dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
X-SRL | SRL is the task of extracting semantic predicate-argument structures from sentences. **X-SRL** is a multilingual parallel Semantic Role Labelling (SRL) corpus for English (EN), German (DE), French (FR) and Spanish (ES) that is based on English gold annotations and shares the same labelling scheme across languages. | Provide a detailed description of the following dataset: X-SRL |
x-stance | A large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). | Provide a detailed description of the following dataset: x-stance |
YASO | YASO is a crowd-sourced TSA evaluation dataset, collected using a new annotation scheme for labeling targets and their sentiments. The dataset contains 2,215 English sentences from movie, business and product reviews, and 7,415 terms and their corresponding sentiments annotated within these sentences. | Provide a detailed description of the following dataset: YASO |
YCBInEOAT Dataset | A new dataset with significant occlusions related to object manipulation. | Provide a detailed description of the following dataset: YCBInEOAT Dataset |
YFCC100M | YFCC100M is a that dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of metadata, e.g. Flickr identifier, owner name, camera, title, tags, geo, media source. The collection provides a comprehensive snapshot of how photos and videos were taken, described, and shared over the years, from the inception of Flickr in 2004 until early 2014. | Provide a detailed description of the following dataset: YFCC100M |
Yoga-82 | Dataset for large-scale yoga pose recognition with 82 classes. | Provide a detailed description of the following dataset: Yoga-82 |
YouCook | This data set was prepared from 88 open-source YouTube cooking videos. The YouCook dataset contains videos of people cooking various recipes. The videos were downloaded from YouTube and are all in the third-person viewpoint; they represent a significantly more challenging visual problem than existing cooking and kitchen datasets (the background kitchen/scene is different for many and most videos have dynamic camera changes). In addition, frame-by-frame object and action annotations are provided for training data (as well as a number of precomputed low-level features). Finally, each video has a number of human provided natural language descriptions (on average, there are eight different descriptions per video). This dataset has been created to serve as a benchmark in describing complex real-world videos with natural language descriptions. | Provide a detailed description of the following dataset: YouCook |
Youtubean | Youtbean is a dataset created from closed captions of YouTube product review videos. It can be used for aspect extraction and sentiment classification.
Source: [https://arxiv.org/pdf/1708.02420.pdf](https://arxiv.org/pdf/1708.02420.pdf) | Provide a detailed description of the following dataset: Youtubean |
YT-BB | YouTube-BoundingBoxes (YT-BB) is a large-scale data set of video URLs with densely-sampled object bounding box annotations. The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. | Provide a detailed description of the following dataset: YT-BB |
YouTube Movie Summaries | This dataset contains 94 movie summary videos from various YouTube channels. | Provide a detailed description of the following dataset: YouTube Movie Summaries |
YUP++ | A new and challenging video database of dynamic scenes that more than doubles the size of those previously available. This dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. | Provide a detailed description of the following dataset: YUP++ |
ZEST | A new English language dataset structured for task-oriented evaluation on unseen tasks. | Provide a detailed description of the following dataset: ZEST |
Zooniverse | The Humbug Zooinverse dataset is a dataset of mosquito audio recordings. With over a thousand contributors, it contains 195,434 labels of two second duration, of which approximately 10 percent signify mosquito events.
