dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
Frustrated Legislators: Replication data and code | Description: Replication data and code for
Aref, S., and Neal, Z.P., "Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance" (2021) Scientific Reports. http://dx.doi.org/10.1038/s41598-021-98139-w | Provide a detailed description of the following dataset: Frustrated Legislators: Replication data and code |
TREx-2p | **TREx-2p** is a dataset to probe whether a pretrained LM possesses “indirect” 2-hop knowledge. It is a 2-hop variant of the T-REx dataset. It has been built by manually examining the 2-hop link existing in the knowledge graph of TREx-1p, and select eight 2- hop relation types that make sense to humans | Provide a detailed description of the following dataset: TREx-2p |
Pano3D | Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. The Pano3D 360 depth estimation benchmark provides a standard Matterport3D train and test split, as well as a secondary GibsonV2 partioning for testing and training as well. The latter is used for zero-shot cross dataset transfer performance assessment and decomposes it into 3 different splits, each one focusing on a specific generalization axis. | Provide a detailed description of the following dataset: Pano3D |
VoicePrivacy 2020 | **VoicePrivacy 2020** is a dataset for developing anonymization solutions for speech technology. It is built from subsets of existing datasets such as: [LibriSpeech](librispeech-1), [LibriTTS](libritts), [VoxCeleb1](voxceleb1), [VoxCeleb2](voxceleb1) and [VCTK](vctk). | Provide a detailed description of the following dataset: VoicePrivacy 2020 |
ImageTBAD | A dataset of A 3D Computed Tomography (CT) image dataset, ImageTBAD, for segmentation of Type-B Aortic Dissection is published. ImageTBAD contains 100 3D Computed Tomography (CT) images, which is of decent size compared with existing medical imaging datasets.
ImageTBAD contains a total of 100 3D CTA images gathered from Guangdong Peoples' Hospital Data from January 1,2013 to April 23, 2019. Images are acquired from a variety of scanners (GE Medical Systems, Siemens, Philips), resulting in large variance in voxel size, resolution and imaging quality. All the images are pre-operative TBAD CTA images whose top and bottom are around the neck and the brachiocephalic vessels, respectively, in the axial view. The segmentation labeling is performed by a team of two cardiovascular radiologists who have extensive experience with TBAD. The segmentation labeling of each patient is fulfilled by one radiologist and checked by the other. The segmentation | Provide a detailed description of the following dataset: ImageTBAD |
WebQA | WebQA, is a new benchmark for multimodal multihop reasoning in which systems are presented with the same style of data as humans when searching the web: Snippets and Images. The system must then identify which information is relevant across modalities and combine it with reasoning to answer the query. Systems will be evaluated on both the correctness of their answers and their sources. | Provide a detailed description of the following dataset: WebQA |
MiniF2F | **MiniF2F** is a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, and Isabelle and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses. | Provide a detailed description of the following dataset: MiniF2F |
mMARCO | **mMARCO** is a multilingual version of the MS MARCO passage ranking dataset comprising 8 languages that was created using machine translation. | Provide a detailed description of the following dataset: mMARCO |
AwA Pose | **AwA Pose** is a large scale animal keypoint dataset with ground truth annotations for keypoint detection of quadruped animals from images. | Provide a detailed description of the following dataset: AwA Pose |
TREK-150 | **TREK-150** is a benchmark dataset for object tracking in First Person Vision (FPV) videos composed of 150 densely annotated video sequences. | Provide a detailed description of the following dataset: TREK-150 |
OAK | **OAK** is a dataset for online continual object detection benchmark with an egocentric video dataset. OAK adopts the KrishnaCam videos, an ego-centric video stream collected over nine months by a graduate student. OAK provides exhaustive bounding box annotations of 80 video snippets (~17.5 hours) for 105 object categories in outdoor scenes. | Provide a detailed description of the following dataset: OAK |
Depth in the Wild | **Depth in the Wild** is a dataset for single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. It consists of images in the wild annotated with relative depth between pairs of random points. | Provide a detailed description of the following dataset: Depth in the Wild |
Fashion-MMT | **Fashion-MNT** is large-scale bilingual product description dataset called Fashion-MMT, which contains over 114k noisy and 40k manually cleaned description translations with multiple product images. | Provide a detailed description of the following dataset: Fashion-MMT |
ComSum | ComSum is a data set of 7 million commit messages for text summarization. When documenting commits, software code changes, both a message and its summary are posted. These messages are gathered and filtered to curate developers' work summarization data set. | Provide a detailed description of the following dataset: ComSum |
Indiscapes2 | Indiscapes2, a new large-scale diverse dataset of Indic manuscripts with semantic layout annotations. Indiscapes2 contains documents from four different historical collections and is 150% larger than its predecessor, Indiscapes. | Provide a detailed description of the following dataset: Indiscapes2 |
BnB | BnB is a large-scale and diverse in-domain VLN (Vision and Language Navigation) dataset. | Provide a detailed description of the following dataset: BnB |
VIL-100 | **VIL-100** is a video instance lane detection dataset, which contains 100 videos with in total 10,000 frames, acquired from different real traffic scenarios. All the frames in each video are manually annotated to a high-quality instance-level lane annotation, and a set of frame-level and video-level metrics are included for quantitative performance evaluation. | Provide a detailed description of the following dataset: VIL-100 |
Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of Interest | Logging—used for system events and security breaches to more informational yet essential aspects of software features—is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, transforms source code to revitalize feature log levels based on the "interestingness" of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with ~3 million lines of code and ~4K log statements. Our tool successfully analyzes 99.26% of logging statements, increases log level distributions by ~20%, identifies logs manually modified with a recall of ~80% and a level-direction match rate of ~87%, and increases the focus of logs in bug fix contexts ~83% of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels. | Provide a detailed description of the following dataset: Automated Evolution of Feature Logging Statement Levels Using Git Histories and Degree of Interest |
COCO 10% labeled data | Semi-Supervised Object Detection on COCO 10% labeled data | Provide a detailed description of the following dataset: COCO 10% labeled data |
VQA-CE | This dataset provides a new split of VQA v2 (similarly to VQA-CP v2), which is built of questions that are hard to answer for biased models.
