**Useful resources for coding, analyzing, and visualizing data** ------------------------------------------------------------ ---------- Here are some resources that can be helpful for you if you are learning to code, or already know how but are looking to improve or learn new tricks. Also included are resources for statistical analyses (behavior and fMRI), version control (git), learning about neural networks, and medial temporal lobe segmentation. # Also: the Department of Statistics offers free statistical consulting to the Columbia community. If you want to use this service, go to [their website][1] for more information, and talk to Mariam to come up with a plan for what to present to them. There are also a variety of summer schools you can apply to which will train you in various topics. [Here is a list][2] of a few that are offered and [here is a github repository][3] of a more comprehensive list. ---------- Programming ----------- *General* [Program better, for fun and profit][4] <br> [Basic and more advanced code snippets][5] (several languages, including Python and R) <br> This [website][6] lists several different programming/analysis tutorials # *Online Experiments* [Mechanical Turk Tutorial][7] <br> [Another Mechanical Turk Tutorial][8] <br> [Matlab Toolbox for communicating with MTurk][9] <br> [psiTurk][10] (for use with Mechanical Turk; includes ready-made experiments shared by others) <br> [Pavlovia – Run PsychoPy (Python) experiments online][11] <br> [Gorilla][12] (for building online experiments) # *Matlab* [Matlab for the behavioral sciences][13] <br> [MathWorks Matlab tutorial][14] <br> [Matlab documentation][15] <br> [Matlab course][16] (via coursera) <br> [Overview of handy Matlab commands][17] <br> [Psychtoolbox tutorial][18] <br> [Psychtoolbox wiki][19] <br> [Princeton MVPA toolbox][20] (Matlab-based) <br> [Quest][21] (Matlab-based toolbox for staircasing) # *Sample Matlab Code* [artmuse_nBack_sampleCode.m][22] (Psychtoolbox code; from [Aly & Turk-Browne, 2016, PNAS][23]) <br> [nBack stimuli][24] (to use with above code) <br> [artmuse_nBack_analysisCode.m][25] (analysis code for n-back data) <br> [addRandomPhase.m][26] (make phase-scrambled images) # *Unix* [Unix Shell Tutorial from Foundations for Research Computing][27]<br> # *R* [An introduction to linear mixed-effects modeling in R][28] <br> [Power analysis in R][29] <br> [Intro to R -- Manual][30] <br> [R for Data Science][31] (free book) <br> [Introduction to R][32] (another free book) <br> [Introductory R course][33] <br> [ANOVA in R][34] (afex) <br> [ggplot2][35] <br> [colors in ggplot2][36] <br> [Elementary statistics with R][37] <br> [More advanced R tutorial][38] <br> [Introduction to mixed models in R][39] <br> [Aly-RCommands-Tutorial.R][40] (Mariam's excessive cheat sheet for R commands) # *Python* [Python on Codecademy][41] <br> [PsychoPy][42] <br> [A really useful PsychoPy tutorial][43] <br> [OpenSesame][44] (experiment builder with a GUI and Python) <br> [Python Data Science Handbook][45] (free book) <br> [Python tutorial from Foundations for Research Computing][46]<br> [scikit-learn][47] (general-purpose machine learning in Python) <br> [Nilearn][48] (machine learning for neuroimaging in Python) <br> [PyMVPA][49] (MVPA in Python) # ---------- Stats and analysis help ----------------------- *General* [An introduction to mixed-effects models for experimental psychology][50] <br> [Best practice guidance for linear mixed-effects models in psychological science][51] <br> [A (visual) introduction to mixed effects models][52] <br> [Interactive guide to basic probability and stats][53] <br> [Andy Conway's stats lectures][54] <br> [Effect Size Calculator][55] <br> [A primer for calculating effect sizes][56] <br> [Transforming stimulus size to visual angle, or vice versa][57] <br> [Robust correlation toolbox][58] (for Matlab) <br> [Two-way repeated-measures ANOVA][59] (Matlab) <br> [ROC toolbox][60] (Matlab) [[related paper]][61] <br> [ROC demo][62] <br> [Shiny app for analytic power analyses of two-way interactions][63] <br> [Shiny app for sample size justification][64] <br> [Simulating power for mixed-effects models][65] <br> [Simulating datasets for mixed-effects models][66] (to pair with above reference on simulating power) <br> [Power analysis in R][67] <br> [Conversions between partial eta squared and Cohen's d][68] <br> # *General fMRI* [Andy's Brain Book][69] (fMRI tutorials) <br> [NIH fMRI course -- slides & videos][70] (summer 2018) <br> [fMRI Bootcamp][71] (videos by Rebecca