NSF Unveils Nine-Point R&D Agenda; Strategic Plans for Big Data, Advanced Computing Infrastructure

As the National Science Foundation (NSF) celebrates its 66_th birthday, NSF’s Director France Córdova unveiled a nine-point research agenda to shape the federal agency’s future for the next several decades. These nine big ideas are intended to “illustrate how increased support for the type of basic research that NSF funds could help answer pressing societal problems,” according to an article from sciencemag.org. NSF’s leadership also hopes to build public and subsequent federal government support for significant federal investments in its agenda as well as spark the interest of industry and foundations to invest alongside the federal government. In addition to NSF’s release of a proposed long-term research agenda, two reports focused on Big Data and advanced computing infrastructures were also released by the federal government in May.

The NSF agenda is divided into two types of idea areas – research ideas and process ideas. The research ideas are intended to help guide the research community with new questions being asked and new challenges being addressed.  They include:

• Harnessing Data for 21st Century Science and Engineering;
• Shaping the New Human–Technology Frontier;
• Understanding the Rules of Life: Predicting Phenotype;
• The Quantum Leap: Leading the Next Quantum Revolution;
• Navigating the New Arctic; and,
• Windows on the Universe: The Era of Multi-messenger Astrophysics.

NSF’s leadership hopes that funding these areas will enable and accelerate future discovery by providing the fundamental techniques, tools, research cyberinfrastructure (CI), and educational foundations to harness the data revolution. Specific efforts may include:

• The creation of a network of national centerscale activities, each with a specific focus, but linked with each other and with industry, government and international partners to maximize collective impact;
• Increased directorate and cross-directorate activities and investments; and,
• The development, deployment, operation and evolution of national-scale, open, data-centric CI, that is integrated with NSF’s existing CI capabilities and evolved through mid-scale pilot activities.

To provide the necessary S&T infrastructure and support for fundamental research in the six research ideas, NSF has identified three process ideas:

• Growing Convergent Research at NSF – strategic support for research projects and programs that are motivated by intellectual opportunities and/or important societal problems, and that would benefit from the convergence of (subsets) of physical sciences, biological sciences, computing, engineering, and the social and behavioral sciences;
• Mid-scale Research Infrastructure – a new approach to research infrastructure for NSF’S science and engineering activities that would rely increasingly on cyberinfrastructure, broadly defined, use infrastructure that is diverse in space, cost, and implementation time, and require dynamic and nimble responses to new challenges; and,
• NSF 2050: Integrative  Foundational Fund (IFF) – an effort similar to NIH’s Common Fund that will identify and support bold, long-term foundational research questions to set the stage for breakthrough science and engineering all the way to NSF’s Centennial in 2050.

In addition to NSF’s release of a proposed long-term research agenda, two reports focused on Big Data and advanced computing infrastructures were also released by the federal government in May.

 Developed by the National Science and Technology Council, The Federal Big Data Research and Development Strategic Plan provides a roadmap to develop and expand each federal agency’s individual mission-driven programs and investments related to Big Data. The strategy identifies seven strategies that represent key areas of importance for Big Data research and development (R&D) that would:

• Create next-generation capabilities by leveraging emerging Big Data foundations, techniques, and technologies;
• Support R&D to explore and understand trustworthiness of data and resulting knowledge, to make better decisions, enable breakthrough discoveries, and take confident action;
• Build and enhance research cyberinfrastructure that enables Big Data innovation in support of agency missions;
• Increase the value of data through policies that promote sharing and management of data;
• Understand Big Data collection, sharing, and use with regard to privacy, security, and ethics;
• Improve the national landscape for Big Data education and training to fulfill increasing demand for both deep analytical talent and analytical capacity for the broader workforce; and,
• Create and enhance connections in the national Big Data innovation ecosystem.

The other recent report – The Future Directions for NSF Advanced Computing Infrastructure to Support U.S. Science and Engineering – was conducted by the National Academies of Sciences, Engineering, and Medicine to examine “priorities and associated trade-offs for advanced computing investments and strategy.” In support of these efforts, the authors of the report provided a series of recommendations for NSF, including:

• Sustain and seek to grow its investments in advanced computing to include hardware and services, software and algorithms, and expertise;
• Pay particular attention to providing support for the revolution in data-driven science along with simulation;
• Collect community requirements and construct and publish roadmaps to allow NSF to set priorities better and make more strategic decisions about advanced computing;
• Adopt approaches that allow investments in advanced computing hardware acquisition, computing services, data services, expertise, algorithms, and software to be considered in an integrated manner;
• Support the development and maintenance of expertise, scientific software, and software tools that are needed to make efficient use of its advanced computing resources;
• Invest modestly to explore next-generation hardware and software technologies to explore new ideas for delivering capabilities that can be used effectively for scientific research, tested, and transitioned into production where successful; and,
• Manage advanced computing investments in a more predictable and sustainable way.