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@ARTICLE{Yarkoni:865043,
      author       = {Yarkoni, Tal and Markiewicz, Christopher and de la Vega,
                      Alejandro and Gorgolewski, Krzysztof and Salo, Taylor and
                      Halchenko, Yaroslav and McNamara, Quinten and DeStasio,
                      Krista and Poline, Jean-Baptiste and Petrov, Dmitry and
                      Hayot-Sasson, Valérie and Nielson, Dylan and Carlin, Johan
                      and Kiar, Gregory and Whitaker, Kirstie and DuPre, Elizabeth
                      and Wagner, Adina Svenja and Tirrell, Lee and Jas, Mainak
                      and Hanke, Michael and Poldrack, Russell and Esteban, Oscar
                      and Appelhoff, Stefan and Holdgraf, Chris and Staden, Isla
                      and Thirion, Bertrand and Kleinschmidt, Dave and Lee, John
                      and di Castello, Matteo and Notter, Michael and Blair, Ross},
      title        = {{P}y{BIDS}: {P}ython tools for {BIDS} datasets},
      journal      = {The journal of open source software},
      volume       = {4},
      number       = {40},
      issn         = {2475-9066},
      reportid     = {FZJ-2019-04605},
      pages        = {1294 -},
      year         = {2019},
      abstract     = {Brain imaging researchers regularly work with large,
                      heterogeneous, high-dimensional datasets. Historically,
                      researchers have dealt with this complexity
                      idiosyncratically, with every lab or individual implementing
                      their own preprocessing and analysis procedures. The
                      resulting lack of field-wide standards has severely limited
                      reproducibility and data sharing and reuse.To address this
                      problem, we and others recently introduced the Brain Imaging
                      Data Standard (BIDS; (Gorgolewski et al., 2016)), a
                      specification meant to standardize the process of
                      representing brain imaging data. BIDS is deliberately
                      designed with adoption in mind; it adheres to a user-focused
                      philosophy that prioritizes common use cases and discourages
                      complexity. By successfully encouraging a large and
                      ever-growing subset of the community to adopt a common
                      standard for naming and organizing files, BIDS has made it
                      much easier for researchers to share, reuse, and process
                      their data (Gorgolewski et al., 2017).The ability to
                      efficiently develop high-quality spec-compliant applications
                      itself depends to a large extent on the availability of good
                      tooling. Because many operations recur widely across diverse
                      contexts—for example, almost every tool designed to work
                      with BIDS datasets involves regular file-filtering
                      operations—there is a strong incentive to develop utility
                      libraries that provide common functionality via a
                      standardized, simple API.PyBIDS is a Python package that
                      makes it easier to work with BIDS datasets. In principle,
                      its scope includes virtually any functionality that is
                      likely to be of general use when working with BIDS datasets
                      (i.e., that is not specific to one narrow context). At
                      present, its core and most widely used module supports
                      simple and flexible querying and manipulation of BIDS
                      datasets. PyBIDS makes it easy for researchers and
                      developers working in Python to search for BIDS files by
                      keywords and/or metadata; to consolidate and retrieve
                      file-associated metadata spread out across multiple levels
                      of a BIDS hierarchy; to construct BIDS-valid path names for
                      new files; and to validate projects against the BIDS
                      specification, among other applications.In addition to this
                      core functionality, PyBIDS also contains an ever-growing set
                      of modules that support additional capabilities meant to
                      keep up with the evolution and expansion of the BIDS
                      specification itself. Currently, PyBIDS includes tools for
                      (1) reading and manipulating data contained in various
                      BIDS-defined files (e.g., physiological recordings, event
                      files, or participant-level variables); (2) constructing
                      design matrices and contrasts that support the new
                      BIDS-StatsModel specification (for machine-readable
                      representation of fMRI statistical models); and (3)
                      automated generation of partial Methods sections for
                      inclusion in publications.PyBIDS can be easily installed on
                      all platforms via pip (pip install pybids), though currently
                      it is not officially supported on Windows. The package has
                      few dependencies outside of standard Python numerical and
                      image analysis libraries (i.e., numpy, scipy, pandas, and
                      NiBabel). The core API is deliberately kept minimalistic:
                      nearly all interactions with PyBIDS functionality occur
                      through a core BIDSLayout object initialized by passing in a
                      path to a BIDS dataset. For most applications, no custom
                      configuration should be required.Although technically still
                      in alpha release, PyBIDS is already being used both as a
                      dependency in dozens of other open-source brain imaging
                      packages –e.g., fMRIPrep (Esteban et al.,2019), MRIQC
                      (Esteban et al., 2017), datalad-neuroimaging
                      (https://github.com/datalad/datalad-neuroimaging), and
                      fitlins (https://github.com/poldracklab/fitlins) – and
                      directly in many researchers’ custom Python workflows.
                      Development is extremely active, with bug fixes and new
                      features continually being added
                      (https://github.com/bids-standard/pybids), and major
                      releases occurring approximately every 6 months. As of this
                      writing, 29 people have contributed code to PyBIDS, and many
                      more have provided feedback and testing. The API is
                      relatively stable, and documentation and testing standards
                      follow established norms for open-source scientific
                      software. We encourage members of the brain imaging
                      community currently working in Python to try using PyBIDS,
                      and welcome new contributions.},
      cin          = {INM-7},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32775955},
      doi          = {10.21105/joss.01294},
      url          = {https://juser.fz-juelich.de/record/865043},
}