The rapid, massive growth of COVID-19 authors in the scientific literature is a dataset (2020). On theSindex it has a DataRank of 1.4, placing it in the top 38.7% of the data-sharing corpus. It has been cited 22 times, with 17 citing works in its 1-hop citation network. Its calibrated FAIR score is 23/100.
ABSTRACT We examined the extent to which the scientific workforce in different fields was engaged in publishing COVID-19-related papers. According to Scopus (data cut, August 1, 2021), 210,183 COVID-19-related publications included 720,801 unique authors, of which 360,005 authors had published at least 5 full papers in their career and 23,520 authors were at the top 2% of their scientific subfield based on a career-long composite citation indicator. The growth of COVID-19 authors was far more rapid and massive compared with cohorts of authors historically publishing on H1N1, Zika, Ebola, HIV/AIDS and tuberculosis. All 174 scientific subfields had some specialists who had published on COVID-19. In 109 of the 174 subfields of science, at least one in ten active, influential (top-2% composite citation indicator) authors in the subfield had authored something on COVID-19. 52 hyper-prolific authors had already at least 60 (and up to 227) COVID-19 publications each. Among the 300 authors with the highest composite citation indicator for their COVID-19 publications, most common countries were USA (n=67), China (n=52), UK (n=32), and Italy (n=18). The rapid and massive involvement of the scientific workforce in COVID-19-related work is unprecedented and creates opportunities and challenges. There is evidence for hyper-prolific productivity.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0.921
From 12 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 34% comes from its base citations and 66% from the citation network (12 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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