COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study
Bibliographic record
Abstract
Abstract Background As per the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. There has not been any evaluation to assess if this COVID-19-related research data complied with FAIR principles (or FAIRness) so far. Objective Our objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness. Methods We conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent , a validated automated tool, we identified articles that included a link to their raw data hosted in a public repository. We then screened the link and included those repositories which included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1–22 and had four components. We reported descriptive analysis for each article type, journal category and repository. We used linear regression models to find the most influential factors on the FAIRness of data. Results 5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD=5.06, n=160), and articles in dental journals (13.67, SD=3.51, n=3) and Harvard Dataverse (15.79, SD=3.65, n=244) had the highest mean FAIRness scores, whereas letters (7.83, SD=4.30, n=55), articles in neuroscience journals (8.16, SD=3.73, n=63), and those deposited in GitHub (4.50, SD=0.13, n=2,152) showed the lowest scores. Regression models showed that the most influential factor on FAIRness scores was the repository (R 2 =0.809). Conclusion This paper underscored the potential for improvement across all facets of FAIR principles, with a specific emphasis on enhancing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.129 | 0.356 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".