Resources
If you have knowledge, let others light their candle at it - Margaret Fuller
For lab members
For everyone
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 ❤️ Jupyter notebooks for analyzing data in python, R, or Julia. Google Colab is our favorite way to Jupyter notebook (because its simple and accessible). But 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: We love the tools developed by the Wharton Credibility Lab (here at UPenn!) including:
- ResearchBox for sharing resources (a simpler alternative to OSF)
- AsPredicted for preregistrations
- Exbuilder is a tool we developed to conduct reproducible research with docker containers
Teaching materials: Katie developed 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
Websites and documentation: Our lab website and internal documentation are made with bearblog (a simple, anti-consumerism blogging platform we ❤️). In the past, we've used gitbook for internal documentation and gitub pages (or google sites) for our lab website.
Database and infrastructure:
We ❤️ simple things that are free, open to everyone, and focused on content.