Data Stewards play an integral role in enabling good research data management practice and help to realise the production FAIR data available for reuse. The majority of research performing organisations that participated in the open consultation do provide in-house research data management support to varying degrees. Some of the key findings are provided below.
We must identify points over the full data lifecycle where RDM support is most effective for enabling FAIR data production. This is particularly true when it comes to selecting and implementing metadata standards to support interoperability and interdisciplinary reuse.
The open consultation showed that the availability of support to help researchers make their data FAIR is considered to be the most positive policy factor influencing researchers’ behaviour with more than 93% of respondents rating this factor as ‘very’ or ‘quite’ positive. Encouragingly, the open consultation also showed that more than two thirds of respondents’ organisations currently provide in-house support.
Based on the findings of the open consultation, RDM support for making data FAIR is most frequently provided centrally by Library or Research Support units in organisations. However many institutions are now introducing dedicated support at the faculty or departmental level. Organisations with limited resources can still provide an essential sign-posting service to a growing body of open training and learning resources such as those developed by Research Infrastructures and related Cluster projects (e.g., ELIXIR, SSHOC).
Respondents identified with a variety of roles, 49% indicated that they fill several roles at their institutions. In many cases, respondents indicated that they held between three and five roles within their organisation. Of the 51% of respondents that have a unique role, the majority (14%) working in research support and liaison followed by policy makers or senior managers (11%) and data stewards or research data librarians (9%).
Based on the results of the open consultation, desk research and interviews D3.2 FAIR Data Practice Analysis provides an analysis of practices to support FAIR data production within a broad selection of research disciplines and research data repositories. D3.4 Recommendations on Practice to support FAIR Data Principles is a set of practical recommendations on making data FAIR and is aimed at research communities and data stewards. The recommendations are grouped under four main ambitions, outlined as themes A - D, with recommendations numbered accordingly. Each recommendation concludes with the summary of actions for various stakeholder groups.
A1: Describe research outputs using agreed terminologies and metadata standards to make data FAIR
A2: Build a culture of data citation
B1: Formalise and support appropriate data management plans (DMPs) for FAIR data
B2: Develop roadmaps, guidance and workflows for machine-actionable data management plans (DMP) to inform FAIR data stewardship
C1: Define and manage FAIR support costs and resources
C2: Develop and implement models for coordinating and supporting data stewards and research software engineers
C3: Develop and implement terminology for competence centres to annotate and retrieve training materials on enabling FAIR
C4: Develop and implement a self-assessment framework for Research Infrastructures, institutions, and other FAIR competence centres
D1: Develop and implement guidance and support for selection of appropriate trusted digital repositories (TDRs)
D2: Develop and implement guidance and support for making sensitive data FAIR for reuse
Drawing on the open consultation results, desk research and interviews D6.1 Overview of needs for competence centres provides an overview of requirements for competence centres in general and a core competence centre for FAIR data stewardship.