Efficient data management and structured digital workflows are essential for transforming experimentalscience toward FAIR datasets. We implement a comprehensive digital lab infrastructure that linksexperimental 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 dataaccording 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 jointlydeveloped, customized Jupyter notebooks. Through this close partnership, both groups produceinteroperable, reusable datasets that support automated analyses and AI-driven applications.In this contribution, we present two workflows developed for Thin Film Catalyst laboratories focusingon Thermocatalysis and Electrocatalysis. These workflows systematically capture and structureresearch data across multiple lab processes, enabling high-throughput analysis, AI-driven insights, andefficient reuse. By highlighting our design decisions for optimizing experimental parameters usingBayesian optimization and for creating linked datasets to efficiently build combinatorial libraries, we aimto share practical strategies for implementing FAIR-aligned, metadata-rich digital lab workflows inheterogeneous experimental environments.