A data navigation model to improve access to research data resources in clinical and translational science

Abstract

Clinical and translational investigators increasingly rely on complex institutional and national data resources, yet barriers related to data discovery, governance, and access pathways remain common. To address fragmentation in data access, we piloted a Data Navigation Program within the Clinical and Translational Science Institute (CTSI) that established a trained Data Navigator as a centralized first point of contact for investigator data inquiries who provided individualized consultations, facilitated connections to data domain experts and honest broker services, and increased awareness of institutional data assets and regulatory requirements. To better characterize investigator needs, a CTSI-wide survey assessing data sources, governance, and training priorities was conducted in collaboration with the Clinical Translational Data Science (CTDS) Workgroup. Results demonstrated strong demand for structured guidance in data discovery and governance navigation. These findings informed refinement of the program, including development of the Research Data Source Match, a self-service decision-support tool implemented in REDCap that generates customized data access roadmaps based on investigator characteristics and data needs. During the pilot year, the Data Navigator conducted consultations addressing electronic health record (EHR), PCORnet resources, and government datasets. Integrating personalized navigation with scalable self-service tools may reduce barriers and support responsible data use in translational research.