NFDI4DS Transfer and Application
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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INFORMATIK 2023: Designing Futures: Zukunfte gestalten, Proceedings; 2 6. – 29. September 2023, Berlin . ed. / Maike Klein; Daniel Krupka; Cornelia Winter; Volker Wohlgemuth. Bonn: Gesellschaft für Informatik e.V., 2023. p. 925-929 (Lecture Notes in Informatics (LNI), Proceedings; Vol. P-337).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - NFDI4DS Transfer and Application
AU - Borisova, Ekaterina
AU - Ahmad, Raia Abu
AU - Rehm, Georg
AU - Usbeck, Ricardo
AU - D'Souza, Jennifer
AU - Stocker, Markus
AU - Auer, Sören
AU - Gilsbach, Judith
AU - Wolschewski, Anastasia
AU - Keller, Johannes
AU - Schneider, Daniel
AU - Neumuth, Thomas
AU - Schimmler, Sonja
N1 - Conference code: 53
PY - 2023
Y1 - 2023
N2 - Due to the ever increasing importance of Data Science and Artificial Intelligence methods for a wide range of scientific disciplines, ensuring transparency and reproducibility of DS and AI methods and research findings have become essential. The NFDI4DS project promotes the findability, accessibility, interoperability, and reusability in DS and AI by developing an open integrated research data infrastructure in which all artefacts (e. g., papers, code, models, datasets) will be interlinked in a FAIR and transparent way. One of the key aspects is to build a bridge between NFDI4DS and other research communities which actively apply DS and AI methods. This paper describes the main actions taken to engage with the relevant (sub)communities.
AB - Due to the ever increasing importance of Data Science and Artificial Intelligence methods for a wide range of scientific disciplines, ensuring transparency and reproducibility of DS and AI methods and research findings have become essential. The NFDI4DS project promotes the findability, accessibility, interoperability, and reusability in DS and AI by developing an open integrated research data infrastructure in which all artefacts (e. g., papers, code, models, datasets) will be interlinked in a FAIR and transparent way. One of the key aspects is to build a bridge between NFDI4DS and other research communities which actively apply DS and AI methods. This paper describes the main actions taken to engage with the relevant (sub)communities.
KW - Artificial Intelligence
KW - Data Science
KW - NFDI
KW - NFDI4DS
KW - Research Data Infrastructures
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85181079336&partnerID=8YFLogxK
UR - https://dl.gi.de/collections/30690b3e-d866-4c83-8b80-3bfc9fa47f2c
UR - https://dl.gi.de/items/36a8d073-7b7d-4b7b-8bbd-b1ecc5c5e2ce/full
M3 - Article in conference proceedings
AN - SCOPUS:85181079336
T3 - Lecture Notes in Informatics (LNI), Proceedings
SP - 925
EP - 929
BT - INFORMATIK 2023
A2 - Klein, Maike
A2 - Krupka, Daniel
A2 - Winter, Cornelia
A2 - Wohlgemuth, Volker
PB - Gesellschaft für Informatik e.V.
CY - Bonn
T2 - 53. Annual Meeting of the German Informatics Society (GI) - INFORMATICS 2023
Y2 - 26 September 2023 through 29 September 2023
ER -