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CORD-19: The COVID-19 Open Research Dataset

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arxiv 2004.10706 v4 pith:3ND2R5QF submitted 2020-04-22 cs.DL cs.CL

Lucy Lu Wang , Kyle Lo , Yoganand Chandrasekhar , Russell Reas , Jiangjiang Yang , Doug Burdick , Darrin Eide , Kathryn Funk
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Yannis Katsis Rodney Kinney Yunyao Li Ziyang Liu William Merrill Paul Mooney Dewey Murdick Devvret Rishi Jerry Sheehan Zhihong Shen Brandon Stilson Alex Wade Kuansan Wang Nancy Xin Ru Wang Chris Wilhelm Boya Xie Douglas Raymond Daniel S. Weld Oren Etzioni Sebastian Kohlmeier
This is my paper · ORCID
classification cs.DLcs.CL
keywords cord-19covid-19datasetresearchtextbeendescribemining
verification ladder T0 review T1 audit T2 compute T3 formal
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The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG

    cs.CL 2026-08 reject novelty 4.0 of 10

    A 15-query CORD-19 study claims hybrid BM25+dense retrieval beats either alone, but the evaluation labels are the hybrid system's own top results, making the comparison self-referential.

  2. CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care

    cs.AI 2024-12 reject novelty 4.0 of 10

    CancerKG.ORG is a knowledge graph augmented LLM retrieval system for colorectal cancer literature, described with claims of unsupervised ingestion and hallucination prevention but without comparative evaluation.

  3. AI Benchmarks and Datasets for LLM Evaluation

    cs.DC 2024-12 conditional novelty 1.0 of 10

    A catalog of 41 existing AI benchmarks and datasets tagged with EU Trustworthy AI categories, with no new benchmarks or experimental results.

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