Towards automated processing and re-use of open-access content in LTP research

In research on low-temperature plasmas (LTP), a wide range of devices, methods and materials are mainly used in table-top experiments. Model-based simulations are often used in conjunction with laboratory experiments to investigate, optimise or develop new plasma processes and applications. This leads to a large number of widely spread, heterogeneous research results, which are difficult to compare and can hardly be brought together. However, this is essential to form a robust overall understanding of the very complex mechanisms and effects in LTP. This contribution deals with modern techniques and exploits the advantages of Open Access (OA) to meet these challenges. OA publications from LTP research are collected in the central public repository RENATE and converted into a machine-readable format. This enables machine learning-based processing and interpretation of OA publications on the used devices, methods, materials, etc., and ultimately their structured storage in the "Open Research Knowledge Graph" (ORKG). In future, the knowledge extracted and semantically described in this way will be easier to find and aggregate and can therefore be re-used to gain new research insights using data-driven methods. The work was funded by the German Federal Ministry of Research, Technology and Space (BMFTR) under the grant marks 16KOA013A and 16KOA013B.