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@INPROCEEDINGS{Denker:851308,
      author       = {Denker, Michael and Yegenoglu, Alper and Grün, Sonja},
      title        = {{C}ollaborative {HPC}-enabled workflows on the {HBP}
                      {C}ollaboratory using the {E}lephant framework},
      reportid     = {FZJ-2018-04998},
      year         = {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.},
      month         = {Aug},
      date          = {2018-08-09},
      organization  = {Neuroinformatics 2018, Montreal
                       (Canada), 9 Aug 2018 - 10 Aug 2018},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / SMHB - Supercomputing and Modelling for the Human
                      Brain (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain Project
                      Specific Grant Agreement 1 (720270)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)785907 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)720270},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/851308},
}