Data sharing in psychology.

Abstract

Routine data sharing, defined here as the publication of the primary data and any supporting materials required to interpret the data acquired as part of a research study, is still in its infancy in psychology, as in many domains. Nevertheless, with increased scrutiny on reproducibility and more funder mandates requiring sharing of data, the issues surrounding data sharing are moving beyond whether data sharing is a benefit or a bane to science, to what data should be shared and how. Here, we present an overview of these issues, specifically focusing on the sharing of so-called "long tail" data, that is, data generated by individual laboratories as part of largely hypothesis-driven research. We draw on experiences in other domains to discuss attitudes toward data sharing, cost-benefits, best practices and infrastructure. We argue that the publishing of data sets is an integral component of 21st-century scholarship. Moreover, although not all issues around how and what to share have been resolved, a consensus on principles and best practices for effective data sharing and the infrastructure for sharing many types of data are largely in place. (PsycINFO Database Record

Data Sharing Practices among Researchers at South African Universities

“…Borgman (2015: 29) defines data as entities used as evidence of phenomena for the purposes of research or scholarship. To Martone et al (2018) data are the measurements, observations or facts taken or assembled for analysis as part of a study and upon which the results and conclusions of the study are based. In its definition, the OECD (2007) chose to focus on the types of data that are produced, which are: factual records (numerical scores, textual records, images and sounds) used as primary sources for scientific research, and that are commonly accepted in the scientific community as necessary to validate research findings.…”

“…In its definition, the OECD (2007) chose to focus on the types of data that are produced, which are: factual records (numerical scores, textual records, images and sounds) used as primary sources for scientific research, and that are commonly accepted in the scientific community as necessary to validate research findings. In other words, data are a means to an end (Martone et al 2018 Martone et al (2018) assert that data sharing is the publication of the primary data and any supporting materials required to interpret the data acquired as part of a research study. However, data sharing is not only about publishing the research data for easy access.…”

Did awarding badges increase data sharing inBMJ Open? A randomized controlled trial

“…A possible explanation behind these striking differences in data sharing rates is that health and medical data are often harder to share than psychological science data, which is largely due to issues around patient confidentiality and consent to share health and medical data publicly. Also, the reproducibility movement is more established in the field of psychology where it is becoming more of a cultural norm, so psychological science researchers might be more primed to share their data than health and medical researchers [20]. We found that the majority of health and medical researchers preferred to share their data upon request (56% of participants in both study groups).…”

Sharing and organizing research products as R packages

“…To maximize the reuse potential of open data and support "discovery through good data management", Wilkinson et al (2016) proposed the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles. Below, we describe how a vertical workflow targets FAIR principles and facilitates accessibility and data reanalyses (FAIR principles paraphrased from, Wilkinson et al, 2016;and Martone et al, 2018) Findable data are discoverable through persistent identifiers (e.g., permanent URLs / DOIs):…”

“…However, the potential added value of these products is not fully realized due to limited sharing and curating practices. Although more transparent communication of these research products has recently been encouraged (Houtkoop et al, 2018;Klein et al, 2018;Lindsay, 2017;Martone, Garcia-Castro, & VandenBos, 2018;Rouder, 2016;Rouder, Haaf, & Snyder, 2019;Vanpaemel, Vermorgen, Deriemaecker, & Storms, 2015;Wicherts, Borsboom, Kats, & Molenaar, 2006), these efforts often focus narrowly on sharing data (and sometimes analysis code). Further, the practical value of sharing is often limited by poor documentation, incompatible file formats, and lack of organization, resulting in low rates of reproducibility 1 (Hardwicke et al, 2018).…”