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naturalistic-data-analysis/naturalistic_data_analysis: Version 1.0

Luke Chang; Jeremy Manning; Christopher Baldassano; Alejandro de la Vega; Gordon Fleetwood; Linda Geerligs; James Haxby; Juha Lahnakoski; Carolyn Parkinson; Heather Shappell; Won Mok Shim; Tor Wager; Tal Yarkoni; Yaara Yeshurun; Emily Finn


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  <identifier identifierType="DOI">10.5281/zenodo.3937849</identifier>
  <creators>
    <creator>
      <creatorName>Luke Chang</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6621-8120</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
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    <creator>
      <creatorName>Jeremy Manning</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7613-4732</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Christopher Baldassano</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3540-5019</nameIdentifier>
      <affiliation>Columbia University</affiliation>
    </creator>
    <creator>
      <creatorName>Alejandro de la Vega</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9062-3778</nameIdentifier>
      <affiliation>University of Texas at Austin</affiliation>
    </creator>
    <creator>
      <creatorName>Gordon Fleetwood</creatorName>
      <affiliation>New Classrooms</affiliation>
    </creator>
    <creator>
      <creatorName>Linda Geerligs</creatorName>
      <affiliation>Donders Institute For Brain, Cognition, And Behavior</affiliation>
    </creator>
    <creator>
      <creatorName>James Haxby</creatorName>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Juha Lahnakoski</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5223-7822</nameIdentifier>
      <affiliation>Forschungszentrum Jülich</affiliation>
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    <creator>
      <creatorName>Carolyn Parkinson</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7128-3480</nameIdentifier>
      <affiliation>University Of California Los Angeles</affiliation>
    </creator>
    <creator>
      <creatorName>Heather Shappell</creatorName>
      <affiliation>Johns Hopkins University</affiliation>
    </creator>
    <creator>
      <creatorName>Won Mok Shim</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9107-0471</nameIdentifier>
      <affiliation>SungKyunKwan University</affiliation>
    </creator>
    <creator>
      <creatorName>Tor Wager</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1936-5574</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Tal Yarkoni</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6558-5113</nameIdentifier>
      <affiliation>University of Texas at Austin</affiliation>
    </creator>
    <creator>
      <creatorName>Yaara Yeshurun</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0843-6998</nameIdentifier>
      <affiliation>Tel-Aviv University</affiliation>
    </creator>
    <creator>
      <creatorName>Emily Finn</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8591-3068</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
  </creators>
  <titles>
    <title>naturalistic-data-analysis/naturalistic_data_analysis: Version 1.0</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>neuroimaging, analysis, fmri, naturalistic, data</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-07-09</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="InteractiveResource"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3937849</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/naturalistic-data-analysis/naturalistic_data_analysis/tree/1.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3937848</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
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