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Data management strategy for a collaborative research center.

Deepti Mittal1, Rebecca Mease2, Thomas Kuner3

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Effective research data management (RDM) is crucial for FAIR neuroscience data. This study presents a sustainable RDM strategy for large consortia, addressing challenges in heterogeneous data generation and promoting incremental adoption.

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Area of Science:

  • Neuroscience
  • Data Science
  • Biomedical Research

Background:

  • Effective research data management (RDM) is essential for generating Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data.
  • Multidisciplinary neuroscience consortia face significant RDM challenges due to diverse data types and research methods.
  • Prioritizing RDM amidst other research demands and implementing coherent plans for complex consortia remains difficult.

Purpose of the Study:

  • To present a practical RDM strategy for large-scale, multidisciplinary neuroscience research consortia.
  • To address the challenges of generating FAIR data from heterogeneous and multimodal sources (animal, human, clinical).
  • To propose sustainable solutions that incentivize incremental RDM adoption while accommodating specific research needs.

Main Methods:

  • Development and implementation of a tailored RDM strategy for the Heidelberg Collaborative Research Consortium.
  • Focus on early-stage RDM and FAIR data generation principles.
  • Integration of solutions to manage diverse data types including neurophysiology, neuroimaging, genetics, and behavior.

Main Results:

  • A concrete RDM strategy was successfully implemented within a large collaborative consortium.
  • The strategy facilitates the generation of FAIR neuroscience data from heterogeneous sources.
  • Sustainable solutions were developed to incentivize ongoing RDM practices.

Conclusions:

  • Implementing a proactive and sustainable RDM strategy is feasible and beneficial for large neuroscience consortia.
  • Such strategies are vital for maximizing the impact of multidisciplinary research and ensuring data usability.
  • Addressing RDM challenges early supports the long-term goals of open science and data sharing in neuroscience.