This post has been in my drafts folder for a while. With the World Cup here, it’s time to post it! It’s a rule that a 3D assembly of hexagons must have at least twelve pentagons in order to be a closed polyhedral shape. This post takes a look at why this is true. First, some examples from nature. The stinkhorn fungus Clathrus ruber , has a largely hexagonal layout, with pentagons inserted.

I’ve written in the past about instrumentalism, the scientific practice of treating theories as tools that can be evaluated by their usefulness, rather than as claims that can be evaluated as true or false. If you haven’t tried this way of looking at science, I highly recommend it! But if theories are tools, what are they used for? What makes a theory more or less useful?

A while back, the lab moved to an electronic lab notebook (details here and here). One of the drivers for this move was the huge number of hard copy lab note books that had accumulated in the lab over >10 years. Switching to an ELN solved this problem for the future, but didn’t make the old lab note books disappear. So the next step was to archive them and free up some space.

This wonderful movie has repeatedly popped up into my twitter feed. http://quantixed.org/wp-content/uploads/2018/04/ctenophore.mp4 It was taken by Tessa Montague and is available here (tweet is here). The movie is striking because of the way that cytokinesis starts at one side and moves to the other. Most model systems for cell division have symmetrical division. Rob de Bruin commented that “it makes total sense to segregate this way”.

I read about Antonio Sánchez Chinchón’s clever approach to use the Travelling Salesperson algorithm to generate some math-art in R. The follow up was even nicer in my opinion, Pencil Scribbles. The subject was Boris Karloff as the monster in Frankenstein. I was interested in running the code (available here and here), so I thought I’d run it on a famous scientist.

We are excited to announce the first release of a new Julia package that let’s you run our Data Retriever software with a native Julia interface. For those of you not familiar with Julia it is a new programming language that is similar to R and Python, has a central focus on data analysis, and is designed from the ground up to be fast. It is an emerging scientific programming and data analysis language.

You might be familiar with Arthur C. Clarke’s Third Law, “Any sufficiently advanced technology is indistinguishable from magic.” Clarke tucked this away in a footnote without explanation, but it fits in with the discussion of magic in Chapter III of James Frazer’s magnum opus The Golden Bough . These two works have shaped a lot of my thoughts about science, technology and the way we interact with our world.

As with children’s toys and clothes, books aimed at children tend to be targeted in a gender-stereotyped way. This is a bit depressing.

The big buzz over the past few years has been Data Science. Corporations are opening Data Science departments and staffing them with PhDs, and universities have started Data Science programs to sell credentials for these jobs. As a linguist I’m particularly interested in this new field, because it includes research practices that I’ve been using for years, like corpus linguistics and natural language processing.

Scaling-up ecological patterns and processes is crucial to understanding the effects of environmental change on natural systems and human society. We are piloting a Data Science Challenge where multiple groups attempt to use the same remote sensing data from low flying airplanes to infer the location and type of trees in forests. This will allow forests to be studied in detail at much larger scales than is currently possible.