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@ARTICLE{Khler:1044241,
      author       = {Köhler, Cristiano A. and Grün, Sonja and Denker, Michael},
      title        = {{I}mproving data sharing and knowledge transfer via the
                      {N}euroelectrophysiology {A}nalysis {O}ntology ({NEAO})},
      journal      = {Scientific data},
      volume       = {12},
      number       = {1},
      issn         = {2052-4436},
      address      = {London},
      publisher    = {Nature Publ. Group},
      reportid     = {FZJ-2025-03129},
      pages        = {907},
      year         = {2025},
      abstract     = {Describing the analysis of data from electrophysiology
                      experiments investigating the function of neural systems is
                      challenging. On the one hand, data can be analyzed by
                      distinct methods with similar purposes, such as different
                      algorithms to estimate the spectral power content of a
                      measured time series. On the other hand, different software
                      codes can implement the same analysis algorithm, while
                      adopting different names to identify functions and
                      parameters. These ambiguities complicate reporting analysis
                      results, e.g., in a manuscript or on a scientific platform.
                      Here, we illustrate how an ontology to describe the analysis
                      process can assist in improving clarity, rigour and
                      comprehensibility by complementing, simplifying and
                      classifying the details of the implementation. We
                      implemented the Neuroelectrophysiology Analysis Ontology
                      (NEAO) to define a vocabulary and to standardize the
                      descriptions of processes for neuroelectrophysiology data
                      analysis. Real-world examples demonstrate how NEAO can
                      annotate provenance information describing an analysis.
                      Based on such provenance, we detail how it supports querying
                      information (e.g., using knowledge graphs) that enable
                      researchers to find, understand and reuse analysis results.},
      cin          = {IAS-6 / INM-10},
      ddc          = {500},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / EBRAINS 2.0 - EBRAINS
                      2.0: A Research Infrastructure to Advance Neuroscience and
                      Brain Health (101147319) / Algorithms of Adaptive Behavior
                      and their Neuronal Implementation in Health and Disease
                      (iBehave-20220812) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / HDS LEE -
                      Helmholtz School for Data Science in Life, Earth and Energy
                      (HDS LEE) (HDS-LEE-20190612) / DFG project
                      G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2025
                      - 2027 / Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(EU-Grant)945539 /
                      G:(EU-Grant)101147319 / G:(DE-Juel-1)iBehave-20220812 /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(DE-Juel1)HDS-LEE-20190612
                      / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1038/s41597-025-05213-3},
      url          = {https://juser.fz-juelich.de/record/1044241},
}