Resources

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

 

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: https://twitter.com/emilynordmann/status/1179653156462051328 + a syllabus: https://twitter.com/juliafstrand/status/1176536652132626433 + more resources: https://twitter.com/juliafstrand/status/1159824738019172352)

self-care in classes – https://twitter.com/SarahE1214/status/1179780512904290306

personal feedback using word – https://twitter.com/brendawyang/status/1178476058389209089

use diverse stock photos – https://twitter.com/sarahegaither/status/1177206175315529728

getting feedback from students – https://twitter.com/GuyBoysen/status/1176541044575539200

best cog psych findings on learning – https://twitter.com/DigitalPromise/status/1167566962593017858

articles demonstrating bias in student evals – https://twitter.com/rebeccakreitzer/status/1162091729052299264

list of psychology podcasts – https://twitter.com/jayvanbavel/status/1145794404545957894

exhaustive list of cognitive biases (good example of data viz): https://twitter.com/jayvanbavel/status/1144963062979604480

information asymmetry & trust games (https://twitter.com/RosasBrain/status/1143553498702741504), voting systems (https://twitter.com/RosasBrain/status/1143552997105909762), tragedy of the commons (https://twitter.com/RosasBrain/status/1143552623322124288), game theory and cooperation (https://twitter.com/RosasBrain/status/1143551112009531393)

list of websites students might use to cheat (https://twitter.com/MattCrump_/status/1139655252695797761)

grad research methods course – https://twitter.com/NeilLewisJr/status/1134459551124807681 (+ https://twitter.com/hardsci/status/1084123254841761792)

teaching materials from ed ted – https://twitter.com/jayvanbavel/status/1143201071759863808

how to read academic articles for undergrads – https://twitter.com/juliafstrand/status/1082375249990221825

 

Coding:

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

fMRIPrep

more mri analysis pipelines – https://twitter.com/karimjerbineuro/status/1179732280656441345

r bootcamp – https://twitter.com/CostelloCK/status/1178361434264064000

r verbal expressions – https://twitter.com/strnr/status/1173736067532558342

awesome R for resources – https://twitter.com/emorinlessard/status/1164299758556442624

making reproducible version of code in R for any browser – https://twitter.com/dsquintana/status/1162002047794864128

resources for how to teach r stats on a short timeline – https://twitter.com/juliafstrand/status/1154406110327169025

(probably should look up these resource books: https://twitter.com/tomfaulkenberry/status/1149422921472978945)

free r resources on simulations: https://twitter.com/EikoFried/status/1147147484508168192

more r stats resources on stats: https://twitter.com/LisaDeBruine/status/1147809399525773312

crash course in docker – https://twitter.com/EmilyRiederer/status/1147317706384990208

reorder plots in ggplot – https://twitter.com/juliasilge/status/1145602792079314944

hannah moshontz’s course on R – https://twitter.com/hmoshontz/status/1144655395782197248

website for data viz in r – https://twitter.com/emilynordmann/status/1136377834493136896

more recs for how to teach r in stats – https://twitter.com/LizBonawitz/status/1135904642779492352

more mri analysis tutorials/resources – https://twitter.com/AndysBrainBlog/status/1135019035152789509

more mri analysis tutorials/resources – https://twitter.com/ptoncompmemlab/status/1130883245753753600

primer on machine learning – https://twitter.com/talyarkoni/status/1131765886174531589

r power analysis package for conflict tasks – https://twitter.com/MattCrump_/status/1120743605675745280

anova r package for power analysis – https://twitter.com/lakens/status/1132897409951064066

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

codebook R package for reproducibility – https://twitter.com/rubenarslan/status/1130465688244621314

r package for preprocessing eyetracking data (https://twitter.com/jgeller_phd/status/1120453554467160064)

arrays in r (https://twitter.com/dvaughan32/status/1118586138594357248)

tips for learning r for first time (https://twitter.com/PsychNeurd/status/1121837144526315520)

intro to r for python programmers (https://twitter.com/gvwilson/status/1121726626402852864)

use python with r (https://twitter.com/StatGarrett/status/1121877687092617216)

html + r (https://twitter.com/JoannaMelon/status/1121847459259531265)

task tracking and project management with r (https://twitter.com/dataandme/status/1122973088415612928)

python based course on analyzing mri data (https://twitter.com/lukejchang/status/1123651203529834497)

saving your installed r packages when upgrading (https://twitter.com/lakens/status/1126800584244383745)

r studio shortcut to make sure lines are not misaligned – https://twitter.com/kyle_e_walker/status/1117801495884386304

