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@INPROCEEDINGS{Sprenger:828548,
      author       = {Sprenger, Julia and Zehl, Lyuba and Canova, Carlos and
                      Grün, Sonja and Denker, Michael},
      title        = {od{ML}-tables: {P}roviding a graphical interface for od{ML}
                      based metadata management},
      reportid     = {FZJ-2017-02502},
      year         = {2017},
      abstract     = {Experimental observations are an essential part of
                      scientific research and are used to validate or reject a
                      scientific hypothesis. In the scientific approach,
                      observations are typically quantified and data are recorded
                      for subsequent analysis. These primary data are always
                      accompanied by information about their origin and the
                      circumstances of their recording. Such information is
                      typically called metadata and includes a variety of
                      information, such as, the date of the recording, a seemingly
                      unimportant change in measurement settings or the
                      expectation of the experimenter (open trial vs. blind trial
                      experiments). Metadata are crucial for performing
                      reproducible data analysis and are essential for the
                      interpretation of the results. They also enable queries to
                      answer scientific questions that researchers did not
                      previously consider (e.g. transversal studies) and are one
                      of the main components for implementing replicable and
                      reproducible research [1]. In addition a comprehensive
                      metadata collection facilitates the communication between
                      members of a project and therefore saves valuable time and
                      effort. In neuroscience, and in particular experimental
                      neurophysiology, the development of approaches to metadata
                      management is 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 as hierarchically
                      organized key-value pairs.In practice however, embedding
                      metadata based on the odML framework into workflows for
                      sharing data in concrete use cases of experimental and
                      theoretical groups revealed that generating the structure of
                      an odML document, and later filling it with metadata from
                      the respective sources, involved extensive programming
                      experience [2]. In addition there are always metadata, which
                      require manual entry during or after the experiment. The
                      lack of software support for certain processing steps and
                      editing capabilities in these use cases 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 that is convenient for
                      editing.odML-tables is an open-source software tool
                      implemented in Python, which offers a graphical user
                      interface (GUI) [4,5]. The main features of odML-tables
                      are:1) Generation of a template (tabular) structure
                      facilitating the initial design of an odML structure2)
                      Conversion between odML files and tabular formats (.xls,
                      .csv) in order to enable manual entry and modifications
                      using spreadsheet software (e.g., Microsoft Excel,
                      LibreOffice).3) Filtering metadata by defined search
                      criteria to simplify access to parts of a complex odML
                      structure4) Merging of multiple odML files5) Generation of a
                      comparison table of similar entries within an odML fileWe
                      show how odML-tables can be applied in a sustainable
                      workflow for metadata management and illustrate the
                      practical usage of odML-tables 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, 10, 26.[3] Grewe,
                      J., Wachtler, T., $\&$ Benda, J. (2011). A Bottom-up
                      Approach to Data Annotation in Neurophysiology. Frontiers in
                      Neuroinformatics, 5, 16.[4] python-odmltables on PyPi:
                      https://pypi.python.org/pypi/python-odmltables/[5]
                      python-odmltables on GitHub:
                      https://github.com/INM-6/python-odmltables},
      month         = {Mar},
      date          = {2017-03-22},
      organization  = {12th Göttingen Meeting of the German
                       Neuroscience Society, Göttingen
                       (Germany), 22 Mar 2017 - 25 Mar 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 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)237833830 /
                      G:(GEPRIS)238707842},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/828548},
}