In practice data-management often poses many different technical and non-technical challenges but it plays a pivotal role when talking about FAIR research data. Furthermore, while ontologies are a great tool for describing data in an interoperable and reuseable way, working with them can be difficult for non-experts. In a live demonstration we show how the DataCharge.io data-management platform, which is developed in the DaMaStE project, enables researchers to create ontology-based data models that describe their experiments and use them to store their research data. Additionally, we demonstrate our ideas for how a federated database system can simplify and improve collaboration and data exchange between research and industry.