dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
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SF-MASK | **SF-MASK** is a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians. | Provide a detailed description of the following dataset: SF-MASK |
OVAD benchmark | Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute detection performance of several foundation models. | Provide a detailed description of the following dataset: OVAD benchmark |
LEPISZCZE | **LEPISZCZE** is an open-source comprehensive benchmark for Polish NLP and a continuous-submission leaderboard, concentrating public Polish datasets (existing and new) in specific tasks. | Provide a detailed description of the following dataset: LEPISZCZE |
Retina Benchmark | The **Retina Benchmark** is a set of real-world tasks that accurately reflect such complexities and are designed to assess the reliability of predictive models in safety-critical scenarios. Specifically, two publicly available datasets of high-resolution human retina images exhibiting varying degrees of diabetic retinopathy, a medical condition that can lead to blindness, are used to design a suite of automated diagnosis tasks that require reliable predictive uncertainty quantification. | Provide a detailed description of the following dataset: Retina Benchmark |
Secim2023 | **Secim2023** is a comprehensive dataset for social media researchers to study the upcoming election, develop tools to prevent online manipulation, and gather novel information to inform the public. | Provide a detailed description of the following dataset: Secim2023 |
ECTSum | **ECTSum** is a dataset with transcripts of earnings calls (ECTs), hosted by public companies, as documents, and short experts-written telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. | Provide a detailed description of the following dataset: ECTSum |
PcMSP | **PcMSP** is a dataset annotated from 305 open access scientific articles for material science information extraction that simultaneously contains the synthesis sentences extracted from the experimental paragraphs, as well as the entity mentions and intra-sentence relations. | Provide a detailed description of the following dataset: PcMSP |
DiscoSense | **DiscoSense** is a benchmark sourced from datasets that contain two sentences connected through a discourse connective. Specifically, it is sourced from two peer reviewed academic datasets, DISCOVERY and DISCOFUSE for commonsense reasoning via understanding a wide variety of discourse connectives. | Provide a detailed description of the following dataset: DiscoSense |
Phee | **Phee** is a dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature. It is designed for biomedical event extraction tasks. | Provide a detailed description of the following dataset: Phee |
HuPR | **HuPR** is a human pose estimation benchmark is created using cross-calibrated mmWave radar sensors and a monocular RGB camera for cross-modality training of radar-based human pose estimation. This dataset contains 235 sequences of data in an indoor environment, with each sequence being one-minute long and totalling about 4 hour-long video data. | Provide a detailed description of the following dataset: HuPR |
YouwikiHow | **YouwikiHow** is a dataset for Weakly-Supervised temporal Article Grounding (WSAG). It contains 47K videos and an average of 20.8 query sentences for each video. | Provide a detailed description of the following dataset: YouwikiHow |
Reddit Engagement Dataset | **Reddit Engagement Dataset (RED)**, a distant-supervision set, with 80k single-turn conversations. RED is sourced from Reddit, sampling from 43 popular subreddits, and processed from a total of 5 million posts, filtering out data that was either non-conversational, toxic, or posts not possible to ascertain popularity. | Provide a detailed description of the following dataset: Reddit Engagement Dataset |
MultiRefKGC | **MultiRefKGC** is a dataset created from conversations from Reddit designed for Knowledge-Grounded Dialogue Generation tasks. | Provide a detailed description of the following dataset: MultiRefKGC |
ORU Diverse radar dataset | * Evaluate radar localization in diverse environments
* Download: https://drive.google.com/drive/folders/1uATfrAe-KHlz29e-Ul8qUbUKwPxBFIhP Download | Provide a detailed description of the following dataset: ORU Diverse radar dataset |
MH-FED | This dataset provides a collection of 162K images and 70 Videos of Meta-Humans. There are 10 Highly realistic Meta-Humans expressing 7 facial expressions. | Provide a detailed description of the following dataset: MH-FED |
RF100 | The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model.
