The availability of precise and comprehensive experimental data is crucial for the usability of Artificial Intelligence (AI) models. To enable the deployment of machine learning models across different platforms, a digitally analysable, system-independent representation of datasets is essential. The overall objective of this contribution is to document research data across process boundaries, as well as across laboratory boundaries and interdisciplinary fields of expertise, empowering researchers to maintain their usual domain specific perspective throughout the data preparation and documentation process. A strategy is proposed in this regard, whereby specialists can focus on data provision by reducing routine activities, rather than attempting to align with other groups. Metadata schemas with synonyms based on ontologies guarantee that research data is understandable, reproducible on a qualitative level, interoperable across laboratory boundaries, and useful for future analysis. The proposed meta-metadata model is formulated in a mathematical setting and its feasibility has been proven. The applicability of the strategy is demonstrated by integrating the model to the research data management of two joint research projects in the engineering domain. To conclude, the proposed strategy supports a paradigm shift away from more or less subjectively designed individualistic conceptions in handling research data towards objectively established harmonised solutions.