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 29,000 scholarly articles, including over 13,000 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.
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.
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.
Latest release contains papers up until 2020-03-13 with over 13,000 full text articles.
Each paper is represented as a single JSON object. The schema is available here.
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 29,000 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.
When including CORD-19 data in a publication or redistribution, please cite the dataset as follows:
COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-13. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed YYYY-MM-DD. doi:10.5281/zenodo.3715506
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.
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 firstname.lastname@example.org .
SciSpacy , a text processing toolkit optimized for scientific text
SciBERT , a BERT model pretrained on scientific text
Create an AI-powered customizable adaptive feed of COVID-19 research
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)
Please contact us email@example.com if you’d like to request to add other resources.