Hanke Michael, Pestilli Franco, Wagner Adina S, Markiewicz Christopher J, Poline Jean-Baptiste, Halchenko Yaroslav O
Institute of Neuroscience and Medicine Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany; and Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany.
Department of Psychology, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, TX, USA.
Neuroforum. 2021;27(1):17-25. doi: 10.1515/nf-2020-0037. Epub 2021 Jan 11.
Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.
去中心化研究数据管理(dRDM)系统在参与节点间处理数字研究对象,而无需严重依赖中央服务。我们提出支持dRDM的四个观点,说明与集中式或联合式研究数据管理解决方案不同,基于异构但可互操作组件的dRDM系统可为科学利益相关者提供可持续、有弹性、包容且适应性强的基础设施,这些利益相关者包括:个体科学家或实验室、研究机构、领域数据存档库或云计算平台以及协作式多站点联盟。所有观点都共享使用一种通用、自包含、便携式数据结构,作为从当前技术和服务选择中抽象出来的内容。同时,这四个观点审视了可扩展、统一的dRDM解决方案如何满足独立科学利益相关者的不同需求,并展示了一个工作系统作为示例性实现。