A few years ago, I discovered Mike Caulfield's The Garden and the Stream: A Technopastoral and understood why I wasn't happy with my blog. Blogs are streams, timelines of posts. Each post has a timestamp, and is considered "finished". Later changes are technically possible, but culturally limited to corrections. A blog post is considered a published essay, and therefore comes with a date of publication.

A few days ago, Google announced its experimental project Open Source Insights, which permits the exploration of the dependency graph of Open Source software. My first look at it ended with a disappointment: in its initial stage, the site considers only the package universes of Java, JavaScript, Go, and Rust. That excludes most of the software I know and use, which tends to be written mainly in C, C++, Fortran, and Python.

In my last post, I have discussed the two main types of scientific models: empirical models, also called descriptive models, and explanatory models. I have also emphasized the crucial role of equations and specifications in the formulation of explanatory models. But my description of scientific models in that post left aside a very important aspect: on a more fundamental level, all models are stories.

It is often said that science rests on two pillars, experiment and theory. Which has lead some to propose one or two additional pillars for the computing age: simulation and data analysis. However, the real two pillars of science are observations and models. Observations are the input to science, in the form of numerous but incomplete and imperfect views on reality. Models are the inner state of science.

Many people are asking for my opinion on the recent impressive success of AlphaFold at CASP14, perhaps incorrectly assuming that I am an expert on protein folding. I have actually never done any research in that field, but it's close enough to my research interests that I have closely followed the progress that has been made over the years. Rather than reply to everyone individually, here is a public version of my comments.

Computational reproducibility has become a topic of much debate in recent years. Often that debate is fueled by misunderstandings between scientists from different disciplines, each having different needs and priorities. Moreover, the debate is often framed in terms of specific tools and techniques, in spite of the fact that tools and techniques in computing are often short-lived.

Over the last years, an interesting metaphor for information and knowledge curation is beginning to take root. It compares knowledge to a landscape in which it identifies in particular two key elements: streams and gardens. The first use of this metaphor that I am aware of is this essay by Mike Caulfield, which I strongly recommend you to read first.

Dear software engineers, Many of you were horrified at the sight of the C++ code that Neil Ferguson and his team wrote to simulate the spread of epidemics. I feel with you. The only reason why I am less horrified than you is that I have seen a lot of similar-looking code before. It is in fact quite common in scientific computing, in particular in research projects that have been running for many years.

In his 1962 classic "The Architecture of Complexity", Herbert Simon described the hierarchical structure found in many complex systems, both natural and human-made. But even though complexity is recognized as a major issue in software development today, the architecture described by Simon is not common in software, and in fact seems unsupported by today's software development and deployment tools.

Malleable systems are software systems that are designed to be modified and extended by their users, eliminating the usually strict borderline between developers and users.

One question I have been thinking about in the context of reproducible research is this: Why is all stable software technology old, and all recent technology fragile? Why is it easier to run 40-year-old Fortran code than ten-year-old Python code? A hypothesis that comes to mind immediately is growing code complexity, but I'd expect this to be an amplifier rather than a cause.