NFDI4DS Transfer and Application
Abstract: 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.
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@inproceedings{DBLP:conf/gi/0002ARU0SAGW0SN23,
author = {Ekaterina Borisova and
Raia Abu Ahmad and
Georg Rehm and
Ricardo Usbeck and
Jennifer D'Souza and
Markus Stocker and
S{\"{o}}ren Auer and
Judith Gilsbach and
Anastasia Wolschewski and
Johannes Keller and
Daniel Schneider and
Thomas Neumuth and
Sonja Schimmler},
editor = {Maike Klein and
Daniel Krupka and
Cornelia Winter and
Volker Wohlgemuth},
title = {{NFDI4DS} Transfer and Application},
booktitle = {53. Jahrestagung der Gesellschaft f{\"{u}}r Informatik, {INFORMATIK}
2023, Designing Future - Zuk{\"{u}}nfte gestalten, Berlin, Germany
September 26-29, 2023},
series = {{LNI}},
volume = {{P-337}},
pages = {925--929},
publisher = {Gesellschaft f{\"{u}}r Informatik, Bonn},
year = {2023},
url = {https://doi.org/10.18420/inf2023\_104},
doi = {10.18420/INF2023\_104},
timestamp = {Fri, 22 Nov 2024 10:56:01 +0100},
biburl = {https://dblp.org/rec/conf/gi/0002ARU0SAGW0SN23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}