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 […]
Category: computing
Rollercoaster III: yet more on Google Scholar
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 […]
Ten Years vs The Spread: Calculating publication lag times in R
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 […]
Cloud Eleven: A cloud-based code sharing solution for IgorPro
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 […]
Rollercoaster II: more on Google Scholar citations
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 […]
I’m not following you II: Twitter data and R
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 […]
Frankly, Mr. Shankly
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 […]
Paintball’s Coming Home: generating Damien Hirst spot paintings
A few days ago, I read an article about Damien Hirst’s new spot paintings. I’d forgotten how regular the spots were in the original spot paintings from the 1990s (examples are on his page here). It made me think that these paintings could be randomly generated and so I wrote a quick piece of code […]
Esoteric Circle
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 […]
The Sound of Clouds: wordcloud of tweets using R
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 […]