---
title: "CORD-19: The COVID-19 Open Research Dataset — Wang 2020"
description: "CORD-19 addresses a practical bottleneck of the early pandemic: the COVID-19 and coronavirus literature was scattered across archives and locked in…"
authors: ["Lucy Lu Wang", "Kyle Lo", "Yoganand Chandrasekhar", "Russell Reas", "Jiangjiang Yang", "Doug Burdick", "Darrin Eide", "Kathryn Funk", "Yannis Katsis", "Rodney Michael Kinney", "Yunyao Li", "Ziyang Liu", "William Merrill", "Paul Mooney", "Dewey A. Murdick", "Devvret Rishi", "Jerry Sheehan", "Zhihong Shen", "Brandon Stilson", "Alex D. Wade", "Kuansan Wang", "Nancy Xin Ru Wang", "Christopher Wilhelm", "Boya Xie", "Douglas M. Raymond", "Daniel S. Weld", "Oren Etzioni", "Sebastian Kohlmeier"]
year: 2020
venue: "Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020"
source: "https://aclanthology.org/2020.nlpcovid19-acl.1/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2020-cord-19-covid-19-open/"
---
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# CORD-19: The COVID-19 Open Research Dataset

**Authors:** [Lucy Lu Wang](https://md.research.elamur.ai/free/en/authors/lucy-lu-wang/), [Kyle Lo](https://md.research.elamur.ai/free/en/authors/kyle-lo/), Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, [Yannis Katsis](https://md.research.elamur.ai/free/en/authors/yannis-katsis/), Rodney Michael Kinney, [Yunyao Li](https://md.research.elamur.ai/free/en/authors/yunyao-li/), Ziyang Liu et al. (28 authors) · **Year:** 2020 · **Venue:** Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.nlpcovid19-acl.1/) · [PDF](https://aclanthology.org/2020.nlpcovid19-acl.1.pdf) · [OpenAlex](https://openalex.org/W3020786614)

**Citations:** 537 citations (openalex-snapshot · 2026-10-14) · [148](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
CORD-19 addresses a practical bottleneck of the early pandemic: the COVID-19 and coronavirus literature was scattered across archives and locked in formats that are hard to process automatically. AI2 and partners including NLM, Microsoft Research, CZI and Kaggle built a single, regularly refreshed corpus. They gathered papers and preprints matching a fixed keyword query from PMC, PubMed, the WHO database, bioRxiv, medRxiv, arXiv and publisher back catalogs, all ingested via Semantic Scholar. The team merged duplicate records using shared identifiers, chose one authoritative metadata record per paper, and converted PDFs and JATS XML into structured S2ORC JSON. For a subset of documents, they also added HTML table parses. The collection started with about 28K papers and grew past 140K, more than 72K of them with full text. In roughly three months it was downloaded over 200K times. It became the basis for search and extraction tools, systematic reviews and shared tasks such as TREC-COVID and the Kaggle challenge. The paper also identifies remaining obstacles: PDF-centric publishing, limited open licensing and the lack of a shared metadata standard.

## Key points
- First release on March 16, 2020 with ~28K papers. By version 2020-06-14 the corpus held >140K papers and >72K full texts, and it is updated daily.
- Full text comes from the S2ORC pipeline (GROBID → TEI XML → S2ORC JSON) plus a custom JATS XML parser for PMC papers. About 48% of papers have a PDF parse and about 37% have an XML parse, and over 90% of PDFs parse successfully.
- Each deduplicated cluster gets a persistent CORD UID, because no single existing identifier (DOI, PMC ID, PubMed ID, arXiv ID, MAG ID, WHO Covidence #) covers all papers.
- Table parsing with IBM Watson Discovery (SDU) and GTE yielded 188K tables from 54K documents. Of these, 33K tables were linked into roughly 19K full-text documents (~25%).
- By MAG subfield, the largest share is Virology (25.5%), followed by Immunology (13.8%) and Surgery (13.5%).

## Interesting ideas
- **Conservative deduplication:** records are merged only when they share an identifier and no other identifier contradicts it, otherwise they stay separate. Any corpus built by merging overlapping sources could use this to avoid silently fusing distinct documents, at the cost of some leftover duplicates.
- **License-aware canonical records:** within each duplicate group, the entry with an available document and the most permissive license wins. Missing fields are then filled in from the other group members. This keeps redistribution legally safe while maximizing metadata completeness.
- **Stable IDs across releases:** a project-specific identifier assigned to each cluster stays the same from one release to the next. Downstream users of a living dataset can then track items over time even as sources and metadata change.
- **One target schema for heterogeneous inputs:** PDFs and publisher XML are both converted into the same JSON format, and each parse keeps a traceable name (a file hash or a PMC ID). Tools work uniformly across sources, and parses of different quality can be compared for the same paper.
- **Cheap cross-pipeline alignment with error analysis:** tables from a separate extractor are attached only when caption similarity is high (Jaccard > 0.9). The authors then examined the failures and found most were caption mismatches. This simple threshold-then-inspect approach suits any task that links outputs of independent extraction systems.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

148 papers of our library cite it (of the 140884 whose bibliographies we read); 13 of them have a page here, 135 not yet.

- [AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset](https://md.research.elamur.ai/free/en/papers/ameur-2021-aracovid19-mfh-arabic-covid-19/)
- [Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/)
- [RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/)
- [Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/)
- [Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation](https://md.research.elamur.ai/free/en/papers/levy-2021-collecting-large-scale-gender-bias/)
- [Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art](https://md.research.elamur.ai/free/en/papers/lewis-2020-pretrained-language-models-biomedical-clinical/)
- [S2ORC: The Semantic Scholar Open Research Corpus](https://md.research.elamur.ai/free/en/papers/lo-2019-s2orc-semantic-scholar-open-research/)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://md.research.elamur.ai/free/en/papers/saakyan-2021-covid-fact-fact-extraction-verification/)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/)
- [Fact or Fiction: Verifying Scientific Claims](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
- [GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://md.research.elamur.ai/free/en/papers/wang-2022-gpl-generative-pseudo-labeling-unsupervised/)
- [Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains](https://md.research.elamur.ai/free/en/papers/zouhar-2024-fine-tuned-machine-translation-metrics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2020cord,
  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},
  year = {2020},
  url = {https://aclanthology.org/2020.nlpcovid19-acl.1/}
}
```

- [← previous ConvoKit: A Toolkit for the Analysis of Conversations](https://md.research.elamur.ai/free/en/papers/chang-2020-convokit-toolkit-analysis-conversations/)
- [next → Coreference for Discourse Parsing: A Neural Approach](https://md.research.elamur.ai/free/en/papers/guz-2020-coreference-discourse-parsing-neural-approach/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/2/)

## References

40 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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