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@INBOOK{Amunts:826140,
      author       = {Denker, Michael and Grün, Sonja},
      editor       = {Amunts, Katrin and Grandinetti, Lucio and Lippert, Thomas
                      and Petkov, Nicolai},
      title        = {{D}esigning {W}orkflows for the {R}eproducible {A}nalysis
                      of {E}lectrophysiological {D}ata},
      volume       = {10087},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2017-00395},
      isbn         = {978-3-319-50861-0 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {58 - 72},
      year         = {2016},
      comment      = {Brain-Inspired Computing / Amunts, Katrin (Editor) ; Cham :
                      Springer International Publishing, 2016, Chapter 5 ; ISSN:
                      0302-9743=1611-3349 ; ISBN:
                      978-3-319-50861-0=978-3-319-50862-7},
      booktitle     = {Brain-Inspired Computing / Amunts,
                       Katrin (Editor) ; Cham : Springer
                       International Publishing, 2016, Chapter
                       5 ; ISSN: 0302-9743=1611-3349 ; ISBN:
                       978-3-319-50861-0=978-3-319-50862-7},
      abstract     = {The workflows that cover the experimental recording of
                      neuronal data up to the publication of figures that
                      illustrate neuroscientific analysis results are interwoven
                      and complex. Unfortunately, current implementations of such
                      workflows of electrophysiological research are far from
                      being automatized, and software supporting such a goal is
                      largely in development or missing. In consequence, the level
                      of reproducibility of data analysis is poor compared to
                      other scientific disciplines. Although the problem is
                      well-known and leads to ineffective, unsustainable science,
                      there is no solution in sight in terms of a complete,
                      provenance-tracked workflow. Here, we outline principle
                      challenges that complicate the design of workflows for
                      electrophysiological research. We detail how existing tools
                      can be integrated to form partial workflows which address
                      some of the challenges. On the basis of a concrete workflow
                      implementation we discuss open questions and urgently needed
                      software components.},
      organization  = {BrainComp 2015,},
      cin          = {INM-6 / IAS-6 / INM-1 / JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)JSC-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP - The Human Brain Project
                      (604102) / BRAINSCALES - Brain-inspired multiscale
                      computation in neuromorphic hybrid systems (269921) / DFG
                      project 238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / DFG project
                      237833830 - Optogenetische Analyse der für kognitive
                      Fähigkeiten zuständigen präfrontal-hippokampalen
                      Netzwerke in der Entwicklung (237833830)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)604102 / G:(EU-Grant)269921 /
                      G:(GEPRIS)238707842 / G:(GEPRIS)237833830},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-319-50862-7_5},
      url          = {https://juser.fz-juelich.de/record/826140},
}