Sprecher
Beschreibung
Implementing (meta)data standards in the research ecosystem remains challenging, especially when operating across disciplines. Beyond technical barriers, varying semantic interpretations, and heterogeneous skill levels, a critical gap persists: how to transform principles into operational workflows ?
This contribution presents the Data Hub (https://datasnack.org/), a self-hostable, open-source framework addressing this implementation gap. Built in Python on top of the Django web framework and PostGIS/PostgreSQL database, it provides interdisciplinary teams with an information infrastructure for reproducible data harmonisation and collaborative metadata management while maintaining institutional sovereignty.
Development follows participatory practice with content matter experts and computer scientists, identifying barriers across domains and iteratively refining technical solutions. This process revealed three core design principles: (i) Open-source code is essential for responding to different (meta)data practices; (ii) Code-based data integration ensures transparency, traceability and reproducibility; (iii) Integrated metadata management incorporates quality assessment into data workflows at each transformation step. In addition, the modular architecture supports incremental adoption: teams may begin with basic harmonization and minimal metadata requirements, expanding capabilities as expertise develops.
Currently piloted in global health research projects (but also applicable to different domains), the Data Hub contributes practical infrastructure for translating (meta)data standards into collaborative research practice, with ongoing development informed by community feedback.
| Alternative Track | 5. From Minimum Requirements to FAIR and AI-Ready: Assessing Metadata Quality |
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