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@INPROCEEDINGS{Zehl:155650,
      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        = {{O}rganizing {M}etadata of {C}omplex {N}europhysiological
                      {E}xperiments},
      issn         = {1662-5196},
      reportid     = {FZJ-2014-04707},
      year         = {2014},
      note         = {Acknowledgements: Supported by the Helmholtz Portfolio
                      Supercomputing and Modeling for the Human Brain (SMHB),
                      Human Brain Project (HBP, EU grant 604102), G-Node (BMBF
                      Grant 01GQ1302), BrainScaleS (EU Grant 269912), ANR-GRASP.
                      References: [1] Grewe J, Wachtler T, and Benda J (2011) A
                      bottom-up approach to data annotation in neurophysiology.
                      Front. Neuroinform. 5:16, [2] Riehle A, Wirtssohn S, Grün
                      S, and Brochier T (2013) Mapping the spatio-temporal
                      structure of motor cortical LFP and spiking activities
                      during reach-to-grasp movements. Front. Neural Circuits
                      7:48},
      abstract     = {Technological progress in neuroscience allows recording
                      from tens to hundreds of neurons simultaneously, both in
                      vitro and in vivo, using various recording techniques (e.g.,
                      multi-electrode recordings) and stimulation methods (e.g.,
                      optogenetics). In addition, recordings can be performed in
                      parallel from multiple brain areas, under more or less
                      natural conditions in (almost) freely behaving animals.
                      Consequently, electrophysiological experiments become
                      increasingly complex. Moreover, to disentangle the
                      relationship to behavior, it is necessary to document animal
                      training, experimental procedures, and details of the setup
                      along with recorded neuronal and behavioral data. Given
                      these various sources of complexity within an experiment,
                      the availability of such information about the experiment,
                      commonly referred to as metadata, is of extreme relevance
                      for reproducible data analysis and correct interpretation of
                      results. Typically, experimenters have developed their own
                      personal procedure to document their experiment, 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. In particular if collaborating
                      groups have different scientific backgrounds, implicit
                      knowledge is often not communicated. In order to perform
                      interpretable analysis of the data, each data set should
                      therefore clearly link to metadata annotations about
                      experimental conditions such as the performed 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-readable way, an XML
                      based file format, odML (open metadata Markup Language), was
                      proposed [1]. Here, we will demonstrate the usefulness of
                      standardized metadata collections for handling the 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 of an odML metadata structure and provide
                      a practical introduction on how to generate an odML file. In
                      addition, we offer odML templates to facilitate the usage of
                      odML across different laboratories and experimental
                      contexts. We demonstrate hands-on the advantages of using
                      odML to screen large numbers of data sets according to
                      selection criteria (e.g., behavioral performance) relevant
                      for subsequent analyses (see companion posters by Denker et
                      al.). Well organized metadata management is a key component
                      to guarantee reproducibility of experiments and to track
                      provenance of performed analyses.},
      month         = {Jul},
      date          = {2014-07-01},
      organization  = {INM Retreat 2014, Juelich (Germany), 1
                       Jul 2014 - 2 Jul 2014},
      cin          = {INM-6 / IAS-6},
      ddc          = {610},
      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) / 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) / 89571
                      - Connectivity and Activity (POF2-89571)},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)604102 / G:(EU-Grant)269921 /
                      G:(DE-HGF)POF2-89571},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/155650},
}