Omkar2118/datasets huggingface dataset:
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)

Source: [Cornell Movie-Dialogs Corpus](http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html)
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.

Source: [You Had Me at Hello: How Phrasing Affects Memorability](/paper/you-had-me-at-hello-how-phrasing-affects)
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. 

Source: [COS960: A Chinese Word Similarity Dataset of 960 Word Pairs](/paper/190600247)

Introducing paper: COS960: A Chinese Word Similarity Dataset of 960 Word Pairs
CO-SKEL dataset

A benchmark dataset for the co-skeletonization task.

Source: [Object Co-Skeletonization With Co-Segmentation](/paper/object-co-skeletonization-with-co)

Introducing paper: Object Co-Skeletonization With Co-Segmentation
CoSQL
Full name: Conversational Text-to-SQL Challenge

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. 

Source: [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://arxiv.org/abs/1909.05378)

Introducing paper: CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
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.

Source: [COSTRA 1.0: A Dataset of Complex Sentence Transformations](/paper/costra-10-a-dataset-of-complex-sentence)

Introducing paper: COSTRA 1.0: A Dataset of Complex Sentence Transformations
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).

Source: [COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval](/paper/cough-a-challenge-dataset-and-models-for)

Introducing paper: COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
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).

Source: [COUNTER](http://ucrel.lancs.ac.uk/textreuse/counter.php)

Introducing paper: COUNTER - corpus of Urdu news text reuse
COVERAGE
Full name: Copy-Move Forgery Database with Similar but Genuine Objects

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.

Source: [COVERAGE](https://github.com/wenbihan/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)

Introducing paper: COVID-19 Data Analysis and Forecasting: Algeria and the World
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)

Introducing paper: Country Image in COVID-19 Pandemic: A Case Study of China
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. 

Source: [COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature](/paper/covid-19-ct-cxr-a-freely-accessible-and)

Introducing paper: COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
COVID-CQ

COVID-CQ is a stance data set of user-generated content on Twitter in the context of COVID-19.

Source: [A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19](/paper/a-stance-data-set-on-polarized-conversations)
Image Source: [https://arxiv.org/pdf/2009.01188v2.pdf](https://arxiv.org/pdf/2009.01188v2.pdf)

Introducing paper: A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19
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)

Introducing paper: COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
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)

Introducing paper: Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation
CovidQA

The beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. 

Source: [Rapidly Bootstrapping a Question Answering Dataset for COVID-19](/paper/rapidly-bootstrapping-a-question-answering)

Introducing paper: Rapidly Bootstrapping a Question Answering Dataset for COVID-19