This blog has been on four different platforms since starting in 2007: a custom blogging engine and then Movable Type on Nature Network 2007-2010, Wordpress on the PLOS Blogs Network 2010-2013, and the static blogging engine Jekyll hosted on Github Pages since 2013. It might be time for yet another blogging platform change.
The main reason to switch from Wordpress to Jekyll was the concept of a static site generator: write posts in markdown format, store them in a Github repository, and then have Jekyll automatically generate the HTML pages hosted on Github Pages. The main attraction was the blog posts in markdown format stored in git version control without the need of a database. Jekyll is the glue to make all this work, and I was able to customize Jekyll to my needs, e.g. by using Pandoc for the markdown to html conversion.
While this workflow still makes sense for this blog, there are a number of shortcomings:
What could we do instead?
The separation into API and frontend is of course old news. But for blogs this seems to still be a fairly new concept, in particular when combined with a backend using documents stored in git version control rather than in a database. Wordpress added a REST API Plugin in 2014, and the Ghost blogging framework (which uses a database backend) also seems to go into that general direction. Please ping me if you like the idea and want to contribute, or have implemented something like this already.
Publishing tabular data as blog post
CSV in many ways is for data what Markdown is for text documents: a very simple format that is both human- and machine-readable, and that – despite a number of shortcomings - is widely used. Given the popularity of Markdown for writing blog posts, ...
Auto generating links to data and resources
A few weeks ago Kafkas et al. (2013) published a paper looking at current patterns of how datasets o biological databases are cited in research articles, based on an analysis of the full text Open Access articles available from Europe PMC. ...
Literate programming is a methodology that combines a programming language with a documentation language, thereby making programs more robust, more portable, more easily maintained, and arguably more fun to write than programs that are written only in a high-level language. ...