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.Apply for 17 534
- 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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NSF BIGDATA (17-534) Grant Opportunity FAQs
What is the NSF BIGDATA solicitation (17-534)?
The NSF BIGDATA solicitation, formally 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 NSF grant program focused on advancing both (1) the core science of data science and (2) its real-world impact across science and engineering domains.
What kinds of problems is this program trying to fund?
The program is aimed at research where "big data" conditions are central to the technical challenge, such as:
- Massive data volume (very large datasets)
- High data velocity (rapid generation, streaming, or near-real-time data)
- High heterogeneity (many formats, sources, modalities, or structures)
- Realistic data complications (missingness, noise, limited data quality, and data bias)
Projects are expected to address what breaks, becomes inadequate, or becomes newly possible under these big data constraints.
What is the overarching goal of the solicitation?
The overarching goal is to produce interdisciplinary advances that connect methodological innovation (for example, computer science, statistics, mathematics, computational science) with impactful applications across sciences and engineering. The solicitation emphasizes that big data should be the reason the research is needed, not just the setting where the work happens.
What are the two proposal tracks supported by this solicitation?
The solicitation accepts proposals in two main tracks:
- Foundations (F): General-purpose big data theory, methods, methodologies, and enabling technologies that are reusable across many contexts.
- Innovative Applications (IA): Translational projects that apply new big data approaches to produce concrete, measurable impact in a specific domain area.
How do I choose between the Foundations (F) track and the Innovative Applications (IA) track?
Choose the track based on the primary contribution of the work:
- Foundations (F) fits if your main outcome is a broadly reusable technique/technology/methodology, grounded in a real big data challenge, and expected to matter beyond a single dataset or domain.
- Innovative Applications (IA) fits if your main outcome is domain-facing impact enabled by new big data methods, demonstrated through concrete use cases, benchmarks, or other evidence in the target domain.
What does NSF mean by "Foundations" research in this context?
Foundations projects develop or study fundamental theory, techniques, methodologies, and enabling technologies for big data that can be reused across many domains. Even though Foundations is about broadly applicable advances, proposals are expected to be motivated by a real big data challenge (for example, what fails at scale, what new principle is required, and why the result generalizes beyond one application).
What does NSF mean by "Innovative Applications" research?
Innovative Applications projects are translational and domain-facing. They use new big data techniques, technologies, or methodologies to solve problems in a specific application area. These proposals should show clear domain impact and should go beyond simply applying existing tools.
Is collaboration required for Innovative Applications (IA) proposals?
Yes. IA proposals must involve collaboration between:
- Researchers from one or more domain disciplines (for example, biology, education, social/behavioral sciences, engineering, physical sciences), and
- Researchers from one or more methodological disciplines (for example, computer science, statistics, mathematics, computational science, simulation, modeling)
This requirement is described as a central requirement for IA projects.
What kinds of impacts are IA proposals expected to demonstrate?
IA proposals are expected to articulate concrete domain impacts, such as:
- What new data-driven capability becomes possible in the domain
- How the approach improves decisions or scientific understanding
- What evidence, benchmarks, or use cases will be used to demonstrate impact
How should proposals describe the "big data aspects" of the work?
Proposals are expected to clearly identify the big data aspects that drive the work. This includes explicitly explaining which features of the data environment motivate the proposed approach, such as:
- Scaling behavior as data volumes increase
- Challenges created by high-rate or streaming data collection
- Complications from integrating heterogeneous sources
- Risks and limitations caused by missingness, noise, low quality data, or biased data
Does NSF treat big data as just a background context for this program?
No. The solicitation emphasizes that big data should not be incidental. Reviewers are being directed to look for projects where big data constraints and opportunities are central to why the proposed research is needed.
Which disciplines and application areas are considered in-scope?
The solicitation indicates proposals are welcome across the full range of sciences and engineering covered by participating NSF directorates. It also notes interests aligned with a partnering agency, the Office of Financial Research (OFR), indicating interest in data-intensive methods and applications relevant to financial systems and related analytic challenges.
