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@INPROCEEDINGS{Sprenger:827697,
      author       = {Sprenger, Julia and Yegenoglu, Alper and Grün, Sonja and
                      Denker, Michael},
      title        = {{S}haring {E}lectrophysiological {D}ata and {M}etadata on
                      {HBP} {P}latforms – {A}n {E}xample {C}ollaboratory
                      {W}orkflow},
      reportid     = {FZJ-2017-01810},
      year         = {2017},
      abstract     = {IntroductionThe Human Brain Project (HBP) [1] aims at
                      creating and operating a European scientific Research
                      Infrastructurefor the neurosciences. A main goal is to
                      gather, organise and disseminate data describing the brain
                      and itsdiseases on the basis of experimental as well as
                      simulated data. Therefore a lot of effort is put into
                      thedevelopment of tools for data registration, storage,
                      access and sharing. The most prominent data type
                      availablethrough the HBP to date are anatomical data and
                      data describing single cell dynamics. However, the need
                      toinclude experimental and simulated large-scale functional
                      data, and in particular, electrophysiological activitydata,
                      has been widely recognized. Such data are primarily created
                      in neuronal network simulations as a core partof the HBP
                      effort. An adapted strategy for data curation is needed, as
                      the established workflows are not yetconsidering the
                      integration of electrophysiological data.Another goal of the
                      HBP is to provide a platform to facilitate collaborative
                      research. For this the Collaboratory[2] has been set up - a
                      web portal which provides a common online workspace (Collab)
                      for all members of acollaboration team. The Collab combines
                      tools which are developed in the context of the HBP
                      platforms and bythis provides access to high performance
                      computing (HPC), simulation tools and shared datasets. In
                      particular, itis thought to act as a platform for
                      interactive data analysis. Next to specialized tools which
                      can be integrated ordeveloped for the Collab, generic
                      analysis can be performed by using Python Jupyter Notebooks
                      [3].MotivationThus, data used in the HBP must be prepared in
                      two respects: (i) integration into the HBP databases and
                      (ii) usein analysis processes on the Collab. Due to the
                      diversity of data (types) in electrophysiological
                      experiments,standardized data and metadata models, and tools
                      operating on these models, have only started to be
                      developed[4,5]. A crucial step in further advancing and
                      disseminating these efforts, and to ensure that
                      individualcomponents can be efficiently linked, is to embed
                      these tools into workflows that recreate the actual
                      scientificroutine of a research project.MethodsHere we
                      consider the combination of 4 open source projects attempt
                      to address these issues:• Neo provides a generic
                      standardized representation for electrophysiological data,
                      which is able tointerface with a range of
                      electrophysiological data formats [6].• The
                      Electrophysiology Analysis Toolkit (Elephant) offers methods
                      ranging from the analysis of spikedata to popluation
                      signals, e.g., local field potentials. Elephant is based on
                      the Neo data representationformat [7].• The open metadata
                      Markup Language (odML) is based on XML and offers a
                      hierarchical structure tostore metadata related to
                      electrophysiological experiments [8].• NIX is a file
                      format designed to combine electrophysiological data and
                      metadata in a single,standardized format [9], and is linked
                      to both the Neo and odML data models.Results $\&$
                      DiscussionHere we show in 3 stages how these open source
                      programs can interact to form a structured,
                      comprehensibleworkflow for electrophysiological spike data.
                      Firstly, we demonstrate the loading of data and metadata and
                      theirintegration into a single data representation. For this
                      we start with the conversion of the raw data into a
                      Neoobject, which is then further annotated with metadata
                      information. To obtain the metadata information,
                      primarymetadata are first converted to the odML format using
                      the odMLtables tool [10], which is then queried for
                      theannotating the Neo object. In a second stage, the final
                      Neo object is saved as NIX file, which preserves the
                      data-metadata relations formed in the Neo structure. In a
                      last stage, the data structure from the NIX file is used
                      forexemplary analysis of the spiking activity using
                      Elephant.In additon to the implementation of such a workflow
                      in Python for use on a local machine, we demonstrate
                      thesetup of the workflow on the Collaboratory of the HBP and
                      indicate how the interaction of multiple
                      collaborationpartners can benefit from the workflow realized
                      in this setting. In addition, we discuss how the data, in
                      particularthe odML-based metadata, can be used for
                      integration in the registration processes developed by
                      theNeuroinformatics platforms.References:[1]
                      https://www.humanbrainproject.eu[2]
                      http://www.collab.humanbrainproject.eu[3]
                      http://jupyter.org/[4] Denker M., Grün S. (2016) Designing
                      Workflows for the Reproducible Analysis of
                      Electrophysiological Data. In: Amunts K., Grandinetti L.,
                      Lippert T., Petkov N. (eds) Brain-Inspired Computing.
                      BrainComp 2015. Lecture Notes in Computer Science, vol
                      10087. Springer, Cham[5] Zehl, L., Jaillet, F., Stoewer, A.,
                      Grewe, J., Sobolev, A., Wachtler, T., Brochier, T., Riehle,
                      A., Denker, M., $\&$ Grün, S. Handling Metadata in a
                      Neurophysiology Laboratory. Frontiers in Neuroinformatics.
                      10, 26.[6] Garcia S., Guarino D., Jaillet F., Jennings T.R.,
                      Pröpper R., Rautenberg P.L., Rodgers C., Sobolev
                      A.,Wachtler T., Yger P. and Davison A.P. (2014) Neo: an
                      object model for handling electrophysiology data in multiple
                      formats. Frontiers in Neuroinformatics 8:10:
                      doi:10.3389/fninf.2014.00010[7]
                      https://github.com/INM-6/elephant[8] Grewe, J., Wachtler,
                      T., $\&$ Benda, J. (2011). A Bottom-up Approach to Data
                      Annotation in Neurophysiology. Frontiers in
                      Neuroinformatics, 5, 16.[9]
                      https://github.com/G-Node/nix[10]
                      https://github.com/INM-6/python-odmltables},
      month         = {Feb},
      date          = {2017-02-08},
      organization  = {First HBP Student Conference, Vienna
                       (Austria), 8 Feb 2017 - 10 Feb 2017},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6},
      cid          = {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)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)720270 / G:(GEPRIS)238707842 /
                      G:(GEPRIS)237833830},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/827697},
}