George Washington University, University of Maryland receive grant to move research data faster and more securely

The George Washington University's Division of Information Technology (IT) and the University of Maryland have received a grant of more than $916,000 from the National Science Foundation to develop a solution that will allow for the quick and secure transport of the universities' research data.

"Being selected as a recipient of this grant is both an honor and a challenge to advance our infrastructure and support for GW's growing research community," said David Steinour, chief information officer of the George Washington University. "This award will help GW move large volumes of research data in a faster, more secure way than ever before, reducing the costs and time involved in our data shipping methods."

This grant will be used to fund a two-year networking integration project in which both universities will collaborate to develop a solution for transferring research data that will limit shipment costs and be compatible in different universities research organizations.

"As GW's Division of IT reaches out to better understand and support the core infrastructural and service needs of the research community, this is a great opportunity to showcase how we can more effectively partner to deliver enhanced shared research capabilities," said Brian Ensor, assistant vice president of technology architecture and research services for the Division of IT and a principal investigator on the project. "We hope this is a model for future engagements on advancing support for the research community at GW."

This effort is funded by the National Science Foundation's campus cyberinfrastructure-network infrastructure (CC-NIE) program, which invests in improvements and re-engineering at the campus level to leverage dynamic network services to support a range of scientific data transfers and movement. The program also supports network integration activities tied to achieving higher levels of performance, reliability and predictability for science applications and distributed research projects.

Provided by George Washington University