It’s 2026 and so it’s time for another edition of “the papers I selected for a module that I teach”. Previous selections are here (2025, 2024, 2023, 2022). The list serves as a snapshot of interesting papers published in the previous 12 months or so. I hope it’s useful to others who are looking for lists […]

In my previous post, I shared my annual running stats which were generated in R using summary data from Garmin Connect. The code I use to generate these summaries was beginning to get a bit unwieldy, so I have now rebased it into a package.

In this post I’ll use R to recap my running data from 2025. Previous round-ups are here (2021, 2022, 2023, not sure why I didn’t post one for 2024…). I had a goal of running a total distance of 3650 km in 2025.

I’m a music fan who is anti-streaming and instead I stubbornly maintain a large music collection. At this time of year, streamers receive a round-up of their year’s listening in a “wrapped” report.

Running a marathon is a big deal. It takes a lot of time to train to run a good time, and it takes a while to recover. So, if you’re chasing a marathon PB (personal best) time, you need to choose which Marathon to target wisely. How can we use data to help our decision?

I’ve dabbled a bit with napari – the python-based image viewer – but never needed to use it seriously. I had a real use case so I thought I’d write up the process of how I got going with napari, in case it helps others.

Here we go again! I ran the Mainova Frankfurt Marathon 2025 and wanted to look at the race results. How can we do this using R? I couldn’t see an easy way to download the data, so I used R to scrape them.

An idea for a masters-level class or journal club alternative: “Where are they now?” but for big splash papers published in the 2000s. Take a few papers published around 20 years ago, then follow what happened subsequently.

It’s plot recreation time! In this post, we’ll look at how we can recreate a plot in R. I thought it might be useful to provide the solution but also to detail the process I went through to get there. We have a FACS plot taken from a BD FACS Aria machine: Briefly, it’s a histogram of the fluorescence levels of 10000 cells from three different sources.

If you are into running, chances are that you will be chasing your PB (personal best) times. This post is about using R to search for your PBs, and to monitor them over time. Usually runners target four distances for PBs: 5 km, 10 km, half marathon and full marathon. It’s likely that a PB will come in a race of exactly that distance, but not necessarily. For example, you can hit a 5K PB during a 10K race.

The software that we use to do our work is our academic software stack . I often see requests for advice on which software works well. Since recommendations from labs that have road-tested a few options are quite valuable, I thought I would document what we’re currently using and why. I have tried to note if we’ve switched, tried alternatives and if we’re happy. The lab is Mac-based and we prefer FOSS solutions.