All of the abstracts from Experimental Biology are public, and sortable by society (i.e. nutrition, anatomy, etc). So I wrote a little program* to scrape all 1,402 of the nutrition poster abstracts to try some visualizations using the software Gephi.

First, here are the top 20 terms appearing in abstracts and the number of times they occur:

Below is the relationship of all of the terms from abstract titles (each point represents a word, and the lines between points reflect when a word appears in titles with that other word). Clearly, much of the language in abstract titles is similar, as it is mostly a large mass of connections, hard to make any sense of.

So, here is a reduced graph with only the terms that appear most frequently. The lines between are weighted by how often the words appear together, but there isn’t a huge difference in frequency.

I tried the same using the authors on each abstract with better results. First, here are the authors who appeared most on posters:

And here is the network graph. Each author is a point, and the lines reflect when they are connected to other authors. This shows us that most research groups do not have authors who appear on other posters, but there are several who have multiple connections among others.

*I used Python to scrape, and R to reduce the number of words and to create a readable file for Gephi.