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000887871 1001_ $$0P:(DE-HGF)0$$aChang, Luke$$b0$$eCorresponding author
000887871 1112_ $$aAnnual meeting of the Organization for Human Brain Mapping 2020$$cVirtual$$d2020-06-23 - 2020-07-03$$gOHBM 2020$$wVirtual
000887871 245__ $$aNeuroimaging Analysis Methods For Naturalistic Data
000887871 260__ $$bZenodo$$c2020
000887871 300__ $$aN/A
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000887871 500__ $$aAll content is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
000887871 520__ $$aVersion 1.0 of the Naturalistic-Data.org educational course. Naturalistic-Data.org is an open access online educational resource that provides an introduction to analyzing naturalistic functional neuroimaging datasets using Python. Naturalistic-Data.org is built using Jupyter-Book and provides interactive tutorials for introducing advanced analytic techniques . This includes functional alignment, inter-subject correlations, inter-subject representational similarity analysis, inter-subject functional connectivity, event segmentation, natural language processing, hidden semi-markov models, automated annotation extraction, and visualizing high dimensional data. The tutorials focus on practical applications using open access data, short open access video lectures, and interactive Jupyter notebooks. All of the tutorials use open source packages from the python scientific computing community (e.g., numpy, pandas, scipy, matplotlib, scikit-learn, networkx, nibabel, nilearn, brainiak, hypertoos, timecorr, pliers, statesegmentation, and nltools). The course is designed to be useful for varying levels of experience, including individuals with minimal experience with programming, Python, and statistics.
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000887871 7001_ $$0P:(DE-HGF)0$$aManning, Jeremy$$b1
000887871 7001_ $$0P:(DE-HGF)0$$aBaldassano, Christopher$$b2
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000887871 7001_ $$0P:(DE-HGF)0$$aFleetwood, Gordon$$b4
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000887871 7001_ $$0P:(DE-HGF)0$$aWager, Tor$$b11
000887871 7001_ $$0P:(DE-HGF)0$$aYarkoni, Tal$$b12
000887871 7001_ $$0P:(DE-HGF)0$$aYeshurun, Yaara$$b13
000887871 7001_ $$0P:(DE-HGF)0$$aFinn, Emily$$b14
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