Saxe) <br> [Neurostars][72] (online community for fMRI help) <br> [Neuropipe][73] (pipeline for fMRI -- highly recommended; our lab's forked version is [here][74], and tips for using Neuropipe on our server are [here][75]) <br> [NeuroElf][76] (Matlab toolbox for fMRI) <br> [Simitar][77] (Matlab-based searchlight analysis toolbox) <br> [Neuroimaging Data Processing Wiki][78] (intro to fMRI analyses) <br> [BIDS][79] (for organization & description of fMRI data) <br> [mriqc][80] (quality control) <br> [fmriprep][81] (preprocessing pipeline) <br> [Mumford Brain Stats][82] (fMRI help) <br> [practiCal fMRI][83] <br> [Neurodesign][84] (toolbox to optimize fMRI expt design) <br> [Power calculation guide for fMRI studies][85] <br> # *FSL* [FEAT user guide][86] (FSL) <br> [Handy FSL functions][87] <br> [Permutation tests in FSL][88] (randomise) <br> [Permutation tests in FSL][89] (PALM) <br> [FSLeyes][90] (the new fslview) <br> # *SPM* [SPM12 Manual][91] <br> [fMRI quality assurance toolbox][92] <br> # *Eye Tracking* [GazeAlyze][93] (Matlab toolbox for eye tracking analyses) <br> [BubbleView][94] (eye tracking proxy that makes use of mouse clicks) <br> [BubbleView adapted for jsPsych][95] <br> [TurkEyes][96] (various interfaces for collecting attention data without eye movements) ---------- Data visualization ------------------ [iWantHue color palette generator][97] <br> [Fundamentals of Data Visualization][98] (book written in R Markdown!) <br> [Beeswarm plots][99] (Matlab) <br> [Beeswarm plots][100] (R) <br> [Bounded line plot][101] -- draw shaded boundaries around a line to represent error/CIs (Matlab) <br> [Circular graph for illustrating network connections][102] (Matlab) <br> [Color calculator][103] <br> [Color wheel][104] <br> [ColorBrewer][105] <br> [Repair polygon artifact in Matlab vector exports][106] <br> ---------- Neural networks --------------- [A neural network playground][107] ---------- Version control --------------- [A book about Git][108] <br> [Git tutorial for behavioral scientists][109] <br> [Git in 10 minutes][110] <br> [A link collection for Git][111] <br> [Version Control with Git][112] (Tutorial from Foundations for Research Computing) <br> ---------- Medial temporal lobe imaging and analysis -------------------------------------------- [Guide to functional imaging of the MTL][113] <br> [MTL segmentation guide][114] <br> [Hippocampal subfield harmonization group][115] <br> [ASHS][116] (automated segmentation approach) <br> [1]: http://stat.columbia.edu/consulting-information/ [2]: https://docs.google.com/spreadsheets/d/1jLM2qWg5Be2QZIZDvCCifIAAqekHPk3zGly9N18X-3E/edit?usp=sharing [3]: https://github.com/PhABC/neuroSummerSchools [4]: https://inattentionalcoffee.wordpress.com/2017/01/13/program-better-for-fun-and-for-profit/ [5]: https://chrisalbon.com/ [6]: https://meta-meta-resources.org/ [7]: https://bradylab.ucsd.edu/ttt/ [8]: http://3dvision.princeton.edu/pvt/turkTemplateExample/mturk_introduction.pdf [9]: https://github.com/adikhosla/mturkMatlab [10]: https://psiturk.org/ [11]: https://pavlovia.org/ [12]: https://gorilla.sc/ [13]: https://www.amazon.com/Matlab-Behavioral-Sciences-Ione-Fine-ebook/dp/B00CPT86NC [14]: https://matlabacademy.mathworks.com/ [15]: https://www.mathworks.com/help/matlab/index.html [16]: https://www.coursera.org/learn/matlab [17]: http://www.hkn.umn.edu/resources/files/matlab/MatlabCommands.pdf [18]: http://peterscarfe.com/ptbtutorials.html [19]: https://github.com/Psychtoolbox-3/Psychtoolbox-3/wiki [20]: https://github.com/PrincetonUniversity/princeton-mvpa-toolbox [21]: http://psychtoolbox.org/docs/Quest [22]: https://www.dropbox.com/s/lhvswbkbp3br4hl/artmuse_nBack_sampleCode.m?dl=0 [23]: http://www.pnas.org/content/113/4/E420.abstract [24]: https://www.dropbox.com/s/n5ib0yg3a3csq9g/nbackStimuli.zip?dl=0 [25]: https://www.dropbox.com/s/3lz44tertrz6mcv/artmuse_nBack_analysisCode.m?dl=0 [26]: https://www.dropbox.com/s/tmwp4ijwxaprvwx/addRandomPhase.m?dl=0 [27]: http://swcarpentry.github.io/shell-novice/ [28]: https://journals.sagepub.com/doi/full/10.1177/2515245920960351 [29]: https://cran.r-project.org/web/packages/pwr/vignettes/pwr-vignette.html [30]: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf [31]: http://r4ds.had.co.nz/ [32]: https://moderndive.com/ [33]: https://www.datacamp.com/courses/free-introduction-to-r [34]: https://www.r-bloggers.com/anova-in-r-afex-may-be-the-solution-you-are-looking-for/ [35]: https://ggplot2.tidyverse.org/ [36]: http://www.cookbook-r.com/Graphs/Colors_%28ggplot2%29/ [37]: http://www.r-tutor.com/elementary-statistics [38]: http://www.r-tutor.com/ [39]: https://gkhajduk.github.io/2017-03-09-mixed-models/ [40]: https://www.dropbox.com/s/y5kzxvg5b9a40tk/Aly-RCommands-Tutorial.R?dl=0 [41]: https://www.codecademy.com/learn/learn-python [42]: http://www.psychopy.org/ [43]: https://www.socsci.ru.nl/wilberth/psychopy/00intro.html [44]: https://osdoc.cogsci.nl/ [45]: https://github.com/jakevdp/PythonDataScienceHandbook [46]: https://swcarpentry.github.io/python-novice-gapminder/ [47]: http://scikit-learn.org/stable/ [48]: https://nilearn.github.io/ [49]: http://www.pymvpa.org/ [50]: http://singmann.org/download/publications/singmann_kellen-introduction-mixed-models.pdf [51]: https://www.sciencedirect.com/science/article/pii/S0749596X20300061?via=ihub [52]: http://mfviz.com/hierarchical-models/ [53]: http://students.brown.edu/seeing-theory/ [54]: https://www.youtube.com/channel/UCQDaVPUG47zdvYTyWxAvpgw/videos [55]: https://katherinemwood.shinyapps.io/lakens_effect_sizes/ [56]: https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00863/full [57]: http://imaging.mrc-cbu.cam.ac.uk/imaging/TransformingVisualAngleAndPixelSize [58]: https://sourceforge.net/projects/robustcorrtool/ [59]: https://www.mathworks.com/matlabcentral/fileexchange/6874-two-way-repeated-measures-anova [60]: https://github.com/jdkoen/roc_toolbox [61]: https://link.springer.com/article/10.3758/s13428-016-0796-z [62]: https://github.com/ritcheym/shinyapps [63]: https://david-baranger.shinyapps.io/InteractionPoweR_analytic/ [64]: https://shiny.ieis.tue.nl/sample_size_justification/ [65]: https://dalejbarr.github.io/aa-powersim/ [66]: https://haiyangjin.github.io/2020/09/simulate-data-v2/ [67]: https://cran.r-project.org/web/packages/pwr/vignettes/pwr-vignette.html [68]: https://haiyangjin.github.io/2020/05/eta2d/ [69]: https://andysbrainbook.readthedocs.io/en/latest/ [70]: https://fmrif.nimh.nih.gov/public/fmri-course/index_html [71]: https://www.youtube.com/watch?v=yA65FuSpOMs&feature=youtu.be [72]: https://neurostars.org/ [73]: https://github.com/ntblab/neuropipe-support/blob/rc-0.3/doc/tutorial_intro/tutorial.rst [74]: https://github.com/alylab/neuropipe [75]: https://www.dropbox.com/s/ra9ec5507osafib/Neuropipe-Tips.docx?dl=0 [76]: http://neuroelf.net/ [77]: http://www.franciscopereira.org/simitar/ [78]: https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing [79]: http://bids.neuroimaging.io/ [80]: https://github.com/poldracklab/mriqc [81]: https://github.com/poldracklab/fmriprep [82]: http://mumfordbrainstats.tumblr.com/ [83]: https://practicalfmri.blogspot.com/ [84]: http://biorxiv.org/content/early/2017/03/23/119594 [85]: https://academic.oup.com/scan/article/7/6/738/1646637/A-power-calculation-guide-for-fMRI-studies [86]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide [87]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils [88]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide [89]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM [90]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes [91]: https://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf [92]: https://github.com/memobc/memolab-fmri-qa [93]: http://gazealyze.sourceforge.net/ [94]: https://bubbleview.namwkim.org/ [95]: https://kywch.github.io/ [96]: http://turkeyes.mit.edu/ [97]: https://medialab.github.io/iwanthue/ [98]: https://serialmentor.com/dataviz/ [99]: https://www.mathworks.com/matlabcentral/fileexchange/37105-plot-spread-points-beeswarm-plot [100]: https://cran.r-project.org/web/packages/beeswarm/index.html [101]: https://www.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m [102]: https://www.mathworks.com/matlabcentral/fileexchange/48576-circulargraph [103]: https://www.sessions.edu/color-calculator/ [104]: https://color.adobe.com/create/color-wheel/ [105]: http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3 [106]: https://github.com/Conclusio/matlab-epsclean [107]: http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.76027&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false [108]: https://git-scm.com/book/en/v2 [109]: https://osf.io/preprints/psyarxiv/6tzh8/ [110]: https://eglerean.wordpress.com/2016/04/19/git-in-10-minutes/ [111]: https://github.com/translationalneurosurgery/translationalneurosurgery.github.io/blob/master/howtogit.md [112]: https://swcarpentry.github.io/git-novice/ [113]: https://osf.io/cqn4z/ [114]: https://www.dropbox.com/sh/h0u5v1c64i8mwn0/AADJN6FbqSuyMG6mlsBHfJLKa?dl=0 [115]: http://www.hippocampalsubfields.com/ [116]: https://sites.google.com/view/ashs-dox/home