Source: [https://github.com/HumBug-Mosquito/ZooniverseData](https://github.com/HumBug-Mosquito/ZooniverseData) | Provide a detailed description of the following dataset: Zooniverse |
ZuBuD+ | A more balanced version of ZuBuD. | Provide a detailed description of the following dataset: ZuBuD+ |
Spot-the-diff | Spot-the-diff is a dataset consisting of 13,192 image pairs along with corresponding human provided text annotations stating the differences between the two images. | Provide a detailed description of the following dataset: Spot-the-diff |
OST300 | **OST300** is an outdoor scene dataset with 300 test images of outdoor scenes, and a training set of 7 categories of images with rich textures. | Provide a detailed description of the following dataset: OST300 |
CoNSeP | The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs, each belonging to an individual patient, and scanned with an Omnyx VL120 scanner within the department of pathology at University Hospitals Coventry and Warwickshire, UK. | Provide a detailed description of the following dataset: CoNSeP |
CUHK Image Cropping | **CUHK Image Cropping** is a dataset for image cropping. The photos are of varying aesthetic quality and span a variety of image categories, including animal, architecture, human, landscape, night, plant and man-made objects. Each image is manually cropped by three expert photographers (graduate students in art whose primary medium is photography) to form three training sets. There are 1,000 photos in the dataset. | Provide a detailed description of the following dataset: CUHK Image Cropping |
MIT Traffic | **MIT Traffic** is a dataset for research on activity analysis and crowded scenes. It includes a traffic video sequence of 90 minutes long. It is recorded by a stationary camera. The size of the scene is 720 by 480 and it is divided into 20 clips. | Provide a detailed description of the following dataset: MIT Traffic |
PCN | **Pedestrian Color Naming (PCN)** is a dataset for pedestrian color naming, which contains 14,213 images, each of which hand-labeled with color label for each pixel. All images in the PCN dataset are obtained from the Market- 1501 dataset. | Provide a detailed description of the following dataset: PCN |
Social Relation Dataset | **Social Relation Dataset** is a dataset for social relation trait prediction from face images. Traits are based on the interpersonal circle proposed by Kiesler, where human relations are divided into 16 segments. Each segment has its opposite side in the circle, such as 'friendly and hostile'. The dataset contains 8,306 images chosen from the internet and movies. Each image is labelled with faces’ bounding boxes and their pairwise relations. | Provide a detailed description of the following dataset: Social Relation Dataset |
WWW Crowd | **WWW Crowd** provides 10,000 videos with over 8 million frames from 8,257 diverse scenes, therefore offering a comprehensive dataset for the area of crowd understanding. | Provide a detailed description of the following dataset: WWW Crowd |
PolyU | PolyU Dataset is a large dataset of real-world noisy images with reasonably obtained corresponding “ground truth” images. The basic idea is to capture the same and unchanged scene for many (e.g., 500) times and compute their mean image, which can be roughly taken as the “ground truth” image for the real-world noisy images. The rational of this strategy is that for each pixel, the noise is generated randomly larger or smaller than 0. Sampling the same pixel many times and computing the average value will approximate the truth pixel value and alleviate significantly the noise. | Provide a detailed description of the following dataset: PolyU |
SPOT | The SPOT dataset contains 197 reviews originating from the Yelp'13 and IMDB collections ([1][2]), annotated with segment-level polarity labels (positive/neutral/negative). Annotations have been gathered on 2 levels of granulatiry:
- Sentences
- Elementary Discourse Units (EDUs), i.e. sub-sentence clauses produced by a state-of-the-art RST parser
This dataset is intended to aid sentiment analysis research and, in particular, the evaluation of methods that attempt to predict sentiment on a fine-grained, segment-level basis. | Provide a detailed description of the following dataset: SPOT |
ToM QA | The data consists of a set of 3 task types and 4 question types, creating 12 total scenarios. The tasks are grouped into stories, which are denoted by the numbering at the start of each line. | Provide a detailed description of the following dataset: ToM QA |
Verse | Verse is a new dataset that augments existing multimodal datasets (COCO and TUHOI) with sense labels. | Provide a detailed description of the following dataset: Verse |
Holl-E | Holl-E is a dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie. | Provide a detailed description of the following dataset: Holl-E |
Wikidata-Disamb | The Wikidata-Disamb dataset is intended to allow a clean and scalable evaluation of NED with Wikidata entries, and to be used as a reference in future research. | Provide a detailed description of the following dataset: Wikidata-Disamb |
Perceptual Similarity | Perceptual Similarity is a dataset of human perceptual similarity judgments. | Provide a detailed description of the following dataset: Perceptual Similarity |
Gutenberg Poem Dataset | Gutenberg Poem Dataset is used for the next verse prediction component. | Provide a detailed description of the following dataset: Gutenberg Poem Dataset |
Visual Beliefs | Visual Beliefs is a dataset of abstract scenes to study visual beliefs. The dataset consists of 8-frame scenes, and in each scene a person has a mistaken belief. The dataset can be used for two tasks: predicting who is mistaken and predicting when are they mistaken. | Provide a detailed description of the following dataset: Visual Beliefs |
MovieShots | MovieShots is a dataset to facilitate the shot type analysis in videos. It is a large-scale shot type annotation set that contains 46K shots from 7,858 movies covering a wide
variety of movie genres to ensure the inclusion of all scale and movement types of shot. Each shot has two attributes, shot scale and shot movement.
Shot scale has five categories: 1) long shot (LS) is taken from a long distance, sometimes as far as a quarter of a mile away; 2) full shot (FS) barely includes the human body in full; 3) medium shot (MS) contains a figure from the knees or waist up; 4) close-up shot (CS) concentrates on a relatively small object, showing the face of the hand of a person; (5) extreme close-up shot (ECS) shows even smaller parts such as the image of an eye or a mouth.
