Research data management in neuroscience

Availability and efficient management of research data is an important ingredient for reproducibility and scientific progress. In Computational Neuroscience, which links experimental neuroscience, theory and modeling, there is a particular need for reuse of research data. To facilitate sharing and reuse, research data should be handled according to established standards, and researchers need support to adopt best practices in research data management.

The Bernstein Network Computational Neuroscience has from its inception been fostering exchange of data and collaboration. As part of the Network, the German Neuroinformatics Node (G-Node) is providing tools and services supporting open science, collaboration, and training in data analysis, neuroinformatics, and research data management. It links the network to the international initiatives in the context of the International Neuroinformatics Coordinating Facility (INCF).

Recently, the FAIR principles have been proposed as guidelines for enhancing the value of research data and other digital resources. Furthermore, the importance of access to digital knowledge and management of data from publicly funded research has led to a national funding program – the National Research Data Infrastructure (NFDI) – to coordinate and develop research data management across the entire scientific landscape. In this context, the Bernstein Network is supporting a consortium initiative for neuroscience, NFDI Neuroscience (https://nfdi-neuro.de). NFDI Neuroscience is committed to organize and sustain cross-community research data management (RDM) in neuroscience. The initiative brings together data collectors, data users and technology providers to develop processes for data management in research with specific emphasis on neuroscience. The consortium works to build a community to develop the conceptual and practical basis of research data management for the neurosciences as part of the NFDI.