A Free, Open Resource for the Global Research Community
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 over 45,000 scholarly articles, including over 33,000 with full text, 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.
CORD-19 Explorer is a quick and easy way to search the CORD-19 corpus, or you can download the complete data below.
Participate in the CORD-19 Challenge
Kaggle is hosting the COVID-19 Open Research Dataset Challenge, a series of important questions designed to inspire the community to use CORD-19 to find new insights about the COVID-19 pandemic including the natural history, transmission, and diagnostics for the virus, management measures at the human-animal interface, lessons from previous epidemiological studies, and more.
Download CORD-19
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.
Download here:
Latest release contains papers up until 2020-03-27 with over 33,000 full text articles. (Changelog from previous release.)
- Commercial use subset (includes PMC content) — 9315 full text (new: 210, removed: 13) 186Mb
- Non-commercial use subset (includes PMC content) — 2350 full text (new: 6, removed: 9), 41Mb
- Custom license subset (includes PMC, Elsevier content) — 20657 full text (new: 4218, removed: 520), 414Mb
- bioRxiv/medRxiv subset (pre-prints that are not peer reviewed) — 1053 full text (new: 194, removed: 26), 15Mb
- Metadata file — 67Mb
- Readme
Each paper is represented as a single JSON object. The schema is available here and previous versions of the dataset are available here.
Description:
The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources:
- PubMed’s PMC open access corpus using this query (COVID-19 and coronavirus research)
- Additional COVID-19 research articles from a corpus maintained by the WHO
- bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research)
We also provide a comprehensive metadata file of 44,000 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. We also thank and acknowledge Unpaywall for providing open access license information for portions of the dataset.
Citation:
When including CORD-19 data in a publication or redistribution, please cite the dataset as follows:
In bibliography:
COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-20. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed YYYY-MM-DD. doi:10.5281/zenodo.3715505
In text:
(CORD-19, 2020)
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.
Contribute to CORD-19
To maximize impact and increase full text available to the global research community, we are actively encouraging publishers to make their research content openly available for AI projects like this that benefit the common good.
If you’re a publisher interested in contributing to the CORD-19 corpus, please contact partnerships@allenai.org.
Join the active discussion and share ideas in the CORD-19 forum.
For research inquiries, please contact kylel@allenai.org (Kyle Lo) and lucyw@allenai.org (Lucy Lu Wang).
Resources from the Allen Institute for AI
- CORD-19 Explorer: full-text search engine
- SciSpacy: a text processing toolkit optimized for scientific text
- SciBERT: a BERT model pretrained on scientific text
- Semantic Scholar API and Open Research Corpus
- Create an AI-powered customizable adaptive feed of COVID-19 research
- View the latest search results for COVID-19 on Semantic Scholar
Additional Resources
- COVID-19 Research Database (provided by the WHO)
- LitCOVID (provided by the NIH)
- COVID-19 Resource Page (provided by Microsoft Academic)
- COVID-19 Research Export File (provided by Dimensions)
- Day-Level COVID-19 Dataset (hosted on Kaggle)
- COVID-19 Global Cases (provided by Johns Hopkins University)
- COVID-19 Open Patent Dataset (hosted by Lens.org)
- COVID-19 Global Rheumatology Registry (provided by the Global Rheumatology Alliance)
- COVID-19 Literature Review Collection (hosted by Cochrane Library)
- Blog Post: Computer Scientists Are Building Algorithms to Tackle COVID-19
Publisher Resources
- American Society for Microbiology
- BMJ
- Elsevier
- New England Journal of Medicine
- SAGE
- Springer Nature
- Wiley
Compute Resources
AWS Resources and programs supporting COVID-19 research:
