Linking citation and retraction data reveals the demographics of scientific retractions among highly cited authors
Linking citation and retraction data reveals the demographics of scientific retractions among highly cited authors is a dataset published in PLOS Biology (2025). On theSindex it has a DataRank of 0.731, placing it in the top 44.8% of the data-sharing corpus. It has been cited 31 times, with 25 citing works in its 1-hop citation network. Its calibrated FAIR score is 41/100.
Abstract
Retractions are becoming increasingly common but still account for a small minority of published papers. It would be useful to generate databases where the presence of retractions can be linked to impact metrics of each scientist. We have thus incorporated retraction data in an updated Scopus-based database of highly cited scientists (top 2% in each scientific subfield according to a composite citation indicator). Using data from the Retraction Watch database (RWDB), retraction records were linked to Scopus citation data. Of 55,237 items in RWDB as of August 15, 2024, we excluded non-retractions, retractions clearly not due to any author error, retractions where the paper had been republished, and items not linkable to Scopus records. Eventually, 39,468 eligible retractions were linked to Scopus. Among 217,097 top-cited scientists in career-long impact and 223,152 in single recent year (2023) impact, 7,083 (3.3%) and 8,747 (4.0%), respectively, had at least 1 retraction. Scientists with retracted publications had younger publication age, higher self-citation rates, and larger publication volume than those without any retracted publications. Retractions were more common in the life sciences and rare or nonexistent in several other disciplines. In several developing countries, very high proportions of top-cited scientists had retractions (highest in Senegal (66.7%), Ecuador (28.6%), and Pakistan (27.8%) in career-long citation impact lists). Variability in retraction rates across fields and countries suggests differences in research practices, scrutiny, and ease of retraction. Addition of retraction data enhances the granularity of top-cited scientists' profiles, aiding in responsible research evaluation. However, caution is needed when interpreting retractions, as they do not always signify misconduct; further analysis on a case-by-case basis is essential. The database should hopefully provide a resource for meta-research and deeper insights into scientific practices.
›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
0.494
From this paper's citation signal
Citation Network Contribution
0.237
From 11 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Bibliometrics: The Leiden Manifesto for research metricsNature20151,838 citationsDataRank 1.1
- A standardized citation metrics author database annotated for scientific fieldPLOS Biology2019350 citationsDataRank 13.6Top 15%
- Quantitative research assessment: using metrics against gamed metricsInternal and Emergency Medicine202350 citationsDataRank 1.2
- Causes for Retraction in the Biomedical Literature: A Systematic Review of Studies of Retraction NoticesJournal of Korean Medical Science202343 citationsDataRank 0.568
- In defense of quantitative metrics in researcher assessmentsPLOS Biology202333 citationsDataRank 0.827
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 68% comes from its base citations and 32% from the citation network (11 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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