dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
Real 3D-AD | Real 3D-AD is the first point cloud anomaly detection dataset for industrial products.
Real3D-AD comprises a total of 1,254 samples that are distributed across 12 distinct categories. These categories include Airplane, Car, Candybar, Chicken, Diamond, Duck, Fish, Gemstone, Seahorse, Shell, Starfish, and Toffees.
Each training sample is an absence of blind spots, and a realistic, high-accuracy prototype. | Provide a detailed description of the following dataset: Real 3D-AD |
NEMO | We present a dataset for the analysis of human affective states using functional near-infrared spectroscopy (fNIRS). Data were recorded from thirty-one participants who engaged in two tasks. In the emotional perception task the participants passively viewed images sampled from the standard international affective picture system database, which provided ground-truth valence and arousal annotation for the stimuli. In the affective imagery task the participants actively imagined emotional scenarios followed by rating these for subjective valence and arousal. Correlates between the fNIRS signal and the valence-arousal ratings were investigated to estimate the validity of the dataset. Source-code and summaries are provided for a processing pipeline, brain activity group analysis, and estimating baseline classification performance. For classification, prediction experiments are conducted for single-trial 4-class classification of arousal and valence as well as cross-participant classifications, and comparisons between high and low arousal variants of the valence prediction tasks. Finally, classification results are presented for subject-specific and cross-participant models. The dataset is made publicly available to encourage research on affective decoding and downstream applications using fNIRS data.
Paper with more information and to be cited upon usage: https://ieeexplore.ieee.org/abstract/document/10286101 | Provide a detailed description of the following dataset: NEMO |
MatrixCity | We build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we developed a pipeline to easily collect aerial and street city views with ground-truth camera poses, as well as a series of additional data modalities. Flexible control on environmental factors like light, weather, human and car crowd is also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size 28km^2. | Provide a detailed description of the following dataset: MatrixCity |
CHAMMI | We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers. | Provide a detailed description of the following dataset: CHAMMI |
ChiMed-VL | # ChiMed-VL Dataset
## ChiMed-VL-Alignment dataset ##
ChiMed-VL-Alignment consists of 580,014 image-text couplings, each pair falling into one of two categories: context information of an image or descriptions of an image. The context category contains 167M tokens, presenting a median text length of 435 (Q1: 211, Q3: 757). Conversely, descriptions, more concise and image-specific, contain inline descriptions and captions. They comprise 63M tokens, with median lengths settling at 59 (Q1: 45, Q3: 83).
## ChiMed-VL-Instruction dataset ##
ChiMed-VL-Instruction comprises 469,441 question-answer pairs. Within this subset, the questions section contains 10M tokens with a median length of 20 (Q1: 16, Q3: 25), posing a concise inquiry reflective of medical queries. The answers consist of 13M tokens with a median length slightly longer at 22 (Q1: 12, Q3: 34), providing clear, direct, and informative responses. | Provide a detailed description of the following dataset: ChiMed-VL |
X-CLAIM | A multilingual dataset for the task of multilingual claim span identification.
X-CLAIM consists of 7K real-world claims, and social media posts containing them, collected from various social media platforms (e.g., Instagram) in English, Hindi, Punjabi, Tamil, Telugu and Bengali. | Provide a detailed description of the following dataset: X-CLAIM |
Nutrition5k | Nutrition5k is a dataset of visual and nutritional data for ~5k realistic plates of food captured from Google cafeterias using a custom scanning rig. We are releasing this dataset alongside our recent CVPR 2021 paper to help promote research in visual nutrition understanding. Please see the paper for more details on the dataset and follow-up experiments. | Provide a detailed description of the following dataset: Nutrition5k |
Condensed Movies | A large-scale video dataset, featuring clips from movies with detailed captions. | Provide a detailed description of the following dataset: Condensed Movies |
Product Reviews 2017 | The corpus contains review sentences mostly of products in electronics domain, annotated and segregated into 4 comparison categories. Each comparison sentence is annotated with names of the products (PROD1 and PROD2), the aspect (ASP) and the predicate (PRED). Dataset contains sentences after auto-labeling on [SNAP dataset](https://snap.stanford.edu/data/web-Amazon-links.html) and manually labeled sentences from the following corpora:
- Jindal and Liu, 2006
- Kessler and Kuhn, 2014
- JDPA Corpus (Kessler et al, 2010) | Provide a detailed description of the following dataset: Product Reviews 2017 |
RVL-CDIP_MP | RVL-CDIP_MP is our first contribution to retrieve the original documents of the IIT-CDIP test collection which were used to create RVL-CDIP. Some PDFs or encoded images were corrupt, which explains that we have around 500 fewer instances. By leveraging metadata from OCR-IDL , we matched the original identifiers from IIT-CDIP and retrieved them from IDL using a conversion.
It has the same label taxonomy as RVL-CDIP (16) with close to 400K documents in PDF format, averaging 5 pages per document. | Provide a detailed description of the following dataset: RVL-CDIP_MP |
RVL-CDIP_N_MP | RVL-CDIP_MP-N can serve its original goal as a covariate shift test set, now for multi-page document classification. We were able to retrieve the original full documents from DocumentCloud and Web Search.