We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub. | Provide a detailed description of the following dataset: RF100 |
GLAMI-1M | We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text. | Provide a detailed description of the following dataset: GLAMI-1M |
FFHQ-UV | **FFHQ-UV** is a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from FFHQ and preserves the most variations in FFHQ. | Provide a detailed description of the following dataset: FFHQ-UV |
MUSIED | **MUSIED** is a large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service, designed for event detection tasks. | Provide a detailed description of the following dataset: MUSIED |
Wild-Time | **Wild-Time** is a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. | Provide a detailed description of the following dataset: Wild-Time |
42Street | The **42Street** dataset is based on a theater play as an example of such an application. The dataset is created using a public recording of the 42Street theatre play [42street]. The play is 1.5 hours long and was split into 5 equally long parts of 20 minutes each, with various clothes changes between the different parts. | Provide a detailed description of the following dataset: 42Street |
KSoF | Stuttering is a complex speech disorder that negatively affects an individual’s ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The clips were labeled with six stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and – specific to therapy – speech modifications. The audio was recorded during therapy sessions at the Institut der Kasseler Stottertherapie. | Provide a detailed description of the following dataset: KSoF |
BAF | **Bank Account Fraud (BAF)** is a large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset. | Provide a detailed description of the following dataset: BAF |
JigsawPlan | **JigsawPlan** contains room layouts and floorplans for 98,780 single-story houses/apartments from a production pipeline, designed for the Extreme Structure from Motion (E-SfM) problem. | Provide a detailed description of the following dataset: JigsawPlan |
S-ODv2 | **SeaDronesSee-Object Detection v2 (S-ODv2)** dataset contains 14,227 RGB images (training: 8,930; validation: 1,547; testing: 3,750). The images are captured from various altitudes and viewing angles ranging from 5 to 260 meters and 0 to 90° degrees (gimbal pitch angle) while providing the respective meta information for altitude, viewing angle and other meta data for almost all frames. | Provide a detailed description of the following dataset: S-ODv2 |
SciRepEval | **SciRepEval** is a comprehensive benchmark for training and evaluating scientific document representations. It includes 25 challenging and realistic tasks, 11 of which are new, across four formats: classification, regression, ranking and search. | Provide a detailed description of the following dataset: SciRepEval |
SLING | **SLING** consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena. Each pair demonstrates the acceptability contrast of a specific syntactic or semantic phenomenon (e.g., The keys are lost vs. The keys is lost), and an LM should assign lower perplexity to the acceptable sentence. | Provide a detailed description of the following dataset: SLING |
TCAB | Text Classification Attack Benchmark (TCAB) is a dataset for analyzing, understanding, detecting, and labeling adversarial attacks against text classifiers. TCAB includes 1.5 million attack instances, generated by twelve adversarial attack targeting three classifiers trained on six source datasets for sentiment analysis and abuse detection in English. The process of generating attacks is automated, so that TCAB can easily be extended to incorporate new text attacks and better classifiers as they are developed. | Provide a detailed description of the following dataset: TCAB |
KD-EmoR | KD-EmoR is socio-behavioral emotion dataset for emotion recognition in realistic conversation scenarios. It consists of total 12289 sentences from 1513 scenes of a Korean TV show named 'Three Brothers'. The dataset is split into Training and testing sets. Each sample consists of sentence_id, person(speaker), sentence, scene_ID, context(Scene description) labeled with one of the following complex emotion labels: euphoria, dysphoria and neutral. This dataset can be used to study Emotion recognition in Korean conversations. | Provide a detailed description of the following dataset: KD-EmoR |
ATLAS v2.0 | Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.
Official Paper: https://www.nature.com/articles/s41597-022-01401-7 | Provide a detailed description of the following dataset: ATLAS v2.0 |
EXPY-TKY | EXPY-TKY contains the traffic speed information and the corresponding traffic incident information in 10-minute interval for 1843 expressway road links in Tokyo over three months (2021/10∼2021/12). Compared with other benchmarks for traffic prediction, EXPY-TKY covers a larger scale and more complex incident situations. Potential tasks of EXPY-TKY include traffic prediction, incident detection, and road type classification. | Provide a detailed description of the following dataset: EXPY-TKY |
CSI Images | The raw .mat data collected in two different scenarios are provided. | Provide a detailed description of the following dataset: CSI Images |
An extensive dataset of handwritten central Kurdish isolated characters | Data collection:
Finding a suitable source of data is considered a first step toward building a database. The first step in building a database is finding a suitable source. Here, the main goal is to collect images of Kurdish handwritten characters written by many writers. So, a form is designed to do so. The form is shown in Figure 1. It consists of 1 alphabet at a time letter that has been printed on the top right corner, and it has 125 empty blocks. The writers have been asked to write each letter three times in the three empty blocks. The total number of writers is 390.
The forms have been distributed among two main categories: The academic staff of the Information Technology department at Tishk International University, the university students of the University of Kurdistan-Hawler, Salahaddin University, and Tishk International University As shown in Table 2. In total there were ten sets of forms, each set with 35 forms for 35 different letters, at first, we decided that nine sets, which will give us at least 1100 images for each letter were the best option for the time that we had. Then there were some problems with the collection process, in first prints of the forms there was confusion for instance in Set 2, there were 2 forms for the letter (چ) and none for (ج), and since we printed and distributed the form at the same time, we were not aware of this problem until the stage of pre-processing, This was creating an inconsistency in the number of samples that we had, for example by the 9th set we had 504 images of the letter (ڤ), which was much less than other letters that they had at least 1000 images. So we decided to add the 10th set as a complementary to other sets, it only contained those letter, which was missing in the first 9 forms, which was (ز،ژ،ش،غ،ڤ،ق،ک،ل،ن،ی), as explained in Table 3, the First column is the letter and columns 2-11 represent several images gathered in each set accordingly, while the first row the header row 2-36 are letters in each set, last row, and last columns are for the total of each letter and each set.
