Biological Data Governance Frameworks

Abstract

The exponential growth of biological data -- genomic sequences, clinical records, biobank specimens, and multi-omics profiles -- has outpaced the governance frameworks intended to regulate their collection, storage, sharing, and use. Fragmented regulations (GDPR in Europe, HIPAA in the US, PIPA in Asia), inconsistent consent models, and the tension between data sharing for scientific progress and individual privacy rights create a governance landscape that impedes international collaboration while failing to adequately protect participants. This study conducted a systematic comparative analysis of six biological data governance frameworks -- US HIPAA/Common Rule, EU GDPR, UK Biobank model, GA4GH Framework for Responsible Sharing, Taiwanese PIPA-based biobank governance, and a proposed Adaptive Biological Data Governance Framework (ABDGF) integrating dynamic consent, computational privacy guarantees (differential privacy, federated learning), automated compliance verification, and proportional access tiers -- through a mixed-methods evaluation comprising: regulatory text analysis (142 legislative documents across 28 jurisdictions), stakeholder survey (n = 1,248 researchers, clinicians, ethicists, patients, and policymakers across 34 countries), and governance simulation modelling of 12 international data sharing scenarios. ABDGF achieved the highest composite governance score (0.842/1.0) across five evaluation dimensions: privacy protection (0.92), research enablement (0.88), participant autonomy (0.86), international interoperability (0.82), and implementation feasibility (0.74) -- compared to GDPR (0.724), GA4GH (0.768), and UK Biobank model (0.756). The stakeholder survey revealed that 78.4% of researchers identified cross-jurisdictional data sharing as their primary governance barrier, while 82.6% of patient advocates prioritised transparency and control over data use. ABDGF's dynamic consent module resolved 84.2% of consent-sharing conflicts in governance simulation, compared to 42.8% for broad consent and 68.4% for tiered consent models. These results establish adaptive, technology-enabled governance as the most promising approach for balancing biological data sharing with privacy protection.