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| Hauptseite > Publikationsdatenbank > Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework |
| Hauptseite > Publikationsdatenbank > Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework |
| Abstract | FZJ-2018-04998 |
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2018
Abstract: The degree of complexity in analyzing massively parallel, heterogeneous data from electrophysiological experiments and network simulations requires work to be performed in larger, multi-disciplinary collaborations that require the availability of robust workflows [1,2] and powerful computing resources [3]. The Human Brain Project (HBP) aims at creating and operating a scientific research infrastructure for the neurosciences to address such needs for integrative software environments. At its core, the HBP features the Collaboratory, a web-based platform to jointly implement research projects. Powerful as this approach is in theory, it is less clear how these developments are most effectively integrated into the daily work routines of the researchers analyzing the data.Here, we show how diverse tools can be successfully combined into a collaborative analysis workflow hosted on the HBP Collaboratory, reproducing [5]. Data are represented in the Neo framework [6], complex metadata [7] are managed using the odML standard [8], and the main analysis is performed by the Elephant library (http://python-elephant.org). These domain-specific tools are combined with generic tools (e.g., version control systems) to form a blueprint for performing collaborative work including access to high-performance computing. Finally, we outline how these building blocks can be assembled into formalized workflows to support reproducible research, e.g., the validation of network simulations.References:[1] Badia, R., Davison, A., Denker, M., Giesler, A., Gosh, S., Goble, C., Grewe, J., Grün, S., Hatsopoulos, N., LeFranc, Y. and Muller, J., 2015. INCF Program on Standards for data sharing: new perspectives on workflows and data management for the analysis of electrophysiological data. https://www. incf.org/about-us/history/incf-scientific-workshops.[2] Denker, M. and Grün, S., 2015. Designing workflows for the reproducible analysis of electrophysiological data. In International Workshop on Brain-Inspired Computing (pp. 58-72). Springer, Cham.[3] Bouchard, K.E., Aimone, J.B., Chun, M., Dean, T., Denker, M., Diesmann, M., Donofrio, D.D., Frank, L.M., Kasthuri, N., Koch, C., et al. (2016). High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. Neuron 92, 628–631.[4] Senk, J., Yegenoglu, A. et al., 2016. A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC. In Jülich Aachen Research Alliance (JARA) High-Performance Computing Symposium (pp. 243-256). Springer, Cham.[5] Denker, M., Zehl, L., Kilavik, B.E., Diesmann, M., Brochier, T., Riehle, A., and Grün, S. (2018). LFP beta amplitude is linked to mesoscopic spatio-temporal phase patterns. Scientific Reports 8, 5200.[6] Garcia, S., Guarino, D., Jaillet, F., Jennings, T.R., Pröpper, R., Rautenberg, P.L., Rodgers, C., Sobolev, A., Wachtler, T., Yger, P. and Davison, A.P., 2014. Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in neuroinformatics, 8, p.10.
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