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. 2021;27(1):17-25.
doi: 10.1515/nf-2020-0037. Epub 2021 Jan 11.

In defense of decentralized research data management

Michael Hanke  1 , Franco Pestilli  2 , Adina S Wagner  3 , Christopher J Markiewicz  4 , Jean-Baptiste Poline  5 , Yaroslav O Halchenko  6
Affiliations

In defense of decentralized research data management

Michael Hanke et al. Neuroforum. 2021.

Abstract
in English, German

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.

Dezentrale Forschungsdatenmanagement (dFDM) Systeme verwalten digitale Forschungsdaten mit mehreren Teilnehmern, ohne dabei von einem zentralen Service abhängig zu sein. Zur Verteidigung von dFDM präsentieren wir vier Perspektiven: Einzelne Wissenschaftler, Institutionen, Datenarchive, Analyse-Plattformen und Konsortien, die zeigen, dass heterogene, aber auf interoperablen Komponenten basierende dFDM Systeme, im Gegensatz zu zentralisierten oder föderierten Lösungen, eine nachhaltige, resiliente, offene und anpassungsfähige Infrastruktur für wissenschaftliche Interessensgruppen sein können. Allen ist die Verwendung einer einheitlichen, portablen Datenstruktur gemein, die als Abstraktion von aktuell verwendeten Technologien zum Einsatz kommt. Zusammengenommen zeigen diese Perspektiven beispielhaft anhand eines in der Praxis verwendeten Systems, wie vielfältige Anforderungen unterschiedlicher Interessengruppen durch eine skalierbare dFDM Lösung adressiert werden können.

Keywords: BrainLife; Canadian Open Neuroscience Platform; DataLad; Interoperability; OpenNeuro.

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Conflict of interest statement

Conflict of interest statement: The authors declare no conflicts of interest.

Figures

Figure 1:
Figure 1:
A common, portable data structure allows establishing interoperability between diverse participant sites. Left: A common data structure can serve as a uniform abstraction layer to interface any number of commercial or institutional storage services, which may be centralized or federated systems. Right: The portable nature of the data structure facilitates data exchange between archive and compute services, as well as collaboration among individual researchers or formal consortia. Moreover, it provides institutions with the flexibility to evolve their infrastructure without needlessly impacting scientific workflows.
See this image and copyright information in PMC

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