National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20892, USA.
Nucleic Acids Res. 2021 Jan 8;49(D1):D1534-D1540. doi: 10.1093/nar/gkaa952.
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.
自 2020 年当前大流行爆发以来,有关 COVID-19 和 SARS-CoV-2 的已发表文章数量迅速增长,每月新增约 10000 篇新文章。这导致信息过载问题日益严重,使得科学家、医疗保健专业人员和普通公众难以及时了解 SARS-CoV-2 和 COVID-19 的最新研究进展。因此,我们开发了 LitCovid(https://www.ncbi.nlm.nih.gov/research/coronavirus/),这是一个经过精心策划的文献中心,用于跟踪 PubMed 中最新的科学信息。LitCovid 每天都会更新,新发现的相关文章会按照精心策划的类别进行组织。为了支持人工策划,我们开发、评估并整合了先进的机器学习和深度学习算法到策划工作流程中。据我们所知,LitCovid 是首个针对 COVID-19 的特定文献资源,其所有收集的文章和策划数据都是免费提供的。自发布以来,LitCovid 得到了广泛应用,全球用户通过它满足了各种信息需求,例如证据综合、药物发现以及文本和数据挖掘等。