Scientific Computing References, Guides, and Notes

This site is a living repository of guides, references, notes, and other miscellanea relating to scientific computing. Likely, this is primarily a resource for myslef, but I’ve made it public in the event that it may prove useful to others. Currently, the site is quite sparse but it will might grow over time.

The goal here is to describe/document tools, workflows, systems, approaches/phiolosophies etc. for scientific computing. It’s primarily not about domain-specific methods or tools, but instead focusses on fairly widely applicable resources for general scientific computing. Not only are strong computational skills necessary across pretty much all the STEM fields, but there is growing concesus that open, reproducible science is paramount, and enabled by such computational approaches. Much of what I describe here is relating to attempts to make workflows more open, reproducible, and useful to others.

Questions, Comments, Collaborations, Etc.

If you have questions or found mistakes/errors/problems, or for any other reason, feel free to reach out to me directly (contact info below).

You can also communicate with me via GitHub. This site is built from a GitHub repository which can be found at: Specifically:

  • Use the ‘Issues’ tab to note problems, errors, etc.
  • If you feel compelled to collaborate on any of the content on this site, you can use the ‘Pull Request’ functionality to suggest modifications to the site. Details on how to do that here.

Finally, if you found anything on this site useful - please let me know, I value that feedback and it helps me justify the time spent making these resources publically available. Similarly, if you use anything on this site in your own work (and I hope that you do!), please cite/acknowledge it as appropriate.


Scott Yanco Postdoctoral Associate
Max Planck - Yale Center for Biodiversity Movement and Global Change
Department of Ecology and Evolutionary Biology, Yale University
Jetz Lab

email: scott.yanco [at]


Conda Guide
Guide to using conda, mamba, and boa for reproducible package management.

Quick Tricks for R
Assorted tips and tricks for R computing (not much here for now…)