Assessing Data Management and Compliance in Large Research Collaborations via Knowledge Bases: A Semi-Structured Interview Approach

Background Large-scale research collaborations rely not only on robust research strategies but also on structured (meta) data management and systematic knowledge exchange. Ensuring compliance with ethical and legal requirements is essential from the beginning. This includes obtaining informed consent and adhering to data protection laws such as the GDPR (for Europe), as well as following Open Science and FAIR principles, particularly when working with personal data. Additionally, the systematic assessment and documentation of project objectives, data characteristics, and other project-specific features are essential for supporting the coordination and long-term development of such collaborations. Methods In this paper, we introduce a methodology designed to identify commonalities across research projects and to enhance data governance within large research consortia. The approach consists of three components: (1) a semi-structured interview designed to help communicate ethical obligations, data protection requirements, and Open Science principles to researchers while systematically collecting metadata on planned studies, data types, participant groups, and methodological procedures; (2) structured processing and organization of the collected information; and (3) visualization of project interrelations through knowledge graphs. The methodology was implemented within a collaborative research centre in linguistics. Results The collected metadata were systematically structured and used to construct knowledge graphs capturing interrelations among projects, data types, methodologies, and participant groups. These visualizations support the identification of potential collaborations, infrastructure planning, and data reuse. Conclusions The proposed methodology offers a systematic workflow to assess data management practices, while supporting compliance and transparency from early stages of collaborative research. Knowledge graph visualizations provide a practical tool for identifying potential synergies, and supporting data reuse.