NYU Part of Initiative to Harness Potential of Data Scientists, Big Data with Support from Moore, Sloan Foundations

New York University has launched a new multi-million dollar collaboration to enable university researchers to harness the full potential of the data-rich world that characterizes all fields of science and discovery. This partnership, which also includes the University of California, Berkeley and the University of Washington, will spur collaborations within and across the three campuses and other partners pursuing similar data-intensive science goals. The new five-year, $37.8 million initiative, with support from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation, was announced today at a meeting sponsored by the White House Office of Science and Technology Policy (OSTP) focused on developing innovative partnerships to advance technologies that support advanced data management and data analytic techniques. At a time when the natural, mathematical, computational, and social 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 both data scientists and those who use data science to advance their research. While data science is already contributing to scientific discovery, substantial systemic challenges need to be overcome to maximize its impact on academic research. To overcome these challenges, this effort seeks to achieve three core goals: • Develop meaningful and sustained interactions and collaborations between researchers with backgrounds in specific subjects (such as astrophysics, genetics, economics) and in the methodology fields (such as computer science, statistics, and applied mathematics), with the specific aim of recognizing what it takes to move each of the sciences forward; • Establish career paths that are long-term and sustainable, using alternative metrics and reward structures to retain a new generation of scientists whose research focuses on the multi-disciplinary analysis of massive, noisy, and complex scientific data and the development of the tools and techniques that enable this analysis; and • Build on current academic and industrial efforts to work toward an ecosystem of analytical tools and research practices that is sustainable, reusable, extensible, learnable, easy to translate across research areas, and enables researchers to spend more time focusing on their science ..."