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

Figure 1

Components of an e-Science ecosystem and the transition of entities from the non-digital to the digital environment. Each of these external entities, e.g., people, concepts is ideally accompanied by a persistent identifier (PID) that uniquely identifies the entity in a machine friendly format. For example, the identifier system for people is the ORCID; for research resources, the Research Resource Identifiers (RRIDs; Bandrowski et al., 2015). Experiments performed within the laboratory result in digital data, which may be held locally, but which should transition into the e-Science ecosystem by deposition in a reputable repository. Deposition should also include a complete accounting of the protocols and methods used to produce the data. Repositories assign a persistent identifier like a DOI (Digital Object Identifier). Additional standards, e.g., minimal information models, common data elements (CDE’s) can be implemented within the laboratory to make this transition more seamless. CDE’s provide standard ways of collecting certain types of data, e.g., demographic data. Once within the eScience ecosystem, these objects are able to be re-used. Provenance is tracked programmatically as data are combined with other data, transformed by software or referenced within an article. Data as well as other research objects are designed so that they are amenable to search and discovery (see discussion of FAIR principles later in the article).