Abstract
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.
Highlights
Research data management (RDM) is an increasingly important topic for individual scientists, institutions, infrastructure providers, and large-scale research collaborators
We present four perspectives on the utility of this type of Decentralized research data management (dRDM)
It has been selected as a strategic component of the NFDI Neuroscience initiative, a consortium that aims to consolidate neuroscience RDM in Germany along these lines
Summary
Research data management (RDM) is an increasingly important topic for individual scientists, institutions, infrastructure providers, and large-scale research collaborators. Researchers utilizing a dRDM model can ensure consistent and robust data management across local and institutional information technology (IT) environments.
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