Google Cloud supports $3M in grant credits for the NSF BIGDATA program

Google Cloud Platform (GCP) serves more than one billion end-users, and we continue to seek ways to give researchers access to these powerful tools. Through the National Science Foundation’s BIGDATA grants program, we're offering researchers $3M in Google Cloud Platform credits to use the same infrastructure, analytics and machine learning that we use to drive innovation at Google.

About the BIGDATA grants

The National Science Foundation (NSF) recently announced its flagship research program on big data, Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA). The BIGDATA program encourages experimentation with datasets at scale. Google will provide cloud credits to qualifying NSF-funded projects, giving researchers access to the breadth of services on GCP, from scalable data management (Google Cloud Storage, Google Cloud Bigtable, Google Cloud Datastore), to analysis (Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, Google Cloud Datalab, Google Genomics) to machine learning (Google Cloud Machine Learning, TensorFlow).

This collaboration combines NSF’s experience in managing diverse research portfolios with Google’s proven track record in secure and intelligent cloud computing and data science. NSF is accepting proposals from March 15, 2017 through March 22, 2017.  All proposals that meet NSF requirements will be reviewed through NSF’s merit review process.

GCP in action at Stanford University

To get an idea of the potential impact of GCP, consider Stanford University’s Center of Genomics and Personalized Medicine, where scientists work with data at a massive scale. Director Mike Snyder and his lab have been involved in a number of large efforts, from ENCODE to the Million Veteran Program. Snyder and his colleagues turned to Google Genomics, which gives scientists access to GCP to help secure, store, process, explore and share biological datasets. With the costs of cloud computing dropping significantly and demand for ever-larger genomics studies growing, Snyder thinks fewer labs will continue relying on local infrastructure.

“We’re entering an era where people are working with thousands or tens of thousands or even million genome projects, and you’re never going to do that on a local cluster very easily,” he says. “Cloud computing is where the field is going.”

“What you can do with Google Genomics — and you can’t do in-house — is run 1,000 genomes in parallel,” says Somalee Datta, bioinformatics director of Stanford University’s Center of Genomics. “From our point of view, it’s almost infinite resources.”