This dataset is designed to penalize biases, and encourage the learning of models that generalize well. | Provide a detailed description of the following dataset: VQA-CE |
CodeXGLUE | CodeXGLUE is a benchmark dataset and open challenge for code intelligence. It includes a collection of code intelligence tasks and a platform for model evaluation and comparison. CodeXGLUE stands for General Language Understanding Evaluation benchmark for CODE. It includes 14 datasets for 10 diversified code intelligence tasks covering the following scenarios:
- code-code (clone detection, defect detection, cloze test, code completion, code repair, and code-to-code translation)
- text-code (natural language code search, text-to-code generation)
- code-text (code summarization)
- text-text (documentation translation)
A brief summary of CodeXGLUE is provided in the figure, including tasks, datasets, language, sizes in various states, baseline systems, providers, and short definitions of each task. Datasets highlighted in BLUE are newly introduced.
Image source: [https://github.com/microsoft/CodeXGLUE](https://github.com/microsoft/CodeXGLUE) | Provide a detailed description of the following dataset: CodeXGLUE |
Medical Wiki Paralell Corpus for Medical Text Simplification | A medical Wiki paralell corpus for medical text simplification. | Provide a detailed description of the following dataset: Medical Wiki Paralell Corpus for Medical Text Simplification |
LiDAR-MOS | # Tasks.
In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes.
# Metric
To assess the labeling performance, we rely on the commonly applied Jaccard Index or intersection-over-union (mIoU) metric over moving parts of the environment. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class.
# Citation
Citation. More information on the task and the metric, you can find in our publication related to the task:
@article{chen2021ral,
title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss},
year={2021},
journal={IEEE Robotics and Automation Letters(RA-L)},
doi = {10.1109/LRA.2021.3093567}
} | Provide a detailed description of the following dataset: LiDAR-MOS |
Spider-Realistic | Spider-Realistic dataset is used for evaluation in the paper "Structure-Grounded Pretraining for Text-to-SQL". The dataset is created based on the dev split of the Spider dataset (2020-06-07 version from https://yale-lily.github.io/spider). We manually modified the original questions to remove the explicit mention of column names while keeping the SQL queries unchanged to better evaluate the model's capability in aligning the NL utterance and the DB schema. For more details, please check our paper at https://arxiv.org/abs/2010.12773. | Provide a detailed description of the following dataset: Spider-Realistic |
Infologic sql queries | Sql queries | Provide a detailed description of the following dataset: Infologic sql queries |
ProcGen | Procgen Benchmark includes 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills. | Provide a detailed description of the following dataset: ProcGen |
5DOF GB Interpolation | These are larger MATLAB .mat files required for reproducing plots from the sgbaird-5DOF/interp repository for grain boundary property interpolation. gitID-0055bee_uuID-475a2dfd_paper-data6.mat contains multiple trials of five degree-of-freedom interpolation model runs for various interpolation schemes. gpr46883_gitID-b473165_puuID-50ffdcf6_kim-rng11.mat contains a Gaussian Process Regression model trained on 46883 Fe simulation GBs. See _Five degree-of-freedom property interpolation of arbitrary grain boundaries via Voronoi fundamental zone framework_ DOI: [10.1016/j.commatsci.2021.110756](https://doi.org/10.1016/j.commatsci.2021.110756) for the peer-reviewed, published version of the paper. | Provide a detailed description of the following dataset: 5DOF GB Interpolation |
Self-stimulatory Behavior Dataset | Autism Spectrum Disorders (ASD), often referred to as autism, are neurological disorders characterised by deficits in cognitive skills, social and communicative behaviours. A common way of diagnosing ASD is by studying behavioural cues expressed by the children.
We introduce a new publicly available dataset (SSBD) of children videos exhibiting self-stimulatory (‘stimming’) behaviours commonly used in autism diagnosis. These videos, posted by parents/caregivers on public domain websites, are collected and annotated for the stimming behaviours.
These videos are extremely challenging for automatic behaviour analysis as they are recorded in uncontrolled natural settings. The dataset contains 75 videos with an average duration of 90 seconds per video, grouped under three categories of stimming behaviours:
- arm flapping,
- head banging, and
- spinning. | Provide a detailed description of the following dataset: Self-stimulatory Behavior Dataset |
2017 Robotic Instrument Segmentation Challenge | Segmentation of robotic instruments is an important problem for robotic assisted minimially invasive surgery. It can be used for simple 2D applications such as overlay masking or 2D tracking but also for more complex 3D tasks such as pose estimation. In this challenge we invite applicants to participate in 3 different tasks: binary segmentation, multi-label segmentation and instrument recognition. Binary segmentation involves just separating the image into instruments and background, whereas multi-label segmentation requires the user to also recognize which parts of the instrument body correspond to the different articulated parts of a da Vinci robotic instrument. The final recogition task tests whether the user can recognize which segmentation corresponds to which da Vinci instrument type.
