FAIR by design (TRACE): A Trusted Research Access & Collaboration Environment

Abstract Background A tension exists between the open science imperative for data transparency through FAIR (Findable, Accessible, Interoperable, Reusable) data sharing and the stringent requirements of current data protection regulations for health data. Researchers lack efficient yet affordable tools to share sensitive data responsibly. Existing solutions are often either difficult to use, expensive, or lack features like variable-level access control and auditable and reproducible workflows. Our objective was to develop a solution that lowers the threshold for researchers to share sensitive data responsibly and that can be operated by existing institutional staff. Results TRACE (Trusted Research Access & Collaboration Environment) was designed to support several data-sharing elements highlighted in the 2025 SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) statements by providing controlled and auditable workflows and supporting reproducibility at the level of data and analysis code. Its features for data governance, sharing, reproducibility and security allow institutions to manage study data access and to establish consistent, transparent standard operating procedures through granular variable selection, integrated study visualisation, and provisioning into a sealed analysis environment. TRACE provides an automated workflow for selected data-access steps that had previously relied on manual curation and ad-hoc sharing. A core feature versions datasets together with the corresponding analysis scripts, supporting reproducibility at the level of data and code; the computational environment is not captured. TRACE has supported data governance across seven clinical studies and processed 56 data access applications. Across three studies, an uncontrolled before-and-after comparison found 42.4 manual interface exports per 30 days before TRACE and 4.7 after implementation; causality cannot be inferred. Conclusions Platforms like TRACE may help shift resource-intensive, expert-dependent data governance towards automated processes. Operated by existing institutional staff, TRACE may make controlled data sharing feasible for institutions with limited resources. By automating previously manual steps, TRACE is intended to reduce the administrative burden of governed data sharing; whether it shortens the interval from data request to analysis has not been measured. TRACE is used as a training environment for preparing and using data in sharing processes.