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The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces. Next to materials synthesis in the form of thin-film material libraries, various material characterization and simulation techniques are used to explore the material data space of the problem. Machine learning and artificial intelligence techniques guide its exploration and navigation. The effective use of the combined heterogeneous data requires more than just a simple research data management plan. Consequently, our research data management system maps different data modalities in different formats and resolutions from different labs to the correct spatial locations on physical samples. Besides a graphical user interface, the system can also be accessed through an application pro- gramming interface for reproducible data-driven workflows. It is implemented by a combination of a bespoke research data management system designed around a relational database, an ontology which builds upon materials science-specific ontologies, and the construction of a Knowledge Graph. Along with the technical solutions of the research data management system and lessons learned, the first use cases are shown, which were not possible (or at least much harder to achieve) without it.