It has the same label taxonomy as RVL-CDIP (16) with close to 1K documents in PDF format, averaging 10 pages per document. | Provide a detailed description of the following dataset: RVL-CDIP_N_MP |
CiNAT-Birds-2021 | CiNAT Birds 2021 (Cross-View iNaturalist-2021 Birds) dataset contains ground-level images of bird species along with satellite images associated with the geolocation of the ground-level images. In total, there are 413,959 pairs for training and 14,831 pairs for validation and testing. The ground-level images are of varying sizes while the satellite images are of size 256x256. Additionally, the dataset comes with rich metadata for each image - geolocation, date, observer id, taxonomy. | Provide a detailed description of the following dataset: CiNAT-Birds-2021 |
Option Smile Volatility and Implied Probabilities Analysis | This study’s sample consists of seven corporations (Black Rock, Google, Meta, JP Morgan, Walgreens, Netflix, and Pepsico) analyzed across seven quarters beginning in 2021. The data includes the implied volatility level (annualized) for the day before, the day of, and the day following the earnings report. This information was obtained from the Bloomberg Terminal dataset BVOL. The data we read from the terminal is based on Bloomberg’s algorithm for calculating the implied volatility for different strikes. The value is the same for both calls and puts, which makes comparisons and calculations more straightforward. The dataset contains a mixture of high-growth, high-risk technology corporations that saw strong market tailwinds during the previous year and steady, high-dividend-paying equities. For a more comprehensive conclusion, we analyze the implied volatility levels across three expirations to determine the influence of each expiration. The shortest maturity spans from 1 to 4 days, while the longest extends from 19 to 22 days. We got the announcement time directly from Bloomberg. The sample comprises blue-chip equities to ensure a highly liquid sample. | Provide a detailed description of the following dataset: Option Smile Volatility and Implied Probabilities Analysis |
LNDST | The Landsat collection contains 400x400 RGB pictures captured by the Landsat 8 satellite. Each image might show areas of water and other areas that are just the background. Your task is to design a classifier that identifies each pixel as either 0 (background) or 1 (water), using integer values. For training, you'll receive the RGB images in jpg format along with a corresponding mask. In this mask, a '0' indicates the background, and a '1' indicates water. However, for testing, you'll only receive the RGB image without the mask. | Provide a detailed description of the following dataset: LNDST |
This is not a Dataset | We introduce a large semi-automatically generated dataset of ~400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms that we use to evaluate LLMs. | Provide a detailed description of the following dataset: This is not a Dataset |
TTE-A&O | The dataset includes two parts corresponding to the cities of Abakan (65524 nodes, 340012 edges) and Omsk (231688 nodes, 1149492 edges). Along with the road network graph, it includes trip records represented as sequences of visited nodes (making the dataset suitable both for path-blind and path-aware settings). There are two types of target values for a regression task: real travel time and real length of a trip. | Provide a detailed description of the following dataset: TTE-A&O |
WHYSHIFT | In our benchmark WHYSHIFT, we explore distribution shifts on 5 real-world tabular datasets from the economic and traffic sectors with natural spatiotemporal distribution shifts.We only pick 7 typical settings out of 22 settings and select only one representative target domain for each setting. In our benchmark, we specify the distribution shift pattern for each setting, and we provide the tools to identify risky regions with large $Y|X$ shifts and to diagnose the performance degradation. | Provide a detailed description of the following dataset: WHYSHIFT |
ManiCups | Multi-domain Image Editing Benchmark | Provide a detailed description of the following dataset: ManiCups |
LOLv2 | The real captured dataset of LOL contains 500 low/normallight image pairs. Most low-light images are collected by changing exposure time and ISO, while other configurations of the cameras are fixed. We capture images from a variety of scenes, e.g., houses, campuses, clubs, streets.
Since camera shaking, object movement, and lightness changing may cause misalignment between the image pairs, inspired by [41], a three-step shooting strategy is used to eliminate such misalignments between the image pairs in our dataset. For one scene, we first shoot two normal-light images $N_1$ and $N_2$. Then, we change the exposure time and ISO to capture a series of low-light images. Finally, we set the exposure time and ISO back to shoot another two normal-light images $N_3$ and $N_4$. The average of $N_i (i = 1,2,3,4)$ is treated as the ground-truth $G=\frac{1}{4}\sum^4_{i=1}N_i$. Then, we check
whether there is object or camera movement. Specifically, the misalignment for these normal-light images is measured by $M=\frac{1}{4}\sum^4_{i=1}MSE(Ni, G)$. If M > 0.1, we abandon the corresponding pair.
These raw images are resized to 400 × 600 and converted to Portable Network Graphics format. The dataset is publicly
available. | Provide a detailed description of the following dataset: LOLv2 |
LOLv2-synthetic | To make synthetic images match the property of real dark photography, we analyze the illumination distribution of low-light images. We collect 270 low-light images from public MEF [42], NPE [6], LIME [8], DICM [43], VV,2 and Fusion [44] dataset, transform the imagesT into YCbCr channel and calculate the histogram of Y channel. We also collect 1000 raw images from RAISE [45] as normal-light images and calculate the histogram of Y channel in YCbCr.
Raw images contain more information than the converted results. For raw images, all operations used to generate pixel values are performed in one step on the base data, making the result more accurate. 1000 raw images in RAISE [45] are used to synthesize low-light images. Interface provided by Adobe Lightroom is used and we try different kinds of parameters to make the histogram of Y channel fit the result in low-light images. Final parameter configuration can be found in the supplementary material. The illumination distribution of synthetic images matches that of low-light images. Finally, we resize these raw images to 400 × 600 and convert them to Portable Network Graphics format. | Provide a detailed description of the following dataset: LOLv2-synthetic |
COESOT | In this work, we propose a general dataset for Color-Event camera based Single Object Tracking, termed COESOT. It contains 1354 color-event videos with 478,721 RGB frames. We split them into a training and testing subset, which contains 827 and 527 videos, respectively. The videos are collected from both outdoor and indoor scenarios (such as the street, zoo, and home) using the DAVIS346 event camera with a zoom lens. Therefore, our videos can reflect the variation in the distance at depth, but other datasets are failed to. Different from existing benchmarks which contain limited categories, our proposed COESOT covers a wider range of object categories (90 classes), as shown in Fig. 3 (a). It mainly reflects four groups, including persons, animals, electronics, and other goods.
The ground truth of the proposed COESOT dataset is densely annotated, i.e., in a frame-by frame way. The absent label is also provided to help researchers design their trackers. Inspired by VisEvent [44], we annotate each testing video sequence with 17 attributes to help researchers evaluate their trackers in specific challenging environments, e.g., full occlusion (FOC), deformation (DEF), rotation (ROT), fast motion (FM), partially occlusion (POC), low illumination (LI), scale variation (SV), background object motion (BOM), motion blur (MB), overexposure (OE), etc. The distribution of videos in each attribute is shown in Fig. 3 (b). The statistical distribution of the ground truth center position is shown in Fig 3 (c). More details can be found in our supplementary materials. | Provide a detailed description of the following dataset: COESOT |
IRIDIA-AF | # A large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database
## Abstract
Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.
