Semi-automated data provenance tracking for transparent data production and linkage to enhance auditing and quality assurance in Trusted Research Environments - O'Sullivan, Katherine; Markovic, Milan; Dymiter, Jaroslaw; Scheliga, Bernhard; Odo, Chinasa; Wilde, Katie (International Journal of Population Data Science, 2025)
Introduction We present a prototype solution for improving transparency and quality assurance of the data linkage process through data provenance tracking designed to assist Data Analysts, researchers and information governance teams in authenticating and auditing data workflows within a Trusted Research Environment (TRE). Methods Using a participatory design process with Data Analysts, researchers and information governance teams, we undertook a contextual inquiry, user requirements interviews, co-design workshops, low-fidelity prototype evaluations. Public Engagement and Involvement activities underpinned the methods to ensure the project and approaches met the public's trust for semi-automating data processing. These helped inform methods for technical implementation, applying the PROV-O ontology to create a derived ontology following the four-step Linked Open Terms methodology and development of automated scripts to collect provenance information for the data processing workflow. Results The resulting Provenance Explorer for Trusted Research Environments (PE-TRE) interactive tool displays the data linkage information extracted from a knowledge graph described using the derived SHP ontology and results of rule-based validation checks. User evaluations confirmed PE-TRE would contribute to better quality data linkage and reduce data processing errors. Conclusion This project demonstrates the next stage in advancing transparency and quality assurance within TREs by semi-automating and systematising data tracking in a single tool throughout the data processing lifecycle, improving transparency, openness and quality assurance.