CORD-19 is a corpus of academic papers about COVID-19 and related coronavirus research. It's curated and maintained by the Semantic Scholar team at the Allen Institute for AI to support text mining and NLP research. Please read our paper for an in-depth description of how it was created: https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1/
The final version of CORD-19 was released on June 2, 2022. Since we launched the dataset on March 13, 2020, we have released an updated version of the dataset almost every week. Starting from around 40K articles in its first version, the dataset has grown to index over 1M papers, and includes full text content for nearly 370K papers. We thank you for your support and feedback throughout this process. For more information, please see this blog post. A list of alternate data resources are provided under Other resources.
- 2022-06-02 - Final release of CORD-19
- 2021-03-01 - Review article published in Briefings in Bioinformatics
- 2020-07-09 - CORD-19 presented at the NLP-COVID workshop.
- 2020-03-13 - CORD-19 initial release
We have performed some data cleaning that is sufficient to fuel most text mining & NLP research efforts. But we do not intend to provide sufficient cleaning for this data to be usable for directly consuming (reading) papers about COVID-19 or coronaviruses. There will always be some amount of error, which will make CORD-19 more/less usable for certain applications than others. We leave it up to the user to make this determination, though please feel free to consult us for recommendations.
While CORD-19 was initially released on 2020-03-13, the current schema is defined base on an update on 2020-05-26. Older versions of CORD-19 will not necessarily adhere to exactly the schema defined in this README. Please reach out for help on this if working with old CORD-19 versions.
All versions of CORD-19 can be found HERE.
First published version (2020-03-13): Download Link (size: 0.3Gb, md5: a36fe181, sha1: 8fbea927)
Last published version (2022-06-02): Download Link (size: 18.7Gb, md5: c557069e, sha1: dd2c32bc)
TREC-COVID Shared Task Website: https://ir.nist.gov/covidSubmit/index.html
| TREC-COVID | Date | Changelog | Link to download | md5 | sha1 |
|---|---|---|---|---|---|
| Round 1 | 2020-04-10 | link | cord-19_2020-04-10.tar.gz (1.5GB) | f4c3e742 |
4980d8ee |
| Round 2 | 2020-05-01 | link | cord-19_2020-05-01.tar.gz (1.7GB) | e8c56920 |
dc22dbc9 |
| Round 3 | 2020-05-19 | link | cord-19_2020-05-19.tar.gz (2.8GB) | 6424de9c |
1781b935 |
| Round 4 | 2020-06-19 | link | cord-19_2020-06-19.tar.gz (3.3GB) | 47b61215 |
fdd0490e |
| Round 5 | 2020-07-16 | link | cord-19_2020-07-16.tar.gz (3.7GB) | 018c4bc4 |
7adcf31a |
EPIC-QA Shared Task Website: https://bionlp.nlm.nih.gov/epic_qa/
| EPIC-QA | Date | Changelog | Link to download | md5 | sha1 |
|---|---|---|---|---|---|
| Preliminary round | 2020-06-19 | link | cord-19_2020-06-19.tar.gz (3.3GB) | 47b61215 |
fdd0490e |
| Primary round | 2020-10-22 | link | cord-19_2020-10-22.tar.gz (5.3GB) | 7cb9e743 |
7efe285f |
CORD-19 is released weekly. Each version of the corpus is tagged with a datestamp (e.g. 2020-05-26). Releases look like:
|-- 2020-05-26/
|-- changelog
|-- cord_19_embeddings.tar.gz
|-- document_parses.tar.gz
|-- metadata.csv
|-- 2020-05-27/
|-- ...
The files in each version are:
changelog: A text file summarizing changes between this and the previous version.cord_19_embeddings.tar.gz: A collection of precomputed SPECTER document embeddings for each CORD-19 paperdocument_parses.tar.gz: A collection of JSON files that contain full text parses of a subset of CORD-19 papersmetadata.csv: Metadata for all CORD-19 papers.
When cord_19_embeddings.tar.gz is uncompressed, it is a 769-column CSV file, where the first column is the cord_uid and the remaining columns correspond to a 768-dimensional document embedding. For example:
ug7v899j,-2.939983606338501,-6.312200546264648,-1.0459030866622925,5.164162635803223,-0.32564637064933777,-2.507413387298584,1.735608696937561,1.9363566637039185,0.622501015663147,1.5613162517547607,...
When document_parses.tar.gz is uncompressed, it is a directory:
|-- document_parses/
|-- pdf_json/
|-- 80013c44d7d2d3949096511ad6fa424a2c740813.json
|-- bfe20b3580e7c539c16ce4b1e424caf917d3be39.json
|-- ...
|-- pmc_json/
|-- PMC7096781.xml.json
|-- PMC7118448.xml.json
|-- ...
We recommend everyone primarily use metadata.csv & augment data when needed with full text in document_parses/. For example, let's say we wanted to collect a bunch of Titles, Abstracts, and Introductions of papers. In Python, such a script might look like:
import csv
import os
import json
from collections import defaultdict
cord_uid_to_text = defaultdict(list)
# open the file
with open('metadata.csv') as f_in:
reader = csv.DictReader(f_in)
for row in reader:
# access some metadata
cord_uid = row['cord_uid']
title = row['title']
abstract = row['abstract']
authors = row['authors'].split('; ')
# access the full text (if available) for Intro
introduction = []
if row['pdf_json_files']:
for json_path in row['pdf_json_files'].split('; '):
with open(json_path) as f_json:
full_text_dict = json.load(f_json)
# grab introduction section from *some* version of the full text
for paragraph_dict in full_text_dict['body_text']:
paragraph_text = paragraph_dict['text']
section_name = paragraph_dict['section']
if 'intro' in section_name.lower():
introduction.append(paragraph_text)
# stop searching other copies of full text if already got introduction
if introduction:
break
# save for later usage
cord_uid_to_text[cord_uid].append({
'title': title,
'abstract': abstract,
'introduction': introduction
})
We recommend everyone work with metadata.csv as the starting point. This file is comma-separated with the following columns:
cord_uid: Astr-valued field that assigns a unique identifier to each CORD-19 paper. This is not necessariy unique per row, which is explained in the FAQs.sha: AList[str]-valued field that is the SHA1 of all PDFs associated with the CORD-19 paper. Most papers will have either zero or one value here (since we either have a PDF or we don't), but some papers will have multiple. For example, the main paper might have supplemental information saved in a separate PDF. Or we might have two separate PDF copies of the same paper. If multiple PDFs exist, their SHA1 will be semicolon-separated (e.g.'4eb6e165ee705e2ae2a24ed2d4e67da42831ff4a; d4f0247db5e916c20eae3f6d772e8572eb828236')source_x: AList[str]-valued field that is the names of sources from which we received this paper. Also semicolon-separated. For example,'ArXiv; Elsevier; PMC; WHO'. There should always be at least one source listed.title: Astr-valued field for the paper titledoi: Astr-valued field for the paper DOIpmcid: Astr-valued field for the paper's ID on PubMed Central. Should begin withPMCfollowed by an integer.pubmed_id: Anint-valued field for the paper's ID on PubMed.license: Astr-valued field with the most permissive license we've found associated with this paper. Possible values include:'cc0', 'hybrid-oa', 'els-covid', 'no-cc', 'cc-by-nc-sa', 'cc-by', 'gold-oa', 'biorxiv', 'green-oa', 'bronze-oa', 'cc-by-nc', 'medrxiv', 'cc-by-nd', 'arxiv', 'unk', 'cc-by-sa', 'cc-by-nc-nd'abstract: Astr-valued field for the paper's abstractpublish_time: Astr-valued field for the published date of the paper. This is inyyyy-mm-ddformat. Not always accurate as some publishers will denote unknown dates with future dates likeyyyy-12-31authors: AList[str]-valued field for the authors of the paper. Each author name is inLast, First Middleformat and semicolon-separated.journal: Astr-valued field for the paper journal. Strings are not normalized (e.g.BMJandBritish Medical Journalcan both exist). Empty string if unknown.mag_id: Deprecated, but originally anint-valued field for the paper as represented in the Microsoft Academic Graph.who_covidence_id: Astr-valued field for the ID assigned by the WHO for this paper. Format looks like#72306.arxiv_id: Astr-valued field for the arXiv ID of this paper.pdf_json_files: AList[str]-valued field containing paths from the root of the current data dump version to the parses of the paper PDFs into JSON format. Multiple paths are semicolon-separated. Example:document_parses/pdf_json/4eb6e165ee705e2ae2a24ed2d4e67da42831ff4a.json; document_parses/pdf_json/d4f0247db5e916c20eae3f6d772e8572eb828236.jsonpmc_json_files: AList[str]-valued field. Same as above, but corresponding to the full text XML files downloaded from PMC, parsed into the same JSON format as above.url: AList[str]-valued field containing all URLs associated with this paper. Semicolon-separated.s2_id: Astr-valued field containing the Semantic Scholar ID for this paper. Can be used with the Semantic Scholar API (e.g.s2_id=9445722corresponds tohttp://api.semanticscholar.org/corpusid:9445722)
This is a very tricky issue, and we have not decided on the best way forward. To explain, let’s take example cord_uid=hox2xwjg. Examining their respective rows in the metadata file, we see that they are the same paper, but sent from different sources (Elsevier, PMC). The Elsevier row has DOI and PDF, but the PMC row doesn’t. Furthermore, the PMC ID, publication date, and URL for each of these rows is different.
