% 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{Sprenger:840027,
      author       = {Sprenger, Julia and Yegenoglu, Alper and Grün, Sonja and
                      Denker, Michael},
      title        = {{M}anagement of data and metadata - an exemplary
                      electrophysiological workflow for collaborative data
                      analysis},
      reportid     = {FZJ-2017-07593},
      year         = {2017},
      abstract     = {The complexity of neuroscientific experiments and their
                      analysis has grown to a degree where special effort
                      andattention is required to guarantee their reproducibility.
                      In addition, collaborations across different laboratoriesand
                      countries are becoming a standard work setting, which
                      increases the need for comprehensivedocumentation of data
                      and metadata, and explicit, formal descriptions of the data
                      analysis process. Theavailability of software tools that
                      support scientists in the various steps of this process is
                      therefore indispensable[Denker and Grün (2016) In:
                      Brain-Inspired Computing: Brain Comp 2015, Springer].
                      Moreover, it is necessary toestablish the workflows that
                      link the various tools, and to develop simple interfaces for
                      them.Here we demonstrate how such a reproducible,
                      structured, and comprehensible workflow for
                      anelectrophysiological experiment, covering the preparation,
                      annotation and analysis data, can be set up in
                      acollaborative environment by the combination of multiple
                      state-of-the-art open-source projects. The workflowfeatures
                      the Electrophysiology Analysis Toolkit (Elephant), which
                      represents the central analysis resourceoffering methods
                      ranging from the analysis of ensemble spike data to
                      population signals, such as local fieldpotentials
                      [http://neuralensemble.org/elephant/]. Elephant is based on
                      the generic standardized datarepresentation for
                      electrophysiological data provided by the Neo library
                      [Garcia et al. (2014) Front Neuroinf8:10]. In addition, Neo
                      is able to interface with a range of data formats commonly
                      used in electrophysiology. Theopen metadata Markup Language
                      (odML) is used as the hierarchical structure to store
                      metadata related toelectrophysiological experiments [Grewe
                      et al. (2011) Front Neuroinf 5:16]. Furthermore, odMLtables
                      extendsthe accessibility of odML by providing an interface
                      to a tabular metadata representation, e.g., using
                      Excel[https://github.com/INM-6/python-odmltables]. Finally,
                      NIX is a newly developed scheme designed to
                      combineelectrophysiological data and metadata in a single,
                      standardized format [https://github.com/G-Node/nix],
                      andlinks the Neo and odML data models. We discuss multiple
                      mechanisms that allow to describe the workflow itself,and
                      show how it may be implemented, e.g., using the
                      Collaboratory of the Human Brain
                      Project[https://collab.humanbrainproject.eu]. While focusing
                      on electrophysiology, many concepts of this workflow arealso
                      transferable to different types of experimental
                      environments.},
      month         = {Nov},
      date          = {2017-11-11},
      organization  = {The 47th annual meeting of the Society
                       for Neuroscience, Washington DC (USA),
                       11 Nov 2017 - 15 Nov 2017},
      subtyp        = {After Call},
      cin          = {INM-10 / INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {571 - Connectivity and Activity (POF3-571) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain Project
                      Specific Grant Agreement 1 (720270) / 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) / DFG project 238707842 - Kausative Mechanismen
                      mesoskopischer Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)720270 / G:(GEPRIS)238707842 /
                      G:(GEPRIS)237833830 / G:(GEPRIS)238707842},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/840027},
}