The emerging field of oxygen-free production research has many facets and addresses research questions ranging from Materials Science, surfaces to process development. The comprehensive study of these problems requires the collaboration of numerous research activities from various disciplines. These activities lead to equally diverse and heterogeneous data from experimental design, measurements, or simulations. Overarching research data management and data science approaches are important assets for such collaborative research efforts. In this work, an integrated Research Data Management System (RDMS) linking several components of an interdisciplinary research environment is proposed and the first modules applied in the Collaborative Research Centre Oxygen-Free Production (CRC1368) are presented. A Knowledge Management System (KMS) allows the general documentation of research activities and the provenance of generated data. It is additionally linked with an Electronic Lab Notebook (ELN) thus including documentation of analytical laboratory activities. The system is supplemented by a Data Repository providing archiving functionality of measurement data or data from simulations. Data and information across these components are semantically annotated and linked based on a shared controlled vocabulary.

Researchers can use the Data Repository to directly visualize and compare datasets from different origins and stages of the process thus identify dependencies of parameters across multiple steps. The combination of KMS, ELN, and Data Repository enables a seamless flow of data from sample creation, defining an analytical lab job, performing analysis, and returning the analytical data to the experimentalist. The overarching and linking approach of the RDMS enables widespread availability of information and data across a collaborative research project fostering shorter development cycles and new insights from holistic data analysis. The application of the RDMS is an important prerequisite to generate data according to FAIR Data Principles (findability, accessibility, interoperability, and reusability).