Full article: Making data meaningful: guidelines for good quality open data

“In the most recent editorial for the The Journal of Social Psychology (JSP), J. Grahe (2021) set out and justified a new journal policy: publishing papers now requires authors to make available all data on which claims are based. This places the journal amongst a growing group of forward-thinking psychology journals that mandate open data for research outputs.1 It is clear that the editorial team hopes to raise the credibility and usefulness of research in the journal, as well as the discipline, through increased research transparency….

This commentary represents a natural and complementary alliance between the ambition of JSP’s open data policy and the reality of how data sharing often takes place. We share with JSP the belief that usable and open data is good for social psychology and supports effective knowledge exchange within and beyond academia. For this to happen, we must have not just more open data, but open data that is of a sufficient quality to support repeated use and replication (Towse et al., 2020). Moreover, it is becoming clear that researchers across science are seeking guidance, training and standards for open data provision (D. Roche et al., 2021; Soeharjono & Roche, 2021). With this in mind, we outline several simple steps and point toward a set of freely available resources that can help make datasets more valuable and impactful. Specifically, we explain how to make data meaningful; easily findable, accessible, complete and understandable. We have provided a simple checklist (Table 1) and useful resources (Appendix A) based on our recommendations, these can also be found on the project page for this article (https:doi.org/10.17605/OSF.IO/NZ5WS). While we have focused mostly on sharing quantitative data, much of what has been discussed remains relevant to qualitative research (for an in-depth discussion of qualitative data sharing, see DuBois et al., 2018)….”

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