Time for an update to a previous post. For the past few years, I have been using an automated process to track citations to my lab’s work on Google Scholar (details of how to set this up are at the end of this post). Due to the nature of how Google Scholar tracks citations, it means that citations get added (hooray!) but might be removed (booo!). Using a daily scrape of the data it is possible to watch this happening.

A while back I visited Artistes & Robots in Paris. Part of the exhibition was on the origins of computer-based art. Nowadays this is referred to as generative art , where computers generate artwork according to rules specified by the programmer.

I was recently reminded of the wonders of paulstretch by a 8-fold slowed down version of Pyramid Song by Radiohead. Paulstretch is an audio manipulation widget that can stretch or compress the time of an audio recording. Note that it doesn’t “slow down” or “speed up” a recording, it resamples the audio and recasts it over a different time scale while maintaining the pitch.

Garmin Connect has a number of plots built in, but to take a deeper dive into all your fitness data, you need to export a CSV and fire up R. This post is a quick guide to some possibilities for running data.  There’s a few things that I wanted to look at. For example, how does my speed change through the year? How does that compare to previous years? If I see some trends, is that the same for short runs and long runs?

We have a new preprint out – it is a cautionary tale about using GFP nanobodies in cells. This short post gives a bit of background to the work. Please read the paper if you are interested in using GFP nanobodies in cells, you can find it here. Paper in a nutshell: Caution is needed when using GFP nanobodies because they can inhibit their target protein in cells.

Anyone that maintains a website is happy that people out there are interested enough to visit. Web traffic is one thing, but I take greatest pleasure in seeing quantixed posts being cited in academic papers. I love the fact that some posts on here have been cited in the literature more than some of my actual papers. It’s difficult to track citations to web resources.

Our lab is international. People born all over the world have come to work in my group. I’m proud of this fact, especially in the current political climate. I’ve previously used the GoogleMaps API to display a heat map on our lab webpage. It shows where in the world people in the lab come from. This was OK, but I wanted to get an R based solution to make this graphic to make it easier to automate updates.

I read this recent paper about very highly cited papers and science funding in the UK. The paper itself was not very good, but the dataset which underlies the paper is something to behold, as I’ll explain below. The idea behind the paper was to examine very highly cited papers in biomedicine with a connection to the UK. Have those authors been successful in getting funding from MRC, Wellcome Trust or NIHR?

This week Erick Martins Ratamero and I put up a preprint on vesicle packing. This post is a bit of backstory but please take a look at the paper, it’s very short and simple. The paper started when I wanted to know how many receptors could fit in a clathrin-coated vesicle. Sounds like a simple problem – but it’s actually more complicated.

I saw today on Twitter that a few labs were examining the gender balance of their papers and posting the ratios of male:female authors. It started with this tweet. This analysis is simple to perform, but interpreting it can be hard. For example, is the research group gender balanced to start with? How many of the authors are collaborators? Nonetheless, I have the data for all of my papers, so I thought I’d take a quick look too.

This is a quick “how to” post. There is a licensed version of PyMol (MacPyMol) available, but the open source version can be installed on a Mac free of charge. The official page has a guide, which is not terribly detailed, and I found this excellent guide which is unfortunately out-of-date.