To achieve this we are providing 8x 225-frame robotic surgical videos, captured at 2 Hz, where a trained team at Intuitive Surgical has manually labelled the different parts and types. The users are invited to test their algorithms on 8x 75-frame videos and 2x 300-frame videos which act as a test set.
Description from: [Robotic Instrument Segmentation Sub-Challenge](https://endovissub2017-roboticinstrumentsegmentation.grand-challenge.org/)
Image source: [https://endovissub2017-roboticinstrumentsegmentation.grand-challenge.org/](https://endovissub2017-roboticinstrumentsegmentation.grand-challenge.org/) | Provide a detailed description of the following dataset: 2017 Robotic Instrument Segmentation Challenge |
Hocalarim: Turkish Student Reviews | We have constructed our dataset by five fields available on the website that
were found convenient for the study of student expectations and experience.
This includes out-of-five star ratings on easiness, understandability, recitation,
accessibility and helpfulness. Average rating was calculated based on these
given five fields. Overall sentiment of the review was determined based on the
average rating where any score higher than 3.5 (>=) was labeled as a positive
review, and anything lower than 2.5 (<) was labeled as a negative review. The
five main aspects students needed to rate was given below.
• Anlaşılırlık (Clarity): Are lectures by the professor clear and understandable?
• Ders Anlatımı (Recitation): Are the lecturing style and material usage of
the professor organized?
• Erişilebilirlik (Accessibility): Is the professor available for further support
outside of classes?
• Yardımseverlik (Helpfulness): How does the professor approach his/her
students?
• Kolaylık (Easiness): How easy are the assessments?
• Ortalama (Average): The final average score was calculated based on the
five different aspects | Provide a detailed description of the following dataset: Hocalarim: Turkish Student Reviews |
COVID-19 Disinfo | With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that focuses on COVID-19, combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual multilingual, and single task vs. multitask settings. | Provide a detailed description of the following dataset: COVID-19 Disinfo |
Waste Classification data | PROBLEM
Waste management is a big problem in our country. Most of the wastes end up in landfills. This leads to many issues like: Increase in landfills, Eutrophication, Consumption of toxic waste by animals, Leachate, Increase in toxins, Land, water and air pollution.
APPROACH
Studied white papers on waste management, Analysed the components of household waste, Segregated into two classes (Organic and recyclable), Automated the process by using IOT and machine learning, Reduce toxic waste ending in landfills
IMPLEMENTATION
Dataset is divided into train data (85%) and test data (15%)
Training data - 22564 images
Test data - 2513 images | Provide a detailed description of the following dataset: Waste Classification data |
MUC-4 | A dataset for evaluate system's understanding of given passages. | Provide a detailed description of the following dataset: MUC-4 |
BiSECT | **BiSECT** is a dataset for sentence simplification, which is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. These were obtained by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. | Provide a detailed description of the following dataset: BiSECT |
HPS Dataset | HPS Dataset is a collection of 3D humans interacting with large 3D scenes (300-1000 $m^2$, up to 2500 $m^2$).
The dataset contains images captured from a head-mounted camera coupled with the reference 3D pose and location of the person in a pre-scanned 3D scene. 7 people in 8 large scenes are captured performing activities such as exercising, reading, eating, lecturing, using a computer, making coffee, dancing. The dataset provides more than 300K synchronized RGB images coupled with the reference 3D pose and location.
The dataset can be used as a testbed for ego-centric tracking with scene constraints, to learn how humans interact and move within large scenes over long periods of time, and to learn how humans process visual input arriving at their eyes. | Provide a detailed description of the following dataset: HPS Dataset |
ASTE-Data-V2 | A benchmark dataset for the Aspect Sentiment Triplet Extraction, an updated version of ASTE-Data-V1. | Provide a detailed description of the following dataset: ASTE-Data-V2 |
DRKG | Drug Repurposing Knowledge Graph (DRKG) is a comprehensive biological knowledge graph relating genes, compounds, diseases, biological processes, side effects and symptoms. DRKG includes information from six existing databases including DrugBank, Hetionet, GNBR, String, IntAct and DGIdb, and data collected from recent publications particularly related to Covid19. It includes 97,238 entities belonging to 13 entity-types; and 5,874,261 triplets belonging to 107 edge-types. These 107 edge-types show a type of interaction between one of the 17 entity-type pairs (multiple types of interactions are possible between the same entity-pair), as depicted in the figure below. It also includes a bunch of notebooks about how to explore and analysis the DRKG using statistical methodologies or using machine learning methodologies such as knowledge graph embedding. | Provide a detailed description of the following dataset: DRKG |
CorruptionDataSet | This original data set includes the following four sheets:
Sheet 1: Raw Data (the original data set)
Sheet 2: Variables (A list with the variables included in the study)
Sheet 3: Countries Scientific Relative Production
Sheet 4: Correlations | Provide a detailed description of the following dataset: CorruptionDataSet |
TRIP | Tiered Reasoning for Intuitive Physics (TRIP) is a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. TRIP serves as a benchmark for physical commonsense reasoning that provides traces of reasoning for an end task of plausibility prediction. The dataset consists of human-authored stories describing sequences of concrete physical actions. Given two stories composed of individually plausible sentences and only differing by one sentence (i.e., Sentence 5), the proposed task is to determine which story is more plausible. To understand stories like these and make such a prediction, one must have knowledge of verb causality and precondition, and rules of intuitive physics.
