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Welcome to our data site of our NSF-funded real-time and historical event data API.  This is part of our project titled "Modernizing Political Event Data for Big Data Social Science Research”. As an overview, our main goal is to do the following:

"Modern political conflict data are needed to understand the complex spatial and temporal dynamics of international relations and civil conflict.  At present, we lack social science datasets with deep temporal and spatial coverages for events that affect regional, national, and international domains.  Our proposal is to create event data on political and social events around the globe, with historical coverage, drawn from multiple language sources, and to make it freely available within hours of the events occurring.  We will construct such an event dataset, along with the software and methodology needed to analyze the data.  The team also will validate the new data and show their value in real time, spatial analysis of the dynamics of civil conflict in Latin America, the Middle East and Africa. We will employ these data to look the relationship between climate change and conflict -- as a test-bed for developing the tools. This will both insure that the system works on real (and difficult) problems, and will appeal immediately to the very substantial research community, both in academia and government.”  

To access the real-time event data coded using the CAMEO framework, see the Data page for instructions.

This material is based upon work supported by the National Science Foundation under Grant No. SBE-SMA-1539302, Resource Implementations for Data Intensive Research in the Social, Behavioral and Economic Sciences (RIDIR).  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  

This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.  This work used the Extreme Science and Engineering Discovery Environment (XSEDE) Jetstream at TACC / Indiana University through allocation SES170012.

© Patrick T. Brandt 2018