## References
Paper: [www.nature.com/articles/s41597-023-02621-1](https://www.nature.com/articles/s41597-023-02621-1) | Provide a detailed description of the following dataset: IRIDIA-AF |
EleThermal | This is the Infrared Elephant Images Dataset (named 'EleThermal dataset') collected from [here](https://github.com/arribada/human-wildlife-conflict#datasets--technology-development-of-arribadas-human-wildlife-conflict-solutions) and annotated by our project, released under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html). Therefore, if you use the annotated 'EleThermal' dataset for any research or other product by any means, please acknowledge the following two works by citing them.
```bibtex
@misc{bandara2023elemantra,
title={Elemantra: An End-to-End Automated Framework Empowered with AI and IoT for Tackling Human-Elephant Conflict in Elephant-Range Countries},
author={Nuwan Sriyantha Bandara and Dilshan Pramudith Bandara},
year={2023},
eprint={2310.15012},
archivePrefix={arXiv},
primaryClass={eess.SY}
}
```
```bibtex
@misc{arribada,
author = {{Arribada Initiative}},
title = {Datasets & technology development of Arribada's human wildlife conflict solutions},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/arribada/human-wildlife-conflict}},
commit = {ed88121d545af408b325508d81410bf17a78751c}
}
```
The dataset is developed during our work on [Elemantra: An End-to-End Automated Framework Empowered with AI and IoT for Tackling Human-Elephant Conflict in Elephant-Range Countries](https://arxiv.org/ftp/arxiv/papers/2310/2310.15012.pdf).
Dataset: https://github.com/NuwanSriBandara/Elemantra/tree/main/data/Thermal_Elephant_Dataset
Project Page: https://nuwansribandara.github.io/Elemantra/
Paper Link: https://arxiv.org/ftp/arxiv/papers/2310/2310.15012.pdf
We believe that this dataset will be instrumental as a benchmark in detecting elephants using thermal signatures.
Human Elephant Conflict, AI-based Detection, Infrared Images | Provide a detailed description of the following dataset: EleThermal |
Supplementary Material | The file contains an annotated list of papers that are included in the literature survey. | Provide a detailed description of the following dataset: Supplementary Material |
Automatic Thermal Modeling | Dataset for reproducing the code of the work: Estimation of Semiconductor Power Losses Through Automatic Thermal Modeling.
Abstract:
Achieving the optimal design of power converters hinges on a deep understanding of the system's dissipation elements to meet desired performance and safety standards. Calorimetric techniques have outperformed classical electrical methods in estimating semiconductor power losses. However, they come with mechanical limitations and depend on analytical electrothermal equivalent circuits. These models are highly topology and technology-dependent, often resulting in either overly simplistic representations that underestimate thermal effects or complex sets of differential equations. To overcome these challenges, we present an innovative data-driven method for characterizing power converter thermal dynamics. This method empowers designers to calculate semiconductor power losses solely based on temperature measurements, which can eliminate the need or be combined with calorimeters. By analyzing sets of power vs. temperature profiles, our approach identifies the most appropriate linear model. This method is rooted in an optimization process that ensures not only precise identification but also the integration of desired modeling requirements, such as dynamics' invertibility for power loss estimation from temperature profiles. This versatile methodology is applicable to any power converter topology, and the derived linear model allows the use of standard control theory techniques for analyzing and controlling thermal dynamics. Real-world experiments validate the proposal's universality and accuracy. | Provide a detailed description of the following dataset: Automatic Thermal Modeling |
FUNSD-r | We introduce FUNSD-r and CORD-r in [Token Path Prediction](https://arxiv.org/abs/2310.11016), the revised VrD-NER datasets to reflect the real-world scenarios of NER on scanned VrDs.
In FUNSD and CORD, segment layout annotations are aligned with labeled entities, which makes them not reflect the reading order issue of NER on scanned VrDs, and thus are unsuitable for evaluating current methods. In FUNSD-r and CORD-r, we automatically reannotate the layouts using PP-OCRv3 OCR system, and manually reannotate the named entities as word sequences based on the new layout annotations. Their segment layout annotations are aligned with real-world situations and entity mentions are labeled on words.
The proposed FUNSD-r consists of 199 document samples including the image, layout annotation of segments and words, and labeled entities of 3 categories. For the detailed summary statistics, please refer to the original paper. | Provide a detailed description of the following dataset: FUNSD-r |
CORD-r | We introduce FUNSD-r and CORD-r in [Token Path Prediction](https://arxiv.org/abs/2310.11016), the revised VrD-NER datasets to reflect the real-world scenarios of NER on scanned VrDs.
In FUNSD and CORD, segment layout annotations are aligned with labeled entities, which makes them not reflect the reading order issue of NER on scanned VrDs, and thus are unsuitable for evaluating current methods. In FUNSD-r and CORD-r, we automatically reannotate the layouts using PP-OCRv3 OCR system, and manually reannotate the named entities as word sequences based on the new layout annotations. Their segment layout annotations are aligned with real-world situations and entity mentions are labeled on words.
