PubMed HealthSearch

SEARCH · PubMed Health

Results for “controlled-access data”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

2 recordsLinked to original sources

Institutional data commons: a federated Data Use Certification-aware architecture for secure and scalable data use in biomedical data ecosystems.

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.

biomedical data ecosystems

Ethical Governance of Open Data Across Biomedical Research, Healthcare, and Public Health: Privacy, Equity, Trust, and Controlled Access.

Open data has become central to biomedical research and public health, but health information is uniquely sensitive and difficult to share responsibly. In this narrative review, open data is considered as a spectrum of health-data sharing arrangements, ranging from public aggregate datasets to controlled-access repositories, federated analysis, and synthetic data. This narrative review synthesizes the scientific and societal rationale for greater openness with the ethical, legal, and governance constraints that shape what "open" can realistically mean in healthcare. We examine how data sharing supports reproducibility, machine learning, and more efficient research, while also enabling public health surveillance and learning health systems. Against these benefits, we analyze privacy and re-identification risks, consent challenges in large-scale secondary use, inequities including data colonialism, and tensions introduced by commercialization. We integrate lessons from prominent case examples spanning pandemic data sharing, genomic initiatives, population registries, patient-led rare disease infrastructures, and regional data spaces. Across these domains, experience suggests that durable progress depends less on unrestricted openness than on calibrated access, privacy-preserving architectures, clear accountability, and sustained public engagement. We conclude by proposing a pragmatic ethical orientation for healthcare open data: treat openness as a spectrum of controlled sharing arrangements, embed equity and reciprocity into governance, and institutionalize trust-building measures that can persist beyond emergencies and political cycles.

Data colonialism