Data management strategy for a collaborative research center

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.