Semantic Scholar

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White House Call to Action to the Tech Community on New Open Access Machine Readable COVID-19 Dataset

Press Release | White House | March 16, 2020

Today, researchers and leaders from the Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University's Center for Security and Emerging Technology (CSET), Microsoft, and the National Library of Medicine (NLM) at the National Institutes of Health released the COVID-19 Open Research Dataset (CORD-19) of scholarly literature about COVID-19, SARS-CoV-2, and the Coronavirus group. Requested by The White House Office of Science and Technology Policy, the dataset represents the most extensive machine-readable Coronavirus literature collection available for data and text mining to date, with over 29,000 articles, more than 13,000 of which have full text.

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Why openly available abstracts are important - overview of the current state of affairs

The value of open and interoperable metadata of scientific articles is increasingly being recognized, as demonstrated by the work of organizations such as Crossref, DataCite, and OpenCitations and by initiatives such as Metadata 2020 and the Initiative for Open Citations. At the same time, scientific articles are increasingly being made openly accessible, stimulated for instance by Plan S, AmeliCA, and recent developments in the US, and also by the need for open access to coronavirus literature. In this post, we focus on a key issue at the interface of these two developments: The open availability of abstracts of scientific articles. Abstracts provide a summary of an article and are part of an article's metadata. We first discuss the many ways in which abstracts can be used and we then explore the availability of abstracts. The open availability of abstracts is surprisingly limited. This creates important obstacles to scientific literature search, bibliometric analysis, and automatic knowledge extraction.

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