In materials science, complex relationships exist between the properties of materials and their composition and processing. Therefore, digital transformation and acceleration in this domain represents a particularly big challenge. Although it is generally agreed that data must be linked by means of semantics and ontologies to form holistic data spaces, there is still a lack of suitable tools for integrating the necessary structures into the everyday work of scientists.[1]

This challenge must be addressed with a broad-based strategy that closely links activities at all relevant levels, including automated lab infrastructure, machine-readable specification and documentation of scientific workflows and the harmonization of generated data structures in accordance with international standards efforts to build common data spaces (cf. MaterialDigital, IDS, GAIA-X).   

With OpenSemanticLab (OSL)[2] we have developed a reference implementation to fulfil this wide spectrum of requirements. Core of the resulting open-source solution architecture is the central web platform that links people (knowledge), machines (data) and algorithms (AI) equally by supporting both unstructured and structured content in an integrated form.[3]

This talk will provide an overview about the features using examples from our everyday laboratory work to create linked data / RDF in a single click and make the connection to the major trends in research data management. In addition, we will demonstrate that our solution in generic and flexible enough to be adapted to any other use case.

[1] Stier, S. P., Räder, A., Gold, L., Popp, M. A., & Triol, A. (2023, September 20). Linked Data Schema Repositories for Interoperable Data Spaces. 19th International Conference on Semantic Systems (SEMANTiCS 2023), Leipzig. https://doi.org/10.5281/zenodo.10528692

[2] S. Stier, L. Gold, A. Räder, M. Popp, A. Triol, The OpenSemanticLab Platform. Zenodo 2023, https://doi.org/10.5281/zenodo.8110655.

[3] Stier, S. P., Xu, X., Gold, L. and Möckel, M. (2024), Ontology-Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence-Empowered Data Analytics. Energy Technol. 2301305. https://doi.org/10.1002/ente.202301305