The proposed CORD-r consists of 999 document samples including the image, layout annotation of segments and words, and labeled entities of 30 categories. For the detailed summary statistics, please refer to the original paper. | Provide a detailed description of the following dataset: CORD-r |
DiaMOS Plant | Abstract
The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
Keywords: plant disease prediction; classification; detection; dataset; survey; machine learning; deep learning | Provide a detailed description of the following dataset: DiaMOS Plant |
NLP Taxonomy Classification Test Data | The dataset contains the titles and abstracts of all EMNLP 22 papers, which were manually labeled according to the NLP taxonomy. This dataset can be used for multi-label classification. | Provide a detailed description of the following dataset: NLP Taxonomy Classification Test Data |
3D-Point Cloud dataset of various geometrical terrains | Depth vision has been recently used in many locomotion devices with the objective to ease the life of disabled people toward reaching more ecological lifestyle. This is due to the fact that such cameras are cheap, compact and can provide rich information about the environment. Our dataset provides many recordings of point cloud and other types of data during different locomotion modes in urban context. If you used this data, please cite the following papers below:
1-Depth Vision based Terrain Detection Algorithm during Human Locomotion
2-Using Depth Vision for Terrain Detection during Active Locomotion | Provide a detailed description of the following dataset: 3D-Point Cloud dataset of various geometrical terrains |
BioFuelQR | BioFuelQR is a dataset consisting of complex reasoning questions related to catalyst discovery in biofuels. This dataset is aimed at benchmarking scientific question answering methods, particularly for search based text generation. | Provide a detailed description of the following dataset: BioFuelQR |
TAPE | A dataset of videos synthetically degraded with Adobe After Effects to exhibit artifacts resembling those of real-world analog videotapes. The original high-quality videos belong to the Venice scene of the Harmonic dataset. The artifacts taken into account are: 1) tape mistracking; 2) VHS edge waving; 3) chroma loss along the scanlines; 4) tape noise; 5) undersaturation. The dataset comprises a total of 26,392 frames corresponding to 40 clips. The clips are randomly divided into training and test sets with a 75%-25% ratio. | Provide a detailed description of the following dataset: TAPE |
ETTh1 (96) | The **Electricity Transformer Temperature** (**ETT**) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months. | Provide a detailed description of the following dataset: ETTh1 (96) |
ETTh1 (192) | The **Electricity Transformer Temperature** (**ETT**) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months. | Provide a detailed description of the following dataset: ETTh1 (192) |
PragmaticCode | PragmaticCode is a dataset of real-world open-source Java projects complete with their development environments and dependencies (through their respective build systems). The authors tried to ensure that all the repositories in PragmaticCode were released publicly only after the determined training dataset cutoff date (31 March 2022) for the CodeGen, SantaCoder and text-davinci-003 family of models, which were used to evaluate MGD. | Provide a detailed description of the following dataset: PragmaticCode |
DotPrompts | DotPrompts is a set of testcases derived from PragmaticCode, such that each testcase consists of a prompt to a dereference location (a code location having the "." operator in Java). It is primarily meant as a benchmark for Code LMs. | Provide a detailed description of the following dataset: DotPrompts |
SourceData-NLP | Introduction: The scientific publishing landscape is expanding rapidly, creating challenges for researchers to stay up-to-date with the evolution of the literature. Natural Language Processing (NLP) has emerged as a potent approach to automating knowledge extraction from this vast amount of publications and preprints. Tasks such as Named-Entity Recognition (NER) and Named-Entity Linking (NEL), in conjunction with context-dependent semantic interpretation, offer promising and complementary approaches to extracting structured information and revealing key concepts.
Results: We present the SourceData-NLP dataset produced through the routine curation of papers during the publication process. A unique feature of this dataset is its emphasis on the annotation of bioentities in figure legends. We annotate eight classes of biomedical entities (small molecules, gene products, subcellular components, cell lines, cell types, tissues, organisms, and diseases), their role in the experimental design, and the nature of the experimental method as an additional class. SourceData-NLP contains more than 620,000 annotated biomedical entities, curated from 18,689 figures in 3,223 papers in molecular and cell biology. We illustrate the dataset's usefulness by assessing BioLinkBERT and PubmedBERT, two transformers-based models, fine-tuned on the SourceData-NLP dataset for NER. We also introduce a novel context-dependent semantic task that infers whether an entity is the target of a controlled intervention or the object of measurement.
Conclusions: SourceData-NLP's scale highlights the value of integrating curation into publishing. Models trained with SourceData-NLP will furthermore enable the development of tools able to extract causal hypotheses from the literature and assemble them into knowledge graphs. | Provide a detailed description of the following dataset: SourceData-NLP |
HC3 Plus | In order to fill the gap of HC3 under semanticinvariant tasks, we extend HC3 and propose a larger ChatGPT-generated text dataset covering translation, summarization, and paraphrasing tasks, called HC3 Plus. | Provide a detailed description of the following dataset: HC3 Plus |
Quasimodo-GenT | A mapping of [Quasimodo](https://paperswithcode.com/dataset/quasimodo) to the relations of [ConceptNet](https://paperswithcode.com/paper/conceptnet-55-an-open-multilingual-graph-of). | Provide a detailed description of the following dataset: Quasimodo-GenT |
Ascent-GenT | A mapping of [Ascent](https://paperswithcode.com/dataset/ascentkb) to the relations of [ConceptNet](https://paperswithcode.com/paper/conceptnet-55-an-open-multilingual-graph-of). | Provide a detailed description of the following dataset: Ascent-GenT |
The EMBO SourceData-NLP dataset | We present the SourceData-NLP dataset produced through the routine curation of papers during the publication process. A unique feature of this dataset is its emphasis on the annotation of bioentities in figure legends. We annotate eight classes of biomedical entities (small molecules, gene products, subcellular components, cell lines, cell types, tissues, organisms, and diseases), their role in the experimental design, and the nature of the experimental method as an additional class. SourceData-NLP contains more than 620,000 annotated biomedical entities, curated from 18,689 figures in 3,223 papers in molecular and cell biology. We illustrate the dataset's usefulness by assessing BioLinkBERT and PubmedBERT, two transformers-based models, fine-tuned on the SourceData-NLP dataset for NER. We also introduce a novel context-dependent semantic task that infers whether an entity is the target of a controlled intervention or the object of measurement. | Provide a detailed description of the following dataset: The EMBO SourceData-NLP dataset |
MT-Bench | This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions. The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. | Provide a detailed description of the following dataset: MT-Bench |
Game of 24 | Game of 24 is a mathematical reasoning challenge, where the goal is to use 4 numbers and basic arithmetic operations (+-*/) to obtain 24. For example, given input “4 9 10 13”, a solution output could be “(10 - 4) * (13 - 9) = 24”. We scrape data from 4nums.com, which has 1,362 games that are sorted from easy to hard by human solving time, and use a subset of relatively hard games indexed 901-1,000 for testing. For each task, we consider the output as success if it is a valid equation that equals 24 and uses the input numbers each exactly once. We report the success rate across 100 games as the metric. | Provide a detailed description of the following dataset: Game of 24 |
Creative Writing | A creative writing task where the input is 4 random sentences and the output should be a coherent passage with 4 paragraphs that end in the 4 input sentences respectively. Such a task is open-ended and exploratory, and challenges creative thinking as well as high-level planning. | Provide a detailed description of the following dataset: Creative Writing |
Mini Crosswords | We scrape data from GooBix, which contains 156 games of 5 × 5 mini crosswords. The goal is not just to solve the task, as more general crosswords can be readily solved with specialized NLP pipelines that leverage large-scale retrieval instead of LM. Rather, we aim to explore the limit of LM as a general problem solver that explores its own thoughts and guides its own exploration with deliberate reasoning as heuristics. | Provide a detailed description of the following dataset: Mini Crosswords |
reader_engagement | Reader eye tracking and engagement scores for two short stories, aggregated by sentence. | Provide a detailed description of the following dataset: reader_engagement |
Race Against the Machine | A fully-annotated, open-design dataset of autonomous and piloted high-speed flight | Provide a detailed description of the following dataset: Race Against the Machine |
InsPLAD | InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them).
