Greetings,

We are excited to share this limited submission opportunity from the Department of Energy (DOE) Office of Science (SC). Harvard University is limited to submitting up to two pre-applications and two applications as the lead institution in response to this funding opportunity. More information about the award and internal selection process can be found in the synopsis and links below.

DOE Data Reduction for Science

Harvard Pre-Proposal Deadline: February 14, 2024

Sponsor Pre-Application Deadline (required, if nominated): March 19, 2024

OSP Deadline (if nominated): April 30, 2024. Departments or areas may require additional time for proposal review and submission. Please discuss a timeline with your grants administrator.

Sponsor Deadline (if nominated): May 7, 2024

Award Amount: $150,000 to $400,000 per year for three years

The DOE SC program in Advanced Scientific Computing Research (ASCR) is accepting research applications that 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.

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. 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. 

The DOE's four priority research directions 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 that can preserve information in features and quantities of interest with quantified uncertainty, and (4) mapping techniques to new architectures and use cases. 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, funding from this opportunity 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. 

This is a limited submission opportunity and Harvard may only submit two pre-applications and two applications as the lead institution. The Office of the Vice Provost for Research will conduct an internal competition to select Harvard’s nominees. To be considered for a Harvard nomination, potential applicants must submit an internal pre-proposal by 11:59 PM on February 14, 2024, via the link above.

More information can be found in the full funding opportunity announcement: https://science.osti.gov/-/media/grants/pdf/foas/2024/DE-FOA-0003266.pdf.

Questions from FAS or SEAS faculty about this opportunity may be directed to research_development@fas.harvard.edu. 

Requests for support related to the application portal may be directed to vpr@harvard.edu.