Minerva-funded researcher Rex Douglass applies advanced computational techniques to social science questions as part of the Center for Peace and Security Studies (cPASS). His DoD funded research with Professor Erik Gartzke, "Forecasting Crisis Dynamics with Machine Coded Data", creates a human-coded dataset of international crisis events and uses machine-learning to measure uncertainty in the human coders, the coding process, and the event itself. That experience has contributed to the creation of the "COVID-19 Dataset Tracking Involuntary Government Restrictions (TIGR)", and to his guide "How to be Curious Instead of Contrarian About COVID-19: Eight Data Science Lessons From ‘Coronavirus Perspective’ (Epstein 2020)" which was picked up by the LATimes and the well-respected "Statistical Modeling, Causal Inference, and Social Science" blog.
Links:
"COVID-19 Dataset Tracking Involuntary Government Restrictions (TIGR)"
https://rexdouglass.github.io/TIGR/TIGR_landing_page.nb.html
"How to be Curious Instead of Contrarian about COVID-19: Eight Data Science Lessons From ‘Coronavirus Perspective’ (Epstein 2020)"
https://rexdouglass.github.io/TIGR/Douglass_2020_How_To_Be_Curious_Instead_of_Contrarian_About_Covid19.nb.html
Andrew Gelman's "Statistical Modeling, Causal Inference, and Social Science"
https://statmodeling.stat.columbia.edu/2020/03/31/how-to-be-curious-instead-of-contrarian-about-covid-19-eight-data-science-lessons-from-coronavirus-perspective
LA Times
https://www.latimes.com/opinion/story/2020-04-02/coronavirus-donald-trump-richard-a-epstein-isaac-chotiner-rex-douglass