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Auflistung nach Schlagwort "Research Data Infrastructures"

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  • Konferenzbeitrag
    NFDI4DS at a Glance
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schimmler, Sonja; Hennig, Christine
    The consortium NFDI4DS supports researchers along all stages of the research data lifecycle to conduct their research in line with the FAIR principles. By conducting interviews and surveys, NFDI4DS continuously identifies the needs and challenges of researchers from various disciplines regarding data science and artificial intelligence, keeping ethical, legal, and social aspects in mind. Those identified needs and challenges are continuously addressed by picking up existing services, developing new ones and integrating them into the NFDI4DS infrastructure. By systematically adding digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) to the NFDI4DS research knowledge graph within the infrastructure, transparency, reproducibility, and fairness are steadily improved. The process is continuously accompanied by providing resources such as educational videos and organizing events such as community challenges. In this short paper, we give an overview of NFDI4DS, and provide details about our approach to address the current challenges. We will report on how we plan to utilize FAIR digital objects (FDOs) and Research Knowledge Graphs (RKGs) as a basis for the infrastructure envisioned. We will also give an overview of the services planned, and how they are meant to interact.
  • Konferenzbeitrag
    NFDI4DS Infrastructure and Services
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schimmler, Sonja; Wentzel, Bianca; Bleier, Arnim; Dietze, Stefan; Karmakar, Saurav; Mutschke, Peter; Kraft, Angelie; Taffa, Tilahun A.; Usbeck, Ricardo; Boukhers, Zeyd; Auer, Sören; Castro, Leyla J.; Ackermann, Marcel R.; Neumuth, Thomas; Schneider, Daniel; Abedjan, Ziawasch; Latif, Atif; Limani, Fidan; Abu Ahmad, Raia; Rehm, Georg; Attar Khorasani, Sima; Lieber, Matthias
    NFDI4DataScience (NFDI4DS) is a consortium founded to support researchers in all stages of the research data lifecycle in order to conduct their research in line with the FAIR principles. The infrastructure developed targets researchers from a wide range of disciplines working in the field of data science and artificial intelligence. NFDI4DS contributes to systematically understanding the needs and challenges of researchers in various disciplines regarding data science and artificial intelligence, keeping in mind ethical, legal and social aspects. The identified needs will be addressed by support structures such as educational videos and challenges. Transparency, reproducibility and FAIRness will be improved by integrating existing and newly developed services into the NFDI4DS infrastructure, and by systematically adding all digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) to the NFDI4DS research knowledge graph. This paper presents the goals of NFDI4DS, and gives an overview on what the consortium is going to contribute to the data science and artificial intelligence communities. It focuses on existing and newly developed services and their integration.
  • Konferenzbeitrag
    NFDI4DS Transfer and Application
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Borisova, Ekaterina; Abu Ahmad, Raia; Rehm, Georg; Usbeck, Ricardo; D’Souza, Jennifer; Stocker, Markus; Auer, Sören; Gilsbach, Judith; Wolschewski, Anastasia; Keller, Johannes; Schneider, Daniel; Neumuth, Thomas; Schimmler, Sonja
    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.