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).

Specifically, G-Node offers infrastructural support for research data including file formats for metadata collection and data organization, and a research data platform for collaboration, sharing and publication. The ANDA course provides training on advanced neural data analysis.

G-Node is part of the NFDI consortium initiative for neuroscience, NFDI Neuroscience. The initiative works to build a community to develop the conceptual and practical basis of research data management for the neurosciences as part of the NFDI.