LitCovid: an open database of COVID-19 literature

• Published in: Nucleic acids research Vol. 49; no. D1; pp. D1534 - D1540
• Main Authors: Chen, Qingyu, Allot, Alexis, Lu, Zhiyong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 08.01.2021
• Subjects: COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 - virology; Data Curation - methods; Data Curation - statistics & numerical data; Data Mining - methods; Data Mining - statistics & numerical data; Database Issue; Databases, Factual; Humans; Internet; Machine Learning; Pandemics; Publications - statistics & numerical data; PubMed - statistics & numerical data; SARS-CoV-2 - isolation & purification; SARS-CoV-2 - physiology
• DOI: 10.1093/nar/gkaa952
• ISSN: 0305-1048; 1362-4962

Abstract Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10 000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.