Organizing Scientific Knowledge From Energy System Research Using the Open Research Knowledge Graph
Oliver Karras, Jan Göpfert, Patrick Kuckertz, Tristan Pelser, Sören Auer
TL;DR
The paper addresses the challenge of organizing and sharing knowledge in energy system research across publications, software, and data to support climate-neutral transformations. It introduces the Open Research Knowledge Graph (ORKG) with the Terminology Service (TS) and the Open Energy Ontology (OEO) as a semantic infrastructure for FAIR data and open science. Through two exemplar use cases, it demonstrates capturing energy-system knowledge as ORKG contributions and comparing them with DOIs via ORKG comparisons and an observatory, including SPARQL-based competency queries and visualizations that reveal insights such as substantial growth in PV and onshore wind where applicable. The work highlights tangible benefits for traceability, reusability, and cross-institution collaboration, aiming to foster adoption of ORKG among energy researchers and to strengthen ties between NFDI4Energy and NFDI4Ing.
Abstract
Engineering sciences, such as energy system research, play an important role in developing solutions to technical, environmental, economic, and social challenges of our modern society. In this context, the transformation of energy systems into climate-neutral systems is one of the key strategies for mitigating climate change. For the transformation of energy systems, engineers model, simulate and analyze scenarios and transformation pathways to initiate debates about possible transformation strategies. For these debates and research in general, all steps of the research process must be traceable to guarantee the trustworthiness of published results, avoid redundancies, and ensure their social acceptance. However, the analysis of energy systems is an interdisciplinary field as the investigations of large, complex energy systems often require the use of different software applications and large amounts of heterogeneous data. Engineers must therefore communicate, understand, and (re)use heterogeneous scientific knowledge and data. Although the importance of FAIR scientific knowledge and data in the engineering sciences and energy system research is increasing, little research has been conducted on this topic. When it comes to publishing scientific knowledge and data from publications, software, and datasets (such as models, scenarios, and simulations) openly available and transparent, energy system research lags behind other research domains. According to Schmitt et al. and Nieße et al., engineers need technical support in the form of infrastructures, services, and terminologies to improve communication, understanding, and (re)use of scientific knowledge and data.