Data Availability Statements in Mega Journals: A Comparative Analysis of Global and Korea‐Affiliated Publications in Health and Medical Research

ABSTRACT Data Availability Statements (DAS) have become a standard mechanism for promoting transparency and reproducibility in open‐access mega journals, yet questions remain about how effectively they support meaningful data sharing in practice and how these practices vary across national research contexts. This study examines data‐sharing practices in the medical and health sciences through a comparative analysis of global publications and Korea‐affiliated articles in three mega journals indexed in PubMed Central: PLOS ONE, Scientific Reports and BMJ Open (2020–2024). DAS from 176,145 articles were collected from PubMed Central using an automated pipeline with manual validation and classified into a seven‐category typology reflecting levels of data accessibility and reuse. Results indicate that although DAS inclusion increased over time, repository‐based data sharing remains limited, while ‘data available upon request’ continues to be prevalent. Clear differences are observed across journals: PLOS ONE shows greater use of repository‐based and in‐article sharing, whereas Scientific Reports and BMJ Open rely more heavily on ‘data available upon request’. Korea‐affiliated articles largely follow global trends, with slightly greater reliance on national public data repositories. Repository use is concentrated among a small number of international multidisciplinary platforms and selected national biomedical databases. The findings reveal a persistent gap between formal DAS compliance and effective data accessibility, indicating the need for clearer, more actionable data‐sharing guidance.

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Publication Details

Journal
Learned Publishing
Published
2026-06-28
DOI
https://doi.org/10.1002/leap.2083
Primary Topic
Research Data Management Practices
Type
article
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article

Data Availability Statements in Mega Journals: A Comparative Analysis of Global and Korea‐Affiliated Publications in Health and Medical Research

Yunseo Park, Sanghee Oh, Seyun Sim
Learned Publishing
Research Data Management Practices
article

Data Availability Statements in Mega Journals: A Comparative Analysis of Global and Korea‐Affiliated Publications in Health and Medical Research

Yunseo Park, Sanghee Oh, Seyun Sim
article en

Abstract

ABSTRACT Data Availability Statements (DAS) have become a standard mechanism for promoting transparency and reproducibility in open‐access mega journals, yet questions remain about how effectively they support meaningful data sharing in practice and how these practices vary across national research contexts. This study examines data‐sharing practices in the medical and health sciences through a comparative analysis of global publications and Korea‐affiliated articles in three mega journals indexed in PubMed Central: PLOS ONE, Scientific Reports and BMJ Open (2020–2024). DAS from 176,145 articles were collected from PubMed Central using an automated pipeline with manual validation and classified into a seven‐category typology reflecting levels of data accessibility and reuse. Results indicate that although DAS inclusion increased over time, repository‐based data sharing remains limited, while ‘data available upon request’ continues to be prevalent. Clear differences are observed across journals: PLOS ONE shows greater use of repository‐based and in‐article sharing, whereas Scientific Reports and BMJ Open rely more heavily on ‘data available upon request’. Korea‐affiliated articles largely follow global trends, with slightly greater reliance on national public data repositories. Repository use is concentrated among a small number of international multidisciplinary platforms and selected national biomedical databases. The findings reveal a persistent gap between formal DAS compliance and effective data accessibility, indicating the need for clearer, more actionable data‐sharing guidance.

Learned PublishingVol. 39(3)
Sungkyunkwan University (KR)
Gender equality
Openalex Percentile: Top 3%
Research Data Management Practices
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