dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
CATS | A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes. | Provide a detailed description of the following dataset: CATS |
caWaC | The corpus represents the largest existing corpus of Catalan containing 687 million words, which is a significant increase given that until now the biggest corpus of Catalan, CuCWeb, counts 166 million words. | Provide a detailed description of the following dataset: caWaC |
CC100 | This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. | Provide a detailed description of the following dataset: CC100 |
CC-19 | **CC-19** is a small new dataset related to the latest family of coronavirus i.e. COVID-19. The proposed dataset “CC-19” contains 34,006 CT scan slices (images) belonging to 98 subjects out of which 28,395 CT scan slices belong to positive COVID patients.
Source: [https://github.com/abdkhanstd/COVID-19](https://github.com/abdkhanstd/COVID-19)
Image Source: [https://github.com/abdkhanstd/COVID-19](https://github.com/abdkhanstd/COVID-19) | Provide a detailed description of the following dataset: CC-19 |
CCD | **Car Crash Dataset** (**CCD**) is collected for traffic accident analysis. It contains real traffic accident videos captured by dashcam mounted on driving vehicles, which is critical to developing safety-guaranteed self-driving systems. CCD is distinguished from existing datasets for diversified accident annotations, including environmental attributes (day/night, snowy/rainy/good weather conditions), whether ego-vehicles involved, accident participants, and accident reason descriptions.
Source: [https://github.com/Cogito2012/CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)
Image Source: [https://github.com/Cogito2012/CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset) | Provide a detailed description of the following dataset: CCD |
CCMatrix | CCMatrix uses ten snapshots of a curated common crawl corpus (Wenzek et al., 2019) totalling 32.7 billion unique sentences. | Provide a detailed description of the following dataset: CCMatrix |
CCPD | The **Chinese City Parking Dataset** (**CCPD**) is a dataset for license plate detection and recognition. It contains over 250k unique car images, with license plate location annotations. | Provide a detailed description of the following dataset: CCPD |
Polish CDSCorpus | Consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. | Provide a detailed description of the following dataset: Polish CDSCorpus |
CECW | The **CECW** dataset is a color-extended version of the Cleanup World (CW) borrowed from the mobile-manipulation robot domain. CW refers to a world equipped with a movable object as well as four rooms in four colors, including "blue," "green," "red," and "yellow," which is designed as a simulation environment where the agent can act based on the instructions received. CW obeys a particular Geometric Linear Temporal Logic (GLTL) to parse commands by grammatical syntax, resulting in a total of 3,382 commands reflecting 39 GLTL expressions.
Source: [https://github.com/MrShininnnnn/CECW](https://github.com/MrShininnnnn/CECW)
Image Source: [https://github.com/MrShininnnnn/CECW](https://github.com/MrShininnnnn/CECW) | Provide a detailed description of the following dataset: CECW |
CED | Contains 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes. | Provide a detailed description of the following dataset: CED |
CelebAMask-HQ | **CelebAMask-HQ** is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA. | Provide a detailed description of the following dataset: CelebAMask-HQ |
CelebA-Spoof | CelebA-Spoof is a large-scale face anti-spoofing dataset with the following properties:
1. Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets.
2. Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors.