Description from: [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://arxiv.org/pdf/2109.04947v1.pdf)
Image source: [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://arxiv.org/pdf/2109.04947v1.pdf) | Provide a detailed description of the following dataset: TRIP |
Graphine | The Graphine dataset contains 2,010,648 terminology definition pairs organized in 227 directed acyclic graphs. Each node in the graph is associated with a terminology and its definition. Terminologies are organized from coarse-grained ones to fine-grained ones in each graph. | Provide a detailed description of the following dataset: Graphine |
MuCo-VQA | MuCo-VQA consist of large-scale (3.7M) multilingual and code-mixed VQA datasets in multiple languages: Hindi (hi), Bengali (bn), Spanish (es), German (de), French (fr) and code-mixed language pairs: en-hi, en-bn, en-fr, en-de and en-es.
Image source: [https://arxiv.org/pdf/2109.04653v1.pdf](https://arxiv.org/pdf/2109.04653v1.pdf) | Provide a detailed description of the following dataset: MuCo-VQA |
LIVECell | The **LIVECell (Label-free In Vitro image Examples of Cells)** dataset is a large-scale microscopic image dataset for instance-segmentation of individual cells in 2D cell cultures.
LIVECell consists of 5,239 manually annotated, expert-validated, Incucyte HD phase-contrast microscopy images with a total of 1,686,352 individual cells annotated from eight different cell types (average 313 cells per image). The LIVECell images have predefined splits into training (3188), validation (539) and test (1512) sets. Each split is also further subdivided into each of the eight cell types. The training set also has splits of different sizes (2, 4, 5, 25, 50%) to allow dataset size experimentation. | Provide a detailed description of the following dataset: LIVECell |
Helix | See https://zenodo.org/record/5500215#.YUCgD51Kg2w | Provide a detailed description of the following dataset: Helix |
Dataset of 3D Garments with Sewing Patterns | The Dataset contains more than 23500 3D garment models with their corresponding sewing patterns, each representing a unique garment design sampled from one of the 19 different categories. The dataset is suitable for training Deep Learning models to solve a variety of clothing-related tasks. | Provide a detailed description of the following dataset: Dataset of 3D Garments with Sewing Patterns |
YorkTag | YorkTag provides pairs of sharp/blurred images containing fiducial markers and is proposed to train and qualitatively and quantitatively evaluate our model. | Provide a detailed description of the following dataset: YorkTag |
GMEG-yahoo | Grammatical error correction dataset for text from Yahoo! Answers | Provide a detailed description of the following dataset: GMEG-yahoo |
GMEG-wiki | Grammatical error correction dataset for text from Wikipedia. | Provide a detailed description of the following dataset: GMEG-wiki |
SituatedQA | **SituatedQA** is an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. Answers to the same question may change depending on the extralinguistic contexts (when and where the question was asked). | Provide a detailed description of the following dataset: SituatedQA |
E-Manual Corpus | **E-Manual Corpus** is a corpus of 307,957 E-manuals, used for pre-training models for Question Answering on e-manuals. | Provide a detailed description of the following dataset: E-Manual Corpus |
CelebA-Dialog | The CelebA-Dialog dataset has the following properties: 1) Facial images are annotated with rich fine-grained labels, which classify one attribute into multiple degrees according to its semantic meaning; 2) Accompanied with each image, there are captions describing the attributes and a user request sample.
Image source: [https://arxiv.org/pdf/2109.04425v1.pdf](https://arxiv.org/pdf/2109.04425v1.pdf) | Provide a detailed description of the following dataset: CelebA-Dialog |
MLFW | The Masked LFW (MLFW), based on [Cross-Age LFW (CALFW)](https://paperswithcode.com/dataset/calfw) database, is built using a simple but effective tool that generates masked faces from unmasked faces automatically.
Image source: [https://arxiv.org/pdf/2109.05804v1.pdf](https://arxiv.org/pdf/2109.05804v1.pdf) | Provide a detailed description of the following dataset: MLFW |
VGaokao | **VGaokao** is a verification style reading comprehension dataset designed for native speakers' evaluation. | Provide a detailed description of the following dataset: VGaokao |
Implicit Hate | The **Implicit Hate** corpus is a dataset for hate speech detection with fine-grained labels for each message and its implication. This dataset contains 22,056 tweets from the most prominent extremist groups in the United States; 6,346 of these tweets contain implicit hate speech. | Provide a detailed description of the following dataset: Implicit Hate |
ZESHEL | ZESHEL is a zero-shot entity linking dataset, which places more emphasis on understanding the unstructured descriptions of entities to resolve the ambiguity of mentions on four unseen domains.
This dataset was constructed using Wikias from FANDOM. | Provide a detailed description of the following dataset: ZESHEL |
GD-VCR | Geo-Diverse Visual Commonsense Reasoning (GD-VCR) is a new dataset to test vision-and-language models' ability to understand cultural and geo-location-specific commonsense.
