Pledging My Time

The end of the month sees the Coventry Half Marathon. I looked at what constitutes a good time over this course, based on 2015 results. I thought I’d post this here in case any one is interested.



The breakdown of runners by category for the 2015 event. Male Senior (MSEN) category has the most runners, constituting a wide age grouping. There were 3565 runners in total, 5 in an undetermined category and 9 DNFs. These 14 were not included in the analysis.

The best time last year was 01:10:21!

Good luck to everyone running this (or any other event) this year.

Edit: The 2016 Coventry Half Marathon happened today. I’m updating this post with the new data.


The width of violins has no special significance compared to 2015. Fastest time this year was 1:08:40 in the MSEN category.


There were more runners this year than last (4212 finishers), across all categories. Also this year there was a wheelchair category, which is not included here as there were only four competitors. FWIW, I placed somewhere in the first violin, in the lower whisker :-).

Congrats to everyone who ran and thanks to the all the supporters out on the course.

The post title is taken from “Pledging My Time” a track from Blonde on Blonde by Bob Dylan

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 selective journals in a bid to achieve a high impact factor is an enormous waste of academics’ time”.

First off, the JIF is a flawed metric in a number of ways but even at face value, what does this analysis really tell us?


This plot is taken from the post by Jon Tennant at Science Open.

As others have pointed out:

  1. The rejection rate is dominated by desk rejects, which although very annoying, don’t take that much time.
  2. Without knowing the journal name it is difficult to know what to make of the plot.

The data are available from Figshare and – thanks to Thomson-Reuters habit of reporting JIF to 3 d.p. – we can easily pull the journal titles from a list using JIF as a key. The list is here. Note that there may be errors due to this quick-and-dirty method.

The list takes on a different meaning when you can see the Journal titles alongside the numbers for rejection rate and JIF.



Looking for familiar journals – whichever field you are in – you will be disappointed. There’s an awful lot of noise in there. By this, I mean journals that are outside of your field.

This is the problem with this analysis as I see it. It is difficult to compare Nature Neuroscience with Mineralium Deposita…

My plan with this dataset was to replot rejection rate versus JIF2014 for a few different journal categories, but I don’t think there’s enough data to do this and make a convincing case one way or the other. So, I think the jury is still out on this question.

It would be interesting to do this analysis on a bigger dataset. Journals releasing their numbers on rejection rates would be a step forward to doing this.

One final note:

The Orthopedic Clinics of North America is a tough journal. Accepts only 2 papers in every 100 for an impact factor of 1!


The post title is from “Throes of Rejection” by Pantera from their Far Beyond Driven LP. I rejected the title “Satan Has Rejected my Soul” by Morrissey for obvious reasons.