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001     851308
005     20240313095005.0
037 _ _ |a FZJ-2018-04998
041 _ _ |a English
100 1 _ |a Denker, Michael
|0 P:(DE-Juel1)144807
|b 0
|e Corresponding author
|u fzj
111 2 _ |a Neuroinformatics 2018
|c Montreal
|d 2018-08-09 - 2018-08-10
|w Canada
245 _ _ |a Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework
260 _ _ |c 2018
336 7 _ |a Abstract
|b abstract
|m abstract
|0 PUB:(DE-HGF)1
|s 1540385005_3258
|2 PUB:(DE-HGF)
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Abstract
|2 DataCite
336 7 _ |a OTHER
|2 ORCID
520 _ _ |a 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.
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 0
536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
|0 G:(EU-Grant)785907
|c 785907
|f H2020-SGA-FETFLAG-HBP-2017
|x 1
536 _ _ |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)
|0 G:(DE-Juel1)HGF-SMHB-2013-2017
|c HGF-SMHB-2013-2017
|f SMHB
|x 2
536 _ _ |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)
|0 G:(EU-Grant)720270
|c 720270
|f H2020-Adhoc-2014-20
|x 3
700 1 _ |a Yegenoglu, Alper
|0 P:(DE-Juel1)161462
|b 1
|u fzj
700 1 _ |a Grün, Sonja
|0 P:(DE-Juel1)144168
|b 2
|u fzj
856 4 _ |u https://doi.org/10.12751/incf.ni2018.0019
909 C O |o oai:juser.fz-juelich.de:851308
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
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|l Decoding the Human Brain
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|v Theory, modelling and simulation
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914 1 _ |y 2018
920 _ _ |l no
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
|k INM-6
|l Computational and Systems Neuroscience
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920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
|k IAS-6
|l Theoretical Neuroscience
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920 1 _ |0 I:(DE-Juel1)INM-10-20170113
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|l Jara-Institut Brain structure-function relationships
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980 _ _ |a abstract
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a I:(DE-Juel1)INM-10-20170113
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)IAS-6-20130828

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