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@INPROCEEDINGS{Chang:887871,
      author       = {Chang, Luke and Manning, Jeremy and Baldassano, Christopher
                      and Vega, Alejandro de la and Fleetwood, Gordon and
                      Geerligs, Linda and Haxby, James and Lahnakoski, Juha and
                      Parkinson, Carolyn and Shappell, Heather and Shim, Won Mok
                      and Wager, Tor and Yarkoni, Tal and Yeshurun, Yaara and
                      Finn, Emily},
      title        = {{N}euroimaging {A}nalysis {M}ethods {F}or {N}aturalistic
                      {D}ata},
      publisher    = {Zenodo},
      reportid     = {FZJ-2020-04493},
      pages        = {N/A},
      year         = {2020},
      note         = {All content is licensed under the Creative Commons
                      Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
                      license.},
      abstract     = {Version 1.0 of the Naturalistic-Data.org educational
                      course. Naturalistic-Data.org is an open access online
                      educational resource that provides an introduction to
                      analyzing naturalistic functional neuroimaging datasets
                      using Python. Naturalistic-Data.org is built using
                      Jupyter-Book and provides interactive tutorials for
                      introducing advanced analytic techniques . This includes
                      functional alignment, inter-subject correlations,
                      inter-subject representational similarity analysis,
                      inter-subject functional connectivity, event segmentation,
                      natural language processing, hidden semi-markov models,
                      automated annotation extraction, and visualizing high
                      dimensional data. The tutorials focus on practical
                      applications using open access data, short open access video
                      lectures, and interactive Jupyter notebooks. All of the
                      tutorials use open source packages from the python
                      scientific computing community (e.g., numpy, pandas, scipy,
                      matplotlib, scikit-learn, networkx, nibabel, nilearn,
                      brainiak, hypertoos, timecorr, pliers, statesegmentation,
                      and nltools). The course is designed to be useful for
                      varying levels of experience, including individuals with
                      minimal experience with programming, Python, and
                      statistics.},
      month         = {Jun},
      date          = {2020-06-23},
      organization  = {Annual meeting of the Organization for
                       Human Brain Mapping 2020, Virtual
                       (Virtual), 23 Jun 2020 - 3 Jul 2020},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.5281/ZENODO.3937849},
      url          = {https://juser.fz-juelich.de/record/887871},
}