Sprecher
Beschreibung
Efficient data management and structured digital workflows are essential for transforming experimental science toward FAIR datasets. We implement a comprehensive digital lab infrastructure that links experimental data across the full sample lifecycle—from synthesis to advanced characterization—ensuring machine-readable, metadata-rich datasets.
At HZB, Data Stewards and Laboratory Scientists collaborate closely to integrate software, metadata, and experimental data. Data Stewards develop the open-source platform NOMAD Oasis to structure data according to FAIR principles and harmonize metadata using collaboratively created vocabularies (e.g., voc4Cat, TFSCO). Laboratory Scientists generate and document heterogeneous experimental data, which are captured through digital laboratory workflows and subsequently analyzed using jointly developed, customized Jupyter notebooks. Through this close partnership, both groups produce interoperable, reusable datasets that support automated analyses and AI-driven applications.
In this contribution, we present two workflows developed for Thin Film Catalyst laboratories focusing on Thermocatalysis and Electrocatalysis. These workflows systematically capture and structure research data across multiple lab processes, enabling high-throughput analysis, AI-driven insights, and efficient reuse. By highlighting our design decisions for optimizing experimental parameters using Bayesian optimization and for creating linked datasets to efficiently build combinatorial libraries, we aim to share practical strategies for implementing FAIR-aligned, metadata-rich digital lab workflows in heterogeneous experimental environments.
| Alternative Track | 6. Harmonisation of Metadata: Closing Semantic Gaps |
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