This project will strengthen the organizational and technical infrastructure of the Center for Historical Information and Analysis (http://chia.pitt.edu), a multi-institutional collaborative of scholars in social, natural, and information sciences structured as a Research Collaborative and a Headquarters. The Research Collaborative links participating institutions that are collecting data on population, climate, and other topics with a crowdsourcing tool to demonstrate the feasibility of building a continuously growing collection of diverse historical data and metadata. The Headquarters assembles and develops knowledge on repository design to develop a repository sufficient to house the incoming data and permit global and interactive analysis. The Center for Historical Information and Analysis?s future plans include expanding its collection and processing of historical data, broadening its community of social and natural science researchers, analyzing historical patterns of global change, and sharing its resources with researchers, policy-makers, teachers and students. CHIA is headquartered at the University of Pittsburgh with participating research groups at Boston University, Harvard University, Michigan State University, and University of California-Merced.

To understand global social patterns as they exist today, it is increasingly clear that we need to understand how they have evolved over recent centuries. The Center for Historical Information and Analysis responds to this need and takes historical analysis into the realm of Big Data. It is expected that the data resources will grow to several terabytes in size. This project will stimulate development of more efficient research collaborations, enabling systematic large-scale consolidation of diverse historical data sources. Once collected and integrated, the data repository and analytical system will allow scholars to address a wider set of questions testing hypotheses about long-term and short-term social change at the global scale and catalyzing an expansion of the evidence base in social sciences. For example, our understanding of important societal issues can advance by linking health to demography and by incorporating climate and health factors into economic studies. Disciplinary theory will advance through interaction among the various scientific fields, so that a global network of social-science researchers will emerge.

Agency
National Science Foundation (NSF)
Institute
Division of Behavioral and Cognitive Sciences (BCS)
Type
Standard Grant (Standard)
Application #
1244796
Program Officer
Saylor Breckenridge
Project Start
Project End
Budget Start
2013-01-01
Budget End
2015-12-31
Support Year
Fiscal Year
2012
Total Cost
$38,052
Indirect Cost
Name
Michigan State University
Department
Type
DUNS #
City
East Lansing
State
MI
Country
United States
Zip Code
48824