Big Data and Cloud Culture - To the Cloud: Big Data in a Turbulent World - page 190

Big data is also increasingly used in the humanities, shaking up tra-
ditional research approaches and stirring considerable debate (Hunter
2011). In the United States, the push to use big data in the liberal arts is
led by the federal government's National Endowment for the Humanities
(NEH). One of the largest funders of liberal-arts research in the United
States, NEH is a federal agency founded in 1965. With an annual budget
of about $170 million, the agency provides grants to cultural institutions
such as libraries, universities, museums, public broadcasters, and individual
scholars in order to strengthen teaching, research, and the institutional
base of the humanities, including expanding access to educational and
cultural resources. NEH created the Digital Humanities Initiative in
2006, and it was raised to the level of an Ofice of Digital Humanities
(ODH) in 2008, a move that helped to legitimize use of the term digital
humanities in the United States. With ODH support, scholars working
in the ield made their presence felt at the 2009 annual meeting of the
Modern Language Association, what many consider a turning point in
the ield. Digital humanists apply computer science to the humanities,
primarily by examining large data sets to carry out research that was
dificult, if not impossible, to complete before computational methods
were available to scholars working in such humanities ields as literature,
history, and philosophy.
Some of the research, such as the ODH-funded Visual Page project,
involves inding new ways to gather big data and analyze it: “All printed
texts convey meaning through both linguistic and graphic signs, but
existing tools for computational text analysis focus only on the linguistic
content. The Visual Page will develop a prototype application to identify
and analyze visual features in digitized Victorian topics of poetry, such
as margin space, line indentation, and typeface attributes” (U.S. National
Endowment for the Humanities 2013). Other projects directly apply
computational methods to analyze large data sets; one of these is an ODH-
funded project on the life cycles of published works: “including not only
scholarly and scientiic literature, but also social networks, blogs, and other
materials.” The goal is to “identify which scholarly activities are indicative
of emerging areas and identify datasets that should no longer be margin-
alized, but built into understandings and measurements of scholarship”
(ibid.). Another funded project demonstrates why the grant program is
called “digging into data”: because it looked at “new ways of exploring