The rapid digital transformation of research and innovation ecosystems has led to an excessive growth in data volume, diversity, and distribution. Sensitive and regulated data, particularly in sectors such as healthcare, energy, and public services, is often dispersed across organisational and national boundaries. This creates a need for infrastructures that enable secure, compliant, and interoperable data sharing and analysis.
Trusted Research Environments (TREs) and data spaces have emerged as two complementary paradigms addressing this challenge. TREs provide controlled environments for secure processing of sensitive data, ensuring strict governance, privacy, and auditability. Data spaces, on the other hand, offer decentralised frameworks for cross-organisational data exchange based on common standards, governance models, and trust mechanisms. Integrating these two approaches combines the strengths of both: TREs safeguard sensitive data, while data spaces enable scalable discovery, negotiation, and collaboration across ecosystems.
In this study, the integration of TREs and data spaces are studied from multiple perspectives.