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@ARTICLE{Khler:1024078,
      author       = {Köhler, Cristiano André and Ulianych, Danylo and Grün,
                      Sonja and Decker, Stefan and Denker, Michael},
      title        = {{F}acilitating the sharing of electrophysiology data
                      analysis results through in-depth provenance capture},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-01958},
      year         = {2023},
      abstract     = {Scientific research demands reproducibility and
                      transparency, particularly in data-intensive fields like
                      electrophysiology. Electrophysiology data is typically
                      analyzed using scripts that generate output files, including
                      figures. Handling these results poses several challenges due
                      to the complexity and interactivity of the analysis process.
                      These stem from the difficulty to discern the analysis
                      steps, parameters, and data flow from the results, making
                      knowledge transfer and findability challenging in
                      collaborative settings. Provenance information tracks data
                      lineage and processes applied to it, and provenance capture
                      during the execution of an analysis script can address those
                      challenges. We present Alpaca (Automated Lightweight
                      Provenance Capture), a tool that captures fine-grained
                      provenance information with minimal user intervention when
                      running data analysis pipelines implemented in Python
                      scripts. Alpaca records inputs, outputs, and function
                      parameters and structures information according to the W3C
                      PROV standard. We demonstrate the tool using a realistic use
                      case involving multichannel local field potential recordings
                      of a neurophysiological experiment, highlighting how the
                      tool makes result details known in a standardized manner in
                      order to address the challenges of the analysis process.
                      Ultimately, using Alpaca will help to represent results
                      according to the FAIR principles, which will improve
                      research reproducibility and facilitate sharing the results
                      of data analyses.},
      keywords     = {Neurons and Cognition (q-bio.NC) (Other) / FOS: Biological
                      sciences (Other)},
      cin          = {IAS-6 / INM-10 / INM-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)INM-6-20090406},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / 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) / HAF - Helmholtz Analytics Framework
                      (ZT-I-0003) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(EU-Grant)945539 /
                      G:(DE-Juel-1)iBehave-20220812 / G:(DE-Juel1)JL
                      SMHB-2021-2027 / G:(DE-HGF)ZT-I-0003 / G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2311.09672},
      url          = {https://juser.fz-juelich.de/record/1024078},
}