001030590 001__ 1030590
001030590 005__ 20241014074411.0
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001030590 037__ $$aFZJ-2024-05343
001030590 041__ $$aEnglish
001030590 088__ $$2arXiv$$a2408.17309
001030590 1001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b0$$eCorresponding author$$ufzj
001030590 245__ $$aMetadata practices for simulation workflows
001030590 260__ $$barXiv$$c2024
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001030590 520__ $$aComputer simulations are an essential pillar of knowledge generation in science.Understanding, reproducing, and exploring the results of simulations relies on tracking and organizing metadata describing numerical experiments.However, the models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata.Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user.These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata.As a proof of concept, we develop the Archivist, a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology.Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring.They support sustainable numerical workflows, facilitating reproducibility and data reuse in generic simulation-based research.
001030590 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001030590 536__ $$0G:(DE-HGF)POF4-1121$$a1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)$$cPOF4-112$$fPOF IV$$x1
001030590 536__ $$0G:(DE-Juel-1)ZT-I-PF-3-026$$aMetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026)$$cZT-I-PF-3-026$$x2
001030590 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x3
001030590 536__ $$0G:(DE-Juel-1)HiRSE_PS-20220812$$aHelmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812)$$cHiRSE_PS-20220812$$x4
001030590 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x5
001030590 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x6
001030590 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x7
001030590 536__ $$0G:(EU-Grant)800858$$aICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)$$c800858$$fH2020-SGA-INFRA-FETFLAG-HBP$$x8
001030590 588__ $$aDataset connected to DataCite
001030590 650_7 $$2Other$$aInformation Retrieval (cs.IR)
001030590 650_7 $$2Other$$aFOS: Computer and information sciences
001030590 7001_ $$0P:(DE-HGF)0$$aKelbling, Matthias$$b1
001030590 7001_ $$0P:(DE-Juel1)190225$$aMore, Heather$$b2$$ufzj
001030590 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b3$$ufzj
001030590 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b4$$ufzj
001030590 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b5$$ufzj
001030590 7001_ $$0P:(DE-HGF)0$$aThober, Stephan$$b6
001030590 773__ $$a10.48550/arXiv.2408.17309
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001030590 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191583$$aForschungszentrum Jülich$$b0$$kFZJ
001030590 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)191583$$aRWTH Aachen$$b0$$kRWTH
001030590 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aDepartment of Computational Hydrosystems, Helmholtz-Centre for Environmental Research, Leipzig, Germany$$b1
001030590 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190225$$aForschungszentrum Jülich$$b2$$kFZJ
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001030590 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b5$$kFZJ
001030590 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aDepartment of Computational Hydrosystems, Helmholtz-Centre for Environmental Research, Leipzig, Germany$$b6
001030590 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001030590 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1121$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x1
001030590 920__ $$lyes
001030590 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001030590 9201_ $$0I:(DE-Juel1)IAS-9-20201008$$kIAS-9$$lMaterials Data Science and Informatics$$x1
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