Posts from 2015-11
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.
My new blog is hosted on GitHub and powered by Frog, a static Web site generator that mixes my posts written as plain Markdown files with HTML templates based on the Bootstrap framework to produce the pages you can read. This setup gives me much more control over my blog, while at the same time making it easier for me to publish new posts.
The one feature that will disappear is the possibility to subscribe to my blog in order to be informed about new posts by e-mail. If you have a GitHub account, you can get the same effect by following updates to the repository that contains my blog. But the easiest way to learn about new posts is to follow me on Twitter.
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. More precisely, I look at "digital scientific knowledge", by which I mean scientific knowledge that is processed using computers. This includes both software and digital datasets. For simplicity, I will concentrate on software, but much of the reasoning applies to datasets as well, if only because the precise meaning of non-trivial datasets is often defined by the software that treats them.
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. Here is an outline of the difficulties, in the hope that it will (1) help others who face similar problems and (2) contributes a little bit to improving the situation.
Tags: computational science, computer-aided research, emacs, mmtk, mobile computing, programming, proteins, python, rants, reproducible research, science, scientific computing, scientific software, social networks, software, source code repositories, sustainable software
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