Image source: [https://arxiv.org/pdf/2109.06860v1.pdf](https://arxiv.org/pdf/2109.06860v1.pdf) | Provide a detailed description of the following dataset: GD-VCR |
Harm-C | **Harm-C** is a dataset for detecting harmful memes related to Covid-19. | Provide a detailed description of the following dataset: Harm-C |
Commonsense-Dialogues | **Commonsense-Dialogues** is a crowdsourced dataset of ~11K dialogues grounded in social contexts involving utilization of commonsense. The social contexts used were sourced from the train split of the [SocialIQA](social-iqa) dataset, a multiple-choice question-answering based social commonsense reasoning benchmark. | Provide a detailed description of the following dataset: Commonsense-Dialogues |
BenchIE | BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese and German. In contrast to existing OIE benchmarks, BenchIE takes into account informational equivalence of extractions: our gold standard consists of fact synsets, clusters in which we exhaustively list all surface forms of the same fact. | Provide a detailed description of the following dataset: BenchIE |
DMO | A large scale dataset to pre-train optical flow prediction network. The data are generated from the DAVIS videos using as-rigid-as-possible principle from Deep-matching and MaskRCNN. The dataset has shown better performance compared to the FlyingChairs dataset. | Provide a detailed description of the following dataset: DMO |
FlyingChairs | The "Flying Chairs" are a synthetic dataset with optical flow ground truth. It consists of 22872 image pairs and corresponding flow fields. Images show renderings of 3D chair models moving in front of random backgrounds from Flickr. Motions of both the chairs and the background are purely planar. | Provide a detailed description of the following dataset: FlyingChairs |
KVQA | It contains manually verified 183K question-answer pairs about more than 18K persons and 24K images. The questions in this dataset require multi-entity, multi-relation and multi-hop reasoning over KG to arrive at an answer. To enable visual named entity linking, it also provides a support set containing reference images of 69K persons harvested from Wikidata as part of the dataset. | Provide a detailed description of the following dataset: KVQA |
WADS | Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather.
Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format).
This outdoor dataset introduces `falling_snow` and `accumulated_snow` along with all the semanticKITTI classes to further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow. | Provide a detailed description of the following dataset: WADS |
Nelson-Plosser | US Macroeconomic dataset containing 14 time series of monthly observations. They have various lengths but all end in 1988. The variables: consumer price index, industrial production, nominal GNP, velocity, employment, interest rate, nominal wages, GNP deflator, money stock, real GNP, stock prices (S&P500), GNP per capita, real wages, unemployment.
The data is available in cleaned form in the R package 'tseries' [1].
First introduction contained data until 1970 [2].
Rerefences:
1. Trapletti, Adrian, and Kurt Hornik. 2020. tseries: Time Series Analysis and Computational Finance. https://cran.r-project.org/package=tseries.
2. Nelson, Charles R., and Charles I. Plosser. 1982. “Trends and Random Walks in Macroeconmic Time Series.” Journal of Monetary Economics 10 (2): 139–162. doi: 10.1016/0304-3932(82)90012-5. | Provide a detailed description of the following dataset: Nelson-Plosser |
BioLAMA | **BioLAMA** is a benchmark comprised of 49K biomedical factual knowledge triples for probing biomedical Language Models. It is used to assess the capabilities of Language Models for being valid biomedical knowledge bases. | Provide a detailed description of the following dataset: BioLAMA |
BLANCA | **BLANCA** (Benchmarks for LANguage models on Coding Artifacts) is a collection of benchmarks that assess code understanding based on tasks such as predicting the best answer to a question in a forum post, finding related forum posts, or predicting classes related in a hierarchy from class documentation. | Provide a detailed description of the following dataset: BLANCA |
ELITR ECA | The ELITR ECA corpus is a multilingual corpus derived from publications of the European Court of Auditors. We use automatic translation together with Bleualign to identify parallel sentence pairs in all 506 translation directions. The result is a corpus comprising 264k document pairs and 41.9M sentence pairs.
Description from: [The ELITR ECA Corpus](https://arxiv.org/pdf/2109.07351v1.pdf) | Provide a detailed description of the following dataset: ELITR ECA |
MindCraft | **MindCraft** is a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. | Provide a detailed description of the following dataset: MindCraft |
M5Product | The **M5Product** dataset is a large-scale multi-modal pre-training dataset with coarse and fine-grained annotations for E-products.
• 6 Million multi-modal samples, 5k properties with 24 Million values
• 5 modalities-image text table video audio
• 6 Million category annotations with 6k classes
• Wide data source (1 Million merchants provide) | Provide a detailed description of the following dataset: M5Product |
Roof-Image Dataset | We created a building-image paired dataset that contains more than 3K samples using our roof modeling tools.
Image source: [https://github.com/llorz/SGA21_roofOptimization/tree/main/RoofGraphDataset](https://github.com/llorz/SGA21_roofOptimization/tree/main/RoofGraphDataset) | Provide a detailed description of the following dataset: Roof-Image Dataset |
AnlamVer | In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.
(from the original abstract of the dataset paper) | Provide a detailed description of the following dataset: AnlamVer |
EDGAR10-Q Dataset | This dataset is built from 10-Q documents (Quarterly Reports) of publicly listed companies on the SEC. | Provide a detailed description of the following dataset: EDGAR10-Q Dataset |
ChFinAnn | Ten years (2008-2018) ChFinAnn documents and human-summarized event knowledge bases to conduct the DS-based event labeling.
Five event types included: Equity Freeze (EF), Equity Repurchase (ER), Equity Underweight (EU), Equity Overweight (EO) and Equity Pledge (EP), which belong to major events required to be disclosed by the regulator and may have a huge impact on the company value.
To ensure the labeling quality, the authors set constraints for matched document-record pairs.
There are 32, 040 documents in total, and this number is ten times larger than 2, 976 of DCFEE and about 53 times larger than 599 of ACE 2005.
These documents are divided into train, development, and test set with the proportion of 8 : 1 : 1 based on the time order.
This DS-generated data are pretty good, achieving high precision and acceptable recall.
In later experiments, the authors directly employ the automatically generated test set for evaluation due to its much broad coverage. | Provide a detailed description of the following dataset: ChFinAnn |
TruthfulQA | TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. The authors crafted questions that some humans would answer falsely due to a false belief or misconception.
