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.

A few days ago, a discussion in my Twitter timeline caught my attention. It was about a very high-level model for the process of scientific research whose conclusions included the affirmation that reproducibility does not improve the convergence of the research process towards truth.

Regular readers of this blog may have noticed that I am not very happy with today's state of computational notebooks, such as they were pioneered by Mathematica and popularized by more recent free incarnations such as Jupyter, R markdown, or Emacs/OrgMode. In this post and the accompanying screencast (my first one!), I will explain what I dislike about today's notebooks, and how I think we can do better.

One of the more interesting things I have been playing with recently is Pharo, a modern descendent of Smalltalk. This is a summary of my first impressions after using it on a small (and unfinished) project, for which it might actually turn out to be very helpful. The first time I read about Smalltalk was in the August 1981 issue of Byte magazine.

There is an important and ubiquitous process in scientific research that scientists never seem to talk about. There isn't even a word for it, as far as I now, so I'll introduce my own: I'll call it knowledge distillation . In today's scientific practice, there are two main variants of this process, one for individual research studies and one for managing the collective knowledge of a discipline.

Since the dawn of computer programming, software developers have been aware of the rapidly growing complexity of code as its size increases.

My most recent paper submission (preprint available) is about improving the verifiability of computer-aided research, and contains many references to the related subject of reproducibility. A reviewer asked the same question about all these references: isn't this the same as for experiments done with lab equipment? Is software worse? I think the answers are of general interest, so here they are.