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naturalistic-data-analysis/naturalistic_data_analysis: Version 1.0

appsOther research productkeyboard_double_arrow_right InteractiveResource 09 Jul 2020 English Publisher:Zenodo

Authors: Chang, Luke; Manning, Jeremy; Baldassano, Christopher; Vega, Alejandro De La; Fleetwood, Gordon; Geerligs, Linda; Haxby, James; +8 Authors

doi: 10.5281/zenodo.3937849 , 10.5281/zenodo.3937848

naturalistic-data-analysis/naturalistic_data_analysis: Version 1.0

- Summary
- Subjects
- Metrics

Abstract

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.

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Keywords

neuroimaging, analysis, fmri, naturalistic, data

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%