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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.

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.

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.

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.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

The purpose of this paper is to examine the relationships between researcher characteristics and their use of metadata in their ResearchGate profiles.

This paper reports on one part of a larger study that examined researchers’ use of and engagement with research information management systems (RIMSs). The study’s design included qualitative, semi-structured interviews with 15 researchers and a survey completed by 412 researchers. Detailed reports of findings from the interviews and survey can be found elsewhere. This paper reports on the part of the study that analyzed the use of metadata elements in the ResearchGate profiles of 126 survey participants.

Most researchers shared metadata related to their research rather than their teaching or service. Statistical analyses revealed statistically significant relationships between researchers’ metadata use and their participation levels in RIMSs, as well as between metadata use and researchers’ seniority.

The study’s findings help to identify researchers’ priorities for different metadata elements, as well as to construct profile metadata templates for each specific participation level.

In the fall of 2002, the University of Illinois Library at Urbana‐Champaign received a grant from the Institute of Museum and Library Services (IMLS) to implement a collection registry and item‐level metadata repository for digital collections and content created by or associated with projects funded under the IMLS National Leadership Grant (NLG) program. When built, the registry and metadata repository will facilitate retrieval of information about digital content related to past and present NLG projects. The process of creating these services also is allowing us to research and gain insight into the many issues associated with implementing such services and the magnitude of the potential benefit and utility of such services as a way to connect, bring together, and make more visible a broad range of heterogeneous digital content. This paper describes the genesis of the project, the rationale for architectural design decisions, challenges faced, and our progress to date.

Prior studies identified a need for further comparison of data-sharing practices across different disciplines and communities. Toward addressing this need, the purpose of this paper is to examine the data-sharing practices of the earthquake engineering (EE) community, which could help inform data-sharing policies in EE and provide different stakeholders of the EE community with suggestions regarding data management and curation.

This study conducted qualitative semi-structured interviews with 16 EE researchers to gain an understanding of which data might be shared, with whom, under what conditions and why; and their perceptions of data ownership.

This study identified 29 data-sharing factors categorized into five groups. Requirements from funding agencies and academic genealogy were frequent impacts on EE researchers’ data-sharing practices. EE researchers were uncertain of data ownership and their perceptions varied.

Based on the findings, this study provides funding agencies, research institutions, data repositories and other stakeholders of the EE community with suggestions, such as allowing researchers to adjust the timeframe they can withhold data based on project size and the amount of experimental data generated; expanding the types and states of data required to share; defining data ownership in grant requirements; integrating data sharing and curation into curriculum; and collaborating with library and information schools for curriculum development.