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
A recent paper in PLOS One made some noise in my twittersphere over the Christmas days. It compares the productivity of writing scientific documents using Microsoft Word and using LaTeX, and concludes that Microsoft Word is so clearly superior that, in the interest of saving taxpayers' money, scientific publishers should abandon LaTeX to allow authors to become more productive.
The release of NumPy 1.9 a few days ago was a bit of a revelation for me. For the first time in the combined history of NumPy and its predecessor Numeric, a new release broke my own code so severely thatI don't see any obvious way to fix it, given the limited means I can dedicate to software maintenance.
The importance of reproducibility in computational science is being more and more recognized, which I think is a good sign. However, I also notice a lot of confusion about what reproducibility means exactly, and also confusion about the difference (if any) between reproducibility and replicability.
Most scientists have found out by now that a lot has been going wrong with scientific publishing over the years. In many fields, scientific journals are no longer fulfilling what used to be their primary role: disseminating and archiving the results of scientific studies.
Over the last few months I have been exploring the Racket language for its potential as a language for computational science, and it's time to summarize my first impressions. Why Racket? There are essentially two reasons for learning a programing language: (1) getting acquainted with a new tool that promises to get some job done better than with other tools, and (2) learning about other approaches to computing and programming.
Why do people write computer programs? The answer seems obvious: in order to produce useful tools that help them (or their clients) do whatever they want to do. That answer is clearly an oversimplification. Some people write programs just for the fun of it, for example.
The other day I was looking at the release notes for the recently published release 1.8 of NumPy, the library that is the basis for most of the Scientific Python ecosystem.
Today I have published the first release of ActivePapers for Python, available on PyPI or directly from the Mercurial repository on Bitbucket. The release coincides with the publication of my first scientific paper for which the complete code and data is in the supplementary material, available through the J. Chem. Phys. Web site or from Figshare.