3. Annotation Richness: CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset. | Provide a detailed description of the following dataset: CelebA-Spoof |
Celeb-DF | **Celeb-DF** is a large-scale challenging dataset for deepfake forensics. It includes 590 original videos collected from YouTube with subjects of different ages, ethnic groups and genders, and 5639 corresponding DeepFake videos. | Provide a detailed description of the following dataset: Celeb-DF |
Cervix93 Cytology Dataset | The dataset has 93 image stacks and their corresponding Extended Depth of Field (EDF) image acquired from cases with grades Nagative, LSIL or HSIL (The Bethesda System):
- Negative: 16
- LSIL: 46
- HSIL: 31
The ground truth includes the grade labels for each frame and manually marked points inside cervical cells in each frame. There are in total 2705 manually marked points inside all frames:
- Negative: 238
- LSIL: 1536
- HSIL: 931
Source: [https://github.com/parham-ap/cytology_dataset](https://github.com/parham-ap/cytology_dataset) | Provide a detailed description of the following dataset: Cervix93 Cytology Dataset |
CFQ | A large and realistic natural language question answering dataset. | Provide a detailed description of the following dataset: CFQ |
CDNET | A video database for testing change detection algorithms. | Provide a detailed description of the following dataset: CDNET |
Charades-Ego | Contains 68,536 activity instances in 68.8 hours of first and third-person video, making it one of the largest and most diverse egocentric datasets available. Charades-Ego furthermore shares activity classes, scripts, and methodology with the Charades dataset, that consist of additional 82.3 hours of third-person video with 66,500 activity instances. | Provide a detailed description of the following dataset: Charades-Ego |
CHECKED | Chinese dataset on COVID-19 misinformation. CHECKED provides ground-truth on credibility, carefully obtained by ensuring the specific sources are used. CHECKED includes microblogs related to COVID-19, identified by using a specific list of keywords, covering a total 2120 microblogs published from December 2019 to August 2020. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, response, and social network information. | Provide a detailed description of the following dataset: CHECKED |
CHiME-5 | The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning. | Provide a detailed description of the following dataset: CHiME-5 |
Chinese AI and Law (CAIL) 2018 | Large-scale Chinese legal dataset for judgment prediction. \dataset contains more than 2.6 million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. | Provide a detailed description of the following dataset: Chinese AI and Law (CAIL) 2018 |
ChineseFoodNet | ChineseFoodNet aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In the dataset, images of each food category of the dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. | Provide a detailed description of the following dataset: ChineseFoodNet |
Chinese Literature NER RE | Chinese Literature NER RE is a Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text. It is constructed from hundreds of Chinese literature articles. | Provide a detailed description of the following dataset: Chinese Literature NER RE |
Chinese Text in the Wild | Chinese Text in the Wild is a dataset of Chinese text with about 1 million Chinese characters from 3850 unique ones annotated by experts in over 30000 street view images. This is a challenging dataset with good diversity containing planar text, raised text, text under poor illumination, distant text, partially occluded text, etc. | Provide a detailed description of the following dataset: Chinese Text in the Wild |
Chinese Traditional Painting dataset | The **Chinese Traditional Painting dataset** for style transfer contains 1000 content images and 100 style images.
The content images are mostly the photorealistic scenes of mountain, lake, river, bridge, and buildings in regions south of the Yangtze River. It includes not only the scenes of China, but also beautiful pictures of Rhine, Alps, Yellow Stone, Grand Canyon, etc. The content images include diverse types of Chinese traditional paintings.
Source: [https://github.com/lbsswu/Chinese_style_transfer](https://github.com/lbsswu/Chinese_style_transfer)
Image Source: [https://github.com/lbsswu/Chinese_style_transfer](https://github.com/lbsswu/Chinese_style_transfer) | Provide a detailed description of the following dataset: Chinese Traditional Painting dataset |
CIC | The dataset is annotated with stance towards one topic, namely, the independence of Catalonia. | Provide a detailed description of the following dataset: CIC |
CITE | CITE is a crowd-sourced resource for multimodal discourse: this resource characterises inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. | Provide a detailed description of the following dataset: CITE |
CITIUS Video Database | This eye tracking video database can be used to validate visual attention models. This dataset includes 72 videos downloaded from Internet and some synthetic videos generated in the lab. The videos can be classified in four categories, natural and synthetic, with fixed or movement camera. It includes 27 synthetic videos with dynamic pop-out effects. The videos have been selected in order to minimize the influence of the top-down effects. | Provide a detailed description of the following dataset: CITIUS Video Database |
CITR Dataset | **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 Dataset |
CityFlow | CityFlow is a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions.
Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). | Provide a detailed description of the following dataset: CityFlow |
Cityscapes Panoptic Parts | The Cityscapes Panoptic Parts dataset introduces part-aware panoptic segmentation annotations for the Cityscapes dataset. It extends the original panoptic annotations for the Cityscapes dataset with part-level annotations for selected scene-level classes. | Provide a detailed description of the following dataset: Cityscapes Panoptic Parts |
CLAD | CLAD (Compled and Long Activities Dataset) is an activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions. The dataset consists of a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. | Provide a detailed description of the following dataset: CLAD |
ClaimBuster | Consist of 23,533 statements extracted from all U.S. general election presidential debates and annotated by human coders. The ClaimBuster dataset can be leveraged in building computational methods to identify claims that are worth fact-checking from the myriad of sources of digital or traditional media. | Provide a detailed description of the following dataset: ClaimBuster |
ClariQ | ClariQ is an extension of the Qulac dataset with additional new topics, questions, and answers in the training set. The test set is completely unseen and newly collected. Like Qulac, ClariQ consists of single-turn conversations (initial_request, followed by clarifying question and answer). In addition, it comes with synthetic multi-turn conversations (up to three turns). ClariQ features approximately 18K single-turn conversations, as well as 1.8 million multi-turn conversations. | Provide a detailed description of the following dataset: ClariQ |
CLEVR-Ref+ | CLEVR-Ref+ is a synthetic diagnostic dataset for referring expression comprehension. The precise locations and attributes of the objects are readily available, and the referring expressions are automatically associated with functional programs. The synthetic nature allows control over dataset bias (through sampling strategy), and the modular programs enable intermediate reasoning ground truth without human annotators. | Provide a detailed description of the following dataset: CLEVR-Ref+ |
Climate Claims | The Climate Change Claims dataset for generating fact checking summaries contains claims broadly related to climate change and global warming from climatefeedback.org. It contains 1k documents from 104 different claims from 97 different domains.
Source: [https://arxiv.org/abs/2010.08570](https://arxiv.org/abs/2010.08570) | Provide a detailed description of the following dataset: Climate Claims |
CLIMATE-FEVER | A new publicly available dataset for verification of climate change-related claims. | Provide a detailed description of the following dataset: CLIMATE-FEVER |
ClipShots | **ClipShots** is a large-scale dataset for shot boundary detection collected from Youtube and Weibo covering more than 20 categories, including sports, TV shows, animals, etc. In contrast to previous shot boundary detection datasets, e.g. TRECVID and RAI, which only consist of documentaries or talk shows where the frames are relatively static, ClipShots contains moslty short videos from Youtube and Weibo. Many short videos are home-made, with more challenges, e.g. hand-held vibrations and large occlusion. The types of these videos are various, including movie spotlights, competition highlights, family videos recorded by mobile phones etc. Each video has a length of 1-20 minutes. The gradual transitions in the dataset include dissolve, fade in fade out, and sliding in sliding out.
Source: [https://github.com/Tangshitao/ClipShots](https://github.com/Tangshitao/ClipShots) | Provide a detailed description of the following dataset: ClipShots |
CLIRMatrix | CLIRMatrix is a large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval. It includes:
* BI-139: A bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,182 language pairs,
* MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages.
In total, 49 million unique queries and 34 billion (query, document, label) triplets were mined, making CLIRMatrix the largest and most comprehensive CLIR dataset to date. | Provide a detailed description of the following dataset: CLIRMatrix |
CloudCast | A satellite-based dataset called "CloudCast". It consists of 70080 images with 10 different cloud types for multiple layers of the atmosphere annotated on a pixel level. The spatial resolution of the dataset is 928 × 1530 pixels (3 × 3 km per pixel) with 15-min intervals between frames for the period January 1, 2017, to December 31, 2018. All frames are centered and projected over Europe. | Provide a detailed description of the following dataset: CloudCast |
ClovaCall | **ClovaCall** is a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people. The raw dataset of ClovaCall includes approximately 112,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain.
