Opportunity Information: Apply for 19 549
The NSF opportunity "Harnessing the Data Revolution (HDR): Institutes for Data-Intensive Research in Science and Engineering - Frameworks (I-DIRSE-FW)" is part of NSF's broader HDR Big Idea, one of the agency's 10 long-term "Big Ideas" launched to push the frontiers of science and engineering through convergence research. The basic premise is that modern research is being reshaped by the explosive growth of data, and that new discoveries will increasingly depend on better ways to manage, understand, and use large, complex, and diverse datasets. While proposals for this program were submitted through NSF's CISE Office of Advanced Cyberinfrastructure (OAC), the program was designed to be managed by a cross-disciplinary team of NSF program directors because it intentionally spans many research communities and NSF directorates.
HDR, as NSF describes it, aims to enable new modes of data-driven discovery so researchers can ask and answer fundamental questions that were previously out of reach. The HDR vision is organized around several tightly linked thrusts: foundations of data science (theory, math, statistics, and principles), algorithms and systems for data science (computational methods and platforms), data-intensive science and engineering (domain-driven applications where data changes what is possible), data cyberinfrastructure (shared computing, storage, software, and services), and education and workforce development (training people to do this work at scale and responsibly). A central theme is that progress does not come from any one piece alone; it comes from combining domain science with data science, computation, and infrastructure, while also building ethical and technical frameworks that make data-driven work trustworthy and impactful for society.
This specific solicitation funded "Frameworks for Data-Intensive Research in Science and Engineering (DIRSE)" as one route into the HDR Institutes activity. Rather than immediately funding fully mature, large-scale institutes, NSF set up a staged approach. The DIRSE Frameworks awards were part of the conceptualization phase, meant to help teams define what an HDR Institute could be in a particular scientific or engineering area, build the right partnerships, and test early prototypes. NSF framed the long-term HDR Institutes vision as an "integrated fabric" of interrelated institutes that collectively accelerate discovery across many fields by sharing approaches, infrastructure concepts, and data science methods, rather than operating as isolated centers.
The conceptualization phase in FY 2019 had two complementary pathways. One pathway used an Ideas Lab model to help individuals self-organize around compelling data-intensive problems and/or technical capabilities and quickly form collaborative teams. The second pathway is this solicitation: it supported more directly proposed, team-based "frameworks" that connect a set of science and engineering challenge problems with the data science solutions and infrastructure concepts needed to tackle them. In practical terms, the program was looking for teams that could articulate a coherent research and collaboration blueprint, identify where the bottlenecks are (data barriers, methodological gaps, infrastructure shortfalls, workforce limitations), and demonstrate how coordinated work across disciplines could unlock a step-change in capability.
The awards produced by this solicitation were intended to be short, two-year efforts focused on community-building and planning plus early technical proof-of-concept work. NSF emphasized outcomes like defining shared research priorities, establishing collaborative structures that cut across departments and institutions, and developing interdisciplinary prototype solutions that make the institute concept credible and actionable. The idea was that the strongest concepts and prototypes from this phase, along with new ideas, could feed into a later "convergence and co-design" phase that NSF anticipated around 2021. That later phase would support larger, more comprehensive HDR Institutes that integrate and scale what worked, bringing multiple domain communities together with computer and computational scientists, mathematicians, statisticians, and information scientists around common data science approaches.
The solicitation's core objective was to foster genuinely convergent, data-driven research in science and engineering. NSF wanted Frameworks teams to pilot new collaboration modalities that go beyond traditional disciplinary boundaries and "institutional walls." That includes building durable links between domain researchers and data scientists and engineers, and also thinking deliberately about integrating research infrastructure (tools, platforms, data pipelines, standards, compute) with education infrastructure (training programs, student pipelines, professional development, curricula, and community practices). The emphasis was not only on producing research results, but also on creating a workable structure for sustained, scalable interdisciplinary collaboration.
NSF also gave clear guidance on what kinds of topic areas were a good fit. Frameworks were expected to target science and engineering domains that are at a "tipping point," meaning the field is ready for a timely data-intensive investment that could have outsized, transformative effects. They also needed to be areas where interdisciplinary investments in data analytics infrastructure would materially change what the community can do, and where there is alignment with investment priorities across relevant NSF directorates during and beyond the HDR Big Idea timeframe. In other words, NSF was not looking for isolated data projects; it was looking for places where the combination of urgent scientific questions, available or emerging data streams, and the right data science advances could reshape an entire research area.
In terms of concrete deliverables, NSF expected Frameworks to (1) identify frontier challenge problems in science and engineering, (2) map the associated data challenges and data-science barriers that currently limit progress (such as data heterogeneity, scale, quality, access, privacy, governance, reproducibility, or algorithmic limitations), and (3) propose and begin testing strategies that can produce breakthroughs by bringing together diverse researchers under a shared framework. This includes considering the theoretical and ethical dimensions of data use, not just the computational ones, since HDR explicitly highlights the need for technical and ethical frameworks that support trustworthy, societally relevant data-driven decision-making.
