While “big data” often connotes new opportunities for understanding the present, largely through the analysis of social media and search engine data, other newly available kinds of rich data sources create huge possibilities for reimagining the past. In recent years, millions of previously difficult-to-access documents and massive archival data structures have become widely available to scholars of human history and the general public.
The project on Big Data and Historical Social Science brings together researchers across a range of disciplines, methods, and research strategies to explore the intersection of classical historical and social science problems with big data. How can access to new kinds of historical data, and new capacities to manipulate and analyze them, allow scholars to address historical questions in new ways?
The first demonstration project of this group is entitled “Reclaiming Lost Data on American Racial Inequality: 1865-1940.” Participants in this project include economists, historians, political scientists, and sociologists from across the country. After several planning meetings, the scholars working on this project were awarded a grant through the Russell Sage Foundation initiative on Computational Social Science to create accessible, linked datasets that will help social scientists of all disciplines gain access to more accurate information about African American populations in United States history.
Marcella Alsan (Stanford University), Audrey Augenbraum (Columbia University) Peter Bearman (Columbia University), Leah Boustan (Princeton University), Karida Brown (University of North Carolina), James Feigenbaum (Boston University), Megan Ming Francis (University of Washington), Trevon Logan (The Ohio State University) Mara Loveman (University of California, Berkeley), Christopher Muller (University of California, Berkeley), Suresh Naidu (Columbia University), Evan Roberts (University of Minnesota), Eric Schickler (University of California, Berkeley), Benjamin Schmidt (Northeastern University), and Vesla Weaver (Johns Hopkins University).