Technically all of this data is representative of paper hox2xwjg so we don’t want to remove any of it. But combining them into one cluster would require a schema change to the data, which would break a lot of people’s code. Hopefully this is not too big an issue because there are only a small percentage of papers affected, but know that this issue exists and we’re debating what’s the best way forward.
Abstracts in the metadata.csv file are “gold” provided directly from publishers or digital archives. Because PMC is very consistent at providing us “gold” abstracts, we do not bother with parsing the PMC XMLs for abstract text (it’s already in the metadata.csv). As such, the PMC JSONs do not contain abstracts. This is not the case for PDF JSONs. We often obtain PDFs through crawling, and in this manner, we would not have “gold” abstracts provided to us. As such, we still opt to parse the PDF for abstract text, which is why that field exists.
The most likely reason is PDF parsing errors. Occasionally, publishers will have different metadata from what is actually displayed on the PDF itself (e.g. slight differences in author names). We encourage users to use fields in the metadata file by default and only fall back on the JSON when it is missing.
The JSONs are only meant for representing the full text of the PDF in a structured, machine-readable format. Many metadata fields like dates and venues don’t commonly appear on the PDF. Please defer to the metadata file for all such fields, since these come from the publishers directly.
Many papers in CORD-19 include HTML table parses. These table parses are available in the document parse files under ref_entries of type table. Note: not all tables will have HTML parses. These parses leverage IBM Watson Discovery capabilities (more details can be found in our paper).
Figure images are currently not available. We’re currently looking into how to best support these. As for equations, we do not do anything special here – the symbols are treated as text and should be included in the text blobs.
We view these as different attempts/views to represent the same paper/document. Some are going to be higher quality than others. Treat these are separate representations of the same document – you can choose to use one, both, neither (i.e. just use the metadata fields). On average, we believe the PMC JSONs are cleaner than the PDF JSONs but that’s not necessarily true.
Let’s take a look at examples cord_uid=d9v5xtx7 and cord_uid=8avkjc84. They both share PDF sha=5d0d0bd116976e1412c10a84902894999df4a342. These are two papers we sourced from Elsevier. If you follow the URLs, you’ll notice that they actually retrieve the same PDF despite different having different DOIs. This is an upstream error from the publisher, which we can’t necessarily do anything about. Hopefully the number of these cases is small.
Subscribe to notifications about CORD-19 at: https://share.hsforms.com/1cM7MMF68RqCdbBKTcyN7VQ3ioxm
Please email [email protected] and [email protected] for any questions or concerns.
Our paper was accepted to the NLP-COVID workshop at ACL 2020. See the reviews on OpenReview: https://openreview.net/forum?id=0gLzHrE_t3z. The paper is available in the ACL Anthology (BibTeX below): https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1
@inproceedings{wang-etal-2020-cord,
title = "{CORD-19}: The {COVID-19} Open Research Dataset",
author = "Wang, Lucy Lu and Lo, Kyle and Chandrasekhar, Yoganand and Reas, Russell and Yang, Jiangjiang and Burdick, Doug and Eide, Darrin and Funk, Kathryn and Katsis, Yannis and Kinney, Rodney Michael and Li, Yunyao and Liu, Ziyang and Merrill, William and Mooney, Paul and Murdick, Dewey A. and Rishi, Devvret and Sheehan, Jerry and Shen, Zhihong and Stilson, Brandon and Wade, Alex D. and Wang, Kuansan and Wang, Nancy Xin Ru and Wilhelm, Christopher and Xie, Boya and Raymond, Douglas M. and Weld, Daniel S. and Etzioni, Oren and Kohlmeier, Sebastian",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1"
}
This is a Google Sheet tracking systems and demos that use CORD-19. Projects are listed in random order. Our focus here is to collect community efforts that might not be discoverable because systems and demos don't always translate to papers (which we can find via citations of CORD-19).
Missing yours or incomplete data? Let us know using this Google Form or email us!
S2ORC-doc2json: We use this library to process PDFs and PubMed JATS XML into the format released in CORD-19. This library can be adapted to produce your own versions of the dataset. Source code and instructions for using the library can be found here.
Semantic Scholar API: Metadata, paper abstracts, and citation information for papers we index are available through our API. Documentation here.
S2ORC: A dataset of millions of full text papers processed in the same way as CORD-19, but covering many different fields of science. Not regularly updated; intended for offline research, like model development. Available here.
PubMed Central: The National Library of Medicine (NLM) continues to collaborate with publishers to make COVID-19 and coronavirus-related publications and associated data immediately accessible in PubMed Central (PMC) in human- and machine-readable forms. Available here.
LitCovid: NLM continues to update its LitCovid dataset of COVID-19 related publications to facilitate text mining. Available here.
