NSF Awards $31M in Big Data Research Grants
Backing up Obama administration rhetoric about the economic impact of big data, the National Science Foundation will fund 17 data science research programs totaling $31 million as part of its Data Infrastructure Building Blocks (DIBBS) program.
NSF said Oct. 1 the new university projects cover areas ranging from data analytics to management to security and specific scientific applications like biomedicine and life sciences.
The agency, which focuses on funding university R&D, stressed in making the awards that many of the benefits of big data “have yet to surface because of a lack of interoperability, missing tools and hardware that is still evolving to meet the needs of scientific communities.”
Hence, the NSF grants focus on developing tools, “cyber-infrastructure” and best practices required for data science. The awards also seek to increase the number of U.S. data scientists at a time when industry is increasingly concerned about a big data skills gap.
The second round of DIBBS awards also will support research in computer science, information technology and related data science fields, NSF said.
The goal of the initiative is “advancing scientific discovery through data,” Irene Qualters, NSF’s division director for advanced cyber-infrastructure, said in a statement announcing the awards. ” “Each project tests a critical component in a future data ecosystem in conjunction with a research community of users,” Qualters added. “This assures that solutions will be applied and use-inspired.”
Two of the 17 awards support ongoing research at Indiana University and Carnegie Mellon University that are deemed to be “more mature.” The other 15 support pilot demonstrations in areas like leveraging social data along with next-generating data analysis and sharing.
The Indiana research aims to create middleware and analytics libraries that would allow data science projects to run on supercomputers. The proposed architecture integrates open source cloud computing software and supercomputing technologies. Along with a data analytics-as-a-service scheme, applications include geospatial information systems, biomedicine, epidemiology and remote sensing.
Carnegie Mellon researchers are investigating the use of data science to develop a distributed data architecture called “Learnsphere” that would make educational data more accessible to course developers. The effort aims to “guide the development of courses that enhance learning while also generating even more data to give us a deeper understanding of the learning process,” said project leader Ken Koedinger, a professor of human computer interaction and psychology at Carnegie Mellon.
Both of the “early implementation” programs received $5 million in DIBBS funding over five years. The cyber-infrastructure demonstration projects will receive $1.5 million over three years.
The University of Illinois at Urbana-Champaign received two grants to conduct research on the scaling capabilities of spatial data synthesis along with an effort to coordinate big data building blocks.
NSF said DIBBS complements other big data efforts, including the DataOne project, the Research Data Alliance and a University of Texas initiative called Wrangler, a data analysis and management tool targeting the open source community.