Abstract
Abstract *Background:* Sharing data is a tenet of science, yet commonplace in only a few subdisciplines. Recognizing that a data sharing culture is unlikely to be achieved without policy guidance, some funders and journals have begun to request and require that investigators share their primary datasets with other researchers. The purpose of this study is to understand the current state of data sharing policies within journals, the features of journals which are associated with the strength of their data sharing policies, and whether the strength of data sharing policies impact the observed prevalence of data sharing. *Methods:* We investigated these relationships with respect to gene expression microarray data in the journals that most often publish studies about this type of data. We measured data sharing prevalence as the proportion of papers with submission links from NCBI's Gene Expression Omnibus (GEO) database. We conducted univariate and linear multivariate regressions to understand the relationship between the strength of data sharing policy and journal impact factor, journal subdiscipline, journal publisher (academic societies vs. commercial), and publishing model (open vs. closed access). *Results:* Of the 70 journal policies, 18 (26%) made no mention of sharing publication-related data within their Instruction to Author statements. Of the 42 (60%) policies with a data sharing policy applicable to microarrays, we classified 18 (26% of 70) as moderately strong and 24 (34% of 70) as strong.Existence of a data sharing policy was associated with the type of journal publisher: half of all commercial publishers had a policy compared to 82% of journals published by academic society. All four of the open-access journals had a data sharing policy. Policy strength was associated with impact factor: the journals with no data sharing policy, a weak policy, and a strong policy had respective median impact factors of 3.6, 4.5, and 6.0. Policy strength was positively associated with measured data sharing submission into the GEO database: the journals with no data sharing policy, a weak policy, and a strong policy had median data sharing prevalence of 11%, 19%, and 29% respectively.*Conclusion:* This review and analysis begins to quantify the relationship between journal policies and data sharing outcomes and thereby contributes to assessing the incentives and initiatives designed to facilitate widespread, responsible, effective data sharing.
Highlights
Sharing data is a tenet of science, yet commonplace in only a few subdisciplines
Recognizing that a data sharing culture is unlikely to be achieved without policy guidance, some funders and journals have begun to request and require that investigators share their primary datasets with other researchers
The purpose of this study is to understand the current state of data sharing policies within journals, the features of journals which are associated with the strength of their data sharing policies, and whether the strength of data sharing policies impact the observed prevalence of data sharing
Summary
Sharing data is a tenet of science, yet commonplace in only a few subdisciplines. Recognizing that a data sharing culture is unlikely to be achieved without policy guidance, some funders and journals have begun to request and require that investigators share their primary datasets with other researchers. Methods: We investigated these relationships with respect to gene expression microarray data in the journals that most often publish studies about this type of data. We measured data sharing prevalence as the proportion of papers with submission links from NCBI's Gene Expression Omnibus (GEO) database. We conducted univariate and linear multivariate regressions to understand the relationship between the strength of data sharing policy and journal impact factor, journal subdiscipline, journal publisher (academic societies vs commercial), and publishing model (open vs closed access). The National Center for Biotechnology Information's (NCBI) Entrez website makes it easy to identify journal articles with associated datasets within GEO, allowing us to study the association between journal policies and observed data sharing practice
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