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Institutional data commons: a federated Data Use Certification-aware architecture for secure and scalable data use in
1Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States.
Background:
Modern biomedical data ecosystems increasingly rely on global cloud platforms to coordinate access to large-scale genomic and clinical datasets. However, operational governance remains largely investigator-centric, shifting the responsibility for complex security, compliance, and infrastructure management to individual laboratories. As data volumes and regulatory requirements expand, this approach fails to scale across the research enterprise. This disjointed approach creates a substantial governance burden and can slow down scientific progress. In centralized cloud environments, investigators face siloed identity management and high costs, leading to inefficient data use and increased risk when integrating local and global datasets.
Materials And Methods:
We examine limitations in the current infrastructure and propose reframing institutional data commons as governance-aware intermediaries to ensure secure, efficient and sustainable use of controlled-access biomedical data.
Results:
This federated architecture decouples storage from authorization, enabling dynamic access linked to active certifications, whether data are analyzed in situ on global platforms or in local governance-aware institutional access environments.
Discussion:
Shifting governance from investigators to institutional infrastructure ensures that biomedical research remains both secure and economically sustainable.
