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ljchang/dartbrains: An online open access resource for learning functional neuroimaging analysis methods in Python

Chang, Luke J.; Huckins, Jeremy; Cheong, Jin Hyun; Brietzke, Sasha; Lindquist, Martin A.; Wager, Tor D.


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  <identifier identifierType="DOI">10.5281/zenodo.3909718</identifier>
  <creators>
    <creator>
      <creatorName>Chang, Luke J.</creatorName>
      <givenName>Luke J.</givenName>
      <familyName>Chang</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7177-4711</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Huckins, Jeremy</creatorName>
      <givenName>Jeremy</givenName>
      <familyName>Huckins</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0106-7808</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Cheong, Jin Hyun</creatorName>
      <givenName>Jin Hyun</givenName>
      <familyName>Cheong</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9255-1648</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Brietzke, Sasha</creatorName>
      <givenName>Sasha</givenName>
      <familyName>Brietzke</familyName>
      <affiliation>Dartmouth College</affiliation>
    </creator>
    <creator>
      <creatorName>Lindquist, Martin A.</creatorName>
      <givenName>Martin A.</givenName>
      <familyName>Lindquist</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2289-0828</nameIdentifier>
      <affiliation>Johns Hopkins University</affiliation>
    </creator>
    <creator>
      <creatorName>Wager, Tor D.</creatorName>
      <givenName>Tor D.</givenName>
      <familyName>Wager</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1936-5574</nameIdentifier>
      <affiliation>Dartmouth College</affiliation>
    </creator>
  </creators>
  <titles>
    <title>ljchang/dartbrains: An online open access resource for learning functional neuroimaging  analysis methods in Python</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>fMRI, neuroimaging, analysis, python, jupyter</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-06-26</date>
  </dates>
  <resourceType resourceTypeGeneral="InteractiveResource"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3909718</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/ljchang/dartbrains/tree/1.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3909717</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;DartBrains.org is an open access online educational resource that provides an introduction to functional neuroimaging analysis methods using Python. DartBrains is built using Jupyter-Book and provides interactive tutorials for introducing the basics of neuroimaging data analysis. This includes the basics of programming, signal processing, preprocessing, univariate analyses using the general linear model, functional connectivity, and multivariate analytic techniques (e.g., prediction/classification and representational similarity analysis). 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.,&amp;nbsp; numpy, pandas, scipy, matplotlib, scikit-learn, networkx,&amp;nbsp;nibabel, nilearn, fmriprep, and nltools). The course is designed to be useful for varying levels of&amp;nbsp;experience, including individuals&amp;nbsp;with minimal experience with programming, Python, and statistics.&lt;/p&gt;</description>
  </descriptions>
</resource>
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