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@ARTICLE{Konradi:1020946,
      author       = {Konradi, Peter and Troglio, Alina and Pérez Garriga,
                      Ariadna and Pérez Martín, Aarón and Röhrig, Rainer and
                      Namer, Barbara and Kutafina, Ekaterina},
      title        = {{P}y{D}apsys: an open-source library for accessing
                      electrophysiology data recorded with {DAPSYS}},
      journal      = {Frontiers in neuroinformatics},
      volume       = {17},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2024-00414},
      pages        = {1250260},
      year         = {2023},
      abstract     = {In the field of neuroscience, a considerable number of
                      commercial data acquisition and processing solutions rely on
                      proprietary formats for data storage. This often leads to
                      data being locked up in formats that are only accessible by
                      using the original software, which may lead to
                      interoperability problems. In fact, even the loss of data
                      access is possible if the software becomes unsupported,
                      changed, or otherwise unavailable. To ensure FAIR data
                      management, strategies should be established to enable
                      long-term, independent, and unified access to data in
                      proprietary formats. In this work, we demonstrate PyDapsys,
                      a solution to gain open access to data that was acquired
                      using the proprietary recording system DAPSYS. PyDapsys
                      enables us to open the recorded files directly in Python and
                      saves them as NIX files, commonly used for open research in
                      the electrophysiology domain. Thus, PyDapsys secures
                      efficient and open access to existing and prospective data.
                      The manuscript demonstrates the complete process of reverse
                      engineering a proprietary electrophysiological format on the
                      example of microneurography data collected for studies on
                      pain and itch signaling in peripheral neural fibers.},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37780458},
      UT           = {WOS:001073043200001},
      doi          = {10.3389/fninf.2023.1250260},
      url          = {https://juser.fz-juelich.de/record/1020946},
}