Research Data-Sharing Practices Through the Lens of Data Availability Statements
TL;DR
Rising DAS adoption was accompanied by a more heterogeneous pooled composition of sharing mechanisms rather than a single pathway towards open data, a shift driven largely by the staggered entry of journals.
Abstract
This study examined the relationships between Data Availability Statement (DAS) categories, citation counts, and the evolution of research data-sharing practices. We analysed 653,490 research articles published between 2010 and 2025 in the ten open-access journals with the most DAS-containing articles, using PubMed Central full-text records matched to Web of Science citation data. DASs were classified into seven categories with a validated large language model (LLM) framework, and associations with citations were evaluated by non-parametric tests and regressions controlling for journal and publication year. The associations varied across journals (DAS × journal interaction: joint Wald test F54 = 30.49, p < 0.001). After adjustment they were generally small: relative to articles reporting no data generated or not applicable (N), reuse of public third-party data (R) was the only category positive and significant in all four specifications; in-article or identifier-free availability (P) was also associated with slightly higher counts in most specifications; and full repository deposition (F) showed no clear advantage over N and lay slightly below author-request-only statements (A; − 0.029, 95% CI: − 0.036 to − 0.023). Longitudinally, rising DAS adoption was accompanied by a more heterogeneous pooled composition of sharing mechanisms rather than a single pathway towards open data, a shift driven largely by the staggered entry of journals. DAS categories thus provide a practical, scalable framework for characterising data-sharing practices, and journal context matters when interpreting them.
