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| Home > Publications database > Workshop: Research Data Management for Data from High Performance Computing – Infrastructure, Best Practice and Tools (JSC contributions) |
| Talk (non-conference) (Other) | FZJ-2025-04949 |
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2025
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Please use a persistent id in citations: doi:10.5281/ZENODO.15236324 doi:10.34734/FZJ-2025-04949
Abstract: This workshop will introduce researchers to the basics of research data management (RDM) as well as tools and systems available at different HPC centers. It shows techniques on how to structure, document and publish your research data and which tools and resources can support you. You will earn knowledge of tools and services for data and metadata collection, storage, publication and transfer of large data. This supports bookkeeping in your own research, exchange with colleagues in your group and dissemination and use of research data with external partners. Publishing research data along journal publications will soon become commonplace and will allow for a wider range of use of comparative research data within the course of your research project. <br><br>Topics: <br>Basics in research data management: FAIR data principles, metadata & terminology, repositories, identifier (DOI), licenses, best-practices<br>Access, hardware systems, data backup, storage, and publication at the different HPC systems (e.g. SuperMUC-NG, Hunter, JUWELS & JURECA ,GWDG)<br>Research data management tools and services for data from HPC: Metadata standards, metadata crawling, cloud storages, interfaces, repositories, containerization, FAIR HPC data, B2SHARE, InHPC-DE etc.<br><br>With contributions from Technical University of Munich: Chair of Aerodynamics and Fluid Mechanics (TUM) High-Performance Computing Center Suttgart (HLRS), Jülich Supercomputing Centre (JSC) Leibniz Supercomputing Centre (LRZ) University of Göttingen / Gesellschaft für wissenschaftliche Datenverarbeitung (GWDG)<br>Note: the HLRS contribution (slides) will be added in a later version
Keyword(s): HPC ; RDM ; CFD ; Research Data Management ; FAIR data principles ; High Performance Computing
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