Communicating through code
Clean code and computational reproducibility.
Expert of the month
Postdoctoral research fellow, Stanford University
Elizabeth is currently a Wu Tsai interdisciplinary postdoctoral research fellow at Stanford University, working between the Poldrack and Linderman labs. As a psychologist and computational neuroscientist, her work focuses on developing methods to characterize complex, naturalistic cognition in health and disease.
As part of her research, she helps to develop several tools used across the open Python ecosystem such as Nilearn. She is also actively involved in community initiatives to promote open, interdisciplinary science. She currently serves as chair of the Organization for Human Brain Mapping (OHBM) Communications Committee and as a handling editor at the Journal of Open Source Software (JOSS). Additionally, she is a certified instructor with Software Carpentry.
Seminar/Workshop
March 28, 1-2 pm ET
The Neuro, Jeanne Timmins Amphitheatre
And on Zoom (register for link)
Academia is driven by publications, and so software is often undervalued and prone to error. However, one could argue that the software and instructions behind a publication are closer to the scholarship itself. Therefore, we need code that is understandable both to other scientists and to our future selves. With the goal of making code more understandable, we will discuss four conventions in coding: naming, organization, documentation, and optimization. We will come to see code as communication of science, enabled by clean code and computational reproducibility.