CORD-19: The Covid-19 Open Research Dataset

By Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Michael Kinney, Ziyang Liu and 15 more. 2020

876 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging, Evaluation & Benchmarks

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “s is a publicly accessible corpus that scientists can work with. As a response to this need, on March 13, 2020, the Allen Institute for AI (AI2) released the COVID-19 Open Research Dataset (CORD-19) [Wang et al., 2020], with regular updates since its initial release (ﬁrst weekly, then daily). CORD-1”
COVIDRead: A Large-scale Question Answering Dataset on COVID-19 (2021, background): “CORD-19 is a collection of various scholarly articles, about COVID-19, SARS-CoV-2, and related corona viruses.”
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering (2022, methodology): “We evaluate our proposed approach on three different datasets from three domains: COVID-19 research (Wang et al., 2020), Conversations (Wu et al.”
RealTime QA: What's the Answer Right Now? (2022, background): “For example, a QA system with timely updates can improve emergency management of natural disasters (Imran et al., 2013, 2015, 2016; Nguyen et al., 2016) or pandemics (e.g., COVID-19; Wang et al., 2020; Lee et al., 2020; Möller et al., 2020; Alzubi et al., 2021).”
MSˆ2: Multi-Document Summarization of Medical Studies (2021, background): “NLP for scientiﬁc text has been gaining interest recently with work spanning the whole NLP pipeline: datasets (S2ORC (Lo et al., 2020), CORD-19 (Wang et al., 2020b)), pretrained transformer models (SciBERT (Beltagy et al., 2019), BioBERT (Lee et al., 2020), ClinicalBERT (Huang et al., 2019),…”
Do Language Models Plagiarize? (2022, background): “• Cord-19: consists of 500,000 scholarly articles about the COVID19 virus [55].”
Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering (2021, background): “In each round of this task, we consider material from the current version of the COVID-19 Open Research Dataset (CORD-19) [41] to reflect the rapid developments in the field.”
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, neutral): “Besides, we have summarized the relevant computation costs from existing studies in Table 8, which aims to provide clear assessment of computation requirements.”
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction (2022, methodology): “We selected seven datasets from the ir_datasets catalogue [35]: Bio medical (TREC Covid [49, 51], TripClick [39], NFCorpus [4]), Entity centric (DBPedia Entity [14]), informal language (Antique [13], TREC Podcast [21]), news cables (TREC Robust 04 [48]).”
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021, methodology): “This prompted the creation of the Covid-19 Open Research Dataset (CORD-19) [81] and the TREC-COVID [60, 73] benchmarking effort on one hand, and a flurry of new research and development of IR systems specializing on this task [9, 69, 80] on the other.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “…algebraic topology as inputs, and relies on two main datasets: information on 84 SARS coronavirus protease inhibitors from the ChEMBL database (Davies et al., 2015; Mendez et al., 2019), and a more general set of 15,843 protein-ligand binding aﬃnities from the PDBbind database (Wang et al., 2004).”