Additional Resources

Internal and External Resources for Reproducibility at the Urban Institute

Contribute

As you come across additional internal or external resources around reproducibility that you would like included here, let us know by opening an issue or pull request in our GitHub repository.

1 Internal Resources

The following are additional resources that are developed and maintained by Urban staff:

• Code Reviews: Fill out the intake form to request that an experienced programmer review your code (e.g. to ensure that the results can be replicated, that the code is logical and readable, etc.).
• TECH Intake Form: Fill out the intake form to request support from an experienced programmer to set up a project to enable reproducible workflows (e.g. to onboard project staff to Git/GitHub, set up directories and data storage on Urban’s infrastructure, automate a manual or error-prone task, etc.).
  
• R Users Group: Visit the group’s website to develop your skills in R, an open-source programming language that is widely used at Urban to read and process data, develop data visualizations and maps, build dashboards, conduct analyses, and more.
• Python Users Group: Visit the group’s website to develop your skills in Python, an open-source programming language that is used at Urban to automate tasks, run microsimulation models, and more.

We also recommend joining the following Slack channels:

• #github
• #reproducible-research
• #r-users-group
• #python-users

2 External Resources

The following are external resources that describe challenges and opportunities to promoting reproducibility:

• Harvard Data Science Review on Reinforcing Reproducibility and Replicability
• How to fix your scientific coding errors
• Exercises for Lab Groups to Prevent Research Mistakes
• The Good Research Code Handbook