Over the four days, the program moved past the foundational theory of FAIR data to focus heavily on the actual tools researchers use day-to-day. Rather than just discussing best practices, participants got hands-on experience structuring and documenting their work.
Highlights from the week included:
Getting comfortable with Electronic Lab Notebooks (ELNs): The group started with a structured introduction before doing a deep dive into PASTA-ELN across two connected modules.
Hands-on in the FAU electronics lab: A major highlight was experiencing a complete, end-to-end workflow in a real lab setting, capturing live measurements with NOMAD CAMELS and taking them all the way through analysis and documentation using NOMAD Oasis and NORTH.
Publishing data: Participants learned how to upload, structure, share, and publish data in NOMAD, including creating datasets with a DOI.
Exploring platforms: The group tested out Coscine for managing research data and discussed how structuring data early in the research process lays the foundation for AI analytics and machine learning.
The Spring School was jointly organized by Dr.-Ing. Flavio Soldera (Universität des Saarlandes / EUSMAT, NFDI-MatWerk), Dr.-Ing. Ullal Pranav Nayak (Universität des Saarlandes, NFDI-MatWerk), Prof. Dr.-Ing. Luca Ghiringhelli (Karlsruhe Institute of Technology (KIT), FAIRmat), and Dr. Ahmed Mansour (Humboldt-Universität zu Berlin, FAIRmat).
Ultimately, the joint NFDI-MatWerk/FAIRmat format bridged the gap between high-level data principles and the reality of working in a lab by creating a coherent pathway from core RDM concepts to concrete infrastructures, tools, and practical, lab-oriented sessions.
For more information on future training opportunities and workshops, please check out the NFDI-MatWerk website, FAIRmat website, or the EUSMAT portal.