Characteristics of Imperial College London's <scp>COVID</scp>‐19 research outputs is a dataset published in Learned Publishing (2021). On theSindex it has a DataRank of 0.187, placing it in the top 55.9% of the data-sharing corpus. It has been cited 3 times, with 3 citing works in its 1-hop citation network. Its calibrated FAIR score is 38/100.
We identified 651 research outputs on the topic of COVID-19 in the form of preprint, report, journal article, dataset, and software/code published by Imperial College London authors between January to September 2020. We sought to understand the distribution of outputs over time by output type, peer review status, publisher, and open access status. Search of Scopus, the institutional repositories, Github, and other databases identified relevant research outputs, which were then combined with Unpaywall open access data and manually-verified associations between preprints and journal articles. Reports were the earliest output to emerge [median: 103 days, interquartile range (IQR): 57.5-129], but journal articles were the most commonly occurring output type over the entire period (60.8%, 396/651). Thirty preprints were identified as connected to a journal article within the set (15.8%, 30/189). A total of 52 publishers were identified, of which 4 publishers account for 59.6% of outputs (388/651). The majority of outputs were available open access through gold, hybrid, or green route (66.1%, 430/651). The presence of exclusively non-peer reviewed material from January to March suggests that demand could not be met by journals in this period, and the sector supported this with enhanced preprint services for authors. Connections between preprints and published articles suggests that some authors chose to use both dissemination methods and that, as some publishers also serve across both models, traditional distinctions of output types might be changing. The bronze open access cohort brings widespread 'free' access but does not ensure true open access.
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
0.165
From this paper's citation signal
Citation Network Contribution
0.0226
From 1 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 88% comes from its base citations and 12% from the citation network (1 citing paper 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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