TL;DR I claim that modern science is synonymous with open source software.\nThis post explains why, why it matters, and what you can (and should) do next.
\nWhy do you care about (open source) software? - Everyone
\nI spend a lot of my time working on software.\nI have been asked why software matters more times than I can remember.\nSoftware is, people say, not science.\nIt’s a time sink, something to rush past in the pursuit of what really matters: results (and papers if you’re in academia).\nPublish or perish.
\nWell. I think software matters. In fact, I think open source software is science. Or, at least computational science.\nAnd this post tells you why.\nWhy we as scientists must insist on the scientific method and why that means working on open and reproducible software.\nThis post is not easy to write.\nIt challenges many of the current trends in academia, but it is an important move towards better science that doesn’t turn us all insane.
\nIf you look up science
\non Wikipedia, here’s what hits you:
\nScience is a systematic discipline that builds and organises knowledge in the form of testable hypotheses and predictions about the universe. - Wikipedia
\nNow, go and grab a random arXiv paper.\nIt clearly contains “knowledge” of some sort.\nBut, does the paper contribute predictions that are testable and can by systematically organized?\nCan you test it?\nCan you systematize it?
\nThe answer is never a flat no
\n, but it’s hard.\nYou rarely have direct access to that knowledge.
\nIf the organism carries a 'small-scale model' of external reality and of its own possible actions within its head, it is able to try out various alternatives, ... and in every way to react in a much fuller, safer, and more competent manner to the emergencies which face it.
\nIn his excellent book The Nature of Explanation
\n, Kenneth James Williams Craik posits that we use small simulations
\nof reality to explain and predict the world outside.
\nThis point seems obvious today, but it highlights the goal of pursuing science in the first place:\nyou, as an acting entity, improves your inner model to the point that you can make better predictions than before.\nThe inner model here is critical: if the arXiv paper does not help their readers predict the world, it is not science.\nThis is why computational reproducibility matters–software is how we encode and share predictive models.
\nRecall that according to Wikipedia, it is not enough to demonstrate results alone.\nResults have to be (1) systematic and they have to be (2) testable.
\nIt is entirely possible that the given paper is too hard to understand or unaccessible to the audience for other reasons.\nThat does not mean that there are no scientific insights to find—readers may find ways to systematize them on their second or third reading.\nNo, it means that you specifically cannot take the idea as your own, test it, and use it to improve your world model.
\nReproducibility, in this context, is not only the duplication of results. It is the ability to take the scientific idea, embed it into your own inner model, adapt it, and build upon it—or discard it because it reduces predictabilitly.
\nIf an idea is not reproducible, the findings cannot be expanded. And are, therefore, useless.
\nThis becomes clear if we do a quick thought-experiment where we replace “software model” with “mathematical model”.\nJust as we wouldn’t accept a physics paper that said our equations predict X but we won’t show the math
\n, we shouldn’t accept (computational) science that hides its methods.
\nHow many fields have been held back, and how many people have had their careers disrupted, because of a buggy program? - Greg Wilson
\nSoftware is ubiquitous in modern science. Anything from CoVid models to search algorithms to lab protocols are build on software built by other people. Researchers are busy people. They don’t bother to look through all software dependencies to verify correctness, understand implementation details, or check for potential errors that could invalidate results.
\nFrom that follows that the scientific results depend on the software.\nIf the software is wrong, the science is wrong.\n(Software bugs already cause numerous retractions, such as here, here, here, and several places here).
\nAnd that is well and good, because at some point we have to trust and rely on other’s work.\nFor that to happen, it (software) needs to be reliable.
\nWe found that software needs to be
\nModifiability is important for science for the same reason that equations are important for scientific predictions.\nReliability is crucial because we want systematic improvement of our knowledge, not flaky and partial results that only work occasionally.
\nThis is what open source software gives us.\nWe can change code and retrofit it to suit our needs (just think about Hugging Face models) and we can iterate upon it to continue to improve it.\nIt already generates trillions in value and there is room for much, much more.
\nOf course, open source software is not a perfect cure.\nThere are IP and security concerns, bugs can still occur, and stability can be a problem.\nBut at least the imperfections are on public record.\nThey can be amended and improved, just like our scientific understanding.\nFrom that perspective, one can claim that open source software is the scientific method—just in simulation.
\nIf we accept these premises we can ask: what would truly open (computational) science look like?
\nEvery result is instantly reproducible.\nWhen 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.
\nScientific software evolves like Wikipedia.\nClimate models aren’t developed in isolation by single labs, but maintained by global communities. When a researcher in Kenya discovers a bug in atmospheric turbulence calculations, the fix propagates instantly to climate simulations worldwide. Models improve continuously rather than languishing in academic silos.
\nThe pace of discovery accelerates. Instead of each researcher building from scratch, we stand on shoulders of giants whose work is not just readable, but runnable and modifiable. Scientific progress compounds at an unprecedented rate.
\nTrust in science strengthens. When climate models, economic forecasts, and medical recommendations are built on transparent, auditable code, public confidence grows. Science communication improves because the models themselves become part of the conversation—not just their conclusions.
\nThis isn’t utopian fantasy. Every piece already exists—open source communities, reproducible environments, collaborative development platforms. We just need to shape them into a coherent vision for how science should work in the digital age.
\nThe question isn’t whether this future is possible. The question is: how quickly can we build it?
\nI posit that open source software is a necessary condition if we are to science in a computerized world.\nSoftware is executable mathematical models
\nthat we should prioritize much higher.
\nWe still have work to do and this is how you can help:
\nThe scientific revolution succeeded because it insisted on transparency, reproducibility, and constant scrutiny. The open source movement embodies these same principles for software, but there is much more work to be done.
\nWill you help make software scientific?
","headings":[{"level":1,"text":"Science is open software","id":"science-is-open-software"},{"level":2,"text":"What is science?","id":"what-is-science"},{"level":3,"text":"The good explanation - inner models","id":"the-good-explanation-inner-models"},{"level":2,"text":"What is reproducibility?","id":"what-is-reproducibility"},{"level":2,"text":"Why is software science?","id":"why-is-software-science"},{"level":2,"text":"Why open source?","id":"why-open-source"},{"level":2,"text":"A vision for future science","id":"a-vision-for-future-science"},{"level":2,"text":"What now?","id":"what-now"}]}}