Data Reduction for Science

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Funding Opportunity ID:332869
Opportunity Number:DE-FOA-0002501
Opportunity Title:Data Reduction for Science
Opportunity Category:Discretionary
Opportunity Category Explanation:
Funding Instrument Type:Grant
Category of Funding Activity:Science and Technology and other Research and Development
Category Explanation:
CFDA Number(s):81.049
Eligible Applicants:Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled “Additional Information on Eligibility”
Additional Information on Eligibility:All types of domestic applicants are eligible to apply, except nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995. Federally affiliated entities must adhere to the eligibility standards below: 1. DOE/NNSA National Laboratories DOE/NNSA National Laboratories are eligible to submit applications under this FOA and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant, funding will be provided through the DOE Field-Work Proposal System and work will be conducted under the laboratory’s contract with DOE. No administrative provisions of this FOA will apply to the laboratory or any laboratory subcontractor. If recommended for funding as a proposed subrecipient, the value of the proposed subaward will be removed from the prime applicant’s award and will be provided to the laboratory through the DOE Field-Work Proposal System and work will be conducted under the laboratory’s contract with DOE. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA. 2. Non-DOE/NNSA FFRDCs Non-DOE/NNSA FFRDCs are eligible to submit applications under this FOA and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant, funding will be provided through an Inter-Agency Award to the FFRDC’s sponsoring Federal Agency. If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an Inter-Agency Award to the FFRDC’s sponsoring Federal Agency. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA. 3. Other Federal Agencies Other Federal Agencies are eligible to submit applications under this FOA and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant, funding will be provided through an Inter-Agency Award. If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an Inter-Agency Award. Additional instructions for providing statutory authorization are found in Section VIII of this FOA.
Agency Code:PAMS-SC
Agency Name:Department of Energy – Office of Science
Office of Science
Posted Date:Apr 15, 2021
Close Date:Jun 04, 2021
Last Updated Date:Apr 14, 2021
Award Ceiling:$800,000
Award Floor:$100,000
Estimated Total Program Funding:$10,000,000
Expected Number of Awards:12
Description: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 INFORMATIONScientific observations, experiments, and simulations are producing data at a rate beyond our capacity to store, analyze, stream, and archive. This data almost always contains redundancies and trivialities that hide the important information of interest to scientists. 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. These efforts should be expanded to include mathematical rigor to ensure that scientifically-relevant constraints on quantities of interest are satisfied, to be integrated into scientific workflows, and to be implemented in a manner that inspires trust that the desired information is preserved.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 [1] 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 Program Announcement is to support applied mathematics and computer science approaches that address one or more of the identified PRDs. 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 Announcement 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 scientific machine learning or trust. Preference may be given to pre-applications that include reduction estimates for at least two science applications.
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