From DeSilva Lab scholar Ellie McNutt:
I took the course Geometric Morphometrics in R hosted by Transmitting Science. GM is an analysis tool based in the programing language R, which allows the user to use 2D or 3D landmark data of an object to quantify shape differences between objects, for example the bones of different species. The course took place in Hostalets de Pierola, which is a small town outside of Barcelona. This course was both an enjoyable and beneficial experience. The course participants were diverse both in the geographic distribution of their home countries and the interests that they were hoping to apply geometric morphometrics (GM). The course is intense and immersive, with participants spending eight hours a day for 5 days learning various ways to apply geometric morphometrics to data. Topics included a basic introduction to R, how to import both 2D and 3D landmark data from multiple sources, how to use R itself to generate landmark data, and how to use multiple R packages to run different types of GM analysis (e.g., elliptical Fourier transform and generalized Procrustes analysis). Participants were encouraged to bring their own data sets, which allowed me to generate the backbone of the code needed for my own experiments while building on the expertise of the course instructor, Dr. Julien Claude.
GM will be a critical component of my dissertation allowing me to make, and statistically quantify, potentially novel differences between different species of primate calcanei. Learning GM in R was ideal for several reasons. First, R allows for much faster and easier handling of large sample sizes compared to other GM programs. Second, the versatility of the program’s coding lets users easily compare subsets of species and landmark data out of the total dataset, which makes it ideal for working with fragmentary fossils. Third, R is a free, open source software which is frequently updated, making it an ideal platform to learn new methodologies. Forth, this course presented an opportunity for me to improve my own understanding of and competence coding in R, which is an extremely useful statistical tool. A working knowledge of both R and GM are valuable skills that improve my CV and marketability when I begin to search for a job.