Shot movement has four categories: 1) in static shot, the camera is fixed but the subject is flexible to move; 2) for motion shot, the camera moves or rotates; 3) the camera zooms in for push shot, and 4) zooms out for pull shot. While all the four movement types are widely used in movies, the use of push and pull shots only takes a very small portion. The usage of different shots usually depends on the movie genres and the preferences of the filmmakers. | Provide a detailed description of the following dataset: MovieShots |
VQA-HAT | VQA-HAT (Human ATtention) is a dataset to evaluate the informative regions of an image depending on the question being asked about it. The dataset consists of human visual attention maps over the images in the original VQA dataset. It contains more than 60k attention maps. | Provide a detailed description of the following dataset: VQA-HAT |
RDD-2020 | The Road Damage Dataset 2020 (RDD-2020) Secondly is a large-scale heterogeneous dataset comprising 26620 images collected from multiple countries using smartphones. The images are collected from roads in India, Japan and the Czech Republic. | Provide a detailed description of the following dataset: RDD-2020 |
Worldtree | Worldtree is a corpus of explanation graphs, explanatory role ratings, and associated tablestore. It contains explanation graphs for 1,680 questions, and 4,950 tablestore rows across 62 semi-structured tables are provided. This data is intended to be paired with the AI2 Mercury Licensed questions. | Provide a detailed description of the following dataset: Worldtree |
Sarcasm Corpus V2 | The Sarcasm Corpus contains sarcastic and non-sarcastic utterances of three different types, which are balanced with half of the samples being sarcastic and half non-sarcastic.
The three types are:
* Generic: 6,520 samples
* Rhetorical Questions: 1,702 samples
* Hyperbole: 1,164 samples | Provide a detailed description of the following dataset: Sarcasm Corpus V2 |
3D-FUTURE | 3D-FUTURE (3D FUrniture shape with TextURE) is a 3D dataset that contains 20,240 photo-realistic synthetic images captured in 5,000 diverse scenes, and 9,992 involved unique industrial 3D CAD shapes of furniture with high-resolution informative textures developed by professional designers. | Provide a detailed description of the following dataset: 3D-FUTURE |
RealNews | **RealNews** is a large corpus of news articles from Common Crawl. Data is scraped from Common Crawl, limited to the 5000 news domains indexed by Google News. The authors used the Newspaper Python library to extract the body and metadata from each article. News from Common Crawl dumps from December 2016 through March 2019
were used as training data; articles published in April 2019 from the April 2019 dump were used for evaluation. After deduplication, RealNews is 120 gigabytes without compression.
Image Source: [https://arxiv.org/pdf/1905.12616v3.pdf](https://arxiv.org/pdf/1905.12616v3.pdf) | Provide a detailed description of the following dataset: RealNews |
CC-Stories | **CC-Stories** (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus. | Provide a detailed description of the following dataset: CC-Stories |
xView | xView is one of the largest publicly available datasets of overhead imagery. It contains images from complex scenes around the world, annotated using bounding boxes. It contains over 1M object instances from 60 different classes. | Provide a detailed description of the following dataset: xView |
BrixIA | **BrixIA Covid-19** is a large dataset of CXR images corresponding to the entire amount of images taken for both triage and patient monitoring in sub-intensive and intensive care units during one month (between March 4th and April 4th 2020) of pandemic peak at the ASST Spedali Civili di Brescia, and contains all the variability originating from a real clinical scenario. It includes 4,707 CXR images of COVID-19 subjects, acquired with both CR and DX modalities, in AP or PA projection, and retrieved from the facility RIS-PACS system. | Provide a detailed description of the following dataset: BrixIA |
MaleX | **MaleX** is a curated dataset of malware and benign Windows executable samples for malware researchers. The dataset contains 1,044,394 Windows executable binaries with 864,669 labelled as malware and 179,725 as benign. This dataset has reasonable number of samples and is sufficient to test data-driven machine learning classification methods and also to measure the performance of the designed models in terms of scalability and adaptability. | Provide a detailed description of the following dataset: MaleX |
AIST++ | **AIST++** is a 3D dance dataset which contains 3D motion reconstructed from real dancers paired with music. The AIST++ Dance Motion Dataset is constructed from the AIST Dance Video DB. With multi-view videos, an elaborate pipeline is designed to estimate the camera parameters, 3D human keypoints and 3D human dance motion sequences:
- It provides 3D human keypoint annotations and camera parameters for 10.1M images, covering 30 different subjects in 9 views. These attributes makes it the largest and richest existing dataset with 3D human keypoint annotations.
