The management of data abroad various disciplines can be a very challenging task due to the variety of expert language and the heterogeneity of data and data formats. In this contribution, we present outcomes and technical solutions developed in the project MEMAS for the efficient and sustainable storage of data and metadata in robotics, manufacturing, testing and simulation of composite parts. Our work follows the FAIR principles by developing a multi-domain ontology that bridges the abovementioned fields of engineering. Data interoperability and reusability is ensured by the use of the research data management system (RDMS) shepard to store heterogenous research data. The development of json schemas allowed for the automatic generation of user interfaces, which are used to enrich data sets and store reusable instances of ontology classes, for instance for testing instruments, machines or test standards.  In a second phase, we focused our work on the generation of automatic parsing tools to extract metadata from research files. Structuring tools allowed for the conversion of human-readable files into machine readable data objects, in particular json or timeseries, for structured storage in RDMS. The heterogeneous data and metadata stored within the RDMS are systematically structured, ensuring findability and semantic coherence. This developed approach and methods establishes a robust foundation for future advancements such as multi-objective optimization and machine learning-driven insights. In this FAIR seminar, we will describe the developed methods for the management of data and metadata in details. Particularly, we will address the difficulties encountered in the development process and discuss challenges related to the accessibility of FAIR processes in research.