Konrad Hinsen's Blog

What can we do to check scientific computation more effectively?

It is widely recognized by now that software is an important ingredient to modern scientific research. If we want to check that published results are valid, and if we want to build on our colleagues’ published work, we must have access to the software and data that were used in the computations. The latest high-impact statement along these lines is a Nature editorial that argues that with any manuscript submission, authors should also submit the data and the software for review. I am all for that, and I hope that more journals will follow.

Data science in ancient Greece

Data science is usually considered a very recent invention, made possible by electronic computing and communication technologies. Some consider it the fourth paradigm of science, suggesting that it came after three other paradigms, though the whole idea of distinct paradigms remains controversial. What I want to point out in this post is that the principles of data science are much older than most of today’s practitioners imagine. Let me introduce you to Apollonius, Hipparchus, and Ptolemy, who applied these principles about 2000 years ago.

Stability in the SciPy ecosystem: a summary of the discussion

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. For the benefit of people discovering it late, here is a summary of the main arguments and my reply to them.

A plea for stability in the SciPy ecosystem

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. The first was the announcement of a plan for dropping support for Python 2. The second was a pointer to a recent presentation by Nathaniel Smith entitled “Inside NumPy” and dealing mainly with the NumPy team’s plans for the near future. Lots of material to think about… and comment on.

There is no such thing as software development

It’s hard to find an aspect of modern life that is not influenced in some way by software. Some of it is very visible, for example the Web browser I start on my computer. Other software is completely invisible, such as the software controlling my car’s diesel engine. Some software is safety critical, for example flight control software in airplanes. Other software is used in a much more futile way, such as playing games. I could go on listing characteristics in which different software packages differ, but I will leave it at that - I don’t really expect anyone to disagree about the ubiquity and diversity of software in our increasingly digital world.

Why Python does so well in scientific computing

A few days ago, I noticed this tweet in my timeline:

That sounded like a good read for the weekend, which it was. The main argument the author makes is that C remains unsurpassed as a system integration language, because it permits interfacing with “alien” code, i.e. code written independently and perhaps even in different languages, down to assembly. In fact, C is one of the few programming languages that lets you deal with whatever data at the byte level. Most more “modern” languages prohibit such interfacing in the name of safety - the only memory you can access is memory allocated through your safe language’s runtime system. As a consequence, you are stuck in the closed universe of your language.

Which mistakes do we actually make in scientific code?

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 programs. Each person/team was asked to do a maximum amount of verification and testing before comparing to the other person’s/team’s work.

Reproducible research in the Python ecosystem: a reality check

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. So how reproducible is my published code over time?

Reproducibility does not imply reproduction

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 computational resources. I think this argument misses the point of computational reproducibility.

Sustainable software and reproducible research: dealing with software collapse

Two currently much discussed issues in scientific computing are the sustainability of research software and the reproducibility of computer-aided research. I believe that the communities behind these two ideals should work together on taming their common enemy: software collapse. As a starting point, I propose an analysis of how the risk of collapse affects sustainability and reproducibility.