Returning to the subject of personal knowledge graphs Kyle Scheer has an interesting repository of Markdown files that describe academic disciplines at https://github.com/kyletscheer/academic-disciplines (see his blog post for more background). If you add these files to Obsidian you get a nice visualisation of a taxonomy of academic disciplines.

It has been an … interesting month. But I did manage to finish a small project.

I discuss the advantages of writing technical documents in plain text. And I give you resources to learn how to do it.

I stumbled across this tweet yesterday (no doubt when I should have been doing other things), and disappeared down a rabbit hole. Emerging, I think the trip was worth it.   Markdown wikis Among the tools listed by @zackfan01 were Obsidian and Roam, neither of which I heard of before.

Whilst working on the blog guide, Stefanie Butland and I consolidated knowledge we had already gained, but it was also the opportunity to up our Rmd/Hugo technical game.Our website uses Hugo but not blogdown ¹ to render posts: every post is based on an .md file that is either written directly or knit from an .Rmd file.We wanted to provide clear guidance for both options, and to stick to the well-documented Hugo way of e.g. inserting

Last year we reported on the joy of using commonmark and xml2 to parseMarkdown content, like the source of this website built withHugo, in particular to extractlinks,at the time merely to count them. How about we go a bit further and usethe same approach to find links to be fixed? In this tech note we shallreport our experience using R to find broken/suboptimal links and fixthem.What is a bad URL?

Tired of presentations made with a full-featured (as in: full of features you are never going to use) software suite stored in unhandy proprietary formats? OK, I am sorry for this rhetorical question.

Remember our recent post showing that one can wrangle Markdown filesprogrammatically without regex? Thattech note showed how to convert Markdown bodies to XML in order toextract information from them.

A while ago weonboarded anexciting package, codemetarby Carl Boettiger. codemetar is an R specificinformation collector and parser for the CodeMetaproject. In particular, codemetar candigest metadata about an R package in order to fill the termsrecognized by CodeMeta. This meansextracting information from DESCRIPTION but also from e.g. continuousintegration ¹ badges in the README!

You might have read my blog post analyzing the social weather ofrOpenScionboarding,based on a text analysis of GitHub issues. I extracted text out ofMarkdown-formatted threads with regular expressions. I basicallyhammered away at the issues using tools I was familiar with until itworked! Now I know there’s a much better and cleaner way, that I’llpresent in this note. Read on if you want to extract insights abouttext, code, links, etc.