TY  - CONF
AU  - Chang, Luke
AU  - Manning, Jeremy
AU  - Baldassano, Christopher
AU  - Vega, Alejandro de la
AU  - Fleetwood, Gordon
AU  - Geerligs, Linda
AU  - Haxby, James
AU  - Lahnakoski, Juha
AU  - Parkinson, Carolyn
AU  - Shappell, Heather
AU  - Shim, Won Mok
AU  - Wager, Tor
AU  - Yarkoni, Tal
AU  - Yeshurun, Yaara
AU  - Finn, Emily
TI  - Neuroimaging Analysis Methods For Naturalistic Data
PB  - Zenodo
M1  - FZJ-2020-04493
SP  - N/A
PY  - 2020
N1  - All content is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
AB  - 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.
T2  - Annual meeting of the Organization for Human Brain Mapping 2020
CY  - 23 Jun 2020 - 3 Jul 2020, Virtual (Virtual)
Y2  - 23 Jun 2020 - 3 Jul 2020
M2  - Virtual, Virtual
LB  - PUB:(DE-HGF)8
DO  - DOI:10.5281/ZENODO.3937849
UR  - https://juser.fz-juelich.de/record/887871
ER  -