Source: [https://github.com/ClovaAI/ClovaCall](https://github.com/ClovaAI/ClovaCall) | Provide a detailed description of the following dataset: ClovaCall |
CLUE | CLUE is a Chinese Language Understanding Evaluation benchmark. It consists of different NLU datasets. It is a community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. | Provide a detailed description of the following dataset: CLUE |
CLUECorpus2020 | CLUECorpus2020 is a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation. It has 100G raw corpus with 35 billion Chinese characters, which is retrieved from Common Crawl. | Provide a detailed description of the following dataset: CLUECorpus2020 |
CLUENER2020 | CLUENER2020 is a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. | Provide a detailed description of the following dataset: CLUENER2020 |
CMCNC | The **Coherent Multiple Choice Narrative Cloze** (**CMCNC**) dataset is an evaluation dataset for the multi-choice narrative cloze task, where the goal is to distinguish which event has been held out from a document from a small set of randomly drawn events.
Source: [https://arxiv.org/pdf/1711.07611.pdf](https://arxiv.org/pdf/1711.07611.pdf) | Provide a detailed description of the following dataset: CMCNC |
CMD | Consists of the key scenes from over 3K movies: each key scene is accompanied by a high level semantic description of the scene, character face-tracks, and metadata about the movie. The dataset is scalable, obtained automatically from YouTube, and is freely available for anybody to download and use. | Provide a detailed description of the following dataset: CMD |
CMRC 2017 | Contains two different types: cloze-style reading comprehension and user query reading comprehension, associated with large-scale training data as well as human-annotated validation and hidden test set. | Provide a detailed description of the following dataset: CMRC 2017 |
CMRC 2018 | **CMRC 2018** is a dataset for **Chinese Machine Reading Comprehension**. Specifically, it is a span-extraction reading comprehension dataset that is similar to SQuAD. | Provide a detailed description of the following dataset: CMRC 2018 |
CMRC 2019 | **CMRC 2019** is a Chinese Machine Reading Comprehension dataset that was used in The Third Evaluation Workshop on Chinese Machine Reading Comprehension. Specifically, CMRC 2019 is a sentence cloze-style machine reading comprehension dataset that aims to evaluate the sentence-level inference ability.
Source: [http://ymcui.com/cmrc2019/](http://ymcui.com/cmrc2019/) | Provide a detailed description of the following dataset: CMRC 2019 |
CMU DoG | This is a document grounded dataset for text conversations. "Document Grounded Conversations" are conversations that are about the contents of a specified document. In this dataset the specified documents are Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. | Provide a detailed description of the following dataset: CMU DoG |
CN-CELEB | CN-Celeb is a large-scale speaker recognition dataset collected `in the wild'. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world. | Provide a detailed description of the following dataset: CN-CELEB |
Coached Conversational Preference Elicitation | **Coached Conversational Preference Elicitation** is a dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'.
Image Source: [https://www.aclweb.org/anthology/W19-5941](https://www.aclweb.org/anthology/W19-5941) | Provide a detailed description of the following dataset: Coached Conversational Preference Elicitation |
CoAID | CoAID include diverse COVID-19 healthcare misinformation, including fake news on websites and social platforms, along with users' social engagement about such news. CoAID includes 4,251 news, 296,000 related user engagements, 926 social platform posts about COVID-19, and ground truth labels. | Provide a detailed description of the following dataset: CoAID |
Coarse Discourse | A large corpus of discourse annotations and relations on ~10K forum threads. | Provide a detailed description of the following dataset: Coarse Discourse |
CoarseWSD-20 | The **CoarseWSD-20** dataset is a coarse-grained sense disambiguation dataset built from Wikipedia (nouns only) targeting 2 to 5 senses of 20 ambiguous words. It was specifically designed to provide an ideal setting for evaluating Word Sense Disambiguation (WSD) models (e.g. no senses in test sets missing from training), both quantitively and qualitatively.
