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| Home > Publications database > An approach to handle provenance-tracked analysis of NEST simulations using Alpaca |
| Conference Presentation (After Call) | FZJ-2024-03793 |
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2024
Abstract: NEST 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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