Abstract
Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams. Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively. The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management. In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group. We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions. We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.
Highlights
For several decades, the research community has been engaged in a debate about the need to make research more reproducible (Baker, 2016), the virtues and dangers of open science and open data (Nosek et al, 2015; Tenopir et al, 2011), and devising better ways to manage data (Perkel, 2019)
research data management (RDM) best practices have emerged such as the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles (Wilkinson et al, 2016), which were developed to enhance the reusability of published datasets, from the point of view of the researchers wanting to reuse data, and from a computational point of view so machines can find and use data automatically without relying on human interpretation
Prior to reviewing the literature, we reflected on our experiences working with researchers, and we observed that the success of RDM implementation often hinges on research teams’ ability to communicate with each other
Summary
The research community has been engaged in a debate about the need to make research more reproducible (Baker, 2016), the virtues and dangers of open science and open data (Nosek et al, 2015; Tenopir et al, 2011), and devising better ways to manage data (Perkel, 2019) From this debate the importance of adopting research data management (RDM) best practices has become increasingly apparent. We will discuss how data management issues in research teams vary based on a range of factors highlighted in the literature on management, organizational psychology, and technology adoption, and the social science of research, with a particular emphasis on exploring how theoretical models might be adapted to better conceptualize the choices and services librarians provide. The purpose of the article is to give librarians who work with research data management a structured way to reflect on their practices and spark new conversations about the influence of research team dynamics on RDM adoption and use
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