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. 

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 processes and the harmonization of generated data structures in accordance with international standards.  

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

OSL does not specify a fixed data model. Instead, schemas can be individually installed from an open repository, imported from an existing ontology, or created and extended by the user. Regardless of the way a schema is created and updated, both program code and visual interfaces for searching, viewing and editing the corresponding JSON-LD datasets are always generated automatically for the user. Thus, OSL makes it possible to apply semantic structures and ontologies in the background without the user needing in-depth knowledge. Conversely, the user can also use the platform to provide pre-structured knowledge for the creation and extension of domain ontologies.

Over all, this platform is easily adaptable even without programming knowledge and without losing the uniform structure. In this way, OpenSemanticLab enables us as scientists to contribute individually and yet in a standardized fashion to future digital materials research.