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@INPROCEEDINGS{Khler:1017079,
      author       = {Köhler, Cristiano and Grün, Sonja and Denker, Michael},
      title        = {{G}aining insight into the analysis of electrophysiology
                      data: the {N}euroelectrophysiology {A}nalysis {O}ntology},
      reportid     = {FZJ-2023-03921},
      year         = {2023},
      abstract     = {Electrophysiology is frequently used to investigate brain
                      function. The analysis of electrophysiology data requires
                      specific transformations and methods of varying complexity,
                      which makes the description of the processes involved and
                      their results challenging. First, there are several
                      variations of methods that can be applied to the data with
                      similar purposes (e.g., different algorithms to compute the
                      power spectral density from local field potentials), leading
                      to multiple levels of granularity in the description.
                      Second, a particular method can be implemented by different
                      software codes (e.g., toolboxes such as Elephant [1] or MNE
                      [2]; see also [3]) that adopt different names for the
                      functions and the parameters used. In the end, this two-fold
                      ambiguity leads to a situation where the outcome of an
                      analysis is difficult to describe, and finding and comparing
                      results based on such descriptions require expert knowledge
                      and is hardly machine-actionable.An ontology defines the
                      concepts within a domain without ambiguity (e.g., the exact
                      method to compute a power spectral density) while providing
                      relationships with semantic information (e.g., a grouping of
                      spectral estimators). Therefore, the description of the
                      processes used for the analysis of electrophysiology data
                      with an ontology will allow their understanding and
                      identification despite the method or implementation used.
                      There are several ontologies in the biomedical sciences
                      including a few for neuroscience and electrophysiology [4].
                      However, their level of description is limited to the data,
                      metadata or general parts of the analysis workflow (e.g.,
                      experiments, subjects, equipment, and basic data
                      input/output).We implemented the Neuroelectrophysiology
                      Analysis Ontology (NEAO) to define a unified vocabulary and
                      standardize the descriptions of the methods involved in the
                      analysis of electrophysiology data. We show real-world
                      examples where the NEAO was used to annotate the provenance
                      information from different analyses of an electrophysiology
                      dataset and highlight how it is possible to query
                      information, facilitating finding and obtaining insights on
                      the results (e.g., using knowledge graphs). In this way,
                      NEAO identifies groups of similar methods while pointing to
                      literature that informs of their differences. We demonstrate
                      how NEAO can seamlessly integrate with Alpaca [5] to capture
                      provenance information. This will help to represent the
                      analysis results according to the FAIR principles
                      [6].References:[1] Elephant $(RRID:SCR_003833);$ Denker, M.,
                      Yegenoglu, A., Grün, S. (2018) Collaborative HPC-enabled
                      workflows on the HBP Collaboratory using the Elephant
                      framework. Neuroinformatics 2018, P19.
                      https://python-elephant.org
                      [doi:10.12751/incf.ni2018.0019][2] MNE $(RRID:SCR_005972);$
                      Gramfort, A. et al. (2013) MEG and EEG data analysis with
                      MNE-Python. Frontiers in Neuroscience 7, 267.
                      https://mne.tools [doi:10.3389/fnins.2013.00267][3]
                      Unakafova, V.A., Gail, A. (2019) Comparing Open-Source
                      Toolboxes for Processing and Analysis of Spike and Local
                      Field Potentials Data. Frontiers in Neuroinformatics 13, 57.
                      [doi:10.3389/fninf.2019.00057][4] NCBO BioPortal,
                      https://bioportal.bioontology.org[5] Alpaca
                      $(RRID:SCR_023739),$ https://alpaca-prov.readthedocs.io [6]
                      Wilkinson, M.D. et al. (2016) The FAIR Guiding Principles
                      for scientific data management and stewardship. Scientific
                      Data 3, 160018. [doi:10.1038/sdata.2016.18]},
      month         = {Sep},
      date          = {2023-09-26},
      organization  = {Bernstein Conference 2023, Berlin
                       (Germany), 26 Sep 2023 - 29 Sep 2023},
      subtyp        = {Other},
      keywords     = {Computational Neuroscience (Other) / Data analysis, machine
                      learning and neuroinformatics (Other)},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / 5231 - Neuroscientific Foundations
                      (POF4-523) / HDS LEE - Helmholtz School for Data Science in
                      Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(DE-HGF)POF4-5231 /
                      G:(DE-Juel1)HDS-LEE-20190612 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.12751/NNCN.BC2023.197},
      url          = {https://juser.fz-juelich.de/record/1017079},
}