Institutions: Data-Science Environments

In November 2013, we announced a bold new partnership and $30M in funding to harness the potential of data scientists and big data for basic research and scientific discovery. With our partners, we launched three Data Science Environments at New York University, the University of California, Berkeley and the University of Washington with funding from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation. This is a five-year, $37.8 million cross-institutional effort to bring data science to the forefront of cross-disciplinary academic research.

The Data Science Environments are working to bring about institutional change via campus-wide experimentation to catalyze a new era of research: cross-disciplinary efforts working towards new approaches to data-intensive discovery. At a time when the life, physical, mathematical, and computational sciences are all producing data with relentlessly increasing volume, variety and velocity, capturing the full potential of a progressively data-rich world has become a daunting hurdle for researchers. At the intersection of natural science, computation and mathematics, data science is already contributing to scientific discovery, yet substantial systemic challenges need to be overcome to maximize its impact on academic research. This ambitious partnership will spur collaborations within and across the three campuses and with other partners pursuing similar data-intensive science goals.

For more details, please see the Moore-Sloan Data Science Environments (MSDSE) website here.

People: Investigator Awards

In November 2014 we launched a portfolio of awards aimed at catalyzing new data-driven scientific discoveries through $21 million in grants to the academic institutions of fourteen highly talented researchers. These Moore Investigators in Data-Driven Discovery will strengthen support for data scientists in academia and create greater opportunities for working between disciplines. The awards support sustained collaborations among data science researchers to build on one another's work, capitalize on the best practices and tools, and create solutions that can be used more broadly by others.

Practices: New Tools, Methods and Training

The Practices strategy focuses on creation, transfer and dissemination of readily usable innovative tools, knowledge and techniques for engaging in data-driven scientific research. Specific tactics in this strategy include the adoption of industrial strength data tools like Jupyter and Julia Language, for specific science domain questions or training courses from organizations like Data Carpentry that engage a larger population to tackle data-driven challenges in science.

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