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@ARTICLE{Wittenburg:906811,
      author       = {Wittenburg, Peter and Hardisty, Alex and Franc, Yann Le and
                      Mozaffari, Amirpasha and Peer, Limor and Skvortsov, Nikolay
                      A. and Zhao, Zhiming and Spinuso, Alessandro},
      title        = {{C}anonical {W}orkflows to {M}ake {D}ata {FAIR}},
      journal      = {Data Intelligence},
      volume       = {4},
      number       = {2},
      issn         = {2096-7004},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {FZJ-2022-01708},
      pages        = {286–305},
      year         = {2022},
      abstract     = {The FAIR principles have been accepted globally as
                      guidelines for improving data-driven science and data
                      management practices, yet the incentives for researchers to
                      change their practices are presently weak. In addition,
                      data-driven science has been slow to embrace workflow
                      technology despite clear evidence of recurring practices. To
                      overcome these challenges, the Canonical Workflow Frameworks
                      for Research (CWFR) initiative suggests a large-scale
                      introduction of self-documenting workflow scripts to
                      automate recurring processes or fragments thereof. This
                      standardised approach, with FAIR Digital Objects as anchors,
                      will be a significant milestone in the transition to FAIR
                      data without adding additional load onto the researchers who
                      stand to benefit most from it. This paper describes the CWFR
                      approach and the activities of the CWFR initiative over the
                      course of the last year or so, highlights several projects
                      that hold promise for the CWFR approaches, including Galaxy,
                      Jupyter Notebook, and RO Crate, and concludes with an
                      assessment of the state of the field and the challenges
                      ahead.},
      cin          = {JSC},
      ddc          = {020},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / IntelliAQ -
                      Artificial Intelligence for Air Quality (787576) /
                      Verbundprojekt DeepRain: Effiziente Lokale
                      Niederschlagsvorhersage durch Maschinelles Lernen
                      (01IS18047A) / Earth System Data Exploration (ESDE) / Earth
                      System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(BMBF)01IS18047A / G:(DE-Juel-1)ESDE / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000850893200011},
      doi          = {10.1162/dint_a_00132},
      url          = {https://juser.fz-juelich.de/record/906811},
}