DOE Data Reduction for Science

Please see the full solicitation for complete information about the funding opportunity. Below is a summary assembled by the Research & Innovation Office (RIO).

Program Summary

The Data Reduction for Science program seeks 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.

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 [5] 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. For additional background, see [6-9].

The principal focus of this program is to support applied mathematics and computer science approaches that address one or more of the identified PRDs. Research proposed may involve methods primarily applicable to high-performance computing, to scientific edge computing, or anywhere scientific data must be collected or processed. 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 FOA 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 artificial intelligence or trust.

Preference may be given to pre-applications that include reduction estimates for at least two science applications.


CU Internal Deadline: 11:59pm MST February 26, 2024

DOE Pre-Application Deadline: 3:00pm MST March 19, 2024

DOE Application Deadline: 9:59pm MST May 7, 2024

Internal Application Requirements (all in PDF format)

  • Project Narrative (3 pages maximum): Please include: 1) Background/Introduction: explain the importance and relevance of the proposed work as well as a review of the relevant literature; 2) Project Objectives: provide a clear, concise statement of the specific objectives/aims of the proposed project; 3) Proposed Research and Methods: identify the hypotheses to be tested (if any) and details of the methods to be used including the integration of experiments with theoretical and computational research efforts; and 4) Promoting Inclusive and Equitable Research (PIER) Plan: describe the activities and strategies to promote equity and inclusion as an intrinsic element to advancing scientific excellence in the research project within the context of the proposing institution and any associated research group(s).
  • Lead PI Curriculum Vitae and Names and Institutional Affiliations of any Coinvestigators
  • Budget Overview (1 page maximum): A basic budget outlining project costs is sufficient; detailed OCG budgets are not required.

To access the online application, visit:


No more than one pre-application or application for each PI at the applicant institution.

The PI on a pre-application, or application may also be listed as a senior or key personnel, including in any role on a proposed subaward, on an unlimited number of separate submissions.

Teams of multiple institutions may submit collaborative applications. Each submitted application in such a team must indicate that it is part of a collaborative project/group. Every partner institution must submit an application through its own sponsored research office. Each multi-institutional team can have only one lead institution.

Limited Submission Guidelines

No more than two pre-applications or applications as the lead institution.

Award Information

Number of Anticipated Awards: 5-10

Period of Performance: 3 years

Ceiling: $150,000 per year  

Floor: $1,000,000 per year

Review Criteria

The internal committee will use DOE’s evaluation criteria (below) for the selection process.


  • What is the scientific innovation of the proposed research?
  • What is the likelihood of achieving valuable results?
  • How might the results of the proposed work impact the direction, progress, and thinking in relevant scientific fields of research?
  • How does the proposed work compare with other efforts in its field, both in terms of scientific and/or technical merit and originality?
  • Does the application specify at least one scientific hypothesis motivating the proposed work? Is the investigation of the specified hypothesis or hypotheses scientifically valuable?
  • Is the Data Management Plan suitable for the proposed research? To what extent does it support the validation of research results? To what extent will research products, including data, be made available and reusable to advance the field of research?


  • How logical and feasible are the research approaches?
  • Does the proposed research employ innovative concepts or methods?
  • Can the approach proposed concretely contribute to our understanding of the validity of the specified scientific hypothesis or hypotheses?
  • Are the conceptual framework, methods, and analyses well justified, adequately developed, and likely to lead to scientifically valid conclusions?
  • Does the applicant recognize significant potential problems and consider alternative strategies?
  • Is the proposed research aligned with the published priorities identified or incorporated by reference in Section I of this FOA?


  • What is the past performance and potential of the research team?
  • How well qualified is the research team to carry out the proposed research?
  • Are the research environment and facilities adequate for performing the research?
  • Does the proposed work take advantage of unique facilities and capabilities?


  • Are the proposed budget and staffing levels adequate to carry out the proposed research?
  • Is the budget reasonable and appropriate for the scope?


  • Is the proposed Promoting Inclusive and Equitable Research (PIER) Plan suitable for the size and complexity of the proposed project and an integral component of the proposed project?
  • To what extent is the PIER plan likely to lead to participation of individuals from diverse backgrounds, including individuals historically underrepresented in the research community?
  • What aspects of the PIER plan are likely to contribute to the goal of creating and maintaining an equitable, inclusive, encouraging, and professional training and research environment and supporting a sense of belonging among project personnel?
  • How does the proposed plan include intentional mentorship and are the associated mentoring resources reasonable and appropriate?