Three image-level computer vision tasks covered by InsPLAD:
* Object detection, evaluated through the AP metric
* Defect classification, evaluated through Balanced Accuracy
* Anomaly detection, evaluated through the AUROC metric | Provide a detailed description of the following dataset: InsPLAD |
RNA-Puzzles | RNA-Puzzles is a collective experiment for blind RNA structure prediction.
The sequence of a solved RNA structure is confidentially communicated to participating modelling groups a couple of weeks prior to publication. The results are assessed and presented in common publications involving structuralists and modellers. | Provide a detailed description of the following dataset: RNA-Puzzles |
WANDS | The dataset contains:
* 42,994 candidate products with data comprising product class, title, description, attributes, category hierarchy, average rating, and number of reviews
* 480 search query strings with predicted product class
* 233,448 (query string, product) human relevance judgments with labels (exact match, partial match, irrelevant)
The purpose of the dataset is to evaluate retrieval models for product search in the e-commerce domain using expert judgment of whether a product is relevant to a given query. It can be used to benchmark different retrieval against each other. As of its publication in 2022, it was to the best of our knowledge the biggest such public dataset.
The accompanying publication describes in depth the annotation guidelines and process used to collect the dataset. It also includes a measure of the quality of the annotation and experimentally compares the dataset's ability to discriminate the effectiveness of different retrieval models vs other comparable evaluation datasets. | Provide a detailed description of the following dataset: WANDS |
ASDiv | We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully. | Provide a detailed description of the following dataset: ASDiv |
WinoMT-Hindi | Test set of sentences in Hindi with complex coreference involving two entities inspired by WinoBias format of sentences in English. Includes grammatical gender cues of Hindi to test gender bias in Hindi-English NMT Systems. | Provide a detailed description of the following dataset: WinoMT-Hindi |
OTSC-Hindi | Test set of sentences in Hindi with simple gender-specific context used to measure gender bias in NMT systems for Hindi-English. | Provide a detailed description of the following dataset: OTSC-Hindi |
BanMANI | A Dataset to Identify Manipulated Social Media News in Bangla
We construct a publicly available Bangla dataset of 800 news-related social media items that are annotated as manipulated or not relative to 500 reference news articles. We present a semi-automatic (use both human and LLM) method for generating such a dataset, which allows scalable dataset collection using annotators efficiently for languages with few available NLP tools. | Provide a detailed description of the following dataset: BanMANI |
Polyp ASH | This dataset was built with data acquired at the Hospital Clinic of Barcelona, Spain. It is composed of a total of 1126 HD polyp images. There are a total of 473 unique polyps, with a variable number of different shots per polyp (minimum: 2, maximum: 24, median: 10). Special attention was paid to ensure that images from the same polyp show different conditions. An external frame-grabber and a white light endoscope were used to capture raw images. The dataset contains images with two different resolutions: 1920 x 1080 and 1350 x 1080. | Provide a detailed description of the following dataset: Polyp ASH |
FoodSG-233 | The **FoodSG-233** dataset contains 209,861 images, covering 13 food groups and 233 food categories.
Specifically, food groups are more coarse-grained whereas food categories are more fine-grained, e.g., the group “Sugars, sweets and confectionery” contains categories such as “Parfait” and “Popcorn”. We guarantee that there are at least 400 food images per food category.
With a specific focus on localized Singaporean cuisine, the FoodSG-233 dataset aims to promote future research directions for the data management community in food computing. | Provide a detailed description of the following dataset: FoodSG-233 |
SSC | The SSC dataset is a spiking version of the Speech Commands dataset release by Google [(Speech Commands)](https://paperswithcode.com/dataset/speech-commands). SSC was generated using Lauscher, an artificial cochlea model. The SSC dataset consists of utterances recorded from a larger number of speakers under controlled conditions. Spikes were generated in 700 input channels, and it contains 35 word categories from a large number of speakers.
A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.
Cramer, B.; Stradmann, Y.; Schemmel, J.; and Zenke, F. "The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 33, 2744–2757, 2022. | Provide a detailed description of the following dataset: SSC |
VRMocap: VR Mocap Dataset for Pose Reconstruction | Data used for the paper SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
It contains over 1GB of high-quality motion capture data recorded with an Xsens Awinda system while using a variety of VR applications in Meta Quest devices. | Provide a detailed description of the following dataset: VRMocap: VR Mocap Dataset for Pose Reconstruction |
BACH | Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. | Provide a detailed description of the following dataset: BACH |
WebRTC QoE Estimation Without Using Application Layer Headers | This dataset contains network traces collected in-lab and in a real-world setting. We also collected ground truth Quality of Experience (QoE) logs from the browser. Researchers can use this dataset to solve a wide range of problems that involve understanding video conferencing quality from a network perspective. | Provide a detailed description of the following dataset: WebRTC QoE Estimation Without Using Application Layer Headers |
LegalBench | The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors.
