A scientometric overview of CORD-19
A scientometric overview of CORD-19 is a dataset published in PLOS ONE (2021). On theSindex it has a DataRank of 3.0, placing it in the top 32.1% of the data-sharing corpus. It has been cited 80 times, with 75 citing works in its 1-hop citation network. Its calibrated FAIR score is 41/100.
Abstract
As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has been made available alongside calls to help mine the information it contains and to create tools to search it more effectively. We analyse the delineation of the publications included in CORD-19 from a scientometric perspective. Based on a comparison to the Web of Science database, we find that CORD-19 provides an almost complete coverage of research on COVID-19 and coronaviruses. CORD-19 contains not only research that deals directly with COVID-19 and coronaviruses, but also research on viruses in general. Publications from CORD-19 focus mostly on a few well-defined research areas, in particular: coronaviruses (primarily SARS-CoV, MERS-CoV and SARS-CoV-2); public health and viral epidemics; molecular biology of viruses; influenza and other families of viruses; immunology and antivirals; clinical medicine. CORD-19 publications that appeared in 2020, especially editorials and letters, are disproportionately popular on social media. While we fully endorse the CORD-19 initiative, it is important to be aware that CORD-19 extends beyond research on COVID-19 and coronaviruses.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
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 →
DataRank Breakdown
Base Score Contribution
0.654
From this paper's citation signal
Citation Network Contribution
2.3
From 60 citing papers with measurable signal
Top 4 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Software survey: VOSviewer, a computer program for bibliometric mappingScientometrics200919,159 citationsDataRank 1.5
- Coronavirus disease 2019: The harms of exaggerated information and non‐evidence‐based measuresEuropean Journal of Clinical Investigation2020384 citationsDataRank 0.893
- Citation Analysis May Severely Underestimate the Impact of Clinical Research as Compared to Basic ResearchPLoS ONE2013249 citationsDataRank 0.828
- Funding COVID-19 research: Insights from an exploratory analysis using open data infrastructuresQuantitative Science Studies202215 citationsDataRank 0.722Top 45%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 22% comes from its base citations and 78% from the citation network (60 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
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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