- It also contains 1,408 sequences of 3D human dance motion, represented as joint rotations along with root trajectories. The dance motions are equally distributed among 10 dance genres with hundreds of choreographies. Motion durations vary from 7.4 sec. to 48.0 sec. All the dance motions have corresponding music. | Provide a detailed description of the following dataset: AIST++ |
ScenicOrNot | ScenicOrNot (SoN) is a dataset of 185,548 images with associated natural beauty rating histograms. Each image in the dataset was rated at least five times. The images also have metadata like title and location. | Provide a detailed description of the following dataset: ScenicOrNot |
TiMoS | **Tropes in Movie Synopses (TiMoS)** is a dataset of movie tropes collected from a Wikipedia-style website, TVTropes3 with 5623 movie synopses associated with 95 most occurred tropes. The movies are diverse in genre, filming year, length, and style, making the task challenging and unable to rely on patterns from a specific domain. The tropes involve character trait, role interaction, situation, and storyline, which could be sensed by a non-expert human but remains challenging for machines that have more than 100 million parameters and pre-trained with 11,000 books and the whole
Wikipedia (23.97 F1 score while a human could reach 64.87). | Provide a detailed description of the following dataset: TiMoS |
Kite | The Kite database is a multi-modal dataset for the control of unmanned aerial vehicles (UAVs). There are three modalities present in the dataset:
- Language, represented by the commands issued to the UAV
- Audio, represented by the spoken instantiation of the commands
- Visual, represented by an image that is likely to be seen when the command is issued
The dataset was created by the members of the [SpeeD](https://speed.pub.ro/) team. | Provide a detailed description of the following dataset: Kite |
COCO Earthquake | **COCO Earthquake** is a dataset similar to Common Objects in Context (COCO) used for cracking segmentation. The images selected in the dataset are at various scales, and the tool referred to as the COCO Annotator is used to label cracks for training. In these labeled images, cracks are in yellow and background is in purple. Size of the training and labeling images is varied from 168×300 to 4600×3070. By excluding steel structures, 2,021 images are labeled when surface cracks appeared on structural or nonstructural materials at various scales. | Provide a detailed description of the following dataset: COCO Earthquake |
VLUC | VLUC (Video-Like Urban Computing) is a benchmark for video-like computing on citywide traffic density and crowd prediction. It consists of two new datasets BousaiTYO and BousaiOSA and existing datasets TaxiBJ, BikeNYC I-II, and TaxiNYC.
| Provide a detailed description of the following dataset: VLUC |
MM-WHS 2017 | The **MM-WHS 2017** dataset is a dataset for multi-modality whole heart segmentation. It provides 20 labeled and 40 unlabeled CT volumes, as well as 20 labeled and 40 unlabeled MR volumes. In total there are 120 multi-modality cardiac images acquired in a real clinical environment. | Provide a detailed description of the following dataset: MM-WHS 2017 |
BosphorusSign22k | BosphorusSign22k is a benchmark dataset for vision-based user-independent isolated Sign Language Recognition (SLR). The dataset is based on the BosphorusSign (Camgoz et al., 2016c) corpus which was collected with the purpose of helping both linguistic and computer science communities. It contains isolated videos of Turkish Sign Language glosses from three different domains: Health, finance and commonly used everyday signs. Videos in this dataset were performed by six native signers, which makes this dataset valuable for user independent sign language studies. | Provide a detailed description of the following dataset: BosphorusSign22k |
EMIDEC | The **MICCAI 2020 EMIDEC** dataset is a dataset for classifying normal and pathological cases from the clinical information with or without DE-MRI, and secondly to automatically detect the different relevant areas (the myocardial contours, the infarcted area and the permanent microvascular obstruction area (no-reflow area)) from a series of short-axis DE-MRI covering the left ventricle. The segmentation allows one to make a quantification of the MI, in absolute value (mm3) or percentage of the myocardium.
The database consists of 150 exams (all from different patients) divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are provided to distinguish normal and pathological cases. The training set has 100 cases.