If you have questions about the project or would like to get involved, please see the website for more information. | Provide a detailed description of the following dataset: LegalBench |
Glot500-c | A dataset of natural language data collected by putting together more than 150 existing mono-lingual and multilingual datasets together and crawling known multilingual websites. The focus of this dataset is on 500 extremely low-resource languages.
Github: https://github.com/cisnlp/Glot500 | Provide a detailed description of the following dataset: Glot500-c |
TII-SSRC-23 | The TII-SSRC-23 dataset offers a comprehensive collection of network traffic patterns, meticulously compiled to support the development and research of Intrusion Detection Systems (IDS). It presents a dual structure: one part provides a tabular representation of extracted features in CSV format, while the other offers raw network traffic data for each type of traffic in PCAP files. This rich dataset captures both benign and malicious network scenarios, serving as an invaluable resource for researchers in the machine learning field.
URL: https://www.kaggle.com/datasets/daniaherzalla/tii-ssrc-23 | Provide a detailed description of the following dataset: TII-SSRC-23 |
LMCQA | This dataset contains a set of multiple-choice questions related to various legal topics. The dataset contains 20 questions covering various aspects of legal knowledge, such as the workings of the European Commission, types of legal documents, procedures in the court system, legal definitions, and European Union+United Kingdom law, among others. | Provide a detailed description of the following dataset: LMCQA |
voraus-AD | voraus-AD contains machine data of a collaborative robot, which moves a can by performing an industrial pick-and-place task. The samples consist of time series of machine data, each recorded over one pick-and-place operation. As usual in anomaly detection, the training set contains only normal data, which includes regular samples without anomalies. The test set contains both, normal data and anomalies, including 12 diverse anomaly types. In order to create a realistic scenario, we have divided the normal data into training and test data as follows: Up to a certain period of time, only training data including 948 samples was recorded. Subsequently, recordings of anomalies (755 samples) and normal data (419 samples) for the test set were taken alternately. This simulates a real application where training data would be recorded first in the same way to train the model before the test case occurs.
To exclude temperature effects, we let robots warm up for half an hour before each recording. | Provide a detailed description of the following dataset: voraus-AD |
Unsynchronized Dynamic Blender Dataset | Unsynchronized dynamic blender dataset for multi-view dynamic NeRFs for evaluating MAE between the predicted time offsets and the ground truth. It contains three unsyncrhonized scenes; box, deer, and fox. | Provide a detailed description of the following dataset: Unsynchronized Dynamic Blender Dataset |
Plenoptic Video Dataset | 3D video data asset of CVPR 2022 Paper "Neural 3D Video Synthesis" | Provide a detailed description of the following dataset: Plenoptic Video Dataset |
Randman | Versatile synthetic classification dataset based on precise input spike timings drawn from smooth random manifolds as previously described | Provide a detailed description of the following dataset: Randman |
PTR_NETS | These are the files containing the Convex Hull and Traveling Salesman Problem dataset present in the “Pointer Networks” paper:
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." NIPS 2015
@inproceedings{vinyals2015pointer,
title={Pointer networks},
author={Vinyals, Oriol and Fortunato, Meire and Jaitly, Navdeep},
booktitle={NIPS},
pages={2692--2700},
year={2015}
}
The following is an example from one of the lines from one of the files. It contains the 2d coordinates (in the format x_1 y_1 x_2 y_2 … x_N y_N) followed by the word “output” as a separator, and then the indices of the corresponding outputs (base 1) of the input points. In the example below, the convex hull was 1 2 3 4 5 1 (note that the last digit is repeated as it forms a tour), i.e., first point (0.99624295 0.92802603), followed by second point (0.24557767 0.80411435), etc.
0.99624295 0.92802603 0.24557767 0.80411435 0.43667577 0.33301639 0.36675234 0.71464094 0.80695481 0.14914953 output 1 2 3 5 1 | Provide a detailed description of the following dataset: PTR_NETS |
Faces Through Time | Faces Through Time (FTT) features 26,247 images of notable people from the 19th to 21st centuries, with roughly 1,900 images per decade on average. It is sourced from Wikimedia Commons, a crowdsourced and open-licensed collection of 50M images. | Provide a detailed description of the following dataset: Faces Through Time |
Turkish Punctuation Restoration | we have prepared a dataset using publicly available TED Talks transcripts [27] and selected the Turkish corpus. The resulting Turkish punctuation restoration dataset currently consists of 146K sentences and 1.8M tokens. The ratio of the train, validation, and test splits are 0.8, 0.1, and 0.1, respectively. Data files contain two columns. The first column has the tokens separated by white space. The second column includes tags for each token. | Provide a detailed description of the following dataset: Turkish Punctuation Restoration |
TimeQA | This dataset is aimed to study the existing reading comprehension models' capability to perform temporal reasoning, and see whether they are sensitive to the temporal description in the given question. | Provide a detailed description of the following dataset: TimeQA |
Tamil Alpaca | Dataset Card for "tamil-alpaca"
This repository includes a Tamil-translated version of the [Alpaca dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned).
This dataset is part of the release of Tamil LLaMA family of models – an important step in advancing LLMs for the Tamil language. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP) ]() that outlines our journey and the model's potential impact.
**GitHub Repository:** [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama)
## Models trained using this dataset
| Model | Type | Data | Base Model | # Params | Download Links |
|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
| Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) |
| Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) |
## Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
- [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/)
## Citation
If you use this model or any of the the Tamil-Llama datasets in your research, please cite:
```bibtex
@misc{balachandran2023tamilllama,
title={Tamil-Llama: A New Tamil Language Model Based on Llama 2},
author={Abhinand Balachandran},
year={2023},
eprint={2311.05845},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | Provide a detailed description of the following dataset: Tamil Alpaca |
Tamil Alpaca Orca | # Dataset Card for "tamil-alpaca"
This repository includes a Tamil-translated versions of the [Alpaca dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) and a subset of [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset.
