Omkar2118/datasets huggingface dataset:
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)

Introducing paper: CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
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. 

Source: [A Dataset and Architecture for Visual Reasoning with a Working Memory](/paper/a-dataset-and-architecture-for-visual)

Introducing paper: A Dataset and Architecture for Visual Reasoning with a Working Memory
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.

Source: [https://github.com/ThoroughImages/CAMEL](https://github.com/ThoroughImages/CAMEL)

Introducing paper: CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
COMETA

Consists of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph.

Source: [COMETA: A Corpus for Medical Entity Linking in the Social Media](/paper/cometa-a-corpus-for-medical-entity-linking-in)

Introducing paper: COMETA: A Corpus for Medical Entity Linking in the Social Media
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. 

Source: [A Commute in Data: The comma2k19 Dataset](/paper/a-commute-in-data-the-comma2k19-dataset)

Introducing paper: A Commute in Data: The comma2k19 Dataset
COMP6
Full name: COmprehensive Machine-learning Potential

**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)

Introducing paper: Less is more: sampling chemical space with active learning
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.

Source: [CompGuessWhat?!](https://compguesswhat.github.io/download/)

Introducing paper: CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning
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.

Source: [Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities](/paper/discriminative-hierarchical-modeling-of)
Image Source: [https://ialillo.sitios.ing.uc.cl/ActionsCVPR2014/](https://ialillo.sitios.ing.uc.cl/ActionsCVPR2014/)

Introducing paper: Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities
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)

Introducing paper: Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
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.

Source: [ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters](/paper/comqa-a-community-sourced-dataset-for-complex)
Image Source: [http://qa.mpi-inf.mpg.de/comqa/](http://qa.mpi-inf.mpg.de/comqa/)

Introducing paper: ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters