Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering | 18 539

Opportunity Information: Apply for 18 539

The National Science Foundation (NSF) BIGDATA solicitation, titled "Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering" (Funding Opportunity Number 18-539), supports research that pushes data science forward in two complementary ways: building better core methods and proving those methods matter in real scientific and engineering settings. At its center, the program is looking for work that tackles genuinely "big data" challenges and produces new capabilities or new understanding that would not be possible without advances in data-intensive computing, statistics, mathematics, and related computational approaches.

The opportunity is aimed at novel research in computer science, statistics, computational science, and mathematics that strengthens the foundations of data science as an interdisciplinary field. At the same time, it encourages ambitious domain-driven projects in areas like social and behavioral sciences, education, physical sciences, and engineering, where large-scale data and modern data science techniques can unlock insights that were previously out of reach. Across all submissions, NSF expects proposals to be clearly motivated by specific big data problems, meaning investigators need to explain what makes the data challenge hard (for example, scale, complexity, heterogeneity, uncertainty, streaming or real-time requirements, privacy constraints, or distributed data sources) and why the proposed research is the right response to those challenges.

NSF divides submissions into two proposal categories. The first category is Foundations (BIGDATA-F). These projects focus on fundamental theories, techniques, methodologies, or technologies that are broadly applicable to big data problems. Even though this is "foundations" work, it still needs to be grounded in concrete data challenges and real requirements, rather than being purely abstract. In practice, this category fits projects that might develop new learning algorithms, statistical frameworks, data management approaches, scalable optimization methods, trustworthy and explainable analytics, privacy-preserving methods, data integration techniques, or rigorous mathematical underpinnings for data-driven discovery, as long as the work is convincingly tied to big-data-motivated needs.

The second category is Innovative Applications (BIGDATA-IA). These projects are explicitly translational, meaning they apply new or emerging big data approaches to solve problems in a specific application domain. A key requirement here is collaboration: proposals must bring together domain experts (for example, in education research, social science, physics, materials, civil infrastructure, or other scientific and engineering areas) with one or more methodological experts (such as computer scientists, statisticians, mathematicians, or simulation and modeling researchers). NSF also expects these proposals to go beyond simply using existing tools; they should demonstrate how the proposed big data techniques, technologies, or methodologies will materially change what can be done in the target domain. Applicants are expected to give clear, concrete examples of impact, such as improved scientific inference, new capabilities to analyze complex systems, better prediction or decision-making, or new forms of discovery enabled by the big data approach.

A notable feature for FY 2018 is the continued "cloud option" introduced the prior year. Through partnerships with Amazon Web Services (AWS), Google Cloud, and Microsoft Azure, the program allows teams to consider cloud resources as part of their research plan, consistent with NSF guidance in the solicitation's program description. In effect, this gives researchers a pathway to propose data-intensive work that may require flexible, scalable computing and storage environments, especially when local infrastructure is not sufficient or when cloud-native methods are part of the research contribution.

NSF also makes it clear that investigators should sanity-check fit before applying, because many projects that involve data or computation may be better served by other NSF programs. For example, biology-focused big data proposals aligned with NSF's BIO directorate are steered toward programs like Advances in Biological Informatics (DBI). Geosciences community-driven cyberinfrastructure and data environment efforts may align better with EarthCube. Projects centered on building robust, shared data- or software-centric cyberinfrastructure are pointed toward the Cyberinfrastructure for Sustained Scientific Innovation - Data and Software (CSSI) program. Work that is broadly about computational and data-enabled research but not specifically about big data challenges may be a better match for CDS&E. If the emphasis is primarily on scaling software performance (rather than data-centric challenges), NSF suggests looking at Scalable Parallelism in the Extreme (SPX). Similarly, math or statistics research not tied to a specific big data problem is directed to the Division of Mathematical Sciences (DMS) programs, and computing research not tied to big data issues may fit better in the CISE core programs (CNS, CCF, or IIS). This guidance signals that NSF is trying to keep BIGDATA focused on projects where "big data" is the motivating constraint and the source of the main scientific and technical contributions, not just a backdrop.

From an administrative standpoint, the opportunity is a discretionary NSF grant program with an unrestricted eligibility statement (open to any type of entity, subject to any clarifications in the solicitation). The posting lists an expected 33 awards and an award ceiling of $2,000,000. The solicitation was created February 15, 2018, with an original closing date of May 14, 2018. The CFDA numbers associated with the opportunity are 47.041, 47.049, 47.070, 47.075, and 47.076, reflecting NSF directorates and accounts that commonly support cross-cutting research in computing, mathematical sciences, and related areas.

In short, this NSF BIGDATA call is designed for research that either (1) creates broadly useful, big-data-driven methods and theory, or (2) demonstrates strong, collaborative, domain-specific breakthroughs powered by new big data techniques. In both cases, success depends on making the "big data" motivation explicit, showing why existing approaches fall short, and explaining what new scientific or engineering value the proposed work will unlock.

• The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering" 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, 47.070, 47.075, 47.076.
• This funding opportunity was created on Feb 15, 2018.
• Applicants must submit their applications by May 14, 2018. (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 $2,000,000.00 in funding.
• The number of recipients for this funding is limited to 33 candidate(s).
• Eligible applicants include: Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled Additional Information on Eligibility.

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