Here is a running list of resources that I have found helpful in my research and teaching:



TeachPsych Project Syllabus – syllabi that are peer-reviewed within Psychology

NYU syllabus for Graduate seminar on writing grants, reviewing papers, mentoring, etc.

Recommendations for including more women and people of color in Intro Cog Psych assignments

Recommendations for nixing textbooks for Intro Psych courses in favor of other materials

Freedom app to reduce technological distraction in the classroom

Some advice from the PsychOne conference & Elon U’s Teaching & Learning conference

A workshop on how to teach yourself programming

Resources on how to teach a course on the Reproducibility crisis in Psychology (here too: + a syllabus: + more resources:

self-care in classes –

personal feedback using word –

use diverse stock photos –

getting feedback from students –

best cog psych findings on learning –

articles demonstrating bias in student evals –

list of psychology podcasts –

exhaustive list of cognitive biases (good example of data viz):

information asymmetry & trust games (, voting systems (, tragedy of the commons (, game theory and cooperation (

list of websites students might use to cheat (

grad research methods course – (+

teaching materials from ed ted –

how to read academic articles for undergrads –



Run Python locally in the browser & interact w/ JavaScript via pyodide [demo here]

Convolutional neural nets course from Ariel Rokem on DataCamp

Resting state HRF toolboxes (Python, Matlab, SPM)

Best practices for RMarkdown notebooks

Intro to MNE Python with links to a Docker

Shiny APP for power analysis involving multilevel logistic regression

Regularizing FIR models with canonical HRF (fMRI regression with informed priors)

Neurohackademy resources (lectures, tutorials, & code)

Using RMarkdown for paper writing tutorial

Matlab to Python migration guide

Automatically creating a docker from Github repository

Machine Learning tutorials, part I, part II, part III

Color conversion module in Python

Grouped stats package in R

Making Matlab code verifiably correct

Efficient R programming textbook (free, open source)

Code for regression and correlation Bayesian analysis

Making your own website using the blogdown package in R

Neurolearn, a package that does machine learning on neurovault data

Eight best practices for writing code


more mri analysis pipelines –

r bootcamp –

r verbal expressions –

awesome R for resources –

making reproducible version of code in R for any browser –

resources for how to teach r stats on a short timeline –

(probably should look up these resource books:

free r resources on simulations:

more r stats resources on stats:

crash course in docker –

reorder plots in ggplot –

hannah moshontz’s course on R –

website for data viz in r –

more recs for how to teach r in stats –

more mri analysis tutorials/resources –

more mri analysis tutorials/resources –

primer on machine learning –

r power analysis package for conflict tasks –

anova r package for power analysis –

(note to self: peter also sent that link before with the power analysis for mixed effects models i think)

codebook R package for reproducibility –

r package for preprocessing eyetracking data (

arrays in r (

tips for learning r for first time (

intro to r for python programmers (

use python with r (

html + r (

task tracking and project management with r (

python based course on analyzing mri data (

saving your installed r packages when upgrading (

r studio shortcut to make sure lines are not misaligned –

browser based python –

building a website in R –

r package that automatically searches stackoverflow for error in console –

formal models of categorization and learning in r –

annotated script on cleaning up qualtrics data in r –

syllabus on teaching r –

learning python’s numerical stack –

rstudio code to run on someone else’s machine –

making osf interact with r –

interactive color package in r –

connecting git and r studio –

course on learning r –



Controlling for confounds & an accompanying blog post

A visual explanation of mixed effects modeling

Using gganimate to generate plots that illustrate how pseudo-correlations can arise, part I and part II

Partitioning variance in meta-analysis

diff in modeling –

power analysis –

recommendations for methodology textbooks –

how to teach statistical thinking –

advice on high powered constraints –

website on visualizing probability distributions –

use mixed models for likert data –

teach all stats as linear models – (teach stats without p values –

cronbach’s alpha is not a good measure of reliability –

comparing analysis measures with rm anovas –

understanding effect sizes –

why you shouldn’t say this study is underpowered –



What is a mental disorder? & Problems with measuring depression

apps for various RT distributions –

reporting guidelines for mri –

power guidelines for correlational research –


Data Visualization:

A set of useful posts compiled by other folks

Raincloud plots with code in R, Python, and Matlab (also here for Matlab)

Estimation plots to visualize differences between groups

Overview of different types of visualization plots

GGridges in R to visualize changes to distributions over time

estimation plots –


Advice from Other Researchers:

A start-up document on resources this researcher wishes he had when he was a student

Advice to your younger self in academia from a host of researchers

Grant writing advice thread

Advice to graduate students

Add your master’s thesis to thesis commons to get indexed

owning the copyright to your figures –

tips on how to say no –

2 academic tools that could be useful –

changing emails to sound like a boss –

hannah schacter’s list of resources:

advice on talks –

red flags for pple in grad school –



Microsoft open databases



A list of women and gender minorities in computational cognitive science

Preregistration template (and another one:

prof dev –

lab handbooks –

more prof dev –

making grad school curricula explicit –

check if citations support or contradict article cited –


To look at & categorize later:

Crowd-sourcing teaching Python sources at this twitter thread