Image source: [https://arxiv.org/pdf/2109.07958v1.pdf](https://arxiv.org/pdf/2109.07958v1.pdf) | Provide a detailed description of the following dataset: TruthfulQA |
SWDE | This dataset is a real-world web page collection used for research on the automatic extraction of structured data (e.g., attribute-value pairs of entities) from the Web. We hope it could serve as a useful benchmark for evaluating and comparing different methods for structured web data extraction. | Provide a detailed description of the following dataset: SWDE |
wikiHow-image | The dataset consists of 53,189 wikiHow articles across various categories of everyday tasks, 155,265 methods, and 772,294 steps with corresponding images. | Provide a detailed description of the following dataset: wikiHow-image |
UESTC RGB-D | UESTC RGB-D Varying-view action database contains 40 categories of aerobic exercise. We utilized 2 Kinect V2 cameras in 8 fixed directions and 1 round direction to capture these actions with the data modalities of RGB video, 3D skeleton sequences and depth map sequences. | Provide a detailed description of the following dataset: UESTC RGB-D |
NLB | **Neural Latents** is a benchmark for latent variable modeling of neural population activity. It consists of four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas. | Provide a detailed description of the following dataset: NLB |
HM3D | **Habitat-Matterport 3D** (HM3D) is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-floor residences, stores, and other private indoor spaces.
HM3D surpasses existing datasets available for academic research in terms of physical scale, completeness of the reconstruction, and visual fidelity. HM3D contains 112.5k m^2 of navigable space, which is 1.4 - 3.7x larger than other building-scale datasets such as [MP3D](matterport3d) and [Gibson](gibson-environment). When compared to existing photorealistic 3D datasets such as [Replica](replica), MP3D, Gibson, and [ScanNet](scannet), images rendered from HM3D have 20 - 85% higher visual fidelity w.r.t. counterpart images captured with real cameras, and HM3D meshes have 34 - 91% fewer artifacts due to incomplete surface reconstruction. | Provide a detailed description of the following dataset: HM3D |
Depth VIDIT | VIDIT is a reference evaluation benchmark and to push forward the development of illumination manipulation methods. Virtual datasets are not only an important step towards achieving real-image performance but have also proven capable of improving training even when real datasets are possible to acquire and available. VIDIT contains 300 virtual scenes used for training, where every scene is captured 40 times in total: from 8 equally-spaced azimuthal angles, each lit with 5 different illuminants. | Provide a detailed description of the following dataset: Depth VIDIT |
ADEFAN | This data set contains 50 low resolution (640 x 360) short videos containing a variety real life activities. | Provide a detailed description of the following dataset: ADEFAN |
VR Curve on Surface Drawing Dataset | The datasets includes curves drawn on 3D surfaces (triangle meshes) in Virtual Reality. A total of 2,880 curves were created using two different techniques by 20 users on 6 meshes. For each curve, a 3D curve executed by the user is provided, the projected curve created on the mesh, and the ground truth target curve on the mesh. For collecting the data, two different task types were employed, which are described in the paper. | Provide a detailed description of the following dataset: VR Curve on Surface Drawing Dataset |
Machine Learning Quantum Reaction Rate Constants | Dataset of 1,517,419 quantum reaction rate constant products kQM(T)QR(T) computed from the transmission coefficient for model single and double barrier minimum energy paths. Here kQM(T) is the quantum reaction rate constant at temperature T and QR(T) is the reactant partition function computed with the rigid rotor and harmonic oscillator approximations.This dataset was created for Ref [1] where it was used to train and test a DNN to predict logkQM(T)QR(T).
See zenodo webpage for details and dataset
https://zenodo.org/record/5510392#.YUkbjWZKhOc | Provide a detailed description of the following dataset: Machine Learning Quantum Reaction Rate Constants |
ReaSCAN | ReaSCAN is a synthetic navigation task that requires models to reason about surroundings over syntactically difficult languages. | Provide a detailed description of the following dataset: ReaSCAN |
CMU Motion Capture | This dataset of motions is free for all uses.
Please don't crawl this database! Check out the FAQs.
This data is free for use in research projects.
You may include this data in commercially-sold products,
but you may not resell this data directly, even in converted form.
If you publish results obtained using this data, we would appreciate it
if you would send the citation to your published paper to jkh+mocap@cs.cmu.edu,
and also would add this text to your acknowledgments section:
The data used in this project was obtained from mocap.cs.cmu.edu.
The database was created with funding from NSF EIA-0196217.
Note: In this database, the same person may appear under more than one subject number.
Each subject, however, has its own calibrated skeleton.
Note: When browsing for motions, start with the higher numbered subjects first.
The lower numbers contain some of our earliest motion capture sessions, and may not be as high quality.
Note: The "toe" and "hand" joints in our motions tend to be noisy, and may require some smoothing.
