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@ARTICLE{Khler:1041471,
      author       = {Köhler, Cristiano and Grün, Sonja and Denker, Michael},
      title        = {{I}mproving data sharing and knowledge transfer via the
                      {N}euroelectrophysiology {A}nalysis {O}ntology ({NEAO})},
      journal      = {arXiv},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-02264},
      pages        = {2412.05021},
      year         = {2024},
      abstract     = {Describing the processes involved in analyzing data from
                      electrophysiology experiments to investigate the function of
                      neural systems is inherently challenging. On the one hand,
                      data can be analyzed by distinct methods that serve a
                      similar purpose, 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
                      algorithm for the analysis while adopting different names to
                      identify functions and parameters. Having reproducibility in
                      mind, with these ambiguities the outcomes of the analysis
                      are difficult to report, e.g., in the methods section of a
                      manuscript or on a platform for scientific findings. Here,
                      we illustrate how using 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 unified vocabulary and to standardize the
                      descriptions of the processes involved in analyzing data
                      from neuroelectrophysiology experiments. Real-world examples
                      demonstrate how the NEAO can be employed to annotate
                      provenance information describing an analysis process. Based
                      on such provenance, we detail how it can be used to query
                      various types of information (e.g., using knowledge graphs)
                      that enable researchers to find, understand and reuse prior
                      analysis results.},
      keywords     = {Quantitative Methods (q-bio.QM) (Other) / FOS: Biological
                      sciences (Other)},
      cin          = {IAS-6 / INM-10},
      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)},
      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},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2412.05021},
      url          = {https://juser.fz-juelich.de/record/1041471},
}