为了跨学科研究的FAIR和联合数据生态系统
Sebastian Beyvers1, Jannis Schlegel1, Lukas Brehm1
1Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen, Germany.
PLoS computational biology
|February 13, 2026
概括
公平和联合数据生态系统为研究数据管理挑战提供了解决方案. 这种方法通过分散数据控制来增强协作和可重复性,同时确保互操作性和治理.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 研究基础设施 研究基础设施
背景情况:
- 科学数据管理面临着指数级数据增长和碎片化带来的挑战.
- 传统的集中式系统难以处理大量的数据,并阻碍跨领域的协作和可重复性.
- 对数据主权和治理的担忧正在增加.
研究的目的:
- 提出FAIR和联合数据生态系统作为研究数据的改进架构模式.
- 将分散系统集成到研究基础设施中,以促进合作.
- 解决科学数据管理中的碎片化和可重复性问题.
主要方法:
- 引入基于数据共享,数据网格和数据空间的分层架构 (治理,数据,服务,应用).
- 专注于自适应元数据管理,简化用户交互,强大的安全性和透明的数据交易.
- 实施一个去中心化的点对点网络,支持计算到数据和数据到计算模式.
主要成果:
- 保持特定领域的专业知识和控制,同时促进数据集成.
- 允许研究基础设施的水平扩展.
- 支持互操作性要求和域自主性.
结论:
- FAIR和联合数据生态系统为实现综合研究基础设施提供了切实可行的途径.
- 该模型使研究人员能够在现有工作的基础上进行构建,同时保持对数据和资源的控制.
- 提高科学可重复性和跨领域的合作.
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