By R. Stuart Geiger, Alejandra Gonzalez-Beltran, Robert Haines, James Hetherington, Chris Holdgraf, Heiko Mueller, Martin O'Reilly, Tomas Petricek, Jake VanderPlas (authors in alphabetical order)
Data and software have enmeshed themselves in the academic world, and are a growing force in most academic disciplines (many of which are not traditionally seen as "data-intensive"). Many universities wish to improve their ability to create software tools, enable efficient data-intensive collaborations, and spread the use of "data science" methods in the academic community.
The fundamentally cross-disciplinary nature of such activities has led to a common model: the creation of institutes or organisations not bound to a particular department or discipline, focusing on the skills and tools that are common across the academic world. However, creating institutes with a cross-university mandate and non-standard academic practices is challenging. These organisations often do not fit into the "traditional" academic model of institutes or departments, and involve work that is not incentivised or rewarded under traditional academic metrics. To add to this challenge, the combination of quantitative and qualitative skills needed is also highly in-demand in non-academic sectors. This raises the question: how do you create such institutes so that they attract top-notch candidates, sustain themselves over time, and provide value both to members of the group as well as the broader university community?…Continue Reading