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Part 1: Document Description
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Citation |
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Title: |
NFDI4ING Task Area Ellen: "Hello, I'm Ellen" |
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Identification Number: |
doi:10.26165/JUELICH-DATA/TSEIX8 |
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Distributor: |
Jülich DATA |
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Date of Distribution: |
2026-06-02 |
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Version: |
1 |
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Bibliographic Citation: |
Kuckertz, Patrick; Karras, Oliver; Göpfert, Jan; Pelser, Tristan; Pflugradt, Noah; Weinand, Jann Michael; Auer, Sören, 2026, "NFDI4ING Task Area Ellen: "Hello, I'm Ellen"", https://doi.org/10.26165/JUELICH-DATA/TSEIX8, Jülich DATA, V1 |
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Citation |
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Title: |
NFDI4ING Task Area Ellen: "Hello, I'm Ellen" |
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Identification Number: |
doi:10.26165/JUELICH-DATA/TSEIX8 |
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Authoring Entity: |
Kuckertz, Patrick (Forschungszentrum Jülich GmbH) |
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Karras, Oliver (Leibniz Universität Hannover, Technische Informationsbibliothek) |
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Göpfert, Jan (Forschungszentrum Jülich GmbH) |
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Pelser, Tristan (Forschungszentrum Jülich GmbH) |
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Pflugradt, Noah (Forschungszentrum Jülich GmbH) |
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Weinand, Jann Michael (Forschungszentrum Jülich GmbH) |
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Auer, Sören (Leibniz Universität Hannover, Technische Informationsbibliothek) |
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Distributor: |
Jülich DATA |
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Access Authority: |
Kuckertz, Patrick |
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Depositor: |
Kuckertz, Patrick |
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Date of Deposit: |
2026-05-27 |
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Study Scope |
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Keywords: |
Computer and Information Science, research data management, FAIR-by-Design, machine-actionable metadata, semantic interoperability, research knowledge graphs, AI-driven knowledge engineering, automated knowledge extraction, heterogeneous data integration, semantic search, data-driven research workflows |
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Abstract: |
The increasing complexity of engineering challenges requires methods and tools that enable researchers to efficiently access, generate, and integrate heterogeneous data. Task Area Ellen presents a FAIR-by-Design approach, embedding machine-actionable metadata directly into texts, software, and data from the outset to minimize documentation effort and maximize reusability. Key tools include SciKGTeX for semantic representation of scholarly contributions, DataDesc for technical metadata of software and data models, and the Open Research Knowledge Graph (ORKG) for connecting and analyzing scientific findings. A best-practice application, ETHOS.REFLOW, demonstrates the reproducible assessment of renewable energy potentials through open-source workflows. To further address information needs, AI-driven knowledge engineering methods—such as vector search, large language models, and automated knowledge extraction—are leveraged to contextualize research content. Tools like ORKG Ask enable semantic exploration across millions of research outputs, while Quinex automates the extraction of quantitative data with contextual detail. Together, these approaches empower researchers to both discover relevant information more effectively and integrate it into customized workflows when suitable data is unavailable. By combining FAIR principles with AI-driven knowledge technologies, this framework significantly enhances transparency, reproducibility, and efficiency in engineering research. <h3>Acknowledgements</h3> The authors would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number: 442146713. This work was also supported by the Helmholtz Association under the program "Energy System Design". |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
NFDI4ING Task Area Ellen.pdf |
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Notes: |
application/pdf |