For researchers

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.

• Data analysis 

  • 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 also love the jupyter/datascience-notebook container and have some tutorials for using that if you prefer

    • How to analyze data with a jupyter/datascience-notebook

    • How to customize the jupyter/datasicence-notebook container

• Reproducible Research

• • 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

• Teaching 

  • Katie teaches several courses at UPenn that you are welcome to take inspiration from

    • Data Science for Studying Language and the Mind

    • Language and the Brain 

    • Neurolinguistics 

    • Recent grad seminars: Acquisition of Variation

• 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).