Harnessing the Data Revolution: Transdisciplinary Research in Principles of Data Science Phase I | 19 550

Opportunity Information: Apply for 19 550

Harnessing the Data Revolution: Transdisciplinary Research in Principles of Data Science (HDR TRIPODS) Phase I is a National Science Foundation (NSF) grant opportunity designed to strengthen the theoretical foundations of data science by funding small, highly collaborative research institutes. The program sits within NSF's broader "Big Ideas" agenda introduced in 2016, which focuses on long-term, high-impact research directions meant to keep the United States at the leading edge of science and engineering. Although proposals are submitted through NSF's Directorate for Computer and Information Science and Engineering, Division of Computing and Communication Foundations (CISE/CCF), the proposals are intended to be handled by a cross-disciplinary team of NSF program directors, reflecting the program's strong emphasis on convergence research rather than work confined to a single field.

At the center of this opportunity is NSF's Harnessing the Data Revolution (HDR) Big Idea, a large, NSF-wide effort aimed at enabling new forms of data-driven discovery across science and engineering. HDR is framed as a national-scale push to make it possible to ask and answer fundamental questions using data at scales, levels of complexity, and levels of societal relevance that were previously out of reach. The solicitation highlights that HDR is not just about building tools, but about developing new knowledge and accelerating innovation through a coordinated set of investments that span foundations of data science, algorithms and systems, data-intensive science and engineering applications, data cyberinfrastructure, and education and workforce development. A key theme is that progress in data science requires deeper theoretical, technical, and ethical frameworks that can guide trustworthy and impactful data-driven decision-making.

HDR TRIPODS specifically targets the "principles" layer of data science: the core theory and conceptual foundations that can unify and advance the field. The program is explicitly structured to bring together four communities that often approach data problems with different languages and methods: electrical engineering, mathematics, statistics, and theoretical computer science. The intent is not simply to place researchers from these areas in the same project, but to require integrated collaboration in which the different disciplinary perspectives are essential to the institute's research and training agenda. For Phase I, each institute must include significant and integral participation from at least three of these four communities, emphasizing true transdisciplinary work rather than parallel efforts.

Phase I funding supports the creation and early development of small collaborative institutes that combine research with training activities, meaning teams are expected to do more than publish results; they are also expected to build capacity, develop talent, and help shape the next generation of data science researchers who can work across disciplinary boundaries. Phase I also serves as a stepping-stone to a planned Phase II: NSF anticipates a future competition (dependent on available funds) that will select a smaller number of larger institutes from among the Phase I awardees. In practice, that means Phase I institutes are meant to establish momentum, demonstrate effective collaboration, and lay out a compelling foundation that could scale.

Administratively, this is a discretionary grant program run by NSF. The funding opportunity number is 19-550, with an original closing date of May 8, 2019, and a posted creation date of February 8, 2019. The award ceiling is $1,500,000, and NSF expected to make about 20 awards under Phase I. The opportunity is categorized under science and technology and other research and development, and it references multiple NSF CFDA numbers (47.041, 47.049, 47.050, 47.070, 47.074, 47.075, 47.076, 47.079, 47.083), reflecting the cross-cutting nature of the program across NSF directorates and research areas. Eligibility is listed broadly as "Others" with further clarification indicated in the solicitation's eligibility text, which is typical for NSF programs that may include universities and other research organizations depending on the specific solicitation language.

Overall, HDR TRIPODS Phase I is best understood as a foundational investment in the underlying theory of data science, deliberately engineered to force deep collaboration across disciplines that traditionally drive core advances in signal processing and electrical engineering, mathematical structures, statistical inference, and computational complexity and algorithms. The goal is to create institute-style teams that can develop unifying principles and rigorous frameworks for data science, while also training researchers who can carry that integrated perspective into future scientific, engineering, and societal challenges.

• The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Harnessing the Data Revolution: Transdisciplinary Research in Principles of Data Science Phase I" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.041, 47.049, 47.050, 47.070, 47.074, 47.075, 47.076, 47.079, 47.083.
• This funding opportunity was created on Feb 08, 2019.
• Applicants must submit their applications by May 08, 2019. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $1,500,000.00 in funding.
• The number of recipients for this funding is limited to 20 candidate(s).
• Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).

Apply for 19 550

[Watch] Creating a grant proposal using the step-by-step wizard inside the applicant portal: