Following the FAIR principles (Findable, Accessible, Interoperable and Reusable), the SPHN Data Coordination Center (DCC) is developing a decentralized infrastructure to enable collaborative research by making the meaning of health-related data understandable to both humans and machines. The SPHN Interoperability Framework is based on a strong semantic layer of information (SPHN Dataset), and a graph technology for the exchange and storage of data. In this training we will demonstrate how you can capture your semantics is a simple way and use the SPHN Tooling to represent the semantics in a formal representation, in our case a RDF schema, further we will create a human-readable visualization, rules for data validation (SHACLs) and queries (SPARQLs) for performing data quality checks. The training is split into three parts: 1. Capturing Semantics: Typically projects first try to capture the semantics of their data in a common human readable format. This is to ensure that the barrier for entry is minimal for both technical and non-technical users. The format of choice is typically an Excel spreadsheet. This allows for domain experts and subject matter experts to focus more on the semantics of the model and ensures that the focus is on data modeling, and defining concepts and properties. In SPHN this representation is called the SPHN Dataset, in the course you will learn how to use the SPHN Dataset and extend it with the concept you need. 2. Representing Semantics: Once the semantics are well defined, the next step is to represent the semantics using a formal language. The most common data model of choice is the Resource Description Framework (RDF). Adopting the RDF as a form for knowledge representation allows for the use of RDF Schema (RDFS) for representing classes and properties. In SPHN this step can be facilitated by the Dataset2RDF tool, which generates the RDF Schema automatically from an Excel spreadsheet, without requiring in depth knowledge of RDF by the user. With the SPHN tool stack a human-readable html document is generated together with the formal RDF Schema, which can be shared within the consortia. 3. Data Validation: Ensuring data quality is of utmost importance for high-quality research. To facilitate quality control and compliance with SPHN data specifications at the data provider level, we will establish a set of Shapes Constraint Language (SHACL) rules that can be run on the data graph before they are shared with researchers. The SHACLer and SPARQLer tool automatically generates SHACLs and SPARQLs using the project’s data schema. This also facilitates the review of new project specifications and ensures harmonized data delivery from all sites.