001052977 001__ 1052977
001052977 005__ 20260128204149.0
001052977 0247_ $$2doi$$a10.5281/ZENODO.15110701
001052977 037__ $$aFZJ-2026-01327
001052977 041__ $$aEnglish
001052977 1001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b0$$eCorresponding author$$ufzj
001052977 245__ $$aMetadata Archivist - Proof of Concept
001052977 260__ $$c2025
001052977 3367_ $$2DCMI$$aSoftware
001052977 3367_ $$0PUB:(DE-HGF)33$$2PUB:(DE-HGF)$$aSoftware$$bsware$$msware$$s1769603113_7433
001052977 3367_ $$2BibTeX$$aMISC
001052977 3367_ $$06$$2EndNote$$aComputer Program
001052977 3367_ $$2ORCID$$aOTHER
001052977 3367_ $$2DataCite$$aSoftware
001052977 520__ $$aThis archive contains an example implementation of a knowledge generation workflow. Here the questions to answer relate to benchmarking and validation of a given simulator and model. In this workflow, 10 simulation runs of a model are performed while collecting metadata. Then the generated data is post processed to ensure an aggregation of simulation statistics over the 10 different runs. These statistics alongside system information are used to structure the metadata. Both raw and post-processed, data and metadata are stored in a MongoDB instance. This instance can then be queried to plot the benchmarks and validation results.
001052977 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001052977 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001052977 588__ $$aDataset connected to DataCite
001052977 650_7 $$2Other$$aSnakemake workflow
001052977 650_7 $$2Other$$aMetadata processing
001052977 650_7 $$2Other$$aSimulation workflow
001052977 7001_ $$0P:(DE-HGF)0$$aKelbling, Matthias$$b1
001052977 7001_ $$0P:(DE-Juel1)190225$$aMore, Heather$$b2$$eResearcher
001052977 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b3$$eResearcher
001052977 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b4$$eResearcher
001052977 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b5$$eResearcher
001052977 7001_ $$0P:(DE-HGF)0$$aThober, Stephan$$b6
001052977 773__ $$a10.5281/ZENODO.15110701
001052977 909CO $$ooai:juser.fz-juelich.de:1052977$$pVDB
001052977 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191583$$aForschungszentrum Jülich$$b0$$kFZJ
001052977 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144807$$aForschungszentrum Jülich$$b3$$kFZJ
001052977 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich$$b4$$kFZJ
001052977 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b5$$kFZJ
001052977 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001052977 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$$x1
001052977 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001052977 980__ $$asware
001052977 980__ $$aVDB
001052977 980__ $$aI:(DE-Juel1)IAS-6-20130828
001052977 980__ $$aUNRESTRICTED