Source: [https://github.com/danlou/bert-disambiguation](https://github.com/danlou/bert-disambiguation) | Provide a detailed description of the following dataset: CoarseWSD-20 |
COCO-QA | **COCO-QA** is a dataset for visual question answering. It consists of:
- 123287 images
- 78736 train questions
- 38948 test questions
- 4 types of questions: object, number, color, location
- Answers are all one-word. | Provide a detailed description of the following dataset: COCO-QA |
COCO-Tasks | Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated. | Provide a detailed description of the following dataset: COCO-Tasks |
COCO-WholeBody | **COCO-WholeBody** is an extension of [COCO](/dataset/coco) dataset with whole-body annotations. There are 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands) annotations for each person in the image. | Provide a detailed description of the following dataset: COCO-WholeBody |
CODEBRIM | Dataset for multi-target classification of five commonly appearing concrete defects. | Provide a detailed description of the following dataset: CODEBRIM |
CodeSwitch-Reddit | A diverse dataset of written code-switched productions, curated from topical threads of multiple bilingual communities on the Reddit discussion platform, and explore questions that were mainly addressed in the context of spoken language thus far. | Provide a detailed description of the following dataset: CodeSwitch-Reddit |
CoDEx Small | CoDEx comprises a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. | Provide a detailed description of the following dataset: CoDEx Small |
CoDraw | The Collaborative Drawing game (**CoDraw**) dataset contains ~10K dialogs consisting of ~138K messages exchanged between human players in the CoDraw game. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate with each other using natural language.
Source: [https://github.com/facebookresearch/CoDraw](https://github.com/facebookresearch/CoDraw)
Image Source: [https://github.com/facebookresearch/CoDraw](https://github.com/facebookresearch/CoDraw) | Provide a detailed description of the following dataset: CoDraw |
COG | A configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures. | Provide a detailed description of the following dataset: COG |
Colorectal Adenoma | Colorectal Adenoma contains 177 whole slide images (156 contain adenoma) gathered and labelled by pathologists from the Department of Pathology, The Chinese PLA General Hospital. | Provide a detailed description of the following dataset: Colorectal Adenoma |
COMETA | Consists of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph. | Provide a detailed description of the following dataset: COMETA |
comma 2k19 | comma 2k19 is a dataset of over 33 hours of commute in California's 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU. | Provide a detailed description of the following dataset: comma 2k19 |
COMP6 | **COMP6** is a benchmark for evaluating the extensibility of machine-learning based molecular potentials. It contains a diverse set of organic molecules.
Source: [https://github.com/isayev/COMP6](https://github.com/isayev/COMP6) | Provide a detailed description of the following dataset: COMP6 |
CompGuessWhat?! | CompGuessWhat?! extends the original GuessWhat?! datasets with a rich semantic representations in the form of scene graphs associated with every image used as reference scene for the guessing games. | Provide a detailed description of the following dataset: CompGuessWhat?! |
Composable activities dataset | The Composable activities dataset consists of 693 videos that contain activities in 16 classes performed by 14 actors. Each activity is composed of 3 to 11 atomic actions. RGB-D data for each sequence is captured using a Microsoft Kinect sensor and estimate position of relevant body joints.
The dataset provides annotations of the activity for each video and the actions for each of the four human parts (left/right arm and leg) for each frame in every video. | Provide a detailed description of the following dataset: Composable activities dataset |
Composed Quora | The **Composed Quora** dataset consists of questions extracted from Quora that are grouped together if they are asking the same thing. The dataset contains 60,400 groups of questions, each group with at least 3 questions that are asking the same.
