"If you have knowledge, let others light their candle at it"
We've assembled a list of resources for researchers, including tools we use, things we like, tutorials we've made, experiment code, and more. We hope these resources are useful to you. Please borrow or adapt anything you like. We love to share and we trust you to cite us or others where appropriate.
We love Jupyter notebooks for analyzing data in python, R, or Julia
Google Colab is our favorite way to Jupyter notebook (because its simple and accessible)
Google Colab opens notebooks in Python by default. R and Julia remain a bit of a hidden secret
You can start Colab in R using colab.to/r and Julia using colab.to/julia
We recommend using Colab in R in conjunction with Groundhog
We also love the jupyter/datascience-notebook container and have some tutorials for using that if you prefer
Exbuilder is a tool we developed to conduct reproducible research with docker containers
We also use and love the tools developed by the Wharton Credibility Lab (here at UPenn!) including:
ResearchBox for sharing data, code, preregistrations, etc (a simpler alternative to OSF)
AsPredicted for preregistrations
Groundhog for writing reproducible R code
Katie teaches several courses at UPenn that you are welcome to take inspiration from
Wiki and websites
Lab Wiki, created with gitbook (free for academic use) and inspired by many others
Lab Website, created with google sites, but formerly created with github pages (tutorial here).