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@INPROCEEDINGS{Villamar:1014307,
      author       = {Villamar, Jose and Kelbling, Matthias and Terhorst, Dennis
                      and More, Heather and Tetzlaff, Tom and Senk, Johanna and
                      Thober, Stephan},
      title        = {{T}racking large-scale simulations through unified metadata
                      handling},
      reportid     = {FZJ-2023-03229},
      year         = {2022},
      abstract     = {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},
      month         = {Oct},
      date          = {2022-10-05},
      organization  = {Helmholtz Metadata Collaboration
                       conference, Virtual (Germany), 5 Oct
                       2022 - 6 Oct 2022},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10 / IAS-9},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5232 - Computational Principles (POF4-523) / MetaMoSim -
                      Generic metadata management for reproducible
                      high-performance-computing simulation workflows - MetaMoSim
                      (ZT-I-PF-3-026) / HBP SGA3 - Human Brain Project Specific
                      Grant Agreement 3 (945539) / ACA - Advanced Computing
                      Architectures (SO-092) / Brain-Scale Simulations
                      $(jinb33_20220812)$},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-Juel-1)ZT-I-PF-3-026 /
                      G:(EU-Grant)945539 / G:(DE-HGF)SO-092 /
                      $G:(DE-Juel1)jinb33_20220812$},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1014307},
}