For how to use the UH Department of Mathematics `compute' servers, see this tutorial. Also take a look at these guidelines for using these `compute' machines.
See how to get reimbursed for trips here.
This link provides a quick guide to use the UH computing clusters (Sabine/Opuntia).
For those just starting, here is a helpful guide. The links point to other useful resources.
Here are some basic, but very nicely done introductory lessons to Python, Git, R, etc.
Here is a tutorial for using git for version control. Here is another guide to the basics.
Here is a Matlab code that simulates a simple predator-prey model. This code serves as a simple example for defining a model and simulating its dynamics.
You can use ChatGPT for coding, but you have to be careful. Here are some tips.
Here is a good online book on data visualization by Claus Wilke.
BioRender is a good resource for creating nice figures related to biological systems.
A set of slides from Sam Way on good data visualizatio practices.
Google Scholar is probably the best way to start. Here are some useful tips on how to use Google Scholar. Web of Science is a good alternative, but you will have to log in through the UH library website .
Try to get the papers through our library website, if pdfs are not openly available. Sci-hub is an alternative, but is, strictly speaking, not legal.
Semantic Scholar is a more sophisticated resource, and is great if you are starting in a field. Connected Papers is built using the Semantic Scholar database, and gives more refined output.
This article gives an overview of other resources you can use to keep abreast with new research, and find older, relevant papers.
Here is an excellent collection of 10 rules. You will have to modify it for a math paper, but the main ideas are universal.
For an alternative view from a mathematician's perspective, you can read this column by Steve Krantz
Some rules for how to present computational research in Jupyter Notebook (provided by PLOS).
Another useful link for how to write nice papers (provided by Nature).
A couple good discussions on how to use LaTeX from Roy Goodman and Dan Larremore (slides).
This presentation about how to speak by Patrick Winston has been viewed 15+ million times, and not without reason.
The difference between different job titles in academia can be quite confusing, and this might be deliberate. Here is a good explanation of the different ranks by Brett Devereaux. There is a bunch of other interesting stuff on this webpage, especailly if you are interested in the history of the ancient world.
Github is best for working on code collaborateively.
We use Dropbox Paper or Google Docs to work on text files collaboratively.
Overleaf is best for working collaboratively on LaTeX documents, although sometimes Dropbox works.
The following is a list of interesting and potentially useful seminars.
UH Department of Mathematics Data Enabled Science/Networks Seminar.
Baylor College of Medicine (BCM) Neuroscience Seminar.
Statistics Colloquium at Rice.
Center for Theoretical Biological Physics (CTBP) holds interesting seminars. See a list of events here.
The Neurofax calendar lists events across Houston institutions.
The following are links that contain a list of resources for a particular subject
A potentially useful tool for reporoducible research in Python.
Astral Codex Ten, formerly Slate Star Codex is my favorite blog. Scott Aaronson's blog is also great. Let me know of others you enjoy.
The following is a list of links to different repositories of tutorials and code mostly for high dimensional inference.
Scott Linderman's collection of state inference method implementations
GLM tutorial by Jonathan Pillow
Find some useful mailing lists to join below.