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In defense of decentralized research data management.

Michael Hanke1, Franco Pestilli2, Adina S Wagner3

  • 1Institute 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.

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|December 12, 2022
PubMed
Summary
This summary is machine-generated.

Decentralized research data management (dRDM) offers a sustainable, resilient, and inclusive infrastructure for diverse scientific stakeholders. This approach utilizes interoperable components and a common data structure for scalable, adaptive data handling.

Keywords:
BrainLifeCanadian Open Neuroscience PlatformDataLadInteroperabilityOpenNeuro

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Area of Science:

  • Computer Science
  • Information Science
  • Scientific Data Management

Background:

  • Centralized research data management (RDM) systems face limitations in scalability and resilience.
  • Federated RDM solutions often require significant coordination and may not fully address diverse stakeholder needs.
  • Decentralized research data management (dRDM) offers an alternative by distributing data handling across nodes without central reliance.

Purpose of the Study:

  • To present a defense of decentralized research data management (dRDM) systems.
  • To illustrate the benefits of dRDM for various scientific stakeholders.
  • To showcase a working dRDM system as an exemplary implementation.

Main Methods:

  • The study proposes four distinct perspectives on dRDM: individual scientist/laboratory, research institute, domain data archive/cloud platform, and multisite consortium.
  • It emphasizes the use of a common, self-contained, portable data structure as a technology-agnostic abstraction.
  • The approach reviews how varying stakeholder requirements can be met by a scalable, uniform dRDM solution.

Main Results:

  • dRDM systems, built on heterogeneous yet interoperable components, provide a sustainable, resilient, inclusive, and adaptive infrastructure.
  • A common data structure abstracts away from specific technology and service choices, promoting flexibility.
  • The proposed dRDM solution addresses the diverse needs of independent scientific stakeholders effectively.

Conclusions:

  • Decentralized research data management (dRDM) presents a viable and advantageous alternative to centralized and federated approaches.
  • The presented dRDM model supports scalability, uniformity, and adaptability for a wide range of scientific applications.
  • A working system demonstrates the practical implementation and benefits of dRDM for the scientific community.