This post has been in my drafts folder for a while. With the World Cup here, it’s time to post it! It’s a rule that a 3D assembly of hexagons must have at least twelve pentagons in order to be a closed polyhedral shape. This post takes a look at why this is true. First, some examples from nature. The stinkhorn fungus Clathrus ruber , has a largely hexagonal layout, with pentagons inserted.

In a previous post I made a little R script to crunch Google Scholar data for a given scientist. The graphics were done in base R and looked a bit ropey. I thought I’d give the code a spring clean – it’s available here. The script is called ggScholar.R (rather than gScholar.R). Feel free to run it and raise an issue or leave a comment if you have some ideas.

There have been several posts on this site about publication lag times. You can read them here. Lag times are the delays in the dissemination of scientific data introduced by the process of publishing the paper in a journal. Nowadays, your paper can be online in a few hours using a preprint server. However, this work is not peer reviewed. Journals organise a formal peer review and provide some sort of certification of the work.

This post is something of a “how to” guide. The problem is how can you share code with a small team and keep it up-to-date? For ImageJ, the solution is simple. You can make an ImageJ update site and then push any updated code to the user when they startup ImageJ. For IgorPro, there is no equivalent. Typically I send ipf files to someone and they run the code, but I have to resend them whenever there’s an update.

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.

As a geek, the added bonus of exercise is the fun that you can have with the data you’ve generated. A recent conversation on Twitter about the accuracy of wrist-based HRMs got me thinking… how does a wrist-based HRM compare with a traditional chest-strap HRM? Conventional wisdom says that the chest-strap is more accurate, but my own experience of chest-strap HRMs is that they are a bit unreliable. Time to put it to the test.

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.

This deserved a bit of further explanation, due to the stupidity involved. My code was giving an unexpected result and I was having a hard time figuring out the problem. The unexpected result was that a resampled set of 2D coordinates were not being rotated randomly. I was fortunate to be able to see this otherwise I would have never found this bug and probably would’ve propagated the error to other code projects.

A while back, the lab moved to an electronic lab notebook (details here and here). One of the drivers for this move was the huge number of hard copy lab note books that had accumulated in the lab over >10 years. Switching to an ELN solved this problem for the future, but didn’t make the old lab note books disappear. So the next step was to archive them and free up some space.

This wonderful movie has repeatedly popped up into my twitter feed. http://quantixed.org/wp-content/uploads/2018/04/ctenophore.mp4 It was taken by Tessa Montague and is available here (tweet is here). The movie is striking because of the way that cytokinesis starts at one side and moves to the other. Most model systems for cell division have symmetrical division. Rob de Bruin commented that “it makes total sense to segregate this way”.

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.