Labeling and Organizing :
Each image is labeled with three numbers and separated by an underscore, the first number is the id of the letter according to its positing in the alphabetical order which is shown in Table 4, the second number being the number of the set of form which there was 10 sets each giving to a specific group of writers, the third number is the order of that character in the form which was between 1 to 126, so each image had a label like following 02_01_94.jpg, 02 is the order of the letter which in this case is Alef (١), then 01 being in the set number 1 which was given to 4th-grade students of Information Technology department in Tishk International University, and 94 is the order of that image in the form. Each letter was stored in a folder with its ID as the name of that folder, with each folder containing approximately 1134 images of that letter. | Provide a detailed description of the following dataset: An extensive dataset of handwritten central Kurdish isolated characters |
Satlas | **Satlas** is a remote sensing dataset and benchmark that is large in both breadth, featuring all of the aforementioned applications and more, as well as scale, comprising 290M labels under 137 categories and 7 label modalities. | Provide a detailed description of the following dataset: Satlas |
HERDPhobia | **HERDPhobia** is an annotated hate speech detection dataset on Fulani herders in Nigeria -- in three languages: English, Nigerian-Pidgin, and Hausa. | Provide a detailed description of the following dataset: HERDPhobia |
HOMER | The **Household Object Movements from Everyday Routines (HOMER)** dataset is composed of routine behaviors for five households, spanning 50 days for the train split and 10 days for test split. The households are based on an identical apartment setting with four rooms and 108 objects and 33 atomic actions such as find, grab, etc. | Provide a detailed description of the following dataset: HOMER |
Gambling Address Dataset | **Gambling Address Dataset** is a collection of 10,423 gambling addresses that have transactions with gambling contracts. Moreover, 51,004 non-gambling addresses are also selected (such as exchanges, wallet addresses, etc.), making the gambling address dataset more complete. In the dataset, accounts are used to refer to addresses (e.g. 0xd1ce...edec95), where 1, 0, and -1 represent the gamble, non-gamble, and other types, respectively. | Provide a detailed description of the following dataset: Gambling Address Dataset |
Gambling Contract Dataset | **Gambling Contract Dataset** is a collection of 260 gambling smart contracts from decentralized gambling websites, such as Dicether, Degens. At the same time, in order to construct the negative samples required for training, 1040 smart contracts that are not involved in gambling (e.g., erc20, erc721, mixer, etc.) are selected . In the dataset, accounts are used to refer to contracts (e.g. 0x3fe2b...f8a33f), where 1, 0, and -1 to represent the gamble, non-gamble, and other types, respectively. | Provide a detailed description of the following dataset: Gambling Contract Dataset |
DRTiD | **DRTiD** is a benchmark dataset for DR grading, consisting of 3,100 two-field fundus images. | Provide a detailed description of the following dataset: DRTiD |
BeGin | **BeGin** provides 23 benchmark scenarios for graph from 14 real-world datasets, which cover 12 combinations of the incremental settings and the levels of problem. In addition, BeGin provides various basic evaluation metrics for measuring the performances and final evalution metrics designed for continual learning. | Provide a detailed description of the following dataset: BeGin |
MIAD | **MIAD** contains more than 100K high-resolution color images in various outdoor industrial scenarios, designed for unsupervised anomaly detection. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. | Provide a detailed description of the following dataset: MIAD |
DeepParliament | **DeepParliament** is a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. There are a total of 5329 documents where 4223 are in the train and 1106 are in the test dataset. Each bill document contains many sentences in both cases, and the document’s length varies greatly. | Provide a detailed description of the following dataset: DeepParliament |
NEREL-BIO | **NEREL-BIO** is an annotation scheme and corpus of PubMed abstracts in Russian and English. It contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL -> NEREL-BIO) and cross-language (English -> Russian) transfer. | Provide a detailed description of the following dataset: NEREL-BIO |
PLABA | **Plain Language Adaptation of Biomedical Abstracts (PLABA)** is a dataset designed for automatic adaptation that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. | Provide a detailed description of the following dataset: PLABA |
MSU BASED | Qualitative dataset with real blurred videos, created by using beam-splitter setup in lab environment | Provide a detailed description of the following dataset: MSU BASED |
Physionet MI | This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2].
Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org) [1]. Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:
- A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.
- A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.
- A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.
- A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.
References
[1] Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering, 51(6), pp.1034-1043.