Lalande, A.; Chen, Z.; Decourselle, T.; Qayyum, A.; Pommier, T.; Lorgis, L.; de la Rosa, E.; Cochet, A.; Cottin, Y.; Ginhac, D.; Salomon, M.; Couturier, R.; Meriaudeau, F. Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI. Data 2020, 5, 89.
doi: https://doi.org/10.3390/data5040089 | Provide a detailed description of the following dataset: EMIDEC |
InstaCities1M | InstaCities1M is a dataset of social media images with associated text. It consists of Instagram images associated associated with one of the 10 most populated English speaking cities all over the world. It has 100K images for each city, which makes a total of 1M images, split in 800K training images, 50K validation images and 150K testing images. All images were resized to 300x300 pixels. | Provide a detailed description of the following dataset: InstaCities1M |
WHU-Hi | WHU-Hi dataset (Wuhan UAV-borne hyperspectral image) is collected and shared by the RSIDEA research group of Wuhan University, and it could serve as a benchmark dataset for precise crop classification and hyperspectral image classification studies. The WHU-Hi dataset contains three individual UAV-borne hyperspectral datasets: WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu. All the datasets were acquired in farming areas with various crop types in Hubei province, China, via a Headwall Nano-Hyperspec sensor mounted on a UAV platform. Compared with spaceborne and airborne hyperspectral platforms, unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution (which we refer to here as H2 imagery). The research was published in Remote Sensing of Environment. | Provide a detailed description of the following dataset: WHU-Hi |
Botswana | **Botswana** is a hyperspectral image classification dataset. The NASA EO-1 satellite acquired a sequence of data over the Okavango Delta, Botswana in 2001-2004. The Hyperion sensor on EO-1 acquires data at 30 m pixel resolution over a 7.7 km strip in 242 bands covering the 400-2500 nm portion of the spectrum in 10 nm windows. Preprocessing of the data was performed by the UT Center for Space Research to mitigate the effects of bad detectors, inter-detector miscalibration, and intermittent anomalies. Uncalibrated and noisy bands that cover water absorption features were removed, and the remaining 145 bands were included as candidate features: [10-55, 82-97, 102-119, 134-164, 187-220]. The data analyzed in this study, acquired May 31, 2001, consist of observations from 14 identified classes representing the land cover types in seasonal swamps, occasional swamps, and drier woodlands located in the distal portion of the Delta. | Provide a detailed description of the following dataset: Botswana |
Houston | **Houston** is a hyperspectral image classification dataset. The hyperspectral imagery consists of 144 spectral bands in the 380 nm to 1050 nm region and has been calibrated to at-sensor spectral radiance units, SRU =$ \mu \text{W} /( \text{cm}^2 \text{ sr nm})$. The corresponding co-registered DSM consists of elevation in meters above sea level (per the Geoid 2012A model). | Provide a detailed description of the following dataset: Houston |
Pavia Centre | **Pavia Centre** is a hyperspectral dataset acquired by the ROSIS sensor during a flight campaign over Pavia, northern Italy. The number of spectral bands is 102 for Pavia Centre. Pavia Centre is a 1096*1096 pixels image. The geometric resolution is 1.3 meters. Image groundtruths differentiate 9 classes each. Pavia scenes were provided by Prof. Paolo Gamba from the Telecommunications and Remote Sensing Laboratory, Pavia university (Italy). | Provide a detailed description of the following dataset: Pavia Centre |
SEVIR | **SEVIR** is an annotated, curated and spatio-temporally aligned dataset containing over 10,000 weather events that each consist of 384 km x 384 km image sequences spanning 4 hours of time. Images in SEVIR were sampled and aligned across five different data types: three channels (C02, C09, C13) from the GOES-16 advanced baseline imager, NEXRAD vertically integrated liquid mosaics, and GOES-16 Geostationary Lightning Mapper (GLM) flashes. Many events in SEVIR were selected and matched to the NOAA Storm Events database so that additional descriptive information such as storm impacts and storm descriptions can be linked to the rich imagery provided by the sensors. | Provide a detailed description of the following dataset: SEVIR |
Chinese Classifier | Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. This dataset of **Chinese Classifiers** can be used to predict Chinese classifiers from context.
The dataset contains a large collection of example sentences for Chinese classifier usage derived from three language corpora (Lancaster Corpus of Mandarin Chinese, UCLA Corpus of Written Chinese and Leiden Weibo Corpus). The data was cleaned and processed for a context-based classifier prediction task.
Source: [https://github.com/wuningxi/ChineseClassifierDataset](https://github.com/wuningxi/ChineseClassifierDataset) | Provide a detailed description of the following dataset: Chinese Classifier |
SimpleDBpediaQA | A new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia. | Provide a detailed description of the following dataset: SimpleDBpediaQA |
Part Whole Relations | The Part-Whole Relations dataset is a dataset of semantic relations between entities. It contains the following subtypes:
- Component-Of
- Member-Of
- Portion-Of
- Stuff-Of
- Located-In
- Contained-In
- Phase-Of
- Participates-In
Source: [https://github.com/pvthuy/part-whole-relations](https://github.com/pvthuy/part-whole-relations) | Provide a detailed description of the following dataset: Part Whole Relations |
ISI-PPT | This is a Dataset for Arabic/English text detection and optical character recognition. All image data are text-slides extracted from PowerPoint files downloaded from Internet through the Google API.