The "finger" and "thumb" joints are added to the skeleton for editing convenience
- we do not actually capture these joints' motions and any such data should be ignored. | Provide a detailed description of the following dataset: CMU Motion Capture |
Berkeley MHAD | Description
The Berkeley Multimodal Human Action Database (MHAD) contains 11 actions performed by 7 male and 5 female subjects in the range 23-30 years of age except for one elderly subject. All the subjects performed 5 repetitions of each action, yielding about 660 action sequences which correspond to about 82 minutes of total recording time. In addition, we have recorded a T-pose for each subject which can be used for the skeleton extraction; and the background data (with and without the chair used in some of the activities). The specified set of actions comprises of the following: (1) actions with movement in both upper and lower extremities, e.g., jumping in place, jumping jacks, throwing, etc., (2) actions with high dynamics in upper extremities, e.g., waving hands, clapping hands, etc. and (3) actions with high dynamics in lower extremities, e.g., sit down, stand up. Prior to each recording, the subjects were given instructions on what action to perform; however no specific details were given on how the action should be executed (i.e., performance style or speed). The subjects have thus incorporated different styles in performing some of the actions (e.g., punching, throwing). | Provide a detailed description of the following dataset: Berkeley MHAD |
Novel COVID-19 Chestxray Repository | ##_Authors of the Dataset_:
- Pratik Bhowal (B.E., Dept of Electronics and Instrumentation Engineering, Jadavpur University Kolkata, India) [[LinkedIn]](https://www.linkedin.com/in/pratik-bhowal-1066aa198?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3B%2BqgwqwxJRIep5K454MTQ6w%3D%3D), [[Github]](https://github.com/prat1999)
- Subhankar Sen (B.Tech, Dept of Computer Science Engineering, Manipal University Jaipur, India) [[LinkedIn]](https://www.linkedin.com/in/subhankar-sen-a62457190lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3BP2gUaNhAT0uL2etYJDiGqw%3D%3D), [[Github]](https://github.com/subhankar01), [[Google Scholar]](https://scholar.google.com/citations?user=MSXb0xoAAAAJ&hl=en)
- Jin Hee Yoon (faculty of the Dept. of Mathematics and Statistics at Sejong University, Seoul, South Korea) [[LinkedIn]](https://www.linkedin.com/in/jin-hee-yoon-2418a069), [[Google Scholar]](https://scholar.google.com/citations?user=Rq_TQc0AAAAJ&hl=en)
- Zong Woo Geem (faculty of College of IT Convergence at Gachon University, South Korea) [[LinkedIn]](https://www.linkedin.com/in/zong-woo-geem-66273113), [[Google Scholar]](https://scholar.google.com/citations?hl=en&user=Je3-B2YAAAAJ)
- Ram Sarkar( Professor at Dept. of Computer Science Engineering, Jadavpur Univeristy Kolkata, India) [[LinkedIn]](https://www.linkedin.com/in/ram-sarkar-0ba8a758?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3BvwKX%2Frm5RNSySsSaIQTiVQ%3D%3D), [[Google Scholar]](https://scholar.google.com/citations?hl=en&user=bDj0BUEAAAAJ&view_op=list_works&citft=1&citft=2&citft=3&email_for_op=subhankarsen2001%40gmail.com&gmla=AJsN-F5CKj5MB0jIcLJssFUKVVcxdf5jt8CBMbzSZf6W9RJvYUYp61X3OC6sXa_lzg1FHW7A8BpuLWwkMtDLWxJje2eowsNWqllMazckf90f5PsxhFZ2D1PcmhyhjJ8OT5q2-3Pc3DcwNuIj4E0s2LfWgQVOZBVVGs76xTjTPWNSKVvqBhvA-u05tkPXamKiItj8RSd_vApWN6jtmvYA9JcJ4ObPprLRFPV10T5a0A4nmrQVxyniapy6XIgng1L8D1qTtb2oFAow)
##Overview
The authors have created a new dataset known as Novel COVID-19 Chestxray Repository by the fusion of publicly available chest-xray image repositories. In creating this combined dataset, three different datasets obtained from the Github and Kaggle databases,created by the authors of other research studies in this field, were utilized.In our study,frontal and lateral chest X-ray images are used since this view of radiography is widely used by radiologist in clinical diagnosis.In the following section, authors have summarized how this dataset is created.
- [COVID-19 Radiography Database](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database): The first release of this dataset reports 219 COVID-19,1345 viral pneumonia and 1341 normal radiographic chest X-ray images. This dataset was created by a team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh in collaboration with medical doctors and specialists from Pakistan and Malaysia.This database is regularly updated with the emergence of new cases of COVID-19 patients worldwide.Related Paper:https://arxiv.org/abs/2003.13145
- [COVID-Chestxray set](https://github.com/ieee8023/covid-chestxray-dataset):Joseph Paul Cohen and Paul Morrison and Lan Dao have created a public image repository on Github which consists both CT scans and digital chest x-rays.The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Children’s medical center.With the aid of metadata information provided along with the dataset,we were able to extract 521 COVID-19 positive,239 viral and bacterial pneumonias;which are of the following three broad categories:Middle East Respiratory Syndrome (MERS),Severe Acute Respiratory Syndrome (SARS), and Acute Respiratory Distress syndrome (ARDS);and 218 normal radiographic chest X-ray images of varying image resolutions. Related Paper: https://arxiv.org/abs/2006.11988
- [Actualmed COVID chestxray dataset](https://github.com/agchung/Actualmed-COVID-chestxray-dataset):Actualmed-COVID-chestxray-dataset comprises of 12 COVID-19 positive and 80 normal radiographic chest x-ray images.
The combined dataset includes chest X-ray images of COVID-19,Pneumonia and Normal (healthy) classes, with a total of 752, 1584, and 1639 images respectively. Information about the Novel COVID-19 Chestxray Database and its parent image repositories is provided in [Table 1](#tab1).
### Table 1: Dataset Description
| Dataset| COVID-19 |Pneumonia | Normal |
| ------------- | ------------- | ------------- | -------------|
| [COVID Chestxray set](https://github.com/ieee8023/covid-chestxray-dataset) | 521 |239|218|
| [COVID-19 Radiography Database(first release)](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database) | 219 |1345|1341|
| [Actualmed COVID chestxray dataset](https://github.com/agchung/Actualmed-COVID-chestxray-dataset)| 12 |0|80|
| **Total**|**752**|**1584**|**1639**|
DATA ACCESS AND USE: Academic/Non-Commercial Use
Dataset License : [Database: Open Database, Contents: Database Contents](https://opendatacommons.org/licenses/dbcl/dbcl-10.txt) | Provide a detailed description of the following dataset: Novel COVID-19 Chestxray Repository |
EmoCause | **EmoCause** is a dataset of annotated emotion cause words in emotional situations from the [EmpatheticDialogues](/dataset/empatheticdialogues) valid and test set. The goal is to recognize emotion cause words in sentences by training only on sentence-level emotion labels without word-level labels (i.e., weakly-supervised emotion cause recognition).
