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.
This post was motivated by Ian Gent's recomputation manifesto and his blog post about it. While I agree with pretty much everything said there, there is one point that I strongly disagree with, and here I'd like to explain the reasons in some detail.
About two years ago I wrote a post about why and how I abandoned Apple's iCal for my agenda management and moved to Emacs org-mode instead. Now I am in the process of making the second step in the same direction: I am abandoning Apple's Address Book and starting to use the "Big Brother DataBase", the most popular contact management system from the Emacs universe.
Altmetrics is one of the hotly debated topics in the Open Science movement today. In summary, the idea is that traditional bibliometric measures (citation counts, impact factors, h factors, ...) are too limited because they miss all the scientific activity that happens outside of the traditional journals.
A while ago I was chatting with two users of my Molecular Modelling Toolkit (MMTK), a library for molecular simulations written in Python. One of them asked me what I would do differently if I were to write MMTK today. That's an interesting question, but not the kind of question I can answer in a sentence or two, so I promised to write a blog post about this. Here it is. First, a bit of history.
This morning I read C. Titus Brown's blog post on how science could be so much better if scientitic data and the software used to work with it were openly available for reuse. One problem he mentions, like many others have done before, is the lack of incentive for publishing anything else but standard scientific papers. What matters for a scientist's career and for grant applications is papers, papers, papers.
Calculators are among the most popular applications for smartphones, and therefore it is not surprising that the Google Play Store has more than 1000 calculators for the Android platform. Having used HP's scientific calculators for more than 20 years, I picked RealCalc when I got my Android phone and set it to RPN mode. It works fine, I have no complaints about it. But I no longer use it because I found something much more powerful.
In an earlier post, I mentioned the Nix package management system as a candidate for ensuring reproducibility in computational science.
When the Greek philosopher Heraclitus pronounced his famous "πάντα ῥεῖ" (everything flows), he most probably was not thinking about software. But it applies to software as much as to other aspects of life: software is in perpetual change, being modified to remove bugs, add features, and adapt it to changing environments.
New programming languages are probably invented every day, and even those that get developed and published are too numerous to mention. New programming languages developed specifically for science and engineering are very rare, however, and that's why such a rare event deserves some publicity.
A two-hour train journey provided the opportunity to watch the video recording of the Panel with Guido van Rossum at the recent PyData Workshop. The lengthy discussion about PEP 225 (which proposes to add additional operators to Python that would enable to have both elementwise and aggregate operations on the same objects, in particular for providing both matrix and elementwise multiplication on arrays with a nice syntax) motivated me