基于规则的科学数据安全治理框架:理解数据"保护-利用"失衡及挑战的新工具OA

With the implementation of various data security laws and regulations centered on privacy protection,and the emergence of new governance factors such as data sovereignty,technological competition,and geopolitics,the requirements for the"protection"of scientific data have been increasingly elevated.This has objectively suppressed the"utilization"functions of data collection,processing,transmission,and analysis,leading to a significant risk of imbalance between the protection and utilization of scientific data.This imbalance is externally manifested in challenges such as the excessive burden of legal compliance and the weakening availability of public scientific data.The academic community,data managers,and policymakers urgently need effective analytical tools to understand and address these challenges in a systematic way.In response to this gap,this paper proposes a rule-based governance framework for scientific data security,aiming to provide a systematic analytical tool to address the protection-utilization imbalance and related challenges from the perspective of governance rules,including laws,ethics,and institutional policies.This framework integrates the major rule types in scientific data security governance and introduces three analytical tools:the"Island-Bridge Model,"the"Law-Ethics Balance,"and the"Moderate Implementation"principle,to explain the interaction mechanisms of these rules.The framework establishes the transmission paths between governance rules and the protection-utilization balance and uses these tools to explain two key challenges—excessive compliance burdens and weakened public scientific data availability—demonstrating its explanatory power and practical value.In the context of the long-term tightening of data security regulations,the rule-based analytical perspective and tools proposed in this paper enrich the theoretical foundation of scientific data security governance and provide practical references for addressing these challenges.The framework also offers essential theoretical support for policy communication among the academic community,data managers,and policymakers,ensuring the sustainable utilization of scientific data in the future.