I’ve previously crunched times for local Half and Full Marathons here on quantixed . Last weekend was the Kenilworth Half Marathon (2018) over a new course. I thought I’d have a look at the distributions of times and paces of the runners. The times are available here. If the Time and Category for finishers are saved as a csv, the script below works to generate the following plots.
Some great scientific data gets posted on Twitter. Sometimes I want to take a closer look and this post describes a strategy to do so. Edit: I received a request to take down the 3D volume images derived from the example dataset I used in this post. I’ve edited the post below so that is now a general guide. Grab the video It can be a bit difficult to the grab video from Twitter. The best way I’ve found is using youtube-dl.
We have a new paper out. It’s not exactly news, because the paper has been up on bioRxiv since December 2016 and hasn’t changed too much. All of the work was done by Nick Clarke when he was a PhD student in the lab. This post is to explain our new paper to a general audience.
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 However, this work is not peer reviewed.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.
