Scientific Data Management and Analysis at Extreme Scale | Apply Online

Frequently Asked Questions (FAQs)

What is the name and number of this funding opportunity?

This opportunity is the Department of Energy (DOE) Office of Science Funding Opportunity Announcement (FOA) DE-FOA-0000256, titled "Scientific Data Management and Analysis at Extreme Scale."

Which DOE office is running the program?

The FOA was released by the DOE Office of Science through the Office of Advanced Scientific Computing Research (ASCR).

What is the main purpose of the FOA?

The purpose is to advance basic research and usable capabilities for managing, analyzing, and extracting knowledge from scientific datasets at extreme scale, with data sizes ranging from petabytes today to exabytes in the near term.

Why is DOE focusing on scientific data management and analysis at extreme scale?

The FOA notes that modern simulations and experiments generate data so rapidly that the bottleneck is often no longer data generation. Instead, the challenge is managing data, analyzing it efficiently, and presenting results in ways that scientists, decision makers, and the public can understand and use.

What kinds of data challenges does the FOA highlight?

The FOA emphasizes that the challenge is not only size (petabyte to exabyte scale), but also complexity. Scientific data can be high-dimensional, heterogeneous, and produced by a mix of instruments, detectors, sensors, and simulation codes. It may also involve multiscale and multiphysics phenomena spanning many spatial and temporal scales and containing hundreds of variables or attributes.

How does high-performance computing (HPC) relate to this FOA?

The FOA states that HPC is essential not only to generate extreme-scale datasets, but also to analyze them, particularly as the community transitions from petascale to exascale systems and increasingly heterogeneous, multicore architectures.

What research areas are within scope?

The scope emphasizes innovative computer science research, with close ties to applied mathematics, statistics, and computational science, focused on extreme-scale scientific data management and analysis.

Is this FOA focused on theory, tools, or both?

Both. The FOA calls for efforts that may blend fundamental research with the creation of algorithms designed for advanced architectures, along with development of practical tools that scientists can adopt for real discovery workflows.

Does the FOA expect incremental improvements or major advances?

The FOA indicates proposals are expected to go beyond incremental improvements and aim for transformational advances that address key obstacles in extreme-scale analytics and data management, grounded in the realities of modern supercomputing systems.

What does the FOA say about alignment with advanced computing architectures?

The FOA highlights the need for algorithms and approaches that are designed for advanced architectures, in the context of increasingly heterogeneous and multicore systems as computing moves toward exascale.

Are multi-institutional proposals encouraged?

Yes. DOE explicitly encourages multi-institutional applications that are cohesive and focused on major breakthroughs.

What types of partnerships are strongly encouraged?

The FOA strongly encourages partnerships connecting universities, DOE National Laboratories, and industry, with the goal of building interdisciplinary teams capable of addressing end-to-end challenges.

What does DOE mean by "purposeful" collaboration?

The FOA signals that collaborations should not be purely additive. Partners should fill genuine gaps in expertise, and the team should be integrated rather than expanding scope without clear coordination.

What kinds of end-to-end challenges are teams expected to address?

The FOA describes end-to-end challenges spanning algorithms, architecture-aware implementations, and tool development that supports real scientific use cases.

What funding instrument was used for this opportunity?

This was a discretionary funding opportunity using cooperative agreements.

What was the estimated total funding level and number of awards?

The FOA estimated a total program funding level of about $5,000,000 and anticipated roughly 15 awards.

Was cost sharing required?

No. The FOA stated there was no cost sharing requirement.

Who was eligible to apply?

Eligibility was broadly open to domestic entities, subject to specific exclusions described in the FOA.

Which entities were not allowed to apply or receive funds under this FOA?

The FOA excluded other federal agencies from applying or receiving funds. Federally Funded Research and Development Centers (FFRDCs) were excluded as applicants. Certain lobbying-related 501(c)(4) nonprofits were also excluded.

Can federal agencies participate as partners if they cannot receive funding?

The FOA notes that while federal agencies could not participate as funded partners, collaborations could include federal laboratories and FFRDCs (including DOE National Labs) in appropriate roles, consistent with the FOA rules.

Can DOE National Laboratories be involved?

Yes. The FOA strongly encourages partnerships that connect universities, DOE National Laboratories, and industry, and it also notes that collaborations could include federal laboratories and FFRDCs (including DOE National Labs) in appropriate roles consistent with FOA rules.

When was the FOA posted, and what were the submission deadlines?

The FOA was posted on January 29, 2010. Both the original closing date and the final closing date were March 18, 2010.

When was the FOA archived?

The archive date was June 18, 2010.

Which office administered the opportunity?

The program was administered through the DOE Chicago Service Center.

Were there official contacts for questions?

Yes. The FOA listed contacts for grants administration questions and for programmatic questions, including the ASCR program manager.

How should this FOA be understood at a high level?

It is a targeted effort to build the computer-science foundations, algorithms, and practical tools needed to make extreme-scale scientific data usable for discovery workflows as computing moves toward exascale performance and more complex hardware platforms.