This dataset is part of the release of Tamil LLaMA family of models – an important step in advancing LLMs for the Tamil language. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP) ]() that outlines our journey and the model's potential impact.
**GitHub Repository:** [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama)
## Models trained using this dataset
| Model | Type | Data | Base Model | # Params | Download Links |
|--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------|
| Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) |
| Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) |
## Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
- [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/)
## Citation
If you use this model or any of the the Tamil-Llama datasets in your research, please cite:
```bibtex
@misc{balachandran2023tamilllama,
title={Tamil-Llama: A New Tamil Language Model Based on Llama 2},
author={Abhinand Balachandran},
year={2023},
eprint={2311.05845},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | Provide a detailed description of the following dataset: Tamil Alpaca Orca |
SMPLMarket | This is an enhanced Market-1501 dataset labeled with SMPL annotations, ie 3D human shape and pose ground truth. | Provide a detailed description of the following dataset: SMPLMarket |
FLD | A deductive reasoning benchmark based on formal logic theory.
A model is required to generate a proof that (dis-) proves a given hypothesis based on a given set of facts. | Provide a detailed description of the following dataset: FLD |
Cifar10Mnist | The **Cifar10Mnist** dataset is created using CIFAR-10 and MNIST data sources.
Since the CIFAR-10 training set consists of 50000 images and the MNIST training set contains 60000 digits, the first 50000 digits from MNIST are padded on top of the CIFAR-10 images after making them slightly translucent. A first training dataset is then obtained (50000 images). Furthermore, the remaining 10000 MNIST digits are padded on top of 10000 random CIFAR10 images (with a fixed seed). This gives the possibility of having a second training dataset of 60000 images.
For the test set, the 10000 CIFAR-10 images are padded over the 10000 MNIST digits. | Provide a detailed description of the following dataset: Cifar10Mnist |
TAMPAR | TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in COCO format. For details see the [paper](https://arxiv.org/abs/2311.03124) and for visual samples the [project page](https://a-nau.github.io/tampar/). Features are:
- >900 annotated real-world images with >2,700 visible parcel side surfaces
- 6 different tampering types
- 6 different distortion strengths | Provide a detailed description of the following dataset: TAMPAR |
Dollar Street Dataset | The MLCommons Dollar Street Dataset is a collection of images of everyday household items from homes around the world that visually captures socioeconomic diversity of traditionally underrepresented populations. It consists of public domain data, licensed for academic, commercial and non-commercial usage, under CC-BY and CC-BY-SA 4.0. The dataset was developed because similar datasets lack socioeconomic metadata and are not representative of global diversity.
It includes 38,479 images collected from 63 different countries, tagged from a set of 289 possible topics. Besides this, the metadata for each image includes demographic information such as region, country, and total household monthly income, allowing for many different use cases, ultimately enhancing image datasets for computer vision.
The dataset was introduced by the paper: [The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World](https://openreview.net/forum?id=qnfYsave0U4) | Provide a detailed description of the following dataset: Dollar Street Dataset |
GneutralSpeech Female | A Brazilian Portuguese TTS dataset featuring a female voice
recorded with high quality in a controlled environment, with
neutral emotion and more than 20 hours of recordings.
with
neutral emotion and more than 20 hours of recordings. Our
dataset aims to facilitate transfer learning for researchers and
developers working on TTS applications: a highly professional
neutral female voice can serve as a good warm-up stage for
learning language-specific structures, pronunciation and other
non-individual characteristics of speech, leaving to further
training procedures only to learn the specific adaptations
needed (e.g. timbre, emotion and prosody). This can surely
help enabling the accommodation of a more diverse range
of female voices in Brazilian Portuguese. By doing so, we
also hope to contribute to the development of accessible and
high-quality TTS systems for several use cases such as virtual
assistants, audiobooks, language learning tools and accessibility solutions.
Possible use cases:
TTS;
Voice Conversion;
ASR;
Speech Enhancement | Provide a detailed description of the following dataset: GneutralSpeech Female |
GneutralSpeech Male | A database containing high sampling rate recordings of a single speaker reading sentences in Brazilian
Portuguese with neutral voice, along with the corresponding text corpus.
Intended for speech synthesis and automatic speech recognition applications, the dataset contains text extracted from a popular Brazilian news
TV program, totalling roughly 20 h of audio spoken by a trained individual in a controlled environment. The text was normalized in the
recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to
a readable text in Portuguese. There are no noticeable accidental sounds
and background noise has been kept to a minimum in all audio samples.
Intended for:
TTS
ASR
Speech Enhancement
Voice Conversion | Provide a detailed description of the following dataset: GneutralSpeech Male |
WSJ0-2mix-extr | WSJ0-2mix-extr is a speech extraction dataset | Provide a detailed description of the following dataset: WSJ0-2mix-extr |
Colors | A large dataset of color names and their respective RGB values stores in CSV. | Provide a detailed description of the following dataset: Colors |
A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market | Dataset for A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market | Provide a detailed description of the following dataset: A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market |
CSIQ | The CSIQ database consists of 30 original images, each is distorted using six different types of distortions at four to five different levels of distortion. CSIQ images are subjectively rated base on a linear displacement of the images across four calibrated LCD monitors placed side by side with equal viewing distance to the observer. The database contains 5000 subjective ratings from 35 different observers, and ratings are reported in the form of DMOS. | Provide a detailed description of the following dataset: CSIQ |
TLUnified-NER | We present the development of a Named Entity Recognition (NER) dataset for Tagalog. This corpus helps fill the resource gap present in Philippine languages today, where NER resources are scarce. The texts were obtained from a pretraining corpora containing news reports, and were labeled by native speakers in an iterative fashion. The resulting dataset contains ~7.8k documents across three entity types: Person, Organization, and Location. The inter-annotator agreement, as measured by Cohen's κ, is 0.81. We also conducted extensive empirical evaluation of state-of-the-art methods across supervised and transfer learning settings. Finally, we released the data and processing code publicly to inspire future work on Tagalog NLP. | Provide a detailed description of the following dataset: TLUnified-NER |
Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification | Dataset of validated OCT and Chest X-Ray images described and analyzed in "Deep learning-based classification and referral of treatable human diseases". The OCT Images are split into a training set and a testing set of independent patients. OCT Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL. | Provide a detailed description of the following dataset: Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification |
ULI-RI | The ULI-RI dataset is generated using the Unreal Engine 4 to simulate various outdoor environments with 115 high-quality 3D human models.
