In the spirit of “if it took you a while to find out how to do something, write about it”, I will detail a method to approximate the surface area of a 3D shape. Our application here was finding the surface area of a cell but it can be used on any shape. We start with a 3D point set, specified by points of interest in a single cell imaged by 3D confocal microscopy.

On a scientist’s Google Scholar page, there is a list of co-authors in the sidebar. I’ve often wondered how Google determines in what order these co-authors appear. The list of co-authors on a primary author’s page is not exhaustive. It only lists co-authors who also have a Google Scholar profile. They also have to be suggested to the primary author and they need to accept the co-author to list them on the page.

I migrated my personal Mastodon account from mastodon.social to biologists.social recently. If you’d like to do the same, I found this guide very useful. Note that, once you move, all your previous posts are left behind on the old instance. Before I migrated, I downloaded all of my data from the old instance. I thought I’d take a look at what I had posted to see if anything was worth reposting on biologists.social.

I’ve posted in the past about analysing race results in R (most recently here). I ran the 2023 MK Marathon and wanted to have a look at the finishing times. The days of race results being made available as a csv or xls for easy analysis seem to be behind us. Instead they tend to be served up on multiple webpages of 50 athletes’ results at a time. Oh no, 29 pages of results and now Download option…. let’s scrape the data!

I’m not a big movie person. Nonetheless I have a media library with quite a few films in and I wondered how many “films to see before you die”-type movies I had in the collection, and how many were missing. I used R to find the answers. I’ve described previously how to get a plain text dump of a Plex database using WebTools-NG. I did that for the Movies library of my Plex Media Server.

A lot of our recent work revolves around working with conversational data, and one thing that’s struck me is that there are no easy ways to create compelling visualizations of conversation as it unfolds over time. The most common form seems to be pixelated screenshots of transcription software not made for this purpose.

There are lots of ways for runners and cyclists to analyse training data. A key question most fitness enthusiasts want to know is “how am I doing?”. “How you are doing” is referred to as form . Unsurprisingly, form can be estimated in many ways. One method is using training stress scores (acute training load and chronic training load) to assess form as training stress balance.

It’s been a while since I posted a breakdown of half marathon times. The last time seems to have been 2018. I decided to give my old code a clean-up and quickly crunched the numbers from the 2022 Kenilworth Half Marathon. First, the results: Briefly, the code below reads in a csv file of race results downloaded from the provider.

I recently made my first R package and was asked how I did it. The answer of course was: I searched, read, and stumbled around until it was done. But having gone through the process I figured it was worthwhile summarising what I did and what I found tricky. First off, there are a ton of resources out there that describe how to go about building a package.

A short post to announce TrackMateR, a new R package to analyse TrackMate XML outputs. Code Instructions Background TrackMate is a plug-in for ImageJ which ships with Fiji. It’s essential for single particle tracking work, particularly for microscopy movies. For example, tracking the movement of fluorescent vesicles inside cells. A tracking session generates a TrackMate XML file.