Source: [https://arxiv.org/pdf/1911.02747.pdf](https://arxiv.org/pdf/1911.02747.pdf) | Provide a detailed description of the following dataset: Composed Quora |
ComQA | ComQA is a large dataset of real user questions that exhibit different challenging aspects such as compositionality, temporal reasoning, and comparisons. ComQA questions come from the WikiAnswers community QA platform, which typically contains questions that are not satisfactorily answerable by existing search engine technology. | Provide a detailed description of the following dataset: ComQA |
ConceptNet | ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. | Provide a detailed description of the following dataset: ConceptNet |
CONCODE | A new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. | Provide a detailed description of the following dataset: CONCODE |
CoNLL-2000 | CoNLL-2000 is a dataset for dividing text into syntactically related non-overlapping groups of words, so-called text chunking. | Provide a detailed description of the following dataset: CoNLL-2000 |
ContentWise Impressions | The **ContentWise Impressions** dataset is a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet. The dataset is distinguished from other already available multimedia recommendation datasets by the availability of impressions, i.e., the recommendations shown to the user, its size, and by being open-source.
The items in the dataset represent the multimedia content that the service provided to the users and are represented by an anonymized numerical identifier. The items refer to television and cinema products belonging to four mutually exclusive categories: movies, movies and clips in series, TV movies or shows, and episodes of TV series.
The interactions represent the actions performed by users on items in the service and are associated with the timestamp when it occurred. Interactions contain the identifier of the impressions, except in those cases where the recommendations came from a row added by the service provider. The interactions are categorized in four different types: views, detail, ratings, and purchases.
The impressions refer to the recommended items that were presented to the user and are identified by their series. Impressions consist of a numerical identifier, the list position on the screen, the length of the recommendation list, and an ordered list of recommended series identifiers, where the most relevant item is in the first position. | Provide a detailed description of the following dataset: ContentWise Impressions |
Contour Drawing Dataset | A new dataset of contour drawings. | Provide a detailed description of the following dataset: Contour Drawing Dataset |
Controversial News Topic Datasets | Corpus of controversial news articles extracted from Twitter. Contains news from three different topics: Beef Ban – controversy over the slaughter and sale of beef on religious grounds (1543
articles) is localised to a particular region, mainly Indian subcontinent, while Gun Control – restrictions on carrying, using, or purchasing firearms (6494 articles) and Capital Punishment – use of the death penalty (7905 articles) are
topical in various regions around the world. | Provide a detailed description of the following dataset: Controversial News Topic Datasets |
CONVERSE | A novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. | Provide a detailed description of the following dataset: CONVERSE |
Cookie | The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation. | Provide a detailed description of the following dataset: Cookie |
Cops-Ref | Cops-Ref is a dataset for visual reasoning in context of referring expression comprehension with two main features. | Provide a detailed description of the following dataset: Cops-Ref |
COQE | Contains more than 5,000 images of 10,000 liquid containers in context labelled with volume, amount of content, bounding box annotation, and corresponding similar 3D CAD models. | Provide a detailed description of the following dataset: COQE |
CORe50 | CORe50 is a dataset designed for assessing Continual Learning techniques in an Object Recognition context. | Provide a detailed description of the following dataset: CORe50 |
Cornell Movie-Dialogs Corpus | This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release year
- IMDB rating
- number of IMDB votes
- IMDB rating
- character metadata included:
- gender (for 3,774 characters)
- position on movie credits (3,321 characters) | Provide a detailed description of the following dataset: Cornell Movie-Dialogs Corpus |
Cornell Movie-Quotes Corpus | A corpus of movie quotes, annotated with memorability information, in which one is able to control for both the speaker and the setting of the quotes. | Provide a detailed description of the following dataset: Cornell Movie-Quotes Corpus |
COS960 | A benchmark dataset with 960 pairs of Chinese wOrd Similarity, where all the words have two morphemes in three Part of Speech (POS) tags with their human annotated similarity rather than relatedness. | Provide a detailed description of the following dataset: COS960 |
CoS-E | CoS-E consists of human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations | Provide a detailed description of the following dataset: CoS-E |
CO-SKEL dataset | A benchmark dataset for the co-skeletonization task. | Provide a detailed description of the following dataset: CO-SKEL dataset |
CoSQL | CoSQL is a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. | Provide a detailed description of the following dataset: CoSQL |
COSTRA 1.0 | COSTRA 1.0 is a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. The first version of the dataset is limited to sentences in Czech but the construction method is universal and the authors plan to use it also for other languages. The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation. | Provide a detailed description of the following dataset: COSTRA 1.0 |
COUGH | A large challenging dataset, COUGH, for COVID-19 FAQ retrieval. Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set. FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). | Provide a detailed description of the following dataset: COUGH |
COUNTER | The **COUNTER** (COrpus of Urdu News TExt Reuse) corpus contains 600 source-derived document pairs collected from the field of journalism. It can be used to evaluate mono-lingual text reuse detection systems in general and specifically for Urdu language.