[2] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. and PhysioBank, P., PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 Volume 101 Issue 23 pp. E215–E220. | Provide a detailed description of the following dataset: Physionet MI |
MCSCSet | **MCSCSet** is a large-scale specialist-annotated dataset, designed for the task of Medical-domain Chinese Spelling Correction that contains about 200k samples. MCSCSet involves: i) extensive real-world medical queries collected from Tencent Yidian, ii) corresponding misspelled sentences manually annotated by medical specialists. | Provide a detailed description of the following dataset: MCSCSet |
CEFR-SP | **CEFR-SP** contains 17k English sentences annotated with the levels based on the Common European Framework of Reference for Languages assigned by English-education professionals. | Provide a detailed description of the following dataset: CEFR-SP |
KurdishInterdialect | a parallel corpus of Sorani (ckb or Central Kurdish) and Kurmanji (kmr or Northern Kurdish) dialects of Kurdish along with English (eng). The parallel corpus contains three manually-aligned corpus in Sorani-Kurmanji, Sorani-English and Kurmanji-English in various formats, namely Translation Memory eXchange file format (.tmx), parallel annotated text useful for ParaConc and raw parallel texts (.txt).
This corpus contains 12,327 translation pairs in the two major dialects of Kurdish, Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in English-Kurmanji and English-Sorani. | Provide a detailed description of the following dataset: KurdishInterdialect |
ZazaGoraniCorpus | A corpus for two endangered languages of the Zaza-Gorani language family: Zazaki and Gorani. | Provide a detailed description of the following dataset: ZazaGoraniCorpus |
A Bi-atrial Statistical Shape Model and 100 Volumetric Anatomical Models of the Atria | This dataset is part of the publication "A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations" by Nagel et al. (https://doi.org/10.1016/j.media.2021.102210). It includes a bi-atrial statistical shape model built based on 47 MR and CT images (Left atrium segmentation challenge (Tobon-Gomez, 2015), Left atrium fibrosis and scar segmentation challenge (Karim, 2013), Left atrial wall thickness challenge (Karim, 2018)). ScalismoLab (https://scalismo.org) was used for parts of the model generation. Further Details are explained in the paper. The SSM is available as an h5 file including information about the mean shape's vertex locations and their triangulation as well as the eigenvectors and -values.
100 random instances derived from the model are available. Each zip file contains the volumetric bi-atrial geometry as vtk file, which was augmented in a post-processing step with a homogeneous wall thickness, fiber orientation, intra-atrial bridges and material tags so that they are ready to use for electrophysiological simulations of atrial signals. Furthermore, the scalar field resulting from computing the gradient of the Laplace equation with the boundary conditions described by Piersanti et al. (Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations, Computer Methods in Applied Mechanics and Engineering, 2020, https://doi.org/10.1016/j.cma.2020.113468) are available on the left and the right atrial instances.
Furthermore, 95 geometries with uniformly distributed left atrial volumes are available in LAE_geometries.zip. | Provide a detailed description of the following dataset: A Bi-atrial Statistical Shape Model and 100 Volumetric Anatomical Models of the Atria |
MedalCare-XL | Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess precisely known ground truth labels of the underlying disease (model parameterization) and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECG signals were used to enrich sparse clinical data for machine learning or even replace them completely during training leading to good performance on real-world clinical test data.
We thus generated a large synthetic database comprising a total of 16,900 12~lead ECGs based on multi-scale electrophysiological simulations equally distributed into 1 normal healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted timing and amplitude features between the virtual cohort and a large publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The novel dataset of simulated ECG signals is split into training, validation and test data folds for development of novel machine learning algorithms and their objective assessment.
This folder WP2_largeDataset_Noise contains the 12-lead ECGs of 10 seconds length. Each ECG is stored in a separate CSV file with one row per lead (lead order: I, II, III, aVR, aVL, aVF, V1-V6) and one sample per column (sampling rate: 500Hz). Data are split by pathologies (avblock = AV block, lbbb = left bundle branch block, rbbb = right bundle branch block, sinus = normal sinus rhythm, lae = left atrial enlargement, fam = fibrotic atrial cardiomyopathy, iab = interatrial conduction block, mi = myocardial infarction). MI data are further split into subclasses depending on the occlusion site (LAD, LCX, RCA) and transmurality (0.3 or 1.0). Each pathology subclass contains training, validation and testing data (~ 70/15/15 split). Training, validation and testing datasets were defined according to the model with which QRST complexes were simulated, i.e., ECGs calculated with the same anatomical model but different electrophysiological parameters are only present in one of the test, validation and training datasets but never in multiple. Each subfolder also contains a "siginfo.csv" file specifying the respective simulation run for the P wave and the QRST segment that was used to synthesize the 10 second ECG segment. Each signal is available in three variations:
run_*_raw.csv contains the synthesized ECG without added noise and without filtering
run_*_noise.csv contains the synthesized ECG (unfiltered) with superimposed noise
run_*_filtered.csv contains the filtered synthesized ECG (fiter settings: highpass cutoff frequency 0.5Hz, lowpass cutoff frequency 150Hz, butterworth filters of order 3).
The folder WP2_largeDataset_ParameterFiles contains the parameter files used to simulate the 12-lead ECGs. Parameters are split for atrial and ventricular simulations, which were run independently from one another.
