Research Data Stewardship in Germany - how a community is moving on!
Today, data stewardship is an important building block for integrating research data management (RDM) into practice. Data stewardship is a cross-functional competence that extends across institutions and research organisations, and it encompasses a broad range of responsibilities. Data stewards provide training, support the development and implementation of research data management (RDM) tools, liaise with researchers and local RDM teams, and design data workflows tailored to disciplinary needs. They play a key role in translating policy into practice, ensuring coherent and sustainable data management strategies across diverse research environments. Insights from the Data Stewardship goes Germany (DSgG) workshops held in 2023 and 2024 [1], [2] revealed significant gaps in the field of data stewardship. Participants emphasised the lack of structured community support, clear career paths, and a central platform for collaboration and knowledge sharing. Their needs and visions for shaping the community were elaborated by using the Community Canvas [3], ultimately leading to the formation of three dedicated working groups: The working group 'Channels and Yellow Pages' aims to establish a central, easily accessible platform for the community that facilitates exchange, collaboration and professional development including a community-driven directory ("Yellow Pages") and is currently discussing technical requirements in particular with a view to providing a first test version. The working group 'Vision and Strategy' formulated the strategic pillars for a sustainable, comprehensive and collaborative data stewardship community in Germany by establishing data stewardship as an essential component of research processes and as a strategic asset. It also emphasises that the professional integration of data stewards into institutional governance and the promotion of cooperation, knowledge transfer and international collaboration are crucial to have a long-term impact on policy, administration and scientific standards. The 'Challenges of Data Stewardship' working group identified key challenges such as clarifying the role of data stewards, legal and technical barriers, and motivating researchers to adopt RDM best practices discussed initial solutions such as knowledge bases and exchange formats, and emphasised the need for institutional support and ongoing collaboration, especially in the area of tension between technical infrastructure and sustainable integration. Beyond this, the DSgG also examined the aspects of the future "Governance and Structure" definition of the emerging community. Drawing on international examples, it became evident that in Germany, there is currently a lack of a central contact point or national framework to support data stewards. The group discussed how such a community could be sustainably established and integrated into the German research data ecosystem. The most frequently mentioned options were the NFDI, due to the fact of shaping RDM across disciplines, and the DINI/nestor AG Forschungsdaten, which is well suited to the workings of a community of practice. This contribution will present the activities and preliminary results of the working groups, outline the envisioned path towards a sustainable and coordinated data stewardship community in Germany and highlight its strategic relevance for the NFDI. We invite all stakeholders to contribute with their expertise, experience, and perspectives to shape a data stewardship community in Germany.
Institutions
- University of Cologne (DE)
- Ernst Abbe University of Applied Sciences Jena (DE)
- Leibniz Institute of Ecological Urban and Regional Development (DE)
- Protestant University of Applied Sciences Rhineland-Westphalia-Lippe (DE)
- Heinrich Heine University Düsseldorf (DE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-08-04
- DOI
- https://doi.org/10.5281/zenodo.16736203
- Primary Topic
- Research Data Management Practices
- Type
- article
- Field-Weighted Citation Impact
- 0.00