Data quality assurance in research data repositories: a theory-guided exploration and model

Purpose This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities. Design/methodology/approach The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework. Findings The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives. Practical implications The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way. Originality/value To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.