See Gillette*, Gsell*, Nagel* et al. "MedalCare-XL: 16,900 healthy and pathological electrocardiograms obtained through multi-scale electrophysiological models" for a description of the model parameters. | Provide a detailed description of the following dataset: MedalCare-XL |
Medical Abstracts | The Medical Abstracts dataset contains 14,438 medical abstracts describing 5 different classes of patient conditions, with all of the dataset being annotated. The dataset is split into training and test sets. | Provide a detailed description of the following dataset: Medical Abstracts |
MIDV-500 | 500 video clips for 50 different identity document types with ground truth.
A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity document recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. The paper presents characteristics of the dataset and evaluation results for existing methods of face detection, text line recognition, and document fields data extraction. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses. | Provide a detailed description of the following dataset: MIDV-500 |
HiAML | HiAML Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 4.6k CIFAR-10 networks with an accuracy range of [91.11%, 93.44%]. | Provide a detailed description of the following dataset: HiAML |
Inception | Inception Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 580 CIFAR-10 networks with an accuracy range of [89.08%, 94.03%]. | Provide a detailed description of the following dataset: Inception |
Two-Path | Two-Path Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 6.9k CIFAR-10 networks with an accuracy range of [85.53%, 92.34%]. | Provide a detailed description of the following dataset: Two-Path |
weather4cast 2022 | Weather4Cast 2022 satellite images for weather prediction. | Provide a detailed description of the following dataset: weather4cast 2022 |
CREPE | **CREPE** is QA dataset containing a natural distribution of presupposition failures from online information-seeking forums. It consists of 8400 Reddit questions with (1) whether there is any false presuppositions annotated, and (2) if any, the presupposition and its correction written. | Provide a detailed description of the following dataset: CREPE |
Fisheye | **Fisheye** dataset comprises of synthetically generated fisheye sequences and fisheye video sequences captured with an actual fisheye camera designed for fisheye motion estimation. | Provide a detailed description of the following dataset: Fisheye |
Geoclidean-Elements | **Geoclidean-Elements** dataset is derived from definitions in the first book of Euclid’s Elements, which focuses on plane geometry. Geoclidean-Elements includes 17 target concepts and 34 tasks. | Provide a detailed description of the following dataset: Geoclidean-Elements |
Geoclidean-Constraints | **Geoclidean-Constraints** dataset consists of 20 concepts and 40 tasks, created from permutations of line and circle construction rules with various constraints describing the relationship between objects. This dataset focuses on explicit constraints between geometric objects. We denote the objects as the following—lines as L, circles as C, and triangles (constructed from three lines) as T. | Provide a detailed description of the following dataset: Geoclidean-Constraints |
HOD | **HOD** is a dataset for 3D object reconstruction which contains 35 objects, divided into two subsets named Sculptures and Daily Objects. The Sculptures has five human sculptures with complex geometries and pure white textures. The Daily Objects consists of 30 daily objects with various shapes and appearances. All of the Sculptures and nine of the Daily Objects are paired with high-fidelity scanned meshes as ground truth geometries for evaluation. | Provide a detailed description of the following dataset: HOD |
TyDiP | **A Dataset for Politeness Classification in Nine Typologically Diverse Languages (TyDiP)** is a dataset containing three-way politeness annotations for 500 examples in each language, totaling 4.5K examples. | Provide a detailed description of the following dataset: TyDiP |
DPM | Don’t Patronize Me! (DPM) is an annotated dataset with Patronizing and Condescending Language towards vulnerable communities. | Provide a detailed description of the following dataset: DPM |
FAUST-partial | FAUST-partial is a 3D registration benchmark dataset created to address the lack of data variability in the existing 3D registration benchmarks such as: 3DMatch, ETH, KITTI.
The original FAUST training dataset is comprised of 100 3D scans of human bodies.
