First Application of a Semantic Framework for the Machine-Readable Representation of Scientific Concepts in Engineering Sciences

The transformation of energy systems is one of the greatest challenges of the 21st century. We have created a machine-readable comparison of greenhouse gas reduction scenarios for Germany, comparing different studies for a future low-carbon energy system.
Published ORKG Comparison of Studies on Germany’s Energy Supply in 2050. Based on a Semantic Framework for Machine-Readable Representation and Mapping of Scientific Concepts in Engineering Sciences, Developed by Task Area Ellen.
Oliver Karras, CC BY 3.0 DE

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This use case is particularly interesting as it involves a large set of multidisciplinary studies and can be used as a basis for complex systems analysis. We are proud to contribute to the collaborative effort of studying sustainable energy systems!

The publication was supported by the NFDI4Ing Task Area Ellen, which aims to sustainably support engineers in their search for data by making research data and software machine-actionable through semantic annotations, increasing their level of integration and reducing the effort required for their reuse.

Patrick Kuckertz, Felix Kullmann, Jan-Maris Göpfert, Oliver Karras, Sören Auer, Felix Engel, Markus Stocker, Peter Markewitz, Jann Weinand, Leander Kotzur, Detlef Stolten