Data Reduction for Science

The summary for the Data Reduction for Science grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Office of Science, which is the U.S. government agency offering this grant.

Data Reduction for Science: 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.