Title: COVID-19 Open Research Dataset (CORD-19)

Type Dataset Sebastian Kohlmeier, Kyle Lo, Lucy Lu Wang, JJ Yang (2020): COVID-19 Open Research Dataset (CORD-19). Zenodo. Dataset. https://zenodo.org/record/3765923

Authors: Sebastian Kohlmeier (Allen Institute for AI) ; Kyle Lo (Allen Institute for AI) ; Lucy Lu Wang (Allen Institute for AI) ; JJ Yang (Allen Institute for AI) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

Important: This dataset is updated regularly and the latest version for download can be found here.

In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community.

This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, medRxiv, and others.

By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file.

Additional licensing information is available on the PMC website, medRxiv website and bioRxiv website.

Dataset content:

Each paper is represented as a single JSON object (see schema file for details).

Description:

The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources:

We also provide a comprehensive metadata file of coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text).

We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available.

This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health came together to provide this service.

Citation:

When including CORD-19 data in a publication or redistribution, please cite our arXiv pre-print.

The Allen Institute for AI and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.

More information

• DOI: 10.5281/zenodo.3765923
• Language: en

Subjects

• COVID-19, Coronavirus, 2019-nCoV, SARS-CoV, MERS-CoV, Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome

Dates

• Publication date: 2020
• Issued: March 16, 2020

Rights

• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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