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

<h4>Background</h4>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 topic...

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Main Authors: Ahmad Sofi-Mahmudi, Eero Raittio, Yeganeh Khazaei, Javed Ashraf, Falk Schwendicke, Sergio E Uribe, David Moher
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2024-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0313991
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author Ahmad Sofi-Mahmudi
Eero Raittio
Yeganeh Khazaei
Javed Ashraf
Falk Schwendicke
Sergio E Uribe
David Moher
author_facet Ahmad Sofi-Mahmudi
Eero Raittio
Yeganeh Khazaei
Javed Ashraf
Falk Schwendicke
Sergio E Uribe
David Moher
author_sort Ahmad Sofi-Mahmudi
collection DOAJ
description <h4>Background</h4>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).<h4>Objective</h4>Our objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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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spelling doaj-art-ed67d3dee844437e9bf0d77a9d8da86f2024-12-23T05:31:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011911e031399110.1371/journal.pone.0313991COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.Ahmad Sofi-MahmudiEero RaittioYeganeh KhazaeiJaved AshrafFalk SchwendickeSergio E UribeDavid Moher<h4>Background</h4>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).<h4>Objective</h4>Our objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.https://doi.org/10.1371/journal.pone.0313991
spellingShingle Ahmad Sofi-Mahmudi
Eero Raittio
Yeganeh Khazaei
Javed Ashraf
Falk Schwendicke
Sergio E Uribe
David Moher
COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.
PLoS ONE
title COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.
title_full COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.
title_fullStr COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.
title_full_unstemmed COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.
title_short COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.
title_sort covid 19 related research data availability and quality according to the fair principles a meta research study
url https://doi.org/10.1371/journal.pone.0313991
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