Plasma Applications
Smart technologies for data and information management in low-temperature plasma physics
4:30 pm – 5:00 pmThe emergence of modern digital solutions in research data management (RDM) is revolutionising scientific research across many disciplines. By streamlining the organisation, accessibility and interoperability of distributed datasets and information sources, smart technologies are providing new opportunities for research and development. For example, these technologies enable automated data processing, simplified information retrieval and advanced data-driven analyses, thereby accelerating scientific discovery and innovation. Following these developments, research in low-temperature plasma (LTP) physics is beginning to benefit from these advancements, e.g. by introducing RDM solutions that adhere to the so-called FAIR data principles, which emphasise findability, accessibility, interoperability and reusability of research data. This contribution outlines recent developments for improving the management of research data and information by innovative tools and technologies throughout the entire research data lifecycle, thereby supporting the use of data-driven research methods. The results achieved so far include the establishment of electronic laboratory notebooks with modular templates for the structured documentation of experiments, topic-specific cross-institutional metadata schemas for the standardised provision of "data about data", ontologies and knowledge graphs for the semantic linking and machine- actionable provision of information, as well as practical tools and RDM workflows for integrating these technologies into research processes. The presentation covers three individual use cases, each addressing specific requirements in LTP research: metadata schemas and guided workflows for data management in laboratories; a toolbox for reproducible simulations of non-thermal plasmas; and public linked data services for semantic information management in LTP research by the entire community.
Funding acknowledgement
The work is partly supported by the DFG (projects 496963457, 501864659, 535827833) and the BMBF (project 16KOA013A).
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