The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of data reduction techniques and algorithms to facilitate more efficient analysis and use of massive data sets produced by observations, experiments and simulation.
Supplementary Information
Scientific observations, experiments, and simulations are producing data at rates beyond our capacity to store, analyze, stream, and archive the data in raw form. Of necessity, many research groups have already begun reducing the size of their data sets via techniques such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction. Once reduced in size, transporting, storing, and analyzing the data is still a considerable challenge – a reality that motivates SC’s Integrated Research Infrastructure (IRI) program and necessitates further innovation in data-reduction methods. These further efforts should continue to increase the level of mathematical rigor in scientific data reduction to ensure that scientifically-relevant constraints on quantities of interest are satisfied, that methods can be integrated into scientific workflows, and that methods are implemented in a manner that inspires trust that the desired information is preserved. Moreover, as the scientific community continues to drive innovation in artificial intelligence (AI), important opportunities to apply AI methods to the challenges of scientific data reduction and apply data-reduction techniques to enable scientific AI, continue to present themselves.
The drivers for data reduction techniques constitute a broad and diverse set of scientific disciplines that cover every aspect of the DOE scientific mission. An incomplete list includes light sources, accelerators, radio astronomy, cosmology, fusion, climate, materials, combustion, the power grid, and genomics, all of which have either observatories, experimental facilities, or simulation needs that produce unwieldy amounts of raw data. ASCR is interested in algorithms, techniques, and workflows that can reduce the volume of such data, and that have the potential to be broadly applied to more than one application. Applicants who submit a pre-application that focuses on a single science application may be discouraged from submitting a full proposal.
Accordingly, a virtual DOE workshop entitled “Data Reduction for Science” was held in January of 2021, resulting in a brochure detailing four priority research directions (PRDs) identified during the workshop. These PRDs are (1) effective algorithms and tools that can be trusted by scientists for accuracy and efficiency, (2) progressive reduction algorithms that enable data to be prioritized for efficient streaming, (3) algorithms which can preserve information in features and quantities of interest with quantified uncertainty, and (4) mapping techniques to new architectures and use cases.
The principal focus of this FOA is to support applied mathematics and computer science approaches that address one or more of the identified PRDs. Research proposed may involve methods primarily applicable to high-performance computing, to scientific edge computing, or anywhere scientific data must be collected or processed. Significant innovations will be required in the development of effective paradigms and approaches for realizing the full potential of data reduction for science. Proposed research should not focus only on particular data sets from specific applications, but rather on creating the body of knowledge and understanding that will inform future scientific advances. Consequently, the funding from this FOA is not intended to incrementally extend current research in the area of the proposed project. Rather, the proposed projects must reflect viable strategies toward the potential solution of challenging problems in data reduction for science. It is expected that the proposed projects will significantly benefit from the exploration of innovative ideas or from the development of unconventional approaches. Proposed approaches may include innovative research with one or more key characteristics, such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction, and may focus on cross-cutting concepts such as artificial intelligence or trust. Preference may be given to pre-applications that include reduction estimates for at least two science applications.
DOE anticipates that, subject to the availability of future year appropriations, a total of $15,000,000 in current and future fiscal year funds will be used to support awards under this FOA.
Interested applicants should upload the following documents in sequence in one PDF file (File name: Last name_DE-FOA-0003266_2024 no later than 4:00 p.m. on the internal submission deadline:
1. Cover Letter (1 page, pdf):
- Descriptive title of proposed activity
- PI name, departmental affiliations(s) and contact information
- Co-PI's names and departmental affiliation(s)
- Names of other key personnel
- Participating institution(s)
- Number and title of this funding opportunity
2. Project Description (no more than two pages, pdf; Figures and references, if included, must fit within the two-page limit.)
- The first page of the pre-application must specify at least one scientific hypothesis whose investigation motivates the proposed work, using no more than 100 words, in a box with a black border. For any hypothesis that is not itself innovative, the pre-application must describe at least one innovative insight into how the hypothesis can be investigated that may be exploited by the planned research.
- This information must be followed by a clear and concise description of the objectives and technical approach of the proposed research.
3. 3-page CV's of Investigators
Formatting Guidelines:
Font/size: No smaller than 11 pt.
Document margins: 1.0” (top, bottom, left and right)
Standard paper size (8 ½” x 11)
Questions concerning the limited submissions process may be submitted to limitedsubs@psu.edu.