A quick post this week. I write “this week” in an attempt to convince regular readers that weekly posting will continue. I noticed that J. Cell Sci. give download metrics for their papers and that these downloads are categorised into abstract, full-text and PDF. I was interested in how one of my papers performed. After […]
Tag: metrics
Rollercoaster IV: ups and downs of Google Scholar citations
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 […]
Five Get Over Excited: Academic papers that cite quantixed posts
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 […]
One With The Freaks – very highly cited papers in biology
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 […]
For What It’s Worth: Influence of our papers on our papers
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. It reminds me that papers can have impact in the field while others might be […]
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 […]
If I Can’t Change Your Mind
I have written previously about Journal Impact Factors (here and here). The response to these articles has been great and earlier this year I was asked to write something about JIFs and citation distributions for one of my favourite journals. I agreed and set to work. Things started off so well. A title came straight to mind. In the […]
Throes of Rejection: No link between rejection rates and impact?
I was interested in the analysis by Frontiers on the lack of a correlation between the rejection rate of a journal and the “impact” (as measured by the JIF). There’s a nice follow here at Science Open. The Times Higher Education Supplement also reported on this with the line that “mass rejection of research papers by […]
The Great Curve II: Citation distributions and reverse engineering the JIF
There have been calls for journals to publish the distribution of citations to the papers they publish (1 2 3). The idea is to turn the focus away from just one number – the Journal Impact Factor (JIF) – and to look at all the data. Some journals have responded by publishing the data that underlie the JIF […]
What Difference Does It Make?
A few days ago, Retraction Watch published the top ten most-cited retracted papers. I saw this post with a bar chart to visualise these citations. It didn’t quite capture what the effect (if any) a retraction has on citations. I thought I’d quickly plot this out for the number one article on the list. The plot […]