I’ve previously written about Google Scholar. Its usefulness and its instability. I just read a post by Jon Tennant on how to harvest Google Scholar data in R and I thought I would use his code as the basis to generate some nice plots based on Google Scholar data. A script for R is below and can be found here. Graphics are base R but do the job. First of all I took it for a spin on my own data.

Ten years are a long time when it comes to (recent) technologies. The first post on this blog was on the topic of how to present chemistry with three intact dimensions.

My activity on twitter revolves around four accounts. I try to segregate what happens on each account, and there’s inevitably some overlap. But what about overlap in followers? What lucky people are following all four? How many only see the individual accounts? It’s quite easy to look at this in R. So there are 36 lucky people (or bots!) following all four accounts.

I read about Antonio Sánchez Chinchón’s clever approach to use the Travelling Salesperson algorithm to generate some math-art in R. The follow up was even nicer in my opinion, Pencil Scribbles. The subject was Boris Karloff as the monster in Frankenstein. I was interested in running the code (available here and here), so I thought I’d run it on a famous scientist.

A few days ago, I read an article about Damien Hirst’s new spot paintings.

Many projects in the lab involve quantifying circular objects. Microtubules, vesicles and so on are approximately circular in cross section. This quick post is about how to find the diameter of these objects using a computer. So how do you measure the diameter of an object that is approximately circular? Well, if it was circular you would measure the distance from one edge to the other, crossing the centre of the object.

FAIR data is increasingly accepted as a description of what research data should aspire to; F indable, A ccessible, I nter-operable and R e-usable, with Context added by rich metadata (and also that it should be Open). But there are two sides to data, one of which is the raw data emerging from say an instrument or software simulations and the other in which some

PIDapalooza is a new forum concerned with discussing all things persistent, hence PID. You might wonder what possible interest a chemist might have in such an apparently arcane subject, but think of it in terms of how to find the proverbial needle in a haystack in a time when needles might look all very similar.

Another post using R and looking at Twitter data. As I was typing out a tweet, I had the feeling that my vocabulary is a bit limited. Papers I tweet about are either “great”, “awesome” or “interesting”. I wondered what my most frequently tweeted words are. Like the last post you can (probably) do what I’ll describe online somewhere, but why would you want to do that when you can DIY in R? First, I requested my tweets from Twitter.

I wondered how many of the people that I follow on Twitter do not follow me back.