What is the role of the Office of Financial Research (OFR) in this solicitation?
The solicitation notes participation from a partnering agency, the Office of Financial Research (OFR). Based on the information provided, this signals interest in data-intensive methods and applications relevant to financial systems and related analytic challenges.
What types of projects might be redirected to other NSF programs instead of BIGDATA?
NSF provides guidance suggesting other programs may be a better fit if the work is not truly centered on big data research questions. Examples mentioned include:
- Data Infrastructure Building Blocks (DIBBs): If the main goal is building shared, robust data-centric cyberinfrastructure.
- Computational and Data Enabled Science and Engineering (CDS&E): If the work is computational/data science research but not specifically driven by big data issues.
- Scalable Parallelism in the Extreme (SPX): If the emphasis is primarily on scaling software systems rather than addressing data-centric challenges.
- EarthCube: For geosciences-focused data environment proposals aligned with community needs.
- DMS programs: For mathematics or statistics research not tied to a specific big data problem.
- CISE core programs (CNS, CCF, IIS): For computer and information science research not tied to a specific big data problem.
If a project is mainly about building cyberinfrastructure, is BIGDATA the right fit?
The solicitation indicates that if the main goal is building shared, robust data-centric cyberinfrastructure, NSF suggests looking at the DIBBs program rather than BIGDATA.
If a project is about scaling software systems, is BIGDATA the right fit?
The solicitation suggests that if the emphasis is primarily on scaling software systems (rather than addressing data-centric challenges), SPX (Scalable Parallelism in the Extreme) may be a better fit.
If a project is math or statistics research but not tied to a specific big data problem, is it a fit for BIGDATA?
The solicitation indicates that 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 rather than BIGDATA.
If a project is computer and information science research but not tied to a specific big data problem, is it a fit for BIGDATA?
The solicitation indicates that computer and information science research not tied to a specific big data problem may be better suited to CISE core programs (CNS, CCF, IIS) rather than BIGDATA.
How does NSF define "big data" in this opportunity?
Based on the provided description, NSF frames "big data" in terms of data environments characterized by scale and complexity (volume, velocity, heterogeneity), plus practical complications like missing data, noise, limited quality, and bias. The emphasis is on research where these characteristics drive the need for new approaches.
What is the funding instrument for this opportunity?
The funding instrument is a Grant.
What is the maximum award amount (award ceiling)?
The award ceiling listed is $2,000,000.
How many awards does NSF expect to make?
The opportunity record indicates an expected 35 awards.
What was the original closing date for the solicitation?
The original closing date listed is March 22, 2017.
When was the opportunity record created?
The creation date listed is December 23, 2016.
Who is eligible to apply?
The opportunity record indicates eligibility is "unrestricted," meaning it is broadly open to any entity type, subject to any clarifications in the full solicitation.
What CFDA numbers are associated with this opportunity?
The CFDA numbers listed are 47.041, 47.049, 47.070, 47.074, 47.075, and 47.076, indicating NSF program areas through which funding may be administered.
What makes a strong Foundations (F) proposal, based on this description?
A strong Foundations proposal typically:
- Explains what breaks or becomes inadequate at big data scale
- Identifies what new principle, approach, or enabling technology is needed
- Shows how the results will generalize beyond a single dataset or domain
- Stays grounded in a real big data challenge (not purely abstract)
What makes a strong Innovative Applications (IA) proposal, based on this description?
A strong IA proposal typically:
- Includes required cross-disciplinary collaboration (domain + methodological researchers)
- Describes the domain problem and data sources in detail
- Explains limitations of current approaches
- Shows how the proposed big data methods change what is possible in the domain
- Defines concrete evidence of impact (benchmarks, use cases, or other demonstrations)
Does the solicitation expect proposals to address data quality and bias issues?
Yes. The description explicitly includes missingness, noise, data quality limitations, and data bias as practical complications associated with big data conditions that proposals may need to address.
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