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@INPROCEEDINGS{Zehl:154644,
      author       = {Zehl, Lyuba and Denker, Michael and Stoewer, Adrian and
                      Jaillet, Florent and Brochier, Thomas and Riehle, Alexa and
                      Wachtler, Thomas and Grün, Sonja},
      title        = {{M}etadata management for complex neurophysiological
                      experiments},
      reportid     = {FZJ-2014-03928},
      year         = {2014},
      note         = {Acknowledgments: SMHB, HBP (EU grant 604102), G-Node (BMBF
                      Grant 01GQ1302), BrainScaleS (EU Grant269912), ANR-GRASP,
                      $Neuro_IC2010,$ CNRS-PEPS, Riken-CNRS Research Agreement.
                      References: [1] Grewe J, Wachtler T, and Benda J (2011)
                      Front. Neuroinform. 5:16[2] Riehle A, Wirtssohn S, Grün S,
                      and Brochier T (2013) Front. Neural Circuits 7:48},
      abstract     = {Technological progress in neuroscience allows recording
                      from tens to hundreds of neuronssimultaneously, both in
                      vitro and in vivo, using various recording techniques (e.g.,
                      multi-electrode recordings) and stimulation methods (e.g.,
                      optogenetics). In addition, recordingscan be performed in
                      parallel from multiple brain areas, under more or less
                      naturalconditions in (almost) freely behaving animals.
                      Consequently, electrophysiologicalexperiments become
                      increasingly complex. Moreover, to disentangle the
                      relationship tobehavior, it is necessary to document animal
                      training, experimental procedures, and detailsof the setup
                      along with recorded neuronal and behavioral data.
                      Considering this, availabilityof the information about the
                      experimental conditions, commonly referred to as metadata,
                      isof extreme relevance for reproducible data analysis and
                      correct interpretation of results.Typically, experimenters
                      have developed their own personal procedure to document
                      theirexperiment, allowing at best other members of the lab
                      to share data and metadata.However, at the latest when it
                      comes to data sharing across labs, details may be missed.
                      Inparticular if collaborating groups have different
                      scientific backgrounds, implicit knowledge isoften not
                      communicated. In order to perform interpretable analysis,
                      each data set shouldtherefore clearly link to metadata
                      annotations about experimental conditions such as
                      theperformed task, quality of the data, or relevant
                      preprocessing (e.g., spike sorting).In order to provide
                      metadata in an organized, easily accessible, but also
                      machine-readableway, an XML based file format, odML (open
                      metadata Markup Language), was proposed [1].Here we will
                      demonstrate the usefulness of standardized metadata
                      collections for handlingthe data and their analysis in the
                      context of a complex behavioral (reach to grasp)experiment
                      with neuronal recordings from a large number of electrodes
                      (Utah array)delivering massively parallel spike and LFP data
                      [2]. We illustrate the conceptual design ofan odML metadata
                      structure and provide a practical introduction on how to
                      generate anodML file. In addition, we offer odML templates
                      to facilitate the usage of odML acrossdifferent laboratories
                      and experimental contexts. We demonstrate hands-on the
                      advantagesof using odML to screen large numbers of data sets
                      according to selection criteria (e.g.,behavioral
                      performance) relevant for subsequent analyses (see companion
                      posters byDenker et al. and Riehle et al.). Well organized
                      metadata management is a key componentto guarantee
                      reproducibility of experiments and to track provenance of
                      performed analyses.},
      month         = {Jun},
      date          = {2014-06-25},
      organization  = {AREADNE 2014, Santorini (Greece), 25
                       Jun 2014 - 29 Jun 2014},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / BRAINSCALES - Brain-inspired multiscale
                      computation in neuromorphic hybrid systems (269921) / HBP -
                      The Human Brain Project (604102) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017) / 89571 -
                      Connectivity and Activity (POF2-89571)},
      pid          = {G:(DE-HGF)POF2-331 / G:(EU-Grant)269921 /
                      G:(EU-Grant)604102 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-HGF)POF2-89571},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/154644},
}