Western Oregon University Sponsored Research Office
Thursday, March 17, 2011
Digging into Data (Information Technology/Sciences)
The creation of vast quantities of Internet-accessible digital data and the development of techniques for large-scale data analysis have led to remarkable new discoveries in genetics, astronomy, and other fields, and—importantly—connections between different academic disciplines. The Digging into Data Challenge seeks to discover how these new research techniques might also be applied to questions in the humanities and social sciences. New techniques of large-scale data analysis allow researchers to discover relationships, detect discrepancies, and perform computations on so-called “big data” sets that are so large that they can be processed only by using computing resources and computational methods that were developed and made economically affordable within the past few years. This “data deluge” has arisen not just from the capture and storage of data on everyday transactions such as Internet searches, consumer purchases, cell phone records, “smart” metering systems and sensors, but also from the digitization of all types of media, with books, newspapers, journals, films, artworks, and sound recordings being digitized on a massive scale. It is possible to apply data linkage and analysis techniques to large and diverse data collections, including survey data, economic data, digitized newspapers, books, music, and other scholarly and scientific resources. How might these techniques help researchers use these materials to ask new questions about and gain new insights into our world? To encourage innovative approaches to this question, eight international research organizations are organizing a joint grant competition to focus the attention of the social sciences, humanities, library, archival, and information sciences communities on large-scale data analysis and its potential applications. The four goals of the initiative are * to promote the development and deployment of innovative research techniques in large-scale data analysis that focus on applications for the humanities and social sciences; * to foster interdisciplinary collaboration among researchers in the humanities, social sciences, computer sciences, library, archive, information sciences, and other fields, around questions of text and data analysis; * to promote international collaboration among both researchers and funders; and * to ensure efficient access to and sharing of the materials for research by working with data repositories that hold large digital collections.