A recurrent theme in computational science (and elsewhere) is the need to combine machine-readable information (which in the following I will call "facts" for simplicity) with a narrative for the benefit of human readers.

Welcome to the last post on this WordPress blog. I have set up a new blog for all my future writing. The reason for the move is that the user interface at WordPress is changing all the time without ever getting better. I like to write my posts on my own computer using Emacs, rather than typing into a rudimentary editing window on a Web site. This is not completely impossible with WordPress, but more hassle than it's worth.

Like all information with a complex structure, scientific knowledge evolves over time. New ideas turn into validated models, and are ultimately integrated into a coherent body of knowledge defined by the concensus of a scientific community. In this essay, I explore how this process is affected by the ever increasing use of computers in scientific research.

We all know that software deployment in a research environment can be a pain, but knowing this as a fact is not quite the same as experiencing it in reality. Over the last days, I spent way more time that I would have imagined on what sounds like a simple task: installing a scientific application written in Python on a Linux machine for use by a group of students in a training session.

Yesterday I participated (as a visitor) in the kickoff meeting for OpenDreamKit, where one recurrent topic of discussion was notebooks, both Jupyter and Sage, including the question if they could be brought together. This reminded me of a recent blog post by Kirill Pomogajko entitled "Why I don't like Jupyter". And it reminded me of my own long-term project of integrating Jupyter with my ActivePapers system for reproducible research.

SciPy 2015 is over, meaning that many non-participants like myself are now busy catching up with what happened by watching the videos.

Three years ago, I first looked at the then-very-new language Julia. Back then, I concluded that there were many interesting features, but also regretted too much bad Matlab influence in the array handling. A hands-on Julia tutorial in my neighborhood was a good occasion to take another look at this language, which has evolved quite a bit since 2012, and continues to evolve rapidly.

Now that the birch pollen season is definitely over, I can draw some conclusions from a two-year experiment with the impressive sample size of one - myself. As you will see, my topic is not so much the experiment itself, but the circumstances in which it happened. I have been allergic to birch pollen for more than thirty years.

In a recent blog post, Titus Brown asks if software is a primary product of science, and basically says "no" (but do read the post for the details). A blog-post length reply by Daniel Katz comes to the opposite conclusion (again, please read the post before continuing here). I left a short comment on Titus' blog but also felt compelled to expand this into a blog post of its own - so here it is. Titus introduces a useful criterion for what

While reading the final report of the reproducibility workshop at XSEDE14, I noticed a statement that I encounter frequently in discussions about reproducible research: In the interest of clarity, let me start by pointing out that within the systematic terminology that I am trying to adopt (see this post for an explanation), I will write "bitwise replicability" from now on, as the problem falls into the technical domain (getting the same result