Enhancing Multiscale Simulation Data Management with Domain Ontologies and an ELN: Addressing Challenges and Implementing Strategies

Data plays a fundamental role in advancing knowledge and driving research across all fields. Despite the abundance of data, several challenges still need to be addressed. These challenges include limited accessibility, heterogeneous data, lack of interconnections between associated topics, difficulty retrieving required information, semantic mismatches, and several other challenges. Ontologies provide a structured method to address these challenges effectively. This study addresses two central aspects of ontology research. First, it details the multidisciplinary ontology development process, highlighting the challenges, mitigation strategies, and impacts on domain data management. It then offers guidelines for beginners and individuals with a background in data management on effective engagement in ontology creation. Second, it introduces the Ontology for Multiscale Simulation methods (Onto-MS), constructed by following the guidelines from the first part. The ontology, developed in Web Ontology Language (OWL) using Protégé, integrates with other ontologies, such as the Elementary Multiperspective Material Ontology (EMMO), aligning this research with the Linked Data concept. A custom Python script was used to incorporate the ontology into an Electronic Laboratory Notebook (ELN), enabling the automatic creation of knowledge graphs and systematic data organization conforming to the ontology. This research successfully answers the fundamental questions in interdisciplinary or domain-level ontology development. Onto-MS provides a robust framework for organizing and linking data in multiscale simulations within computational materials science. Furthermore, ontology incorporation into an ELN simplifies its integration into data management practices. While ontology development is ongoing, the current version is functional and continuously refined with new insights and feedback.