The benchmark generation for a single scan from the FAUST training dataset can be summarized as follows:
1. Make xz-plane the floor by translating the minimal bounding box point of the scan to the origin
2. Surround the scan with a regular icosahaedron. Each point of the icosahaedron acts as a viewpoint
3. For each viewpoint, create a partial point cloud using the hidden point removal algorithm
Finally, for a pair of partial point clouds with the desired overalp, generate a random rotation from the desired rotation range and translation range. | Provide a detailed description of the following dataset: FAUST-partial |
Apron Dataset | The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics. In addition to bounding boxes and object categories the dataset is enriched with meta parameters to quantify the models’ robustness against environmental influences. | Provide a detailed description of the following dataset: Apron Dataset |
jaCappella | **jaCappella** is a corpus of Japanese a cappella vocal ensembles (jaCappella corpus) for vocal ensemble separation and synthesis. It consists of 35 copyright-cleared vocal ensemble songs and their audio recordings of individual voice parts. These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion). They are divided into seven subsets, each of which features typical characteristics of a music genre such as jazz and enka. | Provide a detailed description of the following dataset: jaCappella |
KiloGram | **KiloGram** is a resource for studying abstract visual reasoning in humans and machines. It contains a richly annotated dataset with >1k distinct stimuli. | Provide a detailed description of the following dataset: KiloGram |
ClueWeb22 | **ClueWeb22** is the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high quality, large scale web corpus to support a range of academic and industry research, for example, in information systems, retrieval-augmented AI systems, and model pretraining. Compared with earlier CLUEWeb corpora, the ClUEWeb22 corpus is larger, more varied, of higher-quality, and aligned with the document distributions in commercial web search. Besides raw HTML, the dataset includes rich information about the web pages provided by industry-standard document understanding systems, including the visual representation of pages rendered by a web browser, parsed HTML structure information from a neural network parser, and pre-processed cleaned document text. | Provide a detailed description of the following dataset: ClueWeb22 |
NVD | **Naturalistic Variation Object Dataset (NVD)** is a large simulated dataset of 272k images of everyday objects with naturalistic variations such as object pose, scale, viewpoint, lighting and occlusions. | Provide a detailed description of the following dataset: NVD |
H3WB | **Human3.6M 3D WholeBody (H3WB)** is a large scale dataset with 133 whole-body keypoint annotations on 100K images, made possible by a new multi-view pipeline. It is designed for the three new tasks : i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, iii) 3D whole-body pose estimation from a single RGB image. | Provide a detailed description of the following dataset: H3WB |
G-VUE | **General-purpose Visual Understanding Evaluation (G-VUE)** is a comprehensive benchmark covering the full spectrum of visual cognitive abilities with four functional domains -- Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. | Provide a detailed description of the following dataset: G-VUE |
RSSCN7 | he RSSCN7 dataset contains satellite images acquired from Google Earth, which is originally collected for remote sensing scene classification. We conduct image synthesis on RSSCN7 to make it capable of the image inpainting task. It has seven classes: grassland, farmland, industrial and commercial regions, river and lake, forest field, residential region, and parking lot. Each class has 400 images, so there are total 2,800 images in the RSSCN7 dataset. | Provide a detailed description of the following dataset: RSSCN7 |
OIR | OIR is a financial-domain dataset of the outbound intent recognition task. It aims to identify the intent of customer response in the outbound call scenario. | Provide a detailed description of the following dataset: OIR |
MTC | MTC is a financial-domain dataset of the multi-label topic classification task. It aims to identify the topics of the spoken dialogue. | Provide a detailed description of the following dataset: MTC |
PSM | PSM is a financial-domain dataset of the pairwise search matching task. It aims to identify the semantic similarity of a sentence pair in the search scenario. | Provide a detailed description of the following dataset: PSM |
IEE | IEE is a financial-domain dataset of the Insurance-entity extraction task. Its goal is to locate named entities mentioned in the input sentence. | Provide a detailed description of the following dataset: IEE |
UMD-i Affrodance Dataset | One-Shot Affordance Part Segmentation variant of the UMD dataset. Each object instance in the dataset contains a single image. | Provide a detailed description of the following dataset: UMD-i Affrodance Dataset |
XBT Snapshot | The World Ocean Database (WOD) is world's largest collection of uniformly formatted, quality controlled, publicly available ocean profile data. This dataset is a snapshot of the XBT observations which have been preprocessed for use in a machine learning pipeline.
The data is organised by year in CSV files, covering 1966-2015. This dataset does not include the actual temperature and depth profiles, as this dataset was focused on a project to improve the metadata.
Links
[World Ocean Database](https://www.ncei.noaa.gov/products/world-ocean-database ) | Provide a detailed description of the following dataset: XBT Snapshot |
PIZZA | PIZZA is a dataset for parsing pizza and drink orders, whose semantics cannot be captured by flat slots and intents. | Provide a detailed description of the following dataset: PIZZA |
ExHVV | **ExHVV** is a novel dataset that offers natural language explanations of connotative roles for three types of entities -- heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. | Provide a detailed description of the following dataset: ExHVV |
MatSim | **MatSim** is a synthetic dataset, and natural image benchmark for computer vision-based recognition of similarities and transitions between materials and textures, focusing on identifying any material under any conditions using one or a few examples (one-shot learning), including materials states and subclasses. | Provide a detailed description of the following dataset: MatSim |
EBHI-Seg | **EBHI-Seg** is a dataset containing 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer. | Provide a detailed description of the following dataset: EBHI-Seg |
Perseus | **Perseus** is a dataset for Cross-Lingual Summarization (CLS) which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than two thousand tokens. | Provide a detailed description of the following dataset: Perseus |
Super-CLEVR | **Super-CLEVR** is a dataset for Visual Question Answering (VQA) where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. It contains 21 vehicle models belonging to 5 categories, with controllable attributes. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. | Provide a detailed description of the following dataset: Super-CLEVR |
ArzEn | **Corpus of Egyptian Arabic-English Code-switching (ArzEn)** is a spontaneous conversational speech corpus, obtained through informal interviews held at the German University in Cairo. The participants discussed broad topics, including education, hobbies, work, and life experiences. The corpus currently contains 12 hours of speech, having 6,216 utterances. The recordings were transcribed and translated into monolingual Egyptian Arabic and monolingual English. | Provide a detailed description of the following dataset: ArzEn |
LEVEN | ## Overview
LEVEN is the **largest Legal Event Detection** dataset as well as the **largest Chinese Event Detection** dataset.
