Partnered With IDEA, Data Innovation Hub Gets Boost From National Science Foundation

July 24, 2019

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The National Science Foundation has awarded $4 million to the Northeast Big Data Innovation Hub, an organization dedicated to building partnerships to address societal challenges with data-driven solutions.

The National Science Foundation has awarded $4 million to the Northeast Big Data Innovation Hub, an organization dedicated to building partnerships to address societal challenges with data-driven solutions. The Hub is hosted by Columbia University’s Data Science Institute and is coordinated by several researchers, including James Hendler, director of the Rensselaer Institute for Data Exploration and Applications (IDEA).

Established in 2015 with an earlier grant from the NSF, the Hub has built a network of more than 200 organizations to collaborate on eight priority areas, ranging from data sharing to responsible data science. Initiatives have included collaborations such as the first-ever exposome data warehouse, integrating environmental exposure and clinical data for large-scale health.  

The current grant carries the Hub, which is one of four NSF-funded “Big Data Regional Innovation Hubs,” into its second phase. 

“I am proud of the role the Northeast Big Data Innovation Hub is playing in linking together academic data centers — such as the Rensselaer IDEA — with industrial, government, and other university partners,” Hendler said. “The Hub reduces the barriers to public-private partnerships, and these new initiatives and partnerships help the country remain a leader in data science, AI, and advanced computation. As a member of the coordination group, I look forward to working with Columbia and our other partners in helping to lead this important initiative.” 

Under this new round of funding, the Northeast Hub will place an emphasis on mission-driven projects that coordinate and stimulate translational data science. For example, the Hub will work with its stakeholders on aggregating and helping to develop best practices for responsible data science; creating frameworks for data fluency; fostering better management of data security and privacy; integrating health data from traditional and novel sources; improving education through big data; and reducing barriers for data sharing within and between different sectors.

Written By Mary L. Martialay
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