DOE DE-FOA-0003266: 2024 Data Reduction for Science

Limit: 2 // Tickets Available: 1

M. Chertkov (Applied Math)

Applicant institutions are limited to both:
• No more than two pre-applications or applications as the lead institution.
• No more than one pre-application or application for each PI at the applicant institution.

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.

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 [1] 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 [2-4].

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.