Recent posts
Knowledge refinement in the digital era
Knowledge refinement is the ever ongoing process in science (and beyond it) that shepherds knowledge from lab notebooks into journal articles and then on to review articles, monographs, reference handbooks, university textbooks, and finally professional domain expertise and school education for a wider public. It has been going on for a few centuries, but we hardly talk about it. In fact, I made up the term because I couldn't find an established one. Computational knowledge has not yet found its place in the knowledge refinement process. Why not? And what can we do to make it happen?
Conviviality in computational science
Convivial technology was defined by Ivan Illich in his 1973 book "Tools for conviviality" as technology that supports a convivial society, which is a society that strives to grant each of its members as much agency as is possible without infringing on other members' agency. Conviviality is thus about equality, about the absence of dominance relations. Convivial technology is shaped by its users according to their needs, rather than being controlled by entities such as companies or governments, which then derive power over the user base by exercising control.
Cultures of making and relating
Cultures of Programming - The Development of Programming Concepts and Methodologies is a recent book by Tomáš Petříček that analyses the history of programming from the perspective of five interwoven cultures. It contains a lot of interesting insight, so I encourage you to read it. At the very least, read the first chapter. In this post, I try to relate these five cultures to the wider world of technology, and to the practices of scientific research.
Automating science
The advent of AI agents based on large language models (LLMs) has put the idea of automating the intellectual and cognitive work of researchers on the table. A lively, sometimes even heated discussion is already going on. A frequently missing piece in this debate is the question why we, individually and as a society, actually do science. I will examine this question first, and then consider what it implies for introducing automation into science.
Preparing for scientific deepfakes
By now, most scientists have probably seen figures, tables, and even entire journal articles made by so-called "generative AI", containing more or less subtle mistakes or inconsistencies. What I haven't seen yet, but expect to see soon, is the scientific equivalent of deepfakes: made-up results that come with made-up code that reproduces them. This is likely to become a new challenge for reproducible research.
Explorable explorable explanations
A much cited essay by Bret Victor, "Explorable Explanations", argues for supporting and encouraging active reading in communicating ideas. Explanatory text should thus be complemented by interactive visualizations and computational demonstrations, allowing the reader to actively engage with the ideas. If you haven't read Victor's essay yet, please do so now, and then come back here. It's not very long. What I am going to discuss is a variation on Victor's proposal, and I won't repeat his well-presented arguments.
Explaining software and computational methods
How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it.
Why computational reproducibility matters
Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved. Reproducibility, computational and otherwise, is increasingly recognized as an important aspect of scientific quality control, and mostly considered worth striving for. However, I also note that more and more people, including reproducibility activists, have lost contact with the day-to-day reality in which reproducibility matters. Reproducibility is becoming an item on a checklist, and its precise incarnation the subject of political bickering aimed at making it easy to check off that item. So let's take a look at why computational reproducibility matters for researchers.
Why we should review research software
At the recent SciCodes Symposium, I brought up the question of reviewing research software during the panel discussion. One panelist then raised the question of why we should review research software. I found this question surprising at first, but I do agree that it deserves an answer. Here is mine.
Going for robustness: science
This is a follow-up to my earlier post entitled "Going for robustness", focusing on scientific research.
What is "robust science"? I see at least two interpretations, and I am going to discuss both of them: robustness of scientific findings, and robustness of the process of doing science, which includes in particular the robustness of the web of scientific research institutions: first and foremost universities and research labs, but also learned societies, funding agencies, publishers, etc.
Tags: computational science, computer-aided research, digital scientific notations, 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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