Open science incentives exist in difficult tension with countervailing requirements to hold data secure, and to mitigate downstream disclosures of data. Federated data analysis, and data visitation, has been proposed as a possible solution that captures the benefits of both open and restricted disclosures. This paper assesses the alignment between E.U. and global data protection institutions, and federated analysis. We conclude that the structure of data protection insufficiently accounts for how federated analysis networks are implemented in practice, from both an organizational and technological standpoint. We propose changes to foundational data protection instruments to better align its functioning to enable clear legal compliance on the part of federated analysis networks, enabling their more rapid uptake.

Our aim is to strengthen understanding of access control among EOSC constituencies and RDA members, while also promoting legal awareness of key issues related to secure processing environments. We seek to support the development of legally robust approaches to federated data analysis and to effective data access control within data visitation solutions.

As outlined in the Legal Foundations, considering means for protecting privacy and enhancing access control in data visitation systems is a necessary step for establishing appropriate conditions for inclusive and responsible research and innovation. These legal foundations can ultimately be instrumental for enhancing the processes for visiting personal data held by different data holders in respect of individual fundamental rights for scientific purposes, in particular for data visitation systems’ development. Therefore, the Guidance contributes to UN SDG 9 (Industry, Innovation and Infrastructure),10 (Reduced Inequalities), 16 (Peace, Justice and Strong Institution) and 17 (Partnerships for the Goal).

Bernier, A., Okengwu, U., & RDA Artificial Intelligence and Data Visitation WG. (2025). Secure Processing Environments for Open Science: Proposing Legal Foundations (Version 1). Research Data Alliance. https://doi.org/10.15497/RDA00129