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001027366 037__ $$aFZJ-2024-03793
001027366 1001_ $$0P:(DE-Juel1)180365$$aKöhler, Cristiano$$b0$$eCorresponding author$$ufzj
001027366 1112_ $$aNEST Conference 2024$$cvirtual$$d2024-06-17 - 2024-06-18$$wvirtual
001027366 245__ $$aAn approach to handle provenance-tracked analysis of NEST simulations using Alpaca
001027366 260__ $$c2024
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001027366 520__ $$aNEST simulations are typically executed by a script that configures and runs the simulation. Despite recent improvements in NEST 3.x, where file headers specify the detailed origin of the outputs, users still must interpret the data with respect to the simulation setup. This information is difficult to convey, especially in collaborative contexts with shared simulation results. Moreover, during the explorative process of scientific discovery, results may change without warning when details of the simulation are changed, which could lead to wrong interpretations by collaborators who are unaware of such changes. Therefore, we face two challenges: results are stored in data objects without metadata that describe their role in the simulation, and the simulation outputs are not linked to a description of their provenance with respect to the simulation building.Here we present concepts to tackle both challenges when using the NEST Python interface. We consider a typical simulation experiment and subsequent data analysis using the Elephant (doi:10.5281/zenodo.1186602; RRID:SCR_003833) toolbox [1]. First, we show how data from a NEST simulation can be represented with data objects annotated with simulation details using the Neo library [2]. Second, we demonstrate how the software Alpaca (doi:10.5281/zenodo.10276510; RRID:SCR_023739) can capture workflow provenance when running a simulation (see Figure) [3]. The two approaches allow the semantic description of the simulation experiment that contributes to the FAIR principles [4] by improving the findability of results through detailed provenance, supporting interoperability through a standardized data model, and promoting reuse of simulation data through enhanced data description. References [1] Denker, M., Yegenoglu, A., Grün, S., 2018. Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics 2018, P19. doi:10.12751/incf.ni2018.0019 [2] Garcia, S., Guarino, D., Jaillet, F., Jennings, T., Pröpper, R., Rautenberg, P.L., Rodgers, C.C., Sobolev, A., Wachtler, T., Yger, P., Davison, A.P., 2014. Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8, 10. https://doi.org/10.3389/fninf.2014.00010 [3]	Köhler, C.A., Ulianych, D., Grün, S., Decker, S., Denker, M., 2023. Facilitating the sharing of electrophysiology data analysis results through in-depth provenance capture. https://doi.org/10.48550/arXiv.2311.09672 [4] Wilkinson, M.D., Dumontier, M., Aalbersberg, Ij.J., Appleton, G., Axton, M., Baak, A., Blomberg, N. et al., 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 160018. https://doi.org/10.1038/sdata.2016.18
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001027366 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x2
001027366 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
001027366 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
001027366 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x5
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001027366 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x7
001027366 7001_ $$0P:(DE-Juel1)190659$$aKern, Moritz$$b1$$ufzj
001027366 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b2$$ufzj
001027366 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b3$$ufzj
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001027366 9141_ $$y2024
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001027366 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001027366 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
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