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001     887871
005     20210130010703.0
024 7 _ |a 10.5281/ZENODO.3937849
|2 doi
037 _ _ |a FZJ-2020-04493
041 _ _ |a en
100 1 _ |a Chang, Luke
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|e Corresponding author
111 2 _ |a Annual meeting of the Organization for Human Brain Mapping 2020
|g OHBM 2020
|c Virtual
|d 2020-06-23 - 2020-07-03
|w Virtual
245 _ _ |a Neuroimaging Analysis Methods For Naturalistic Data
260 _ _ |c 2020
|b Zenodo
300 _ _ |a N/A
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
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336 7 _ |a Output Types/Conference Paper
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336 7 _ |a Contribution to a conference proceedings
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500 _ _ |a All content is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
520 _ _ |a 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.
536 _ _ |a 571 - Connectivity and Activity (POF3-571)
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588 _ _ |a Dataset connected to DataCite
700 1 _ |a Manning, Jeremy
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700 1 _ |a Baldassano, Christopher
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700 1 _ |a Vega, Alejandro de la
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700 1 _ |a Fleetwood, Gordon
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700 1 _ |a Geerligs, Linda
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700 1 _ |a Haxby, James
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700 1 _ |a Lahnakoski, Juha
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700 1 _ |a Parkinson, Carolyn
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700 1 _ |a Shappell, Heather
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700 1 _ |a Shim, Won Mok
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700 1 _ |a Wager, Tor
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700 1 _ |a Yarkoni, Tal
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700 1 _ |a Yeshurun, Yaara
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700 1 _ |a Finn, Emily
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773 _ _ |a 10.5281/ZENODO.3937849
856 4 _ |u http://naturalistic-data.org
909 C O |o oai:juser.fz-juelich.de:887871
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
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|v Connectivity and Activity
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914 1 _ |y 2020
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
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980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a UNRESTRICTED

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