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@INPROCEEDINGS{Denker:1044589,
      author       = {Denker, Michael},
      title        = {{U}sing {P}rovenance for {FAIR} {S}haring of {R}esults of
                      {W}orkflows {A}nalyzing {N}eural {A}ctivity {D}ata},
      reportid     = {FZJ-2025-03264},
      year         = {2025},
      abstract     = {Computational workflows for the analysis of
                      electrophysiology data are often implemented as scripts that
                      read input datasets and produce result files [1]. As
                      available data grows in size due to technological advances,
                      collaboration between researchers becomes increasingly
                      important to handle the involved analysis complexity.
                      Moreover, the derived analysis outputs are more valuable for
                      reuse because modern analysis methods often require
                      competitive high-performance compute resources. To
                      facilitate these structural changes in a way that scientists
                      engage in data analysis, we consider the FAIR-ness [2] of
                      results of computational analysis workflows that are derived
                      from experimental data. To do so, we investigate
                      descriptions of the analysis process beyond source codes and
                      free-text descriptions that enable query, introspection and
                      seamless reuse of the results. We identify multiple
                      challenges throughout the workflow that complicate the
                      generation of such descriptions due to the iterative nature
                      of analysis scenarios, lacking technical and semantic
                      standardization, and the availability of competing software
                      implementations [3].To address those challenges, we
                      developed Alpaca (Automatic Lightweight Provenance Capture)
                      as a framework to generate machine-readable descriptions of
                      the workflow execution that are enriched with the relevant
                      semantic information with minimal user intervention [4].
                      This produces a detailed provenance record of the atomic
                      analysis steps using the W3C PROV standard [5]. The record
                      is enriched using the Neuroelectrophysiology Analysis
                      Ontology (NEAO), which we developed as a unified vocabulary
                      to standardize the descriptions of the methods involved in
                      the analysis of extracellular electrophysiology data [6]. We
                      show how to obtain insights on the results (e.g., using
                      knowledge graphs) of real-world workflows for analyzing and
                      comparing heterogeneous data based on Elephant [7] and
                      Cobrawap [8]. We discuss extensions to other computational
                      workflows (e.g., neural simulation) to help in representing
                      their results according to the FAIR principles.[1] Denker et
                      al., 2021. Neuroforum 27, 27[2] Wilkinson et al., 2016. Sci
                      Data 3, 160018[3] Unakafova and Gail, 2019. Front Neuroinf.,
                      13, 57[4] Köhler et al., 2024. eNeuro 11,
                      ENEURO.0476-23.2024[5]
                      https://www.w3.org/TR/prov-overview[6] Köhler et al., 2024.
                      arXiv:2412.05021[7] Denker et al., 2018. Neuroinformatics
                      2018, doi:10.12751/incf.ni2018.0019[8] Gutzen et al., 2024.
                      Cell Rep Meth 4, 100681},
      month         = {Jul},
      date          = {2025-07-24},
      organization  = {48th Annual Meeting of the Japanse
                       Neuroscience Society, Niigata (Japan),
                       24 Jul 2025 - 27 Jul 2025},
      subtyp        = {Invited},
      cin          = {IAS-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / 5231 - Neuroscientific Foundations
                      (POF4-523) / 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)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(DE-HGF)POF4-5231 /
                      G:(EU-Grant)101147319 / G:(DE-Juel-1)iBehave-20220812 /
                      G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1044589},
}