| 贺帆1 甄思圆1 韩蓓蓓2 高曼3.科学数据治理共同体生成机理研究及价值分析[J].中国科技资源导刊,2026,(2):27~39 |
| 科学数据治理共同体生成机理研究及价值分析 |
| Research on the Generative Mechanism and Value Analysis of Scientific Data Governance Communities |
| 投稿时间：2026-02-04 |
| DOI： |
| 中文关键词: 科学数据治理;治理共同体;协同治理;生成机理;价值分析 |
| 英文关键词: scientific data governance, governance community, collaborative governance, formation mechanism, value analysis |
| 基金项目:2024年中国中医科学院基本科研业务费自主选题项目(17批)“面向中医药领域的学术论文创新性内涵与评价路径研究”(ZZ170312);中国图书馆学会医学图书馆分会2025年科研资助项目“面向科研范式变革的医学图书馆学术智能体服务模式构建与发展策略研究”(2025LSCYXZZQN004)。 |
| 作者 | 单位 | | 贺帆1 甄思圆1 韩蓓蓓2 高曼3 | (1. 天津市中医药研究院中医药信息研究所，天津 300120
2. 甘肃农业大学图书馆，甘肃兰州 730070
3. 中国中医科学院中医药信息研究所，北京 100700) |
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| 中文摘要: |
| 科学数据治理面临碎片化困境，单一主体难以应对日益复杂的治理需求。基于共同体视角，立足国内科学数据治理实践，界定科学数据治理共同体的内涵，系统分析其生成动因、演化阶段与多维价值。研究认为，科研范式变革、数据流动困境和利益关系张力三重动因的交互作用推动了共同体的生成，其发展经历要素积累、主体聚合、目标协同、机制建设和共同体形成5个阶段，核心价值体现为治理创新、知识创新、可持续发展和韧性治理4个维度。在此基础上，从政策体系、组织架构、利益机制和技术底座4个方面提出共同体的建设路径，以期为科学数据治理体系的完善提供理论参考。 |
| 英文摘要: |
| Scientific data governance faces the challenge of fragmentation, and a single entity struggles to address increasingly complex governance needs. This paper introduces a community-based perspective, drawing on domestic scientific data governance practices to define the concept of a scientific data governance community and systematically analyze its driving forces, evolutionary stages, and multidimensional value. The study argues that the interplay of three driving forces—paradigm shifts in scientific research, challenges in data flow, and tensions among stakeholder interests—has propelled the emergence of these communities. Their development progresses through five stages: accumulation of elements, aggregation of actors, coordination of objectives, mechanism building, and community formation. Their core value is manifested across four dimensions: governance innovation, knowledge innovation, sustainable development, and resilient governance. Building on this foundation, the study proposes a development pathway for the community across four dimensions—policy systems, organizational structures, incentive mechanisms, and technological infrastructure—with the aim of providing a theoretical framework for the refinement of the scientific data governance system. |
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