Recent posts
The rise of community-owned monopolies
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. In this pose, I will look at another candidate: the dominance of Open Source communities in the development of scientific software.
Pharo year one
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.
Industrialization of scientific software: a case study
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.
The industrialization of scientific research
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? In this post, I will make an attempt at formulating an hypothesis: the underlying cause for the reproducibility crisis is the ongoing industrialization of scientific research.
The computational notebook of the future (part 2)
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.
Is reproducibility good for scientific progress? (a paper review)
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. The Twitter discussion set off some alarm bells for me, in particular the use of the term "reproducibility" in the abstract, without specifying which of its many interpretations and application contexts everybody referred. But that's just the Twitter discussion, let's turn to the more relevant question of what to think of the paper itself (preprint on arXiv).
The computational notebook of the future
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.
Exploring Pharo
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.
Knowledge distillation in computer-aided research
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. I'll briefly present both of them, before coming to the main point of this post, which is the integration of digital knowledge, and in particular software, into the knowledge distillation process.
Literate computational science
Since the dawn of computer programming, software developers have been aware of the rapidly growing complexity of code as its size increases. Keeping in mind all the details in a few hundred lines of code is not trivial, and understanding someone else's code is even more difficult because many higher-level decisions about algorithms and data structures are not visible unless the authors have carefully documented them and keep those comments up to date.
Tags: computational science, computer-aided research, emacs, mmtk, mobile computing, polycrisis, programming, proteins, python, rants, reproducible research, science, scientific computing, scientific software, social networks, software, source code repositories, sustainable software
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