% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Denker:892654,
      author       = {Denker, Michael},
      title        = {{O}rchestrating analysis workflows using {E}lephant and
                      {N}eo},
      reportid     = {FZJ-2021-02240},
      year         = {2021},
      abstract     = {In order to deal with the increasing complexity of data
                      from electrophysiological experiments and spiking neural
                      network simulations, concepts and tools to perform data
                      acquisition and analysis in a reproducible fashion are in
                      high demand. Here, following [1], we demonstrate open-source
                      software solutions that support such workflows, each
                      addressing different aspects of the process: (i)
                      electrophysiological data of different origins are
                      represented in a standard description using Neo
                      $(RRID:SCR_000634)$ [2], (ii) complex metadata accumulating
                      in the electrophysiological experiment [3] are organized [4]
                      using the open metadata markup language (odML,
                      $RRID:SCR_001376)$ [5], and (iii) analysis is performed
                      using the Electrophysiology Analysis Toolkit (Elephant,
                      $RRID:SCR_003833,$ http://python-elephant). Elephant acts as
                      the central modular software component that provides generic
                      library functions to perform standard and advanced analysis
                      methods for parallel, multi-scale activity data. We outline
                      how the integration of such workflows into the EBRAINS
                      infrastructure facilitates interdisciplinary, collaborative
                      work including access to high-performance computing. In
                      particular, we demonstrate how such tools form the basis for
                      rigorous approaches to model validation [6].},
      month         = {May},
      date          = {2021-05-19},
      organization  = {NeuroFrance 2021, Online (Online), 19
                       May 2021 - 21 May 2021},
      cin          = {INM-6 / INM-10 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {523 - Neuromorphic Computing and Network Dynamics
                      (POF4-523) / 5235 - Digitization of Neuroscience and
                      User-Community Building (POF4-523) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / HAF -
                      Helmholtz Analytics Framework (ZT-I-0003) / HDS LEE -
                      Helmholtz School for Data Science in Life, Earth and Energy
                      (HDS LEE) (HDS-LEE-20190612) / HMC - Helmholz Metadata
                      Collaboration $((DE-HGF)HMC_20200306)$},
      pid          = {G:(DE-HGF)POF4-523 / G:(DE-HGF)POF4-5235 /
                      G:(EU-Grant)945539 / G:(DE-HGF)ZT-I-0003 /
                      G:(DE-Juel1)HDS-LEE-20190612 / $G:(DE-HGF)HMC_20200306$},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/892654},
}