Research transparency in dental research: A programmatic analysis

Raittio, Eero; Sofi‐Mahmudi, Ahmad; Uribe, Sergio

doi:10.1111/eos.12908

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…In general, adherence to transparent practices, except for COI disclosure, was at a similar level in COVID-19-related literature than in other biomedical literature analyzed with the same methods (see Fig 3 in [ 9 ] and also [ 20 , 21 ]). This is surprising, particularly when considering worldwide, remarkable and noble initiatives to enhance open science to tackle the pandemic.…”

Section: Discussionmentioning

confidence: 78%

Transparency of COVID-19-related research: A meta-research study

Sofi‐Mahmudi¹,

Raittio²,

Uribe³

2023

PLoS ONE

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Background We aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles. Methods We searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses. Results The total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7–85.8 95%) and protocol registration (53.5%, 95% CI: 50.7–56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3–2.8) and code (0.4%, 95% CI: 0.4–0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited. Conclusion While most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders.

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Section: Discussionmentioning

confidence: 78%

Transparency of COVID-19-related research: A meta-research study

Sofi‐Mahmudi¹,

Raittio²,

Uribe³

2023

PLoS ONE

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“…Some funders and journals, such as CIHR (4), NIH (5), BMJ (6), and PLOS (7), have aligned themselves with open science. Central to open science are three components: open protocols, open-access publications, and open data; collectively, they enhance transparency, collaboration and dissemination (8).…”

Section: Introductionmentioning

confidence: 99%

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

Sofi-Mahmudi,

Raittio,

Khazaei

et al. 2023

Preprint

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BackgroundAs 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.ObjectiveOur objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.MethodsWe 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 themetareadrpackage. Usingrtransparent, 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) andrfujipackage. 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.Results5,700 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 (R2=0.809).ConclusionThis 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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