browser based python – https://twitter.com/RosasBrain/status/1118202101283397632

building a website in R – https://twitter.com/Andreasheenn/status/1113374992140525568

r package that automatically searches stackoverflow for error in console – https://twitter.com/pawel_appsilon/status/1109545516264841216

formal models of categorization and learning in r – https://twitter.com/todd_gureckis/status/1100370766237974528

annotated script on cleaning up qualtrics data in r – https://twitter.com/hmoshontz/status/1100862721795469316

syllabus on teaching r – https://twitter.com/danyurovsky/status/1094393254848663552

learning python’s numerical stack – https://twitter.com/GaelVaroquaux/status/1090142057878994944

rstudio code to run on someone else’s machine – https://twitter.com/juliafstrand/status/1085638720395624453

making osf interact with r – https://twitter.com/BrianNosek/status/1085264465594068993

interactive color package in r – https://twitter.com/AchimZeileis/status/1084641988941672448

connecting git and r studio – https://twitter.com/HeidiBaya/status/1082211221334564866

course on learning r – https://twitter.com/ozanjaquette/status/1077300020544765952

 

Statistics:

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 – https://twitter.com/ch402/status/1179053652025188352

power analysis – https://twitter.com/EricDWilkey/status/1178762421319675904

recommendations for methodology textbooks – https://twitter.com/gard_arianna/status/1173949618410926080

how to teach statistical thinking – https://twitter.com/deevybee/status/1172097761342365701

advice on high powered constraints – https://twitter.com/lakens/status/1160212113362735105

website on visualizing probability distributions – https://twitter.com/RosasBrain/status/1143553877129564160

use mixed models for likert data – https://twitter.com/mcxfrank/status/1122629866841448448

teach all stats as linear models – https://twitter.com/jonaslindeloev/status/1110907133833502721 (teach stats without p values – https://twitter.com/JeffRouder/status/1109466434407587844)

cronbach’s alpha is not a good measure of reliability – https://twitter.com/saraweston09/status/1109486565158379520

comparing analysis measures with rm anovas – https://twitter.com/Eshjolly/status/1097644614700421123

understanding effect sizes – https://twitter.com/page_eco/status/1090924043807543297

why you shouldn’t say this study is underpowered – https://twitter.com/richarddmorey/status/1089871429078827008

 

Research:

What is a mental disorder? & Problems with measuring depression

apps for various RT distributions – https://twitter.com/jonaslindeloev/status/1171784311126798336

reporting guidelines for mri – https://twitter.com/RemiGau/status/1163212790213095424

power guidelines for correlational research – https://twitter.com/R__INDEX/status/1114925907112808449

 

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 – https://twitter.com/strnr/status/1115238569696792576

 

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 – https://twitter.com/alexpghayes/status/1176979953507868672

tips on how to say no – https://twitter.com/jayvanbavel/status/1172569958854926336

2 academic tools that could be useful – https://twitter.com/NeilLewisJr/status/1138187538831749120

changing emails to sound like a boss – https://twitter.com/LucyStats/status/1131285346455625734

hannah schacter’s list of resources: https://twitter.com/hannah_schacter/status/1116080011981676544

advice on talks – https://twitter.com/BucarLiz/status/1096488970379317249

red flags for pple in grad school – https://twitter.com/StacyTShaw/status/1090371752297787392

 

Databases:

Microsoft open databases

 

Other:

A list of women and gender minorities in computational cognitive science

Preregistration template (and another one: https://twitter.com/annaveer/status/1133097905898889216)

prof dev – https://twitter.com/NicholasStrayer/status/1181254905018761216

lab handbooks – https://twitter.com/samuelmehr/status/1139733291899080705

more prof dev – https://twitter.com/abbysussman/status/1129134900408389634

making grad school curricula explicit – https://twitter.com/BrookeMacnamara/status/1129432848232898560

check if citations support or contradict article cited – https://twitter.com/chrisgorgo/status/1125125705941958656

 

To look at & categorize later:

Crowd-sourcing teaching Python sources at this twitter thread

https://github.com/IndrajeetPatil/ggstatsplot

https://www.ucl.ac.uk/pals/research/experimental-psychology/blog/tms-cognitive-neuroscience-workshops/

https://www.sciencedirect.com/science/article/pii/S0092656613000858

https://osf.io/82dsj/

http://shinyapps.org/apps/RGraphCompendium/index.php?utm_content=bufferb2277&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

http://www.cns.nyu.edu/malab/applying.html

https://osf.io/93znh/