Research Data Management across disciplines
Effective research data management (RDM) is increasingly becoming a central element of all research collaborations due to the rapidly increasing amounts of available and generated data. RDM varies across disciplines, each with unique needs and tools.
Here is a snapshot of how we approach #RDM across our four research areas at the SmartProSys research initiative:
➡ Process Labs (PL): LabFolder ELN (Labforward) for streamlined data capture and sharing in experimental workflows
➡ Molecular Labs (ML): eLabFTW for precise documentation and collaboration in chemistry-focused research].
➡ System Level studies & questionnaires (SL): Leibniz-Institut für Psychologie (ZPID)’s Research Data Center supports psychological data management.
➡ Computational and Mathematical Programming (CMA): GitLab for secure, versioned code and model development.
By tailoring RDM to each field, we ensure data that is Findable, Accessible, Interoperable, and Reusable (FAIR) over the entire research range.
➡ See also RDM@OVGU
