ePoster

Empowering collaborative neuroscience: Optimizing FAIR data sharing with a tailored open-source repository for CRC 1280 “Extinction Learning”

Tobias Otto, Marlene Pacharra, Johannes Frenzel, Nina O. C. Winter
FENS Forum 2024(2024)
Messe Wien Exhibition & Congress Center, Vienna, Austria

Conference

FENS Forum 2024

Messe Wien Exhibition & Congress Center, Vienna, Austria

Resources

Authors & Affiliations

Tobias Otto, Marlene Pacharra, Johannes Frenzel, Nina O. C. Winter

Abstract

Since 2017, researchers of the interdisciplinary Collaborative Research Center (CRC) 1280 have investigated the mechanisms of extinction learning. To ensure sustainable research data management (RDM), the CRC established an RDM board representing all career stages, jointly developed an RDM policy, and agreed on common standards for folder structure and metadata. For neuroimaging, the BIDS Structure was adopted. However, sharing data from in-progress research projects for comprehensive analysis by CRC collaborators requires the use of trusted workflows within a reliable, preferably local, repository.Thus, the CRC’s experimental research data are stored with metadata in the new open-source repository ReSeeD of the Ruhr University Bochum that is based on Hyrax (Samvera community) and enables publication of FAIR data and 10-year archiving. This system was fine-tuned for the CRC to support the entire data lifecycle in neuroscience, emphasizing user-friendly daily uploads, metadata assignment, and intuitive navigation of multilevel data structures within ReSeeD’s user interface. Aligned with CRC 1280's organizational framework, a three-tier roles and rights system was implemented, encompassing roles such as researcher, group manager, CRC's data steward, and central publication manager. This is the basis for policy-compliant CRC-wide data sharing of ongoing projects, as well as 3-step quality assurance for data publication and archiving.Collaboration with central library and IT ensures the sustainability of this open-source repository solution, which can easily be adopted and further developed by the community. The repository not only facilitates the sharing of FAIR neuroscience data but also promotes agile collaboration, fostering a cohesive research environment.

Unique ID: fens-24/empowering-collaborative-neuroscience-d629b1d7