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@ARTICLE{Zhao:1024665,
      author       = {Zhao, Chenying and Jarecka, Dorota and Covitz, Sydney and
                      Chen, Yibei and Eickhoff, Simon B. and Fair, Damien A. and
                      Franco, Alexandre R. and Halchenko, Yaroslav O. and
                      Hendrickson, Timothy J. and Hoffstaedter, Felix and
                      Houghton, Audrey and Kiar, Gregory and Macdonald, Austin and
                      Mehta, Kahini and Milham, Michael P. and Salo, Taylor and
                      Hanke, Michael and Ghosh, Satrajit S. and Cieslak, Matthew
                      and Satterthwaite, Theodore D.},
      title        = {{A} reproducible and generalizable software workflow for
                      analysis of large-scale neuroimaging data collections using
                      {BIDS} {A}pps},
      journal      = {Imaging neuroscience},
      volume       = {2},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {FZJ-2024-02338},
      pages        = {1 - 19},
      year         = {2024},
      abstract     = {Neuroimaging research faces a crisis of reproducibility.
                      With massive sample sizes and greater data complexity, this
                      problem becomes more acute. Software that operates on
                      imaging data defined using the Brain Imaging Data Structure
                      (BIDS)—the BIDS App—has provided a substantial advance.
                      However, even using BIDS Apps, a full audit trail of data
                      processing is a necessary prerequisite for fully
                      reproducible research. Obtaining a faithful record of the
                      audit trail is challenging—especially for large datasets.
                      Recently, the FAIRly big framework was introduced as a way
                      to facilitate reproducible processing of large-scale data by
                      leveraging DataLad—a version control system for data
                      management. However, the current implementation of this
                      framework was more of a proof of concept, and could not be
                      immediately reused by other investigators for different use
                      cases. Here, we introduce the BIDS App Bootstrap (BABS), a
                      user-friendly and generalizable Python package for
                      reproducible image processing at scale. BABS facilitates the
                      reproducible application of BIDS Apps to large-scale
                      datasets. Leveraging DataLad and the FAIRly big framework,
                      BABS tracks the full audit trail of data processing in a
                      scalable way by automatically preparing all scripts
                      necessary for data processing and version tracking on high
                      performance computing (HPC) systems. Currently, BABS
                      supports jobs submissions and audits on Sun Grid Engine
                      (SGE) and Slurm HPCs with a parsimonious set of programs. To
                      demonstrate its scalability, we applied BABS to data from
                      the Healthy Brain Network (HBN; n = 2,565). Taken together,
                      BABS allows reproducible and scalable image processing and
                      is broadly extensible via an open-source development
                      model.Reproducibility, BIDS Apps, software, MRI, big data,
                      image processing},
      cin          = {INM-7},
      ddc          = {050},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37645999},
      UT           = {WOS:001531527700014},
      doi          = {10.1162/imag_a_00074},
      url          = {https://juser.fz-juelich.de/record/1024665},
}