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 post is about a citation analysis that didn’t quite work out. I liked this blackboard project by Manuel Théry looking at the influence of each paper authored by David Pellman’s lab on the future directions of the Pellman lab.
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.
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 However, this work is not peer reviewed.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.
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.
So quantixed occasionally gets correspondence from other researchers asking for advice. A recent email came from someone who had been “scooped”. What should they do? Before we get into this topic we have to define what we mean by being scooped. You were working on something that someone else was also working on – maybe you knew about this or not and vice versa – but they got their work out before you did.
Here’s a quick tech tip. We’ve been writing papers in TeX recently, using Overleaf as a way to write collaboratively. This works great but sometimes, a Word file is required by the publisher. So how do you convert from one to the other quickly and with the least hassle? If you Google this question (as I did), you will find a number of suggestions which vary in the amount of effort required. Methods include latex2rtf or pandoc.
Back in 2014, I posted an analysis of the time my lab takes to publish our work. This post is very popular. Probably because it looks at the total time it takes us to publish our work. It was time for an update. Here is the latest version.
We have a new paper out! This post is to explain what it’s about. Cancer cells often have gene fusions . This happens because the DNA in cancer cells is really messed up. Sometimes, chromosomes can break and get reattached to a different one in a strange way. This means you get a fusion between one gene and another which makes a new gene, called a gene fusion.
