A review of journal policies for sharing research data

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.

Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories

“…However, Campbell and Bendavid (2003) found that government agencies sometimes provide scientists with funding under strict restrictive policies about data sharing if a national security issue is at stake. In terms of journal policy, Piwowar and Chapman (2008) found that there is a positive correlation between the strength of journals' data sharing policies and the rate at which scientists deposit microarray data in a public database. Another study by Savage and Vickers (2009), however, investigated whether the authors whose articles were published in journals with strong data sharing policies provided raw datasets when requested and found that only one author sent data out of the 10 requests made.…”

“…With regard to institutional factors, prior studies on data sharing in the sciences (broadly conceived) have investigated funding requirements (McCullough, McGeary, & Harrison, 2008;Piwowar, 2011), journal policies (Piwowar & Chapman, 2008;Savage & Vickers, 2009), and contracts with industry sponsors (Campbell & Bendavid, 2003;Louis et al, 2002). Scholars found that scientists who received funding from government agencies (e.g.…”

Public sharing of research datasets: A pilot study of associations

“…For example, some journals require the submission of detailed biomedical datasets to publicly available databases as a condition of publication (McCain, 1995; Piwowar & Chapman, 2008). Many funders require data sharing plans as a condition of funding: since 2003, the National Institutes of Health (NIH) in the USA has required a data sharing plan for all large funding grants(NIH, 2003) and has more recently introduced stronger requirements for genome-wide association studies (NIH, 2007).…”

“…Others have used surveys and interviews to analyze opinions about the effectiveness of mandates (Ventura, 2005) and the value of various incentives (Giordano, 2007; Hedstrom, 2006; Hedstrom & Niu, 2008; Niu, 2006). A few inventories list the data-sharing policies of funders (Lowrance, 2006; University of Nottingham) and journals (Brown, 2003; McCain, 1995), and some work has been done to correlate policy strength with outcome (McCullough, McGeary, & Harrison, 2008; Piwowar & Chapman, 2008). Surveys and case studies have been used to develop models of information behavior in related domains, including knowledge sharing within an organization(Constant, Kiesler, & Sproull, 1994; Matzler et al, 2008), physician knowledge sharing in hospitals (Ryu, Ho, & Han, 2003), participation in open source projects(Bitzer, Schrettl, & Schröder, 2007), academic contributions to institutional archives(Kim, 2007; Seonghee & Boryung, 2008), the choice to publish in open access journals(Warlick & Vaughan, 2007), sharing social science datasets (Hedstrom, 2006), and participation in large-scale biomedical research collaborations (Lee, Dourish, & Mark, 2006).…”

On the shoulders of giants

“…Piwowar and Chapman [17] recently found that journals with stronger data sharing policies have higher median impact factors and that more academic journals have data sharing policies (82%) than commercial journals (46%). The NIH requires all grant proposals exceeding $500,000 per year address data sharing in their applications [2].…”