Data management in practice with FAIRDOM-SEEK
The next FAIRDOM user meeting is scheduled for Monday, 16 November 2026 at 14:00 - 16:00 CET.
The purpose of the FAIRDOM user meetings is to establish an active FAIRDOM user community to allow networking between various FAIRDOM-SEEK users and learn from each other’s experiences. Within the two-hours event invited users will showcase the use of FAIRDOMHub and other FAIRDOM-SEEK instances for their research projects. After the presentations we’ll have plenty of time for discussions and networking.
Agenda:
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14:00 - Welcome
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14:10 - Niels Geudens (VIB, Belgium)
“Supporting collaborative Design–Build–Test–Learn cycle with DataHub”
This presentation describes our work on metadata collection in DataHub to support Design–Build–Test–Learn (DBTL) research in the deCYPher project. We focus on how metadata templates and relationships between computational models and experimental results help maintain context and traceability across DBTL iterations.
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14:30 - Timo Mühlhaus (RPTU University, Germany)
“ARC it the ISA way”
Annotated Research Contexts (ARCs) provide a structured, FAIR-oriented framework for organizing research data, metadata, and computational workflows. A key conceptual distinction is the separation between the workspace, where research data and metadata are actively created, annotated, and maintained, and the publication space, where research outputs are represented as FAIR Digital Objects to enable FAIR data publication, discovery, and reuse. Building on the RO-Crate standard, the ARC profile collection introduces a modular approach to describing research objects while supporting interoperability, extensibility, and reuse. This approach is founded on three core profiles: Administrative Crate, which captures administrative metadata and attribution; Process Core Crate, which represents experimental and computational processes and their provenance; and Semantic Designation, which enables structured semantic annotation of research entities. Together, these profiles establish coherent annotation principles for expressing research context and meaning. A central concept is the composition of profiles through inheritance, specialization, and multityping, allowing research entities to fulfill multiple semantic roles and combine complementary metadata models without unnecessary duplication. This modular architecture provides the foundation for ISA ARCs, which integrate the Investigation–Study–Assay (ISA) model as a first-class representation within the ARC ecosystem. Within DataPLANT, ISA ARCs are first-class citizens, placing ISA-based research data annotation at the core of research data management. Dedicated tools and services, complemented by Data Steward support, enable researchers to create, annotate, validate, and maintain ISA-structured research data throughout the research lifecycle. This combination of a shared conceptual model, practical tooling, and human expertise supports consistent metadata annotation while accommodating diverse experimental workflows and disciplinary requirements. Together, these concepts establish a flexible, machine-actionable foundation for representing complex research contexts, connecting active research workflows with FAIR data publication, and enabling semantic clarity, provenance, and interoperability across research data infrastructures.
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14:50 - Alain Becam (University of Heidelberg, Germany)
“Two documentation sites for DM system choice and their (cloud) setup”
Effective research data management (RDM) requires more than selecting a storage solution: researchers must identify an RDM platform that fits their scientific workflows, data types, collaboration needs, FAIR requirements, and technical constraints. They must also decide which technical architecture and implementation strategy best support their research objectives throughout the data lifecycle. The FAIR-DMP Registry supports researchers in identifying suitable RDM platforms and services based on their project requirements. The Data Management On The Cloud Technical guide helps users in selecting appropriate technical approaches, from bare metal setup to cloud-based solutions, for managing and processing research data. It also gives a first explanation of all technologies and when complexity ask for an IT support or an existing solution. Both resources aim to simplify the increasingly complex landscape of research data management technologies and help researchers align technical choices with FAIR and reproducibility principles.
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15:10 - Discussion about matters raised
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15:25 - Q&A: ask me anything
Please register for free to get the ZOOM access details: Registration.