(CLOSED )Data Reduction for Science (DE-FOA-0002501)

Sponsor Name: 
DOE
Description of the Award: 

Summary

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 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.

Limit (Number of applicants permitted per institution): 
2
Sponsor LOI Deadline: 
May 06, 2021
Sponsor Final Deadline: 
Jun 04, 2021
OVPR Application or NOI Instructions: 

Interested applicants should upload the following documents in sequence in one PDF file (File name: Last name_DE-FOA-0002051_2021 no later than 4:00 p.m. on the internal submission deadline:

1. Cover Letter (1 page, pdf):

  • Descriptive title of proposed activity
  • Name(s), address(es), and telephone number(s) of the PD(s)/PI(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) identifying the project scope that addresses the key aspects and elements of the sponsor's solicitation, principal investigators, collaborators, and partner organizations.

3. 2-page CV's of Investigators

Formatting Guidelines:

Font/size: No smaller than 11 point
Document margins: 1.0” (top, bottom, left and right)
Standard paper size (8 ½” x 11)

To be considered as a Penn State institutional nominee, please submit a notice of intent by the date provided directly below.
This limited submission is in downselect: 
Penn State may only submit a specific number of proposals to this funding opportunity. The number of NOIs received require that an internal competition take place, thus, a downselect process has commenced. No Penn State researchers may apply to this opportunity outside of this downselect process. To apply for this limited submission, please use this link:
OVPR Downselect Deadline: 
Tuesday, April 27, 2021 - 4:00pm
Notes: 
Mahmut Kandemir (CoE);