Marta Dembska is a recognised expert in research data management and semantic web technologies, specialising in ontology development, process modelling, and the digitalisation of laboratory work. Her research focuses on scientific workflow modelling, metadata management for laboratory processes, the FAIRification of laboratory data, and the integration of ontologies into electronic laboratory notebooks (ELNs) within materials science and engineering (MSE).
She takes an interdisciplinary approach, bridging experimental science with data science to enhance the structuring and management of research data.
She graduated with honours with a BSc in Technical Physics, specialising in computers in physical measurement (2007), followed by an MSc in Computer Physics (2010) from the University of Zielona Góra. In 2013, she earned her PhD in Radio Astronomy at the same university, with her dissertation receiving honours.
Since 2014, she has been a research fellow at the German Aerospace Center (DLR), initially working on laboratory research in MSE and later shifting her focus towards scientific workflow modelling, metadata management for laboratory processes, and the digitalisation of laboratory work.
Ontology-Driven Digitalisation of Laboratory Research in Materials Science
The digital transformation of materials science and engineering (MSE) is reshaping how laboratory experiments are documented, shared, and reused. Electronic Laboratory Notebooks (ELNs) play a central role in this transition, enhancing data quality and reproducibility by structuring experimental records and integrating measurement data. However, ensuring FAIR (Findable, Accessible, Interoperable, and Reusable) data across MSE subfields remains a challenge due to diverse methodologies and domain-specific vocabularies.
Ontologies provide a semantic foundation for standardising metadata, structuring research workflows, and enabling cross-disciplinary collaboration. By embedding semantic models into ELNs and research data management systems, we enhance data provenance, support automation, and foster interoperability across experimental and computational approaches. This presentation explores strategies for FAIRification of laboratory processes, focusing on ontology (re)use, alignment with top-level ontologies, and the role of semantic technologies in improving research data management in MSE.
