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. 2024 Nov 18;19(11):e0313991.
doi: 10.1371/journal.pone.0313991. eCollection 2024.

COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study

Ahmad Sofi-Mahmudi  1   2 , Eero Raittio  3   4 , Yeganeh Khazaei  5 , Javed Ashraf  3 , Falk Schwendicke  6   7 , Sergio E Uribe  8   9   10 , David Moher  11   12
Affiliations

COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study

Ahmad Sofi-Mahmudi et al. PLoS One. .

Abstract

Background: According to 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. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).

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 with links to their raw data hosted in a public repository. We then screened the link and included those repositories that 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), 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 repository was the most influential factor on FAIRness scores (R2 = 0.809).

Conclusion: This paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The flow diagram of the study.
Fig 2
Fig 2. FAIR level by metric (percentage).
Fig 3
Fig 3. Yearly trend for FAIR components.
See this image and copyright information in PMC

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