Policy Needs to Go Hand in Hand with Practice: The Learning and Listening Approach to Data Management

Abstract

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), "Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment". However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-today research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.

Cross-Functional Policy Development for a Data Repository

“…Literature on a library-based data repository policy exists in a few different categories. These include the following: explorations on the scope and content of a policy, including exploring the concepts and purpose of a repository policy (Riddle, 2015), emerging workflows in data management in a repository (Austin et al, 2017), and content analysis of existing policies (Higman & Pinfield, 2015); how policies are developed and who should be included, including the importance of stakeholders in policy development (Erway, 2013;Shearer, 2015;Tenopir et al, 2017;Van Zeeland & Ringersma, 2017;Verhaar et al, 2017) and the development of policies occurring from the "bottom up" (Lee and Stvilia, 2017;Cruz et al, 2019); tying policy to services, including the relationship between data management policy and services (Higman & Pinfield, 2015); the lack of policy and the need for standards, including discussions on the lack of standards (Briney et al, 2015;Austin et al, 2017); and the lack of research data management policy coupled with the lack of strategic development of these policies (Yu, 2017). Unfortunately, these resources did not provide guidance on how to approach writing our own policy, what has worked for others, what challenges they faced, and how their policy changed over time.…”

Making Common Fund data more findable: catalyzing a data ecosystem

“…To make Common Fund data more findable, the CFDE has created a flexible system of data federation that enables users to discover datasets from across the CF at a centralized portal [ 23 ] without requiring CF programs to move, reformat, or rehost their data, similar to the federation strategy of the Research Data Alliance [ 18 ], the Australian Research Data Commons [ 19 ], and the Earth System Grid Federation [ 20 ]. The CFDE uses a sociotechnical federation system that combines proven, explicitly community-driven approaches [ 18 , 21 , 22 ] with a model-driven catalog that integrates detailed descriptions of datasets submitted by individual programs’ DCCs into a shared metadata structure that is then indexed and made searchable via a centralized portal.…”

Making Common Fund data more findable: Catalyzing a Data Ecosystem

“…To make Common Fund data more findable, the CFDE has created a flexible system of data federation that enables users to discover datasets from across the CF at a centralized portal without requiring Common Fund programs to move, reformat, or rehost their data, similar to the federation strategy of the Research Data Alliance [17], The Australian Research Data Commons [18], and the Earth System Grid Federation [19]. The CFDE uses a sociotechnical federation system that combines proven, explicitly community driven approaches [17,20,21] with a model-driven catalog that incorporates metadata submitted by individual CF Program Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal.…”

“…Each stage presents an individually large challenge for a typical biomedical researcher or clinician which motivates labs to hire dedicated bioinformaticians (at considerable cost to NIH); confronting all of these challenges together is prohibitive for integrative analysis.To make Common Fund data more findable, the CFDE has created a flexible system of data federation that enables users to discover datasets from across the CF at a centralized portal without requiring Common Fund programs to move, reformat, or rehost their data, similar to the federation strategy of the Research Data Alliance (Plante et al, 2021) and The Australian Research Data Commons (Barker, Wilkinson and Treloar, 2019). The CFDE uses a sociotechnical federation system that combines proven, explicitly community driven approaches (Cruz et al, 2019;DeBarry et al, 2020;Plante et al, 2021) with a model driven catalog that incorporates metadata submitted by individual CF Program Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This uniform Crosscut Metadata Model (C2M2), supports the wide variety of dataset types, vocabularies, and metadata terms used by the individual CF DCCs.…”