The corpus has 600 source and 600 derived documents. It contains in total 275,387 words (tokens), 21,426 unique words and 10,841 sentences. It has been manually annotated at document level with three levels of reuse: wholly derived (135), partially derived (288) and non derived (177). | Provide a detailed description of the following dataset: COUNTER |
COVERAGE | COVERAGE contains copymove forged (CMFD) images and their originals with similar but genuine objects (SGOs). COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged–original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance. | Provide a detailed description of the following dataset: COVERAGE |
COVID19-Algeria-and-World-Dataset | A coronavirus dataset with 98 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:
Source: [https://github.com/SamBelkacem/COVID19-Algeria-and-World-Dataset](https://github.com/SamBelkacem/COVID19-Algeria-and-World-Dataset)
Image Source: [https://github.com/SamBelkacem/COVID19-Algeria-and-World-Dataset](https://github.com/SamBelkacem/COVID19-Algeria-and-World-Dataset) | Provide a detailed description of the following dataset: COVID19-Algeria-and-World-Dataset |
COVID19-CountryImage | The Covid19-CountryImage dataset is a Twitter dataset which contains COVID-19-related tweets.
Source: [https://github.com/thunlp/COVID19-CountryImage](https://github.com/thunlp/COVID19-CountryImage) | Provide a detailed description of the following dataset: COVID19-CountryImage |
COVID-19-CT-CXR | A public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. | Provide a detailed description of the following dataset: COVID-19-CT-CXR |
COVID-19 Image Data Collection | Contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. | Provide a detailed description of the following dataset: COVID-19 Image Data Collection |
COVID-19 Twitter Chatter Dataset | A large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. | Provide a detailed description of the following dataset: COVID-19 Twitter Chatter Dataset |
COVID-CQ | COVID-CQ is a stance data set of user-generated content on Twitter in the context of COVID-19. | Provide a detailed description of the following dataset: COVID-CQ |
COVID-CT | Contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbreak of this pandemic. | Provide a detailed description of the following dataset: COVID-CT |
COVIDGR | Under a close collaboration with an expert radiologist team of the Hospital Universitario San Cecilio, the **COVIDGR**-1.0 dataset of patients' anonymized X-ray images has been built. 852 images have been collected following a strict labeling protocol. They are categorized into 426 positive cases and 426 negative cases. Positive images correspond to patients who have been tested positive for COVID-19 using RT-PCR within a time span of at most 24h between the X-ray image and the test. Every image has been taken using the same type of equipment and with the same format: only the posterior-anterior view is considered.
Source: [https://github.com/ari-dasci/covidgr](https://github.com/ari-dasci/covidgr) | Provide a detailed description of the following dataset: COVIDGR |
Covid-HeRA | **Covid-HeRA** is a dataset for health risk assessment and severity-informed decision making in the presence of COVID19 misinformation. It is a benchmark dataset for risk-aware health misinformation detection, related to the 2019 coronavirus pandemic. Social media posts (Twitter) are annotated based on the perceived likelihood of health behavioural changes and the perceived corresponding risks from following unreliable advice found online.
Source: [https://github.com/TIMAN-group/covid19_misinformation](https://github.com/TIMAN-group/covid19_misinformation) | Provide a detailed description of the following dataset: Covid-HeRA |
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