### Large Scale
LEVEN contains 8,116 legal documents, 63,616 sentences, and 150,977 event mentions.
### High Coverage
LEVEN contains 108 event types in total, including 64 charge-oriented events and 44 general events.
## Miscs
LEVEN is adopted for the [Event Detection Track of CAIL 2022](http://cail.cipsc.org.cn/task1.html?raceID=1&cail_tag=2022).
Check out the LEVEN [repo](https://github.com/thunlp/LEVEN/) for more details. | Provide a detailed description of the following dataset: LEVEN |
PhD Exchange Network-Rawdata-5848 | This dataset is part of the journal paper "Discipline Reputation Evaluation Based on PhD Exchange Network", Author: Shudong YANG @dalian University of Technology. | Provide a detailed description of the following dataset: PhD Exchange Network-Rawdata-5848 |
SolarDK | **SolarDK** is a dataset for the detection and localization of solar. It comprises images from GeoDanmark with a variable Ground Sample Distance (GSD) between 10 cm and 15 cm, all sampled between March 1st and May 1st during 2021, containing 23,417 hand labelled images for classification and 880 segmentation masks, in addition to a set of about 100,000+ images for classification covering most variations of Danish urban and rural landscapes. | Provide a detailed description of the following dataset: SolarDK |
DialogUSR | **DialogUSR** dataset covers 23 domains with a multi-step crowd-sourcing procedure. It comprises 36.7 Chinese characters by assembling 3.6 single-intent queries (including initial and follow-up queries) and is designed for dialogue utterance splitting and reformulation task. | Provide a detailed description of the following dataset: DialogUSR |
BottleCap | The BottleCap dataset contains over 1100 color images and 7 types of real
defects. All the images are collected from the real production line
and verified by professionals. | Provide a detailed description of the following dataset: BottleCap |
Allergen30 | Allergen30 is created with the goal of building a robust detection model that can assist people in avoiding possible allergic reactions.
It contains more than 6,000 images of 30 commonly used food items that can cause an adverse reaction within the human body. This dataset is one of the first research attempts in training a deep learning based computer vision model to detect the presence of such food items from images. It also serves as a benchmark for evaluating the efficacy of object detection methods in learning the otherwise difficult visual cues related to food items. | Provide a detailed description of the following dataset: Allergen30 |
CLEVR-MRT | **CLEVR Mental Rotation Tests (CLEVR-MRT)** is a new version of the CLEVR dataset. It contains 20 images generated for each scene holding a constant altitude and sampling over azimuthal angle. It is a controlled setting whereby questions are posed about the properties of a scene if that scene was observed from another viewpoint. | Provide a detailed description of the following dataset: CLEVR-MRT |
Crypto related tweets from 10.10.2020 to 3.3.2021 | The dataset contains 30 million cryptocurrency-related tweets from 10.10.2020 to 3.3.2021. See https://github.com/meakbiyik/ask-who-not-what for more details. | Provide a detailed description of the following dataset: Crypto related tweets from 10.10.2020 to 3.3.2021 |
RoomEnv-v1 | # The Room environment - v1
For the documentation of [RoomEnv-v0](./documents/README-v0.md), click the corresponding buttons.
This document, RoomEnv-v1, is the most up-to-date one.
We have released a challenging [OpenAI Gym](https://www.gymlibrary.dev/) compatible
environment. The best strategy for this environment is to have both episodic and semantic
memory systems. See the [paper](https://arxiv.org/abs/2212.02098) for more information.
## Prerequisites
1. A unix or unix-like x86 machine
1. python 3.8 or higher.
1. Running in a virtual environment (e.g., conda, virtualenv, etc.) is highly recommended so that you don't mess up with the system python.
1. This env is added to the PyPI server. Just run: `pip install room-env`
## RoomEnv-v1
```python
import gym
import room_env
import random
env = gym.make("RoomEnv-v1")
observation, info = env.reset()
rewards = 0
while True:
# There is one different thing in the RoomEnv from the original AAAI-2023 paper:
# The reward is either +1 or -1, instead of +1 or 0.
observation, reward, done, truncated, info = env.step(random.randint(0, 2))
rewards += reward
if done:
break
print(rewards)
```
Every time when an agent takes an action, the environment will give you three memory
systems (i.e., episodic, semantic, and short-term), as an `observation`. The goal of the
agent is to learn a memory management policy. The actions are:
- 0: Put the short-term memory into the episodic memory system.
- 1: Put it into the semantic.
- 2: Just forget it.
The memory systems will be managed according to your actions, and they will eventually
be used to answer questions. You don't have to worry about the question answering. It's done
by the environment. The better you manage your memory systems, the higher chances that
your agent can answer more questions correctly!
