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@INPROCEEDINGS{Sprenger:826006,
      author       = {Sprenger, Julia and Canova, Carlos and Pick, Jana and Zehl,
                      Lyuba and Grün, Sonja and Denker, Michael},
      title        = {od{ML}-tables: {A} graphical approach to metadata
                      management based on od{ML}},
      reportid     = {FZJ-2017-00278},
      year         = {2016},
      abstract     = {Central to quantitative sciences is the measurement of data
                      with the aim to capture empirical, experimental observations
                      or the outcome of model simulations. These primary data, and
                      derived data resulting from post-processing steps, are
                      always accompanied by information about the origin of the
                      data and the circumstances of recording. Such information is
                      typically called metadata. It is relevant to facilitate the
                      communication between members of a project and is essential
                      for the interpretation of the data. It also enables queries
                      to answer scientific questions that researchers did not
                      previously consider (e.g. transversal studies) and is one of
                      the main components for implementing reproducibility [1]. In
                      neuroscience, and in particular experimental
                      neurophysiology, the development of approaches to metadata
                      management are still an ongoing effort [2]. A promising
                      metadata framework in this field is odML (open metadata
                      Markup Language) [3]. This XML-based language is designed to
                      represent complex metadata collections hierarchically
                      organized as key-value pairs.In practice however, embedding
                      odML-based metadata within multiple collaborations of INM-6
                      [4] revealed that setting up an odML document involves
                      extensive programming and the manual entry of metadata into
                      it during or after the experiment is cumbersome. The lack of
                      software support effectively prevented our experimental
                      partners from using odML to capture metadata into one
                      coherent collection. To address this shortcoming, we
                      developed odML-tables, a software solution that bridges the
                      gap between hierarchical odML and a tabular representation
                      of metadata and which is suitable for easy
                      editing.odML-tables is accessible by a graphical user
                      interface as well as from a Python interface and offers
                      multiple features which simplify the generation and
                      modification of odML metadata files:*Generation of a
                      template (tabular) structure facilitating the initial design
                      of an odML structure*Conversion of existing odML files to
                      more easily accessible tabular formats (.xls, .csv) in order
                      to enable manual entry and modifications using common
                      graphical software tools (e.g., Microsoft Excel,
                      LibreOffice).*Reverse transformation of the modified data in
                      a standardized tabular format to the odML format*Filtering
                      odML metadata by defined search criteria to generate
                      overview files or simplify access parts of a complex odML
                      structure*Merging of multiple odML files*Generation of a
                      comparison table of similar entries within an odML fileWe
                      show how odML-tables serves to complement a sustainable
                      workflow for metadata management in an example use case,
                      where we illustrate the practical usage of odML-tables
                      ranging from structuring available metadata to daily
                      enrichment of the metadata collection (cf. also
                      [1,2]).References:[1] Denker, M., $\&$ Grün, S. (2016).
                      Designing workflows for the reproducible Analysis of
                      Electrophys-iological Data. In: Brain Inspired Computing,
                      eds: Katrin Amunts, Lucio Grandinetti, Thomas Lippert,
                      Nicolai Petkov. Lecture Notes in Computer Science, Springer.
                      (in press)[2] 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. (under
                      revision)[3] Grewe, J., Wachtler, T., $\&$ Benda, J. (2011).
                      A Bottom-up Approach to Data Annotation in Neurophysiology.
                      Frontiers in Neuroinformatics, 5, 16.[4] Sprenger, J.,
                      Canova, C., Denker, M. $\&$ Grün, S. (2015). Data workflow
                      management and analysis for complex electrophysiological
                      experiments. 4th INM-Retreat, Jülich, Germany. Poster},
      month         = {Dec},
      date          = {2016-12-05},
      organization  = {1st Symposium of the Institute for
                       Advanced Simulations, Jülich
                       (Germany), 5 Dec 2016 - 6 Dec 2016},
      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) / 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) / HBP
                      SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(GEPRIS)237833830 / G:(GEPRIS)238707842 /
                      G:(EU-Grant)720270},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/826006},
}