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.

It's the season when everyone writes about the past year, or even the past decade for a year number ending in 9. I'll make a modest contribution by summarizing my experience with Pharo after one year of using it for projects of my own. My first contact with Pharo happened a bit more than one year ago, when I signed up for the Pharo MOOC in October 2018. But following a MOOC means working on exercice problems defined by someone else.

A coffee break conversion at a scientific conference last week provided an excellent illustration for the industrialization of scientific research that I wrote about in a recent blog post. It has provoked some discussion on Twitter that deserves being recorded and commented on a more permanent medium. Which is here. I was chatting with a colleague who I have been meeting at such occasions for about 15 years.

Over the last few years, I have spent a lot of time thinking, speaking, and discussing about the reproducibility crisis in scientific research. An obvious but hard to answer question is: Why has reproducibility become such a major problem, in so many disciplines? And why now?

A while ago I wrote about my ideas for a successor of today's computational notebooks. Since then I have made some progress on a prototype implementation, which is the topic of this post. Again I have made a companion screencast so that you can get a better idea of how all this works in practice.