Administrative details from the opportunity listing include that it was a discretionary NSF grant program (Funding Opportunity Number 19-549) with an original closing date of May 7, 2019. It anticipated roughly 10 awards. The opportunity was associated with multiple NSF CFDA numbers (47.041, 47.049, 47.050, 47.070, 47.074, 47.075, 47.076, 47.079, 47.083), reflecting its cross-cutting scope. Eligibility was described broadly as "Others (see text...)," signaling that applicants needed to consult the full solicitation for specific eligibility rules and any constraints tied to institution types or partnership structures.
Taken as a whole, I-DIRSE-FW was essentially NSF's way of funding the "blueprint stage" for future HDR Institutes: short, focused awards to organize interdisciplinary communities, pinpoint high-leverage data bottlenecks in key science and engineering areas, build early prototypes, and lay the groundwork for larger, institute-scale efforts designed to change how data-intensive research is done across the US research ecosystem.Apply for 19 549
- 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 (HDR): Institutes for Data-Intensive Research in Science and Engineering - Frameworks (I-DIRSE-FW)" 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 07, 2019.
- Applicants must submit their applications by May 07, 2019. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The number of recipients for this funding is limited to 10 candidate(s).
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
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Frequently Asked Questions (FAQs)
What is the NSF "Harnessing the Data Revolution (HDR)" opportunity described here?
This opportunity is part of NSF's broader Harnessing the Data Revolution (HDR) Big Idea, one of NSF's long-term "Big Ideas" intended to push the frontiers of science and engineering through convergence research. It focuses on enabling new modes of data-driven discovery as modern research is reshaped by rapid growth in large, complex, and diverse datasets.
What is the specific program name and focus of this solicitation?
The solicitation is "Harnessing the Data Revolution (HDR): Institutes for Data-Intensive Research in Science and Engineering - Frameworks (I-DIRSE-FW)." It funded "Frameworks for Data-Intensive Research in Science and Engineering (DIRSE)" as a conceptualization route into the broader HDR Institutes activity.
What does I-DIRSE-FW fund, in plain terms?
I-DIRSE-FW funded short, two-year efforts that function as a blueprint stage for potential future HDR Institutes. These awards were meant to support community-building, planning, partnership formation, and early technical proof-of-concept work that makes an institute concept credible and actionable.
How does this fit into NSF's longer-term HDR Institutes vision?
NSF framed the long-term HDR Institutes vision as an "integrated fabric" of interrelated institutes. The goal is for institutes to share approaches, infrastructure concepts, and data science methods across fields, rather than operate as isolated centers.
Why did NSF create a staged approach instead of immediately funding large institutes?
Rather than immediately funding fully mature, large-scale institutes, NSF designed a staged approach so teams could first define what an HDR Institute could be in a specific area, build the right partnerships, and test early prototypes. The Frameworks awards were part of this conceptualization phase.
What were the two pathways in the FY 2019 conceptualization phase?
The FY 2019 conceptualization phase included two complementary pathways:
- An Ideas Lab model, where individuals self-organize around compelling data-intensive problems and/or technical capabilities and rapidly form teams.
- This solicitation (I-DIRSE-FW), which supported directly proposed, team-based "frameworks" connecting domain challenge problems to data science solutions and infrastructure concepts.
What kinds of teams or collaborations was NSF looking for?
The program sought teams that could articulate a coherent research and collaboration blueprint, identify key bottlenecks (data barriers, methodological gaps, infrastructure shortfalls, and workforce limitations), and show how coordinated cross-disciplinary work could unlock step-change improvements in capability.
What does NSF mean by "convergence research" in this context?
In this context, convergence research refers to intentionally combining domain science with data science, computation, and infrastructure. NSF emphasized that progress depends on integrating these elements rather than advancing any single piece in isolation.
What are the main thrusts of the HDR vision described in the opportunity?
NSF described HDR as organized around several tightly linked thrusts:
- Foundations of data science (theory, mathematics, statistics, and principles)
- Algorithms and systems for data science (computational methods and platforms)
- Data-intensive science and engineering (domain-driven applications enabled by data)
- Data cyberinfrastructure (shared computing, storage, software, and services)
- Education and workforce development (training people to do this work at scale and responsibly)
What types of topic areas were considered a good fit for Frameworks awards?
Frameworks were expected to target science and engineering domains at a "tipping point" where timely data-intensive investment could have transformative effects. NSF was not looking for isolated data projects; it was looking for areas where urgent scientific questions, available or emerging data streams, and appropriate data science advances could reshape an entire research area.
