From policy to practice: building a data management Learning Hub for diverse data types

The UK Data Service increasingly supports researchers working with a broad and evolving range of social science data, from traditional surveys and qualitative studies to newer and expanding forms of data, such as administrative and linked data, digital trace and web-scraped sources, and sensor and wearable data. As we support both depositors and users of these data, our approach to data management training and guidance has needed to evolve alongside our collections; a single set of general principles is no longer sufficient. This presentation tells the practical story of redesigning and re-scoping the UK Data Service Data Management Learning Hub in response to a broad, policy-driven remit. Our aim was to move beyond an abstract “good practice” and instead provide concrete, data-specific support, while keeping our Learning Hub usable, maintainable, and consistent. We describe how we carried out a lightweight classification and mapping exercise to make visible a fuller and more diverse range of data types, assessed existing training provision, and translated this analysis into a prioritised content creation plan. Rather than treating data types in isolation, we focused on supporting real-world, mixed-methods projects. A key focus of this project is the often overlooked “middle layer” of learning design: the work of turning typologies and policy requirements into practical navigation structures, worked examples, checklists, and decision aids that researchers can use at the point of need. Finally, we reflect on governance and resourcing decisions, including how we decide what to develop in-house versus commission externally, to ensure content development remains sustainable and responsive as both data practices and policy expectations continue to evolve.