Generating FAIR research data and enabling its reuse is the overall goal of research data management. However, establishing machine-readable knowledge representation - the “I” in FAIR - as the foundation for FAIR data and metadata remains a major challenge for many research communities. AIMS envisions a research ecosystem where machine-readable metadata is seamlessly integrated into everyday research workflows. We have developed an approach to create subject-specific RDF-compliant metadata profiles (in the sense of SHACL shapes) that enable precise and flexible documentation of research processes and data. This enables domain experts to leverage existing Linked Data technologies when developing machine-interpretable metadata profiles that support semantic encoding and validation of (meta)data. To facilitate the modelling process and make it accessible to users with only limited knowledge of ontologies, we have developed a web service that provides a graphical user interface for creating metadata profiles. The AIMS Metadata Profile Service is open source and allows the integration into other tools, such as the RDM platform Coscine. Its frontend allows searching for suitable terms from existing terminologies and adding them to a profile along with restrictions on the permitted value nodes, such as expected data types, classes, or node types, as well as the cardinality of attributes. This allows users without prior knowledge of SHACL to create metadata profiles. In the second funding phase of the project AIMS 2 focuses on improving the user interface and the integration of metadata profiles into electronic laboratory notebooks (ELNs). Using the ELN eLabFTW as an example, the project demonstrates how semantic metadata can be recorded without requiring researchers to change their existing workflows. In addition, we will improve support for exploring and displaying profile-conformant metadata in the metadata profile service and facilitate the creation of interdependent metadata profiles, thereby supporting a modular and hierarchical modelling approach that increases the reusability of the created profiles.
Windeck et al. (2026) studied this question.