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| 001 | 1014307 | ||
| 005 | 20240313095004.0 | ||
| 037 | _ | _ | |a FZJ-2023-03229 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Villamar, Jose |0 P:(DE-Juel1)191583 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Helmholtz Metadata Collaboration conference |c Virtual |d 2022-10-05 - 2022-10-06 |w Germany |
| 245 | _ | _ | |a Tracking large-scale simulations through unified metadata handling |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1695110044_7536 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Simulation is an essential pillar of knowledge generation in science. The numerical models used to describe, predict, and understand real-world systems are typically complex. Consequently, applying these models by means of simulation often poses high demands on computational resources, and requires high-performance computing (HPC) or other dedicated hardware architectures. Metadata describing the details of a numerical experiment arise at all stages of the simulation process: the conceptual description of the model, the model implementation, and the tools and machines used to run the simulation. Capturing these metadata and provenance information along the processing chain is a vital requirement for several purposes, e.g. reproducibility, benchmarking and validation, assessment of the reliability of the simulations, and data exploration [1,2]. The ability to search, share, and evaluate metadata and provenance traces from heterogeneous simulations and environments is a major challenge in provenance-driven analysis. The availability of a common metadata framework, which can be adopted by scientists from different scientific domains, would foster the meta-analysis of HPC simulation workflows [3]. Here, we develop a metadata management framework for generic HPC-based simulation research comprising concepts and tools for efficiently generating, organizing, and exploring metadata along a given simulation workflow. The derived solutions cope with the modularity and flexibility demands of rapidly progressing science and are applicable to diverse research fields. As a proof of concept, we will apply these solutions to use cases from environmental research and computational neuroscience.[1] Guilyardi, E., et. al. (2013) doi: 10.1175/BAMS-D-11-00035.1[2] Manninen, T., et. al. (2018) doi: 10.3389/fninf.2018.00020[3] Ivie, P., & Thain, D. (2018). doi: 10.1145/3186266 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a MetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026) |0 G:(DE-Juel-1)ZT-I-PF-3-026 |c ZT-I-PF-3-026 |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 2 |
| 536 | _ | _ | |a ACA - Advanced Computing Architectures (SO-092) |0 G:(DE-HGF)SO-092 |c SO-092 |x 3 |
| 536 | _ | _ | |a Brain-Scale Simulations (jinb33_20220812) |0 G:(DE-Juel1)jinb33_20220812 |c jinb33_20220812 |f Brain-Scale Simulations |x 4 |
| 700 | 1 | _ | |a Kelbling, Matthias |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Terhorst, Dennis |0 P:(DE-Juel1)169778 |b 2 |u fzj |
| 700 | 1 | _ | |a More, Heather |0 P:(DE-Juel1)190225 |b 3 |u fzj |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 4 |u fzj |
| 700 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 5 |u fzj |
| 700 | 1 | _ | |a Thober, Stephan |0 P:(DE-HGF)0 |b 6 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1014307 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)191583 |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)162130 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5232 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-9-20201008 |k IAS-9 |l Materials Data Science and Informatics |x 3 |
| 980 | _ | _ | |a poster |
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| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-9-20201008 |
| 980 | _ | _ | |a UNRESTRICTED |
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