What does "tipping point" mean in this solicitation?
"Tipping point" refers to a domain being ready for a timely data-intensive investment that could produce outsized, transformative impacts, especially where interdisciplinary data analytics and infrastructure investments would materially change what the community can do.
What were the expected deliverables or outputs of a DIRSE Frameworks project?
NSF expected Frameworks projects to:
- Identify frontier challenge problems in science and engineering.
- Map the associated data challenges and data-science barriers limiting progress (for example, heterogeneity, scale, quality, access, privacy, governance, reproducibility, and algorithmic limitations).
- Propose and begin testing strategies to produce breakthroughs by bringing diverse researchers together under a shared framework.
Was the emphasis only on research results?
No. NSF emphasized not only research results but also creating a workable structure for sustained, scalable interdisciplinary collaboration, including durable links across disciplines and institutions.
What role did prototypes play in these awards?
Prototypes were part of making the institute concept credible and actionable. The awards were intended to include early technical proof-of-concept work alongside planning and community-building.
What kinds of bottlenecks were Frameworks teams expected to identify?
Teams were expected to identify where progress is limited by issues such as data barriers, methodological gaps, infrastructure shortfalls, and workforce limitations, including challenges like data heterogeneity, scale, quality, access, privacy, governance, reproducibility, and algorithmic constraints.
How did NSF expect teams to address ethical or trustworthy data use?
The HDR framing explicitly highlights the need for technical and ethical frameworks that support trustworthy, societally relevant data-driven decision-making. Frameworks were expected to consider theoretical and ethical dimensions of data use, not just computational aspects.
How were these proposals submitted and who managed the program?
Proposals for this program were submitted through NSF's CISE Office of Advanced Cyberinfrastructure (OAC). However, the program was designed to be managed by a cross-disciplinary team of NSF program directors because it intentionally spans many research communities and NSF directorates.
What does it mean that the opportunity was "cross-cutting" across NSF?
The opportunity reflected a cross-directorate scope, intended to span multiple research communities. This cross-cutting nature is also reflected in the listing of multiple NSF CFDA numbers associated with the program.
What was the project duration for awards under this solicitation?
The solicitation described the awards as short, two-year efforts focused on planning, community-building, and early proof-of-concept work.
How many awards were anticipated?
The opportunity anticipated roughly 10 awards.
What is the Funding Opportunity Number and original closing date?
The Funding Opportunity Number listed is 19-549, and the original closing date was May 7, 2019.
What are the CFDA numbers associated with this opportunity?
The opportunity was associated with multiple NSF CFDA numbers: 47.041, 47.049, 47.050, 47.070, 47.074, 47.075, 47.076, 47.079, and 47.083.
Who was eligible to apply based on the listing information provided?
Eligibility was described broadly as "Others (see text...)," indicating applicants needed to consult the full solicitation for specific eligibility rules and any constraints tied to institution types or partnership structures.
What was NSF's expected impact of funding DIRSE Frameworks?
NSF positioned I-DIRSE-FW as the blueprint stage for future HDR Institutes: organizing interdisciplinary communities, pinpointing high-leverage data bottlenecks in key science and engineering areas, building early prototypes, and laying groundwork for larger institute-scale efforts intended to change how data-intensive research is done across the US research ecosystem.
Was there an anticipated next phase after these Frameworks awards?
Yes. NSF anticipated that strong concepts and prototypes from this phase (along with new ideas) could feed into a later "convergence and co-design" phase anticipated around 2021, supporting larger, more comprehensive HDR Institutes that integrate and scale what worked.
What does it mean that DIRSE Frameworks should connect challenge problems to data science solutions and infrastructure concepts?
It means the Frameworks were expected to clearly tie real science and engineering frontier questions to the data science methods, computational approaches, and cyberinfrastructure (tools, platforms, pipelines, standards, compute, storage, and services) needed to address them, showing why coordinated interdisciplinary work is necessary.
How did NSF describe the role of education and workforce development in this program?
Education and workforce development were identified as a core HDR thrust. Frameworks were encouraged to integrate education infrastructure (training programs, student pipelines, professional development, curricula, and community practices) with research and technical infrastructure so the community can do data-intensive work at scale and responsibly.
What kinds of collaboration changes did NSF want teams to pilot?
NSF wanted teams to pilot collaboration modalities that go beyond traditional disciplinary boundaries and "institutional walls," establishing durable links between domain researchers and data scientists and engineers.
Is this opportunity primarily about building a standalone center?
Not as described here. The emphasis was on conceptualizing and prototyping what could become an institute, and on contributing to an integrated fabric of interrelated institutes that share approaches and concepts across fields.
Was this a discretionary grant program?
Yes. The opportunity listing described it as a discretionary NSF grant program.
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