Building Computational Capacity Among Global Data Service Staff: A Focus Group Analysis To Inform Learning Module Design

Data service staff, including librarians, data specialists, and research support engineers, play a critical role in the computational social science (CSS) ecosystem. They require broad conceptual knowledge to support diverse research communities without becoming topic specialists. These professionals face the challenge of maintaining a wide understanding across multiple computational areas amidst rapid technological advances.

This study presents findings from focus groups with data service professionals, part of the UKRI-funded project Building Computational Capacity among Global Data Service Staff (UKRI302) in collaboration with the UK Data Service. The research aimed to inform the design of a series of CSS asynchronous training modules by exploring participants' training needs and knowledge gaps, preferred learning formats, and views on appropriate technical depth for different roles.

Analysis of the focus group transcripts employed a robust three-phase mixed-methods approach. Three researchers independently coded responses to key questions, generating 378 codes. The qualitative data was then examined quantitatively using Python to analyse topic frequency and coding consistency. Finally, themes were collaboratively identified and validated against the original data.

Results revealed a high demand for practical data skills, and foundational knowledge of artificial intelligence and machine learning, alongside ethical considerations like bias and privacy. Participants highlighted a gap between theory and practice and recommended using role-specific, context-specific examples. The findings suggest that training should be tailored: support staff require conceptual understanding, whereas technical staff need advanced skills. Participants favour principle-based, flexible, and modular training formats that incorporate interactivity and certification.

These insights have directly informed the development of the CSS training modules, which are structured around the prioritised topics and learning preferences identified in this research. The paper will outline the outputs from focus group analysis and discuss their implications for global data services aiming for evidence-based workforce development in computational social science.