**EmoCause** is based on the fact that humans do not recognize the cause of emotions with supervised learning on word-level cause labels. Thus, we do not provide a training set.
* Number of emotion categories: 32
* Average number of cause words per utterance: 2.3
* Total number of utterances: 4.6K (valid: 3.8K / test: 0.8K) | Provide a detailed description of the following dataset: EmoCause |
CodeQA | CodeQA is a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated. CodeQA contains a Java dataset with 119,778 question-answer pairs and a Python dataset with 70,085 question-answer pairs.
Description from: [CodeQA: A Question Answering Dataset for Source Code Comprehension](https://paperswithcode.com/paper/codeqa-a-question-answering-dataset-for) | Provide a detailed description of the following dataset: CodeQA |
Draper VDisc Dataset | Draper VDISC Dataset - Vulnerability Detection in Source Code
The dataset consists of the source code of 1.27 million functions mined from open source software, labeled by static analysis for potential vulnerabilities. For more details on the dataset and benchmark results, see https://arxiv.org/abs/1807.04320.
The data is provided in three HDF5 files corresponding to an 80:10:10 train/validate/test split, matching the splits used in our paper. The combined file size is roughly 1 GB. Each function's raw source code, starting from the function name, is stored as a variable-length UTF-8 string. Five binary 'vulnerability' labels are provided for each function, corresponding to the four most common CWEs in our data plus all others:
* CWE-120 (3.7% of functions)
* CWE-119 (1.9% of functions)
* CWE-469 (0.95% of functions)
* CWE-476 (0.21% of functions)
* CWE-other (2.7% of functions)
Functions may have more than one detected CWE each.
Please cite our paper if you use this dataset in a publication: [https://arxiv.org/abs/1807.04320](https://arxiv.org/abs/1807.04320) | Provide a detailed description of the following dataset: Draper VDisc Dataset |
VISUELLE | VISUELLE is a repository build upon the data of a real fast fashion company, Nunalie, and is composed of 5577 new products and about 45M sales related to fashion seasons from 2016-2019. Each product in VISUELLE is equipped with multimodal information: its image, textual metadata, sales after the first release date, and three related Google Trends describing category, color and fabric popularity.
Download <a href="https://drive.google.com/file/d/11Bn2efKfO_PbtdqsSqj8U6y6YgBlRcP6/view?usp=sharing">here</a>
Image source: [https://arxiv.org/pdf/2109.09824v1.pdf](https://arxiv.org/pdf/2109.09824v1.pdf) | Provide a detailed description of the following dataset: VISUELLE |
ARCA23K | ARCA23K is a dataset of labelled sound events created to investigate real-world label noise. It contains 23,727 audio clips originating from Freesound, and each clip belongs to one of 70 classes taken from the AudioSet ontology. The dataset was created using an entirely automated process with no manual verification of the data. For this reason, many clips are expected to be labelled incorrectly. | Provide a detailed description of the following dataset: ARCA23K |
EntityQuestions | **EntityQuestions** is a dataset of simple, entity-rich questions based on facts from Wikidata (e.g., "Where was Arve Furset born? "). | Provide a detailed description of the following dataset: EntityQuestions |
OPV2V | **OPV2V** is a large-scale open simulated dataset for Vehicle-to-Vehicle perception. It contains over 70 interesting scenes, 11,464 frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 towns in CARLA and a digital town of Culver City, Los Angeles. | Provide a detailed description of the following dataset: OPV2V |
ObjectFolder | **ObjectFolder** is a dataset for multisensory object-centric perception, reasoning, and interaction. It consists of 100 virtualized objects. ObjectFolder encodes the visual, auditory, and tactile sensory data for all objects, enabling a number of multisensory object recognition tasks. | Provide a detailed description of the following dataset: ObjectFolder |
Bentham | Bentham manuscripts refers to a large set of documents that were written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). Volunteers of the Transcribe Bentham initiative transcribed this collection. Currently, >6 000 documents or > 25 000 pages have been transcribed using this public web platform.
For our experiments, we used the BenthamR0 dataset a part of the Bentham manuscripts. | Provide a detailed description of the following dataset: Bentham |
Saint Gall | Saint Gall dataset contains handwritten historical manuscripts written in Latin that date back to the 9th century. It consists of 60 pages, 1 410 text lines and 11 597 words. | Provide a detailed description of the following dataset: Saint Gall |
Konzil | Konzil dataset was created by specialists of the University of Greifswald. It contains manuscripts written in modern German. Train sample consists of 353 lines, validation - 29 lines and test - 87 lines. | Provide a detailed description of the following dataset: Konzil |
Schiller | Schiller contains handwritten texts written in modern German. Train sample consists of 244 lines, validation - 21 lines and test - 63 lines. | Provide a detailed description of the following dataset: Schiller |
Ricordi | Ricordi contains handwritten texts written in Italian. Train sample consists of 295 lines, validation - 19 lines and test - 69 lines. | Provide a detailed description of the following dataset: Ricordi |
Patzig | Patzig contains handwritten texts written in modern German. Train sample consists of 485 lines, validation - 38 lines and test -118 lines. | Provide a detailed description of the following dataset: Patzig |
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