Science Is Open Software

Science should be more like this, in current times, yes.

But until much of academia is burned to the ground, or until science can be properly separated from modern academia, this will never be so. The current academic incentives are all wrong: low-quality research is rewarded and results in publications, whereas high-quality research (that takes time, and usually reveals that most exciting publications depend on p-hacking or other highly data-dependent analyses and selective presentations) is not published or actively blocked during peer review.

So instead you get BS arguments about how data can't be released for various privacy concerns (when in reality the vast majority of most datasets are trivial to scrub of identifying factors, and even in more complex datasets where you need to consider k-anonymity, it is still trivial to release data that allows replication of core analyses), and academic science is increasingly irrelevant unless it is tied to tech and industry, where producing junk actually has real negative economic and personal consequences.

I don't know what world this article / post lives in, but it isn't the messy world of actual reality.

> Every result is instantly reproducible. When you read a paper claiming that a new drug reduces symptoms by 30%, you click a link and watch the exact analysis run in your browser. The data processing, statistical tests, and visualizations execute in seconds using the same environment the authors used—preserved perfectly through reproducible containers.

At least some journals have this as a stipulation e.g. https://www.nature.com/nature-portfolio/editorial-policies/r...

Particularly the "data availability" and "Availability and peer review of computer code and algorithm".

However, in my limited experience, of trying to reproduce certain scRNA-seq processing pipelines, in practice it's never available as just a Github link. I can understand that some/many researcher's code is not in good shape, so I think it'll be quite a stretch to have this available.

I do think it's laudable though, to try and make it available. It would certainly have been very useful for me in the past.

I agree with the general sentiment, but there's one major caveat. We should implement reproducible programs on top of a virtual machine spec like JVM or WebAssembly rather than replicate the entire environment. It's more practical to do and doesn't push software towards further centralization. Let people use whatever OS and VM implementation they want, or even write their own.

> modern science is synonymous with open source software.

Another problem with reproducibility is the openness of the underlying data. Many academics are terrified of giving away the golden goose and the software is often useless without the data.

However many scientists do work openly, e.g. The Journal of Open Source Software:

https://joss.theoj.org/

Science is open, but science is not software and software definitely not science.

Replace "Open" with free as in "freedom" gnu.org

Nice read. I believe that traditional software is a great way to showcase the proofs when it comes to physics and mathematics. You can easily code up a theorem in a language of your choice and justify that 'Okay, the output matches the expected value'.

I am particularly fascinated by labs like DeepMind [https://deepmind.google/science/]. The recent advances in their frontier models that are able to predict diseases before they're diagnosed is incredible. This is what AI should be built for and actually do!

We really need to ban tech people from using the word "open".

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TL;DR I claim that modern science is synonymous with open source software. This post explains why, why it matters, and what you can (and should) do next.