Elizabeth is a PhD candidate in the Integrated Program in Neuroscience at McGill University, where she works under the supervision of Dr. Jean-Baptiste Poline. Her work focuses on improving the inferences we can draw with high-dimensional, naturalistic datasets. To do this, she is benchmarking emerging methods and developing open source tools to improve reproducible workflows.
Modern neuroscience research generates significant amounts of code and data, but these research objects do not fit nicely into a PDF and so are often invisible to readers. Even when explicitly shared, it can be difficult for researchers to directly link these objects to the results of a published paper; for example, it may be unclear which version of a dataset was used to generate a specific figure. With the support of CONP, I am working to adapt Jupyter Book — an open source tool for combining code, data, and narrative text into a single document — to better fit into open publishing workflows for neuroscience research.