Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering | 17 534

Opportunity Information: Apply for 17 534

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 No. 17-534), is a discretionary grant program aimed at pushing forward the core science and real-world use of data science. The program is designed for research that tackles genuinely "big data" conditions such as massive data volume, high data velocity (rapid generation and streaming), high heterogeneity (many different formats and sources), and practical complications like missingness, noise, data quality limitations, and data bias. The overall goal is to produce new interdisciplinary advances that connect methodological innovation (computer science, statistics, mathematics, computational science) with impactful applications across the sciences and engineering, including social and behavioral sciences, education, biology, physical sciences, and engineering. NSF also notes participation from a partnering agency, the Office of Financial Research (OFR), signaling interest in data-intensive methods and applications relevant to financial systems and related analytic challenges.

The solicitation accepts proposals in two main tracks, and the choice between them depends on whether the work is primarily advancing general-purpose big data methods or translating such methods into concrete domain results. The first track, Foundations (F), supports projects that develop or study fundamental theory, techniques, methodologies, and enabling technologies that can be reused across many big data contexts. While this category is about broadly applicable advances, proposals are still expected to be motivated by a real big data challenge; in other words, the foundational contribution should be grounded in specific requirements that arise because of scale, complexity, or the nature of modern data. A strong Foundations proposal typically explains what breaks or becomes inadequate at big data scale, what new principle or approach is needed, and why the outcome would matter beyond a single dataset or domain.

The second track, Innovative Applications (IA), is oriented toward translational, domain-facing work that uses new big data techniques, technologies, or methodologies to solve problems in a specific application area. A central requirement here is collaboration: IA projects must involve researchers from one or more domain disciplines working together with researchers from one or more methodological disciplines (for example, pairing a biologist, education researcher, or engineer with experts in computer science, statistics, mathematics, simulation, or modeling). These proposals are expected to show clear, concrete impacts in the chosen domain, not just a plan to apply existing tools. In practice, that means articulating what new data-driven capability the project enables in the domain, how the new approach improves decisions or scientific understanding, and what evidence, benchmarks, or use cases will demonstrate that impact.

Across both categories, the solicitation emphasizes that proposals must clearly identify the "big data aspects" that drive the work. This includes explaining which features of the data environment motivate the proposed approach, such as scaling behavior as data volumes increase, challenges created by high-rate data collection, complications from combining heterogeneous sources, and risks introduced by poor data quality or biased data. In effect, reviewers are being asked to look for projects where big data is not just a backdrop but the actual reason the proposed research is needed. For IA proposals specifically, NSF expects explicit examples of domain impact, so applicants should describe the domain problem in detail, the data sources involved, the limitations of current approaches, and how the proposed big data methods change what is possible.

In terms of scope and fit, NSF signals that proposals are welcome across the full range of sciences and engineering covered by participating NSF directorates, along with OFR interests. At the same time, the solicitation is careful to steer applicants toward other NSF programs if their work is not truly centered on big data research questions. If the main goal is building shared, robust data-centric cyberinfrastructure, NSF suggests looking at Data Infrastructure Building Blocks (DIBBs). If the project is computational and data science research but not specifically driven by big data issues, NSF points to Computational and Data Enabled Science and Engineering (CDS and E). If the emphasis is primarily on scaling software systems rather than addressing data-centric challenges, NSF suggests Scalable Parallelism in the Extreme (SPX). Geosciences-focused data environment proposals aligned with community needs may be a better fit for EarthCube. Similarly, mathematics or statistics research not tied to a specific big data problem is expected to go to the appropriate Division of Mathematical Sciences (DMS) program, and computer and information science research not tied to a specific big data problem may be better suited to CISE core programs (CNS, CCF, IIS). This guidance is essentially NSF saying that BIGDATA is for research where the big data constraints and opportunities are central, not incidental.

Key administrative details from the opportunity record include that it is an NSF grant (Funding Instrument: Grant) with an award ceiling of $2,000,000, an expected 35 awards, and an original closing date of March 22, 2017 (with a creation date of December 23, 2016). The opportunity is broadly open in terms of eligibility ("unrestricted," meaning open to any entity type, subject to any clarifications in the full solicitation). The CFDA numbers listed (47.041, 47.049, 47.070, 47.074, 47.075, 47.076) indicate NSF program areas through which funding may be administered. Taken together, the solicitation is best understood as NSF's effort to fund both the next generation of scalable, reliable, and principled data science methods and the high-impact, cross-disciplinary projects that prove those methods matter in real scientific and engineering domains.

• 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, 47.074, 47.075, 47.076.
• This funding opportunity was created on Dec 23, 2016.
• Applicants must submit their applications by Mar 22, 2017. (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 35 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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