Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Conference object . 2025
License: CC BY
Data sources: Datacite
ZENODO
Conference object . 2025
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Rhine-Ruhr Center for Scientific Data Literacy (DKZ.2R) - Introduction and upcoming highlights

Authors: Immel, Katharina; Janz, Alicia; Mueller, Matthias; Sandfeld, Stefan;

Rhine-Ruhr Center for Scientific Data Literacy (DKZ.2R) - Introduction and upcoming highlights

Abstract

Researchers from all disciplines are confronted with ever-increasing amounts of data. The newly founded Rhine-Ruhr Center for Scientific Data Literacy (DKZ.2R) has set itself the goal of supporting and promoting researchers from a variety of research disciplines with regard toincreasingly complex data analysis, data management, and high-performance computing. The DKZ.2R is one of eleven data literacy centers in Germany funded by the Federal Ministry of Education and Research (BMBF) and will be funded for a period of three years with a total sum of around three million euros. The availability of ever larger and more complex amounts of data requires comprehensive skills that esearchers often have to learn independently. This begins with the consideration of how scientific data should be collected so that, for example, artificial intelligence methods can be used effectively. But it also includes questions about how data should be stored, annotated, processed, evaluated and published.The DKZ.2R therefore focuses on a combined methodological data competence, which consists in particular of data science and machine learning, high-performance computing and research data management. With the wide-ranging key domains of mathematics and computer science as well as life sciences, natural sciences and engineering, the Data Literacy Center thus offers services for a broad range of domain scientists. It enables them to break through data-related barriers and promotes synergies between the disciplines. The DKZ.2R offers researchers at different career stages a range of different services to improve their skills in dealing with research data. These include curated training courses, a range of advisory services including scientific consulting, scientific hackathons and data challenges, data cafés and software tool development and integration. Overall, the Data Competence Center is intended to serve as a point of contact for researchers and create a place for learning and networking. This will strengthen interdisciplinary research and create a strong basis for promoting data-based innovations. In addition to the institutions directly involved in DKZ.2R, the project also maintains close links with Helmholtz School for Data Science in Life, Earth, and Energy (HDS-LEE), National Research Data Infrastructure (NFDI), LAMARR Institute for Machine Learning and Artificial Intelligence, the State initiative for research data management fdm.nrw, and many more.

This publication has been made possible through the DKZ.2R "Datenkompetenzkolleg Rhein-Ruhr" (16DKZ2030.) by the Federal Ministry of Research, Technology and Space financed by the European Union programme "NextGenerationEU".

Keywords

Machine Learning, Datenkompetenzzentrum, Research Data Management, High Performance Computing, data literacy center

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green