Towards the end of 2015, I started distance running. I thought it’d be fun to look at the frequency of my runs over the course of 2016. Most of my runs were recorded with a GPS watch. I log my cycling data using Rubitrack, so I just added my running data to this. This software is great but to do any serious number crunching, other software is needed.

Our recent paper on “the mesh” in kinetochore fibres (K-fibres) of the mitotic spindle was our first adventure in 3D electron microscopy. This post is about some of the new data analysis challenges that were thrown up by this study. I promised a more technical post about this paper and here it is, better late than never.

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This post follows on from a previous post on citation distributions and the wrongness of Impact Factor.

Following on from the last post about publication lag times at cell biology journals, I went ahead and crunched the numbers for all journals in PubMed for one year (2013). Before we dive into the numbers, a couple of points about this kind of information. Some journals “reset the clock” on the received date with manuscripts that are resubmitted. This makes comparisons difficult.

My interest in publication lag times continues. Previous posts have looked at how long it takes my lab to publish our work, how often trainees publish and I also looked at very long lag times at Oncogene. I recently read a blog post on automated calculation of publication lag times for Bioinformatics journals. I thought it would be great to do this for Cell Biology journals too.

I thought I’d share this piece of analysis looking at productivity of people in the lab. Here, productivity means publishing papers. This is unfortunate since some people in my lab have made some great contributions to other peoples’ projects or have generally got something going, but these haven’t necessarily transferred into print. Also, the projects people have been involved in have varied in toughness.

Having recently got my head around violin plots, I thought I would explain what they are and why you might want to use them. There are several options when it comes to plotting summary data. I list them here in order of granularity, before describing violin plots and how to plot them in some detail.

I realised recently that I’ve maintained a consistent iTunes library for ~10 years. For most of that time I’ve been listening exclusively to iTunes, rather than to music in other formats.