Virtual Infrastructure for Data Intensive Analysis (VIDIA)

VIDIA.

Photo credit: Michael Forster Rothbart/SUNY Oneonta 

Published June 3, 2014 This content is archived.

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“This scalable, community-driven infrastructure exposes students and faculty at the SUNY comprehensive colleges to data-intensive computing and analysis techniques. ”


Large datasets culled from social media can easily grow to a size beyond the analytical capability of common software tools. Undergraduate institutions often lack the computing infrastructure and support personnel needed to allow students and researchers to create, manipulate, and analyze such extremely large datasets. In order to provide the tools necessary to expose students to data intensive computing and analysis techniques, CCR has teamed with SUNY Oneonta to establish a collaborative virtual community focusing on data intensive computing education in the social sciences.

Virtual Infrastructure for Data Intensive Analysis (VIDIA) unites SUNY Oneonta and CCR to support the analysis of large datasets by social science students at Primarily Undergraduate Institutions (PUIs).  Using the VIDIA platform, SUNY Oneonta has integrated the analysis of large datasets into coursework in Sociology, Political Science and Philosophy.

The use of these data centers upon a social-scientific examination of how moral claims and discourses are created, sustained, altered and challenged within the electronic sphere. Students and faculty members capture data using the Twitter Application Programming Interface (API), then analyze it using VIDIA. SUNY Oneonta has developed a strong working relationship with UB, fostering a collaborative environment where their students and faculty can conduct intensive data analysis that is not otherwise possible.

VIDIA is hosted by the CCR and was made possible through a 2013 SUNY Innovative Instruction Technology Grant (IITG) grant. The VIDIA site is powered by the HUBzero Platform for Scientific Collaboration, originally developed at Purdue University. HUBzero was specifically designed to help a scientific community share resources. Users can upload their own content, launch computations, and view results with an ordinary web browser, without having to download, compile, or install any code. The tools they access are not just web forms, but powerful graphical tools that support visualization and comparison of results.