Between Policy and Practice: Adoption of Research Data Management in DFG’s Individual Research Grants Programme (2017 – 2024)
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This paper applies natural language processing, machine learning and repository linking to examine the adoption of research data management (RDM) practices in proposals submitted to the German Research Foundation’s (DFG) Individual Research Grants Programme between 2017 and 2024. The analysis reveals a growing awareness of RDM, reflected in longer and more substantive statements as well as an increasing number of references to specific data repositories. Significant disciplinary differences emerge in the frequency, profile, and concreteness of RDM references, pointing to varying levels of maturity across fields. By linking proposal texts with re3data metadata, we identify the most frequently cited repositories and their disciplinary coverage. A marked shift toward more professionalized RDM following the introduction of a DFG checklist in 2021 suggests that funding agencies can actively shape RDM practices through policy instruments. These findings demonstrate how funders can influence data practices through guidance rather than coercion, offering novel insights for the monitoring of Open Science policies.
