Opportunity Information: Apply for DE FOA 0002306
The Department of Energy (DOE) Office of Science, through its Advanced Scientific Computing Research (ASCR) program, offered this funding opportunity to improve how research data and artificial intelligence (AI) models are shared and reused across the scientific community. The core goal is to make both datasets and AI/ML models FAIR: findable, accessible, interoperable, and reusable. By pushing the community toward FAIR practices, ASCR aims to speed up the creation of new AI applications that support the Office of Science mission areas and strengthen AI research and development more broadly.
The opportunity focuses on two main needs. First, ASCR is interested in FAIR benchmark data for AI, meaning well-documented, well-curated datasets that can be reliably used to train and evaluate AI systems. Benchmarks are especially important because they let researchers compare methods fairly, reproduce results, and understand where models succeed or fail. Second, ASCR wants FAIR frameworks that connect data and AI models, essentially approaches, standards, or infrastructure that capture and expose the relationship between training data, model development, and model use during inference. This includes the ability to understand what data produced a model, how it was processed, what task the model performs, and how the model should be applied or combined with additional data when making predictions or classifications.
In this announcement, "AI" is used broadly and includes machine learning, deep learning, neural networks, computer vision, and natural language processing. The FOA also clarifies key definitions: data are the digital artifacts used to generate AI models and/or used alongside models during inference, while an AI model is an inference method used to perform a task such as prediction, diagnosis, or classification. An AI task is the actual inference activity carried out by an intelligent system. In practical terms, the funding is aimed at improving the full lifecycle around AI in science: producing trustworthy datasets, creating models tied to clear provenance and documentation, and enabling others to discover, access, integrate, and reuse those assets without redoing foundational work.
Administratively, this was a discretionary funding opportunity issued as a cooperative agreement, indicating DOE expected substantial involvement during the project period rather than a hands-off grant structure. It fell under the Science and Technology and other Research and Development activity category (CFDA 81.049). Eligibility was broadly open to any type of applicant entity, subject to any additional eligibility details in the full announcement. The FOA was released on April 2, 2020, with an original closing date of May 15, 2020. DOE anticipated making about seven awards, with a maximum award size (ceiling) of $750,000 per project. The funding opportunity number was DE-FOA-0002306, titled "FAIR Data and Models for Artificial Intelligence and Machine Learning."Apply for DE FOA 0002306
- The Department of Energy - Office of Science, Office of Science in the science and technology and other research and development sector is offering a public funding opportunity titled "FAIR Data and Models for Artificial Intelligence and Machine Learning" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 81.049.
- This funding opportunity was created on Apr 02, 2020.
- Applicants must submit their applications by May 15, 2020. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $750,000.00 in funding.
- The number of recipients for this funding is limited to 7 candidate(s).
- Eligible applicants include: Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled Additional Information on Eligibility.
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Frequently Asked Questions (FAQs)
What is the name of this funding opportunity?
The funding opportunity is titled "FAIR Data and Models for Artificial Intelligence and Machine Learning."
What is the funding opportunity number?
The funding opportunity number is DE-FOA-0002306.
Which DOE office and program offered this opportunity?
The opportunity was offered by the Department of Energy (DOE) Office of Science through its Advanced Scientific Computing Research (ASCR) program.
What is the main purpose of this FOA?
The core purpose is to improve how research data and artificial intelligence (AI) models are shared and reused across the scientific community by making datasets and AI/