We describe computational treatments of archival collections through a case study of World War II Japanese-American Incarceration Camps. Camp staff and police officers compiled so-called "internal security" reports relating to alleged cases of "disorderly conduct, assault, theft, loss of property, and accidents" in the camps, and an index to these reports comprising over 25,000 index cards to the reports. The sheer size of these collections is pushing archivists and researchers to consider new forms of processing for collections at scale.
Natural disasters such as hurricanes, floods or tornadoes affect millions of individuals every year. As a result, governments spend millions of dollars in emergency response allocating resources to mitigate the damages. Effective resource allocation requires a deep understanding of how humans react when a disaster takes place. Due to the multiplicity of human behavior, however, it is not trivial to understand human behaviors at large scale during and after the disaster.
Urban renewal was a national initiative from the 1960s through 70s aimed at improving so-called “blighted” areas, and resulted in the displacement of many vibrant communities. While the underlying mechanisms of urban renewal have been examined, there have been very few data-driven, evidence-based studies that take into account the histories and interests of former residents. The “Human Face of Big Data” project started as a digital curation effort to design and develop a web-based, big data platform that provides insights and analytics into the mechanisms of this process.