Today we are pleased to announce that we have received new funding from the Gordon and Betty Moore Foundation. The $894k grant will help us improve infrastructure for R packages and enable us to move towards a science first package ecosystem for the R community. You may have already noticed some developments on this front when we announced our automated documentation server back in June.

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.

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.

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 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.

Over the past week this tweet was doing the rounds. I’m not sure where it comes from or precisely what its original context was, but it appeared in my feed from folks in various student analytics and big data crowds. The message I took was “measurement looks complicated until you pin it down”. But what I took from this was something a bit different. Once upon a time the idea of temperature was a complex thing.

**Shallow Impact. Tis the season. **In case people didn’t know— the world of scientific publishing has seasons: There is the Inundation season, which starts in November as authors rush to submit their papers before the end of year. Then there is the Recovery season beginning in January as editors come back from holidays to tackle the glut.

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.

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.

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 (EMBO J, Peer J, Royal Soc, Nature Chem). It would be great if more journals did this.