Konrad Hinsen's blog

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 d…

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 oth…

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.

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.

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.

A much cited essay by Bret Victor, "Explorable Explanations", argues for supporting and encouraging active reading in communicating ideas.

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?

Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved.

At the recent SciCodes Symposium, I brought up the question of reviewing research software during the panel discussion.

This is a follow-up to my earlier post entitled "Going for robustness", focusing on scientific research.

I suspect that most people in the Western world (at least) are realizing that we are living in interesting times.

This is a contribution to the Challenge problem: Fearless extensibility by the Malleable Systems Collective.

Yesterday I participated in the International workshop “Software, Pillar of Open Science”, organized by the French Committee for Open Science.

Regular visitors to my blog have probably noticed that it looks different now.

This post is a follow-up to my previous one, Deconstructing the Mastodon client.

Ever since I joined Twitter in 2011, and then moved to Mastodon in 2022, I have been unhappy with the timeline view proposed by both of these communication platforms as their main interface.

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.

A few days ago, Google announced its experimental project Open Source Insights, which permits the exploration of the dependency graph of Open Source software.

In my last post, I have discussed the two main types of scientific models: empirical models, also called descriptive models, and explanatory models.

It is often said that science rests on two pillars, experiment and theory.

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.

Computational reproducibility has become a topic of much debate in recent years.

Over the last years, an interesting metaphor for information and knowledge curation is beginning to take root.

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.

In his 1962 classic "The Architecture of Complexity", Herbert Simon described the hierarchical structure found in many complex systems, both natural and human-made.

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?

It's the season when everyone writes about the past year, or even the past decade for a year number ending in 9.

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.

Over the last few years, I have spent a lot of time thinking, speaking, and discussing about the reproducibility crisis in scientific research.

A while ago I wrote about my ideas for a successor of today's computational notebooks.

A few days ago, a discussion in my Twitter timeline caught my attention.

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.

One of the more interesting things I have been playing with recently is Pharo, a modern descendent of Smalltalk.

There is an important and ubiquitous process in scientific research that scientists never seem to talk about.

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 recent article in "The Atlantic" has been the subject of many comments in my Twittersphere.

It is widely recognized by now that software is an important ingredient to modern scientific research.

Data science is usually considered a very recent invention, made possible by electronic computing and communication technologies.

The plea for stability in the SciPy ecosystem that I posted last week on this blog has generated a lot of feedback, both as comments and in a lengthy Twitter thread.

Two NumPy-related news items appeared on my Twitter feed yesterday, just a few days after I had accidentally started a somewhat heated debate myself concerning the poor reproducibility of Python-based computer-aided research.

It's hard to find an aspect of modern life that is not influenced in some way by software.

A few days ago, I noticed this tweet in my timeline: I 'still' program in C.

Over the last few years, I have repeated a little experiment: Have two scientists, or two teams of scientists, write code for the same task, described in plain English as it would appear in a paper, and then compare the results produced by the two program…

A few years ago, I decided to adopt the practices of reproducible research as far as possible within the technical and social constraints I have to live with.

In discussions about computational reproducibility (or replicability, or repeatability, according to the preference of each author), I often see the argument that reproducing computations may not be worth the investment in terms of human effort and comput…

Two currently much discussed issues in scientific computing are the sustainability of research software and the reproducibility of computer-aided research.

The importance of reproducibility in computer-aided research (and elsewhere) is by now widely recognized in the scientific community.

Think of all the things you hate about using computers in doing research.