The default parameters for the environment are
```json
{
"des_size": "l",
"seed": 42,
"policies": {"encoding": "argmax",
"memory_management": "RL",
"question_answer": "episodic_semantic"},
"capacity": {"episodic": 16, "semantic": 16, "short": 1},
"question_prob": 1.0,
"observation_params": "perfect",
"allow_random_human": False,
"allow_random_question": False,
"total_episode_rewards": 128,
"pretrain_semantic": False,
"check_resources": True,
"varying_rewards": False
}
```
If you want to create an env with a different set of parameters, you can do so. For example:
```python
env_params = {"seed": 0,
"capacity": {"episodic": 8, "semantic": 16, "short": 1},
"pretrain_semantic": True}
env = gym.make("RoomEnv-v1", **env_params)
```
Take a look at [this repo](https://github.com/tae898/explicit-memory) for an actual
interaction with this environment to learn a policy.
## Data collection
Data is collected from querying ConceptNet APIs. For simplicity, we only collect triples
whose format is (`head`, `AtLocation`, `tail`). Here `head` is one of the 80 MS COCO
dataset categories. This was kept in mind so that later on we can use images as well.
If you want to collect the data manually, then run below:
```
python collect_data.py
```
## [The RoomDes](room_env/des.py)
The DES is part of RoomEnv. You don't have to care about how it works. If you are still
curious, you can read below.
You can run the RoomDes by
```python
from room_env.des import RoomDes
des = RoomDes()
des.run(debug=True)
```
with `debug=True` it'll print events (i.e., state changes) to the console.
```console
{'resource_changes': {'desk': -1, 'lap': 1},
'state_changes': {'Vincent': {'current_time': 1,
'object_location': {'current': 'desk',
'previous': 'lap'}}}}
{'resource_changes': {}, 'state_changes': {}}
{'resource_changes': {}, 'state_changes': {}}
{'resource_changes': {},
'state_changes': {'Michael': {'current_time': 4,
'object_location': {'current': 'lap',
'previous': 'desk'}},
'Tae': {'current_time': 4,
'object_location': {'current': 'desk',
'previous': 'lap'}}}}
```
## Contributing
Contributions are what make the open source community such an amazing place to be learn,
inspire, and create. Any contributions you make are **greatly appreciated**.
1. Fork the Project
1. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
1. Run `make test && make style && make quality` in the root repo directory,
to ensure code quality.
1. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
1. Push to the Branch (`git push origin feature/AmazingFeature`)
1. Open a Pull Request
## [Cite our paper](https://arxiv.org/abs/2212.02098)
```bibtex
@misc{https://doi.org/10.48550/arxiv.2212.02098,
doi = {10.48550/ARXIV.2212.02098},
url = {https://arxiv.org/abs/2212.02098},
author = {Kim, Taewoon and Cochez, Michael and François-Lavet, Vincent and Neerincx, Mark and Vossen, Piek},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {A Machine with Short-Term, Episodic, and Semantic Memory Systems},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
## Cite our code
[](https://zenodo.org/badge/latestdoi/477781069)
## Authors
- [Taewoon Kim](https://taewoon.kim/)
- [Michael Cochez](https://www.cochez.nl/)
- [Vincent Francois-Lavet](http://vincent.francois-l.be/)
- [Mark Neerincx](https://ocw.tudelft.nl/teachers/m_a_neerincx/)
- [Piek Vossen](https://vossen.info/)
## License
[MIT](https://choosealicense.com/licenses/mit/) | Provide a detailed description of the following dataset: RoomEnv-v1 |
VOT2020 | VOT2020 is a Visual Object Tracking benchmark for short-term tracking in RGB. | Provide a detailed description of the following dataset: VOT2020 |
Computer Vision Arxiv Figures | **Computer Vision Figures** dataset consists of 88645 images that more closely resemble the structure of our visual prompts. The dataset was collected from Arxiv, the open-access web archive for scholarly articles from a variety of academic fields. | Provide a detailed description of the following dataset: Computer Vision Arxiv Figures |
LIB-HSI | The LIB-HSI dataset contains hyperspectral reflectance images and their corresponding RGB images of building façades in a light industrial environment. The dataset also contains pixel-level annotated images for each hyperspectral/RGB image. The LIB-HSI dataset was created to develop deep learning methods for segmenting building facade materials.
The images were captured using a Specim IQ hyperspectral camera. The hyperspectral images are in the ENVI format. There are 393 training images, 45 validation images and 75 test images. | Provide a detailed description of the following dataset: LIB-HSI |
DocCVQA | DocCVQA is a Document Visual Question Answering dataset, where the questions are posed over a whole collection of 14,362 scanned documents. Therefore, the task can be seen as a retrieval-style evidence seeking task where given a question, the aim is to identify and retrieve all the documents in a large document collection that are relevant to answering this question as well as provide the answer. | Provide a detailed description of the following dataset: DocCVQA |
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