For each person identity, we controlled and quantitatively labeled the illumination intensity, view point (model z-rotation angle), and background to create 512 images.
There are total 115 x 512 = 58880 images in the ULI-RI dataset. | Provide a detailed description of the following dataset: ULI-RI |
Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks | Dataset used in the publication of Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks. The paper is published at npj Computational Materials (https://www.nature.com/articles/s41524-023-01125-1) | Provide a detailed description of the following dataset: Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks |
Wikidata5M | Wikidata5m is a million-scale knowledge graph dataset with aligned corpus. This dataset integrates the Wikidata knowledge graph and Wikipedia pages. Each entity in Wikidata5m is described by a corresponding Wikipedia page, which enables the evaluation of link prediction over unseen entities.
The dataset is distributed as a knowledge graph, a corpus, and aliases. We provide both transductive and inductive data splits used in the original paper. | Provide a detailed description of the following dataset: Wikidata5M |
UNER v1 | UNER v1 adds an NER annotation layer to 18 datasets (primarily treebanks from UD) and covers 12 geneologically and ty- pologically diverse languages: Cebuano, Danish, German, English, Croatian, Portuguese, Russian, Slovak, Serbian, Swedish, Tagalog, and Chinese4. Overall, UNER v1 contains nine full datasets with training, development, and test splits over eight languages, three evaluation sets for lower-resource languages (TL and CEB), and a parallel evaluation benchmark spanning six languages. | Provide a detailed description of the following dataset: UNER v1 |
AIR | Adverbs in Recipes (AIR) is a dataset specifically collected for adverb recognition. AIR is a subset of HowTo100M where recipe videos show actions performed in ways that change according to an adverb (e.g. chop thinly/coarsely). AIR was carefully reviewed to ensure reliable annotations. | Provide a detailed description of the following dataset: AIR |
HowTo100M Adverbs | HowTo100M Adverbs is a subset from HowTo100M with mined adverbs from 83 tasks in HowTo100M. The annotations were obtained from automatically transcribed narrations of instructional videos. The dataset contains originally 5,824 clips annotated with action-adverb pairs from 72 verbs and 6 adverbs.
Source: [How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs](https://arxiv.org/abs/2203.12344) | Provide a detailed description of the following dataset: HowTo100M Adverbs |
VATEX Adverbs | VATEX Adverbs is a subset from VATEX with extracted verb-adverb annotations. VATEX Adverbs contains 34 adverbs appearing across 135 actions, forming 1,550 unique action-adverb pairs in 14,617 video clips. | Provide a detailed description of the following dataset: VATEX Adverbs |
ActivityNet Adverbs | ActivityNet Adverbs is a subset from the ActivityNet dataset with extracted verb-adverb annotations. ActivityNet Adverbs contains 20 adverbs appearing across 114 actions, forming 643 unique action-adverb pairs in 3,099 video clips. | Provide a detailed description of the following dataset: ActivityNet Adverbs |
MSR-VTT Adverbs | MSR-VTT Adverbs is a subset from MSR-VTT with extracted verb-adverb annotations. MSR-VTT Adverbs contains 18 adverbs appearing across 106 actions, forming 464 unique action-adverb pairs in 1,824 video clips. | Provide a detailed description of the following dataset: MSR-VTT Adverbs |
SynFoot | 50K synthetic renders of the human foot, with surface normals, masks and keypoints. | Provide a detailed description of the following dataset: SynFoot |
Conic10K | Conic10K is an open-ended math problem dataset on conic sections in Chinese senior high school education. This dataset contains 10,861 carefully annotated problems, each one has a formal representation, the corresponding text spans, the answer, and natural language rationales. These questions require long reasoning steps while the topic is limited to conic sections. It could be used to evaluate models with 2 tasks: semantic parsing and mathematical question answering (mathQA). | Provide a detailed description of the following dataset: Conic10K |
GSCAN | Grounded SCAN poses a simple task, where an agent must execute action sequences based on a synthetic language instruction.
The agent is presented with a simple grid world containing a collection of objects, each of which is associated with a vector of features. The agent is evaluated on its ability to follow one or more instructions in this environment. Some instructions require interaction with particular kinds of objects.
 | Provide a detailed description of the following dataset: GSCAN |
PWISeg | ## **Overview**
The Surgical Instruments Recognition Dataset is a groundbreaking collection of high-resolution images (1280x960 pixels) specifically designed for the recognition and categorization of surgical instruments. This dataset captures the intricate details and complexity of surgical tools, particularly when arranged in scenarios reminiscent of an operating room.
## **Data Acquisition**
Our data collection approach was multifaceted to encompass a wide range of scenarios in which surgical instruments are typically viewed. We utilized:
1. **Ceiling-mounted, program-controlled camera:** This camera systematically rotated to capture the instruments from various angles.
2. **Handheld camera:** It was employed to take pictures of instruments in trays on the floor, creating images with natural shadows and lighting.
3. **Real-world operating room imagery:** We included close-up shots of instruments in use, offering a view akin to that of the surgical team, and capturing the tools in motion.
## **Annotations**
The dataset adheres to the COCO results format, supporting object and keypoint detection tasks. For object detection, bounding boxes and class labels are provided for each instrument. Key point annotations identify precise instrument points, with over 10,000 instruments annotated throughout the dataset.
## **Dataset Structure**
The dataset is divided into three parts:
- Training set: 1788 images with annotations
- Validation set: 200 images with annotations
- Testing set: 185 images with annotations | Provide a detailed description of the following dataset: PWISeg |
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