [7 - espece_arbre (nominal)],
8: [8 - controle (numeric)],
9: [9 - situation (nominal)],
10: [1... | {'MajorityClassSize': 1.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 1.0,
'NumberOfFeatures': 57.0,
'NumberOfInstances': 1.0,
'NumberOfInstancesWithMissingValues': 1.0,
'NumberOfMissingValues': 11.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 35.0,
'cos... | dgf_96f4164d-956d-4c1c-b161-68724eb0ccdc | [
"id_arbre",
"commune",
"quartier",
"site",
"cote_voirie",
"matricule_arbre",
"genre_arbre",
"espece_arbre",
"controle",
"situation",
"type_sol",
"surf_permeable",
"date_plantation",
"classe_age",
"hauteur",
"classe_hauteur",
"diametre",
"circonference-en-cm",
"classe_circonferenc... | [
true,
true,
true,
true,
false,
false,
true,
true,
false,
true,
true,
false,
false,
true,
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
tr... | 2,932 |
361,301 | predictive_accuracy | accuracy_score | sick | Thyroid disease records supplied by the Garavan Institute and J. Ross Quinlan, New South Wales Institute, Syndney, Australia. 1987. | {0: [0 - age (numeric)],
1: [1 - TSH (numeric)],
2: [2 - TT4 (numeric)],
3: [3 - T4U (numeric)],
4: [4 - FTI (numeric)],
5: [5 - sex (nominal)],
6: [6 - on_thyroxine (nominal)],
7: [7 - query_on_thyroxine (nominal)],
8: [8 - on_antithyroid_medication (nominal)],
9: [9 - sick (nominal)],
10: [10 - pregnant (no... | {'MajorityClassSize': 2888.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 215.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 3103.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 17.0,... | sick | [
"age",
"TSH",
"TT4",
"T4U",
"FTI",
"sex",
"on_thyroxine",
"query_on_thyroxine",
"on_antithyroid_medication",
"sick",
"pregnant",
"thyroid_surgery",
"I131_treatment",
"query_hypothyroid",
"query_hyperthyroid",
"lithium",
"goitre",
"tumor",
"hypopituitary",
"psych",
"T3_measure... | [
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 2,933 |
361,314 | predictive_accuracy | accuracy_score | eye_movements | Jarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/Competition... | {0: [0 - lineNo (numeric)],
1: [1 - assgNo (numeric)],
2: [2 - prevFixDur (numeric)],
3: [3 - firstfixDur (numeric)],
4: [4 - firstPassFixDur (numeric)],
5: [5 - nextFixDur (numeric)],
6: [6 - firstSaccLen (numeric)],
7: [7 - lastSaccLen (numeric)],
8: [8 - prevFixPos (numeric)],
9: [9 - landingPos (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 7608.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 3.0,
'co... | eye_movements | [
"lineNo",
"assgNo",
"prevFixDur",
"firstfixDur",
"firstPassFixDur",
"nextFixDur",
"firstSaccLen",
"lastSaccLen",
"prevFixPos",
"landingPos",
"leavingPos",
"totalFixDur",
"meanFixDur",
"regressLen",
"regressDur",
"pupilDiamMax",
"pupilDiamLag",
"timePrtctg",
"titleNo",
"wordNo",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true
] | 2,934 |
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