What Are Open Resources?

If you've ever looked into computational science, you've probably run into the assumed barriers -- expensive software, the need for specialized training, etc. The thing is, you can get around a lot of that thanks to open resources. Even the issue of computing hardware is less of a barrier these days thanks to generous free tiers in services like GitHub Codespaces and computing resources from programs like the NSF ACCESS allocation scheme.

At its core, "open resources" is just a term for a collection of tools, knowledge, and communities that are free and available for anyone to use, share, and even build upon.

This includes things like:

• Open-source software. The main languages used in science, like Python and R, are open-source. The same goes for the key libraries you need for analysis or machine learning. You don't buy a license; you just download and use them. This will be true of 99% of the tools you need to get started and the remaining 1% are going to be tools you need to write yourself to do things exactly the way you want.
• Open educational materials. This is everything from free online courses to the kind of informal guides that get people up and running on a topic or analysis technique fast. It's learning on your own time, without the cost.
• Open data. These are publicly available datasets from research, government, or other organizations that you can use to practice on real-world problems. Dealing with field-specific data is always going to be the best-case scenario for applying novel techniques in your own research or interests, but a good surrogate source of data would be any dataset that is roughly the same in terms of number of features, dimensionality, etc.
• Open communities. This is a big one. When you get stuck—and you will—you can go to forums, GitHub, or places like Stack Overflow and get help from people who are already deep in the field.

The main impact of all this is that it pretty much removes the cost barrier to getting started. It makes your own initiative the most important factor, though we strongly recommend reaching out to others working in this space so that you have a peer mentor that can push you in the right direction if you're feeling stuck. Also, if you find there's a skill or tool you need that doesn't exist, embrace the open source philosophy and make it! You can find other interested people to help build it in the ASCSN forums (forum.ascsn.net), or reach out on GitHub (github.com/ascsn)

This is the first post in a series where we'll be breaking these areas down. In the future, we'll focus specifically on open-source software and how you can get started with a language like Python. If you want to get a head start, head over to the GitHub organization and take a look at what people have been building there: github.com/ascsn