%0 Conference Paper
%A Chang, Luke
%A Manning, Jeremy
%A Baldassano, Christopher
%A Vega, Alejandro de la
%A Fleetwood, Gordon
%A Geerligs, Linda
%A Haxby, James
%A Lahnakoski, Juha
%A Parkinson, Carolyn
%A Shappell, Heather
%A Shim, Won Mok
%A Wager, Tor
%A Yarkoni, Tal
%A Yeshurun, Yaara
%A Finn, Emily
%T Neuroimaging Analysis Methods For Naturalistic Data
%I Zenodo
%M FZJ-2020-04493
%P N/A
%D 2020
%Z All content is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
%X 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.
%B Annual meeting of the Organization for Human Brain Mapping 2020
%C 23 Jun 2020 - 3 Jul 2020, Virtual (Virtual)
Y2 23 Jun 2020 - 3 Jul 2020
M2 Virtual, Virtual
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%R 10.5281/ZENODO.3937849
%U https://juser.fz-juelich.de/record/887871