All annotations are automatically generated mainly through the WinCom32 Python API. Postprocess is also applied to place a more accurate text bounding box or to suppress false-alarms, e.g. a text box only containing spaces. Finally, all annotation results are briefly reviewed by human to reject extreme bad samples, e.g. a slide with a large portion of copied table as image. In summary, this dataset contains 10,692 images, and roughly 100K line samples.
Source: [https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset)
Image Source: [https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset) | Provide a detailed description of the following dataset: ISI-PPT |
MeQSum | **MeQSum** is a dataset for medical question summarization. It contains 1,000 summarized consumer health questions.
Source: [https://www.aclweb.org/anthology/P19-1215.pdf](https://www.aclweb.org/anthology/P19-1215.pdf)
Image Source: [https://www.aclweb.org/anthology/P19-1215.pdf](https://www.aclweb.org/anthology/P19-1215.pdf) | Provide a detailed description of the following dataset: MeQSum |
Visual Choice of Plausible Alternatives | Visual Choice of Plausible Alternatives (VCOPA) is an evaluation dataset containing 380 VCOPA questions and over 1K images with various topics, which is amenable to automatic evaluation, and present the performance of baseline reasoning approaches as initial benchmarks for future systems. | Provide a detailed description of the following dataset: Visual Choice of Plausible Alternatives |
QTuna | The QTUNA dataset is the result of a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions in order to inform possible Natural Language Generation algorithms that mimic humans' use of quantified expressions.
Source: [https://github.com/a-quei/qtuna](https://github.com/a-quei/qtuna) | Provide a detailed description of the following dataset: QTuna |
StockNet | The **StockNet** dataset is a comprehensive dataset for stock movement prediction from tweets and historical stock prices.
It consists of two-year price movements from 01/01/2014 to 01/01/2016 of 88 stocks, coming from all the 8 stocks in the Conglomerates sector and the top 10 stocks in capital size in each of the other 8 sectors.
Source: [https://github.com/yumoxu/stocknet-dataset](https://github.com/yumoxu/stocknet-dataset) | Provide a detailed description of the following dataset: StockNet |
LDDRS | The **LWIR DoFP Dataset of Road Scene** (**LDDRS**) is a road detection dataset with 2,113 annotated images. It contains both day and night scenes, with multiple cars and pedestrians per image.
Source: [https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700460.pdf](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700460.pdf) | Provide a detailed description of the following dataset: LDDRS |
VegFru | **VegFru** is a domain-specific dataset for fine-grained visual categorization. VegFru categorizes vegetables and fruits according to their eating characteristics, and each image contains at least one edible part of vegetables or fruits with the same cooking usage. Particularly, all the images are labelled hierarchically. The current version covers vegetables and fruits of 25 upper-level categories and 292 subordinate classes. And it contains more than 160,000 images in total and at least 200 images for each subordinate class.
Source: [https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf](https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf)
Image Source: [https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf](https://openaccess.thecvf.com/content_ICCV_2017/papers/Hou_VegFru_A_Domain-Specific_ICCV_2017_paper.pdf) | Provide a detailed description of the following dataset: VegFru |
Para-Quality | Used to investigate common crowdsourced paraphrasing issues and for detecting the quality issues. | Provide a detailed description of the following dataset: Para-Quality |
Metaphorical Connections | The **Metaphorical Connections** dataset is a poetry dataset that contains annotations between metaphorical prompts and short poems. Each poem is annotated whether or not it successfully communicates the idea of the metaphorical prompt.
Source: [https://github.com/kgero/metaphorical-connections](https://github.com/kgero/metaphorical-connections) | Provide a detailed description of the following dataset: Metaphorical Connections |
German affixoid dataset | German affixoids are a type of morpheme in between affixes and free stems. | Provide a detailed description of the following dataset: German affixoid dataset |
Cross-Modal Comments Dataset | Cross Modal Automatic Commenting (CMAC) is a task which aims to automatically generate comments for graphic news. The CMAC dataset is a large-scale dataset for this task which consists of 24,134 graphic news. Each instance is composed of several news photos, news title, news body, and corresponding high-quality comments.
Source: [https://github.com/lancopku/CMAC](https://github.com/lancopku/CMAC) | Provide a detailed description of the following dataset: Cross-Modal Comments Dataset |
STREETS | A novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. | Provide a detailed description of the following dataset: STREETS |
PubMed PICO Element Detection Dataset | PICO is a framework to formulate a well-defined focused clinical question. This framework identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). The PubMed PICO Element Detection dataset is a dataset for evaluating models that automatically detect PICO elements.
Source: [https://github.com/jind11/PubMed-PICO-Detection](https://github.com/jind11/PubMed-PICO-Detection) | Provide a detailed description of the following dataset: PubMed PICO Element Detection Dataset |
SpatialVOC2K | A multilingual image dataset with spatial relation annotations and object features for image-to-text generation, built using 2,026 images from the PASCAL VOC2008 dataset. | Provide a detailed description of the following dataset: SpatialVOC2K |
CC-DBP | **CC-DBP** is a dataset for knowledge base population research using Common Crawl and DBpedia. | Provide a detailed description of the following dataset: CC-DBP |
Earnings Call | The Earning Calls dataset consists of processed earning conference calls data (text and audio). It can be used to predict financial risk from both textual and vocal features from conference calls.
Source: [https://www.aclweb.org/anthology/P19-1038/](https://www.aclweb.org/anthology/P19-1038/) | Provide a detailed description of the following dataset: Earnings Call |
Processed Twitter | Processed Twitter is a dataset that is used for Twitter topic recognition. It contains tweets from 6 different topics. | Provide a detailed description of the following dataset: Processed Twitter |
Grasp MultiObject | Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.
This dataset is annotated using the same protocol as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Source: [https://github.com/ivalab/grasp_multiObject](https://github.com/ivalab/grasp_multiObject)
Image Source: [https://github.com/ivalab/grasp_multiObject](https://github.com/ivalab/grasp_multiObject) | Provide a detailed description of the following dataset: Grasp MultiObject |
OFEQ-10k | The **OFEQ-10k** dataset contains 12,548 detailed questions with corresponding math headlines from MathOverflow.
Source: [https://arxiv.org/pdf/1912.00839.pdf](https://arxiv.org/pdf/1912.00839.pdf) | Provide a detailed description of the following dataset: OFEQ-10k |
Pascal Panoptic Parts | The Pascal Panoptic Parts dataset consists of annotations for the part-aware panoptic segmentation task on the PASCAL VOC 2010 dataset. It is created by merging scene-level labels from PASCAL-Context with part-level labels from PASCAL-Part | Provide a detailed description of the following dataset: Pascal Panoptic Parts |
JSS Dataset | The **Jejueo Single Speaker Speech** (JSS) dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file.
Source: [https://arxiv.org/abs/1911.12071](https://arxiv.org/abs/1911.12071) | Provide a detailed description of the following dataset: JSS Dataset |
needadvice | **needadvice** is a dataset for advice classification extracted from Reddit. In this dataset, posts are annotated for whether they contain advice or not. It contains 6,148 samples for training, 816 for validation and 898 for testing.
Source: [https://github.com/venkatasg/Advice-EMNLP2020](https://github.com/venkatasg/Advice-EMNLP2020) | Provide a detailed description of the following dataset: needadvice |
FB1.5M | The **FB1.5M** dataset is a benchmark for Knowledge Graph Completion. It is based on Freebase and it contains 30 relations with less than 500 triplets as low-resource relations.
Source: [https://arxiv.org/pdf/1911.03091.pdf](https://arxiv.org/pdf/1911.03091.pdf) | Provide a detailed description of the following dataset: FB1.5M |
Wiki-zh | **Wiki-zh** is an annotated Chinese dataset for domain detection extracted from Wikipedia. It includes texts from 7 different domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW), “Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). It contains 26,280 documents split into training, validation and test.
Source: [https://arxiv.org/pdf/1907.11499.pdf](https://arxiv.org/pdf/1907.11499.pdf) | Provide a detailed description of the following dataset: Wiki-zh |
Simulated Flying Shapes | The dataset consists of 90 000 grayscale videos that show two objects of equal shape and size in which one object approaches the other one. The object speed during the process of approaching is hereby modelled by a proportional-derivative controller. Overall, three different shapes (Rectangle, Triangle and Circle) are provided. Initial configuration of the objects such as position and color were randomly sampled. Different from the moving MNIST dataset, the samples comprise a goal-oriented task, namely one object has to fully cover the other object rather than randomly moving, making it better suitable for testing prediction capabilities of an ML model.
For instance, one can use it as a toy dataset to investigate the capacity and output behavior of a deep neural network before testing it on real-world data.
Source: [https://github.com/ferreirafabio/FlyingShapesDataset](https://github.com/ferreirafabio/FlyingShapesDataset)
Image Source: [https://github.com/ferreirafabio/FlyingShapesDataset](https://github.com/ferreirafabio/FlyingShapesDataset) | Provide a detailed description of the following dataset: Simulated Flying Shapes |
PKU-Reid | This dataset contains 114 individuals including 1824 images captured from two disjoint camera views. For each person, eight images are captured from eight different orientations under one camera view and are normalized to 128x48 pixels. This dataset is also split into two parts randomly. One contains 57 individuals for training, and the other contains 57 individuals for testing.
Source: [https://github.com/charliememory/PKU-Reid-Dataset](https://github.com/charliememory/PKU-Reid-Dataset)
Image Source: [https://arxiv.org/pdf/1605.02464.pdf](https://arxiv.org/pdf/1605.02464.pdf) | Provide a detailed description of the following dataset: PKU-Reid |
YFCC100M Fine-Grained Geolocation | The **YFCC100M Fine-Grained Geolocation** dataset is a subset of 100 a set of 36,146 YFCC100M images that had Flickr tags that could be identified as corresponding to one of the labels in the iNaturalist 2017 dataset. The 36,146 images that were selected so have the following characteristics:
the image must have geolocation available,
the image must have at most one iNaturalist label,
at most ten examples were retained for each label.
Source: [https://github.com/visipedia/fg_geo](https://github.com/visipedia/fg_geo)
Image Source: [https://github.com/visipedia/fg_geo](https://github.com/visipedia/fg_geo) | Provide a detailed description of the following dataset: YFCC100M Fine-Grained Geolocation |
AuxAI | **AuxAI** is a distantly supervised dataset for acronym identification.
Source: [https://github.com/PrimerAI/sdu-data](https://github.com/PrimerAI/sdu-data) | Provide a detailed description of the following dataset: AuxAI |
PART-OF | The **PART-OF** dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are parts of the human body. The dataset has 16,894 nodes with 19,436 edges between them.
Source: [https://arxiv.org/pdf/1906.05939.pdf](https://arxiv.org/pdf/1906.05939.pdf) | Provide a detailed description of the following dataset: PART-OF |
ITG | **In The Groove** (**ITG**) is an audio dataset where given a raw audio track, the goal is to produce a choreography step chart, similar to those used in the Dance Dance Revolution video game. It contains 133 songs choreographed by a three different authors, with 652 charts for the 133 songs.
Source: [https://arxiv.org/pdf/1703.06891.pdf](https://arxiv.org/pdf/1703.06891.pdf)
Image Source: [https://github.com/chrisdonahue/ddc](https://github.com/chrisdonahue/ddc) | Provide a detailed description of the following dataset: ITG |
KorSTS | **KorSTS** is a dataset for semantic textural similarity (STS) in Korean. The dataset is constructed by automatically the STS-B dataset. To ensure translation quality, two professional translators with at least seven years of experience who specialize in academic papers/books as well as business contracts post-edited a half of the dataset each and cross-checked each other’s translation afterward.
The KorSTS dataset comprises 5,749 training examples translated automatically and 2,879 evaluation examples translated manually. | Provide a detailed description of the following dataset: KorSTS |
Detection of Traffic Anomaly | Contains 4,677 videos with temporal, spatial, and categorical annotations. | Provide a detailed description of the following dataset: Detection of Traffic Anomaly |
IN2LAAMA | IN2LAAMA is a set of lidar-inertial datasets collected with a Velodyne VLP-16 lidar and a Xsens MTi-3 IMU. | Provide a detailed description of the following dataset: IN2LAAMA |
TartanAir | A dataset for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects. | Provide a detailed description of the following dataset: TartanAir |
Snopes | Fact-checking (FC) articles which contains pairs (multimodal tweet and a FC-article) from snopes.com. | Provide a detailed description of the following dataset: Snopes |
DailyDialog++ | Consists of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context. | Provide a detailed description of the following dataset: DailyDialog++ |
Multilingual LibriSpeech | Multilingual LibriSpeech is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages. | Provide a detailed description of the following dataset: Multilingual LibriSpeech |
CITR & DUT | Consists of two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role.
CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI.
DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. | Provide a detailed description of the following dataset: CITR & DUT |
JNC | The JNC data provides common supervision data for headline generation. | Provide a detailed description of the following dataset: JNC |
JHU-CROWD++ | JHU-CROWD++ is A large-scale unconstrained crowd counting dataset with 4,372 images and 1.51 million annotations. This dataset is collected under a variety of diverse scenarios and environmental conditions. In addition, the dataset provides comparatively richer set of annotations like dots, approximate bounding boxes, blur levels, etc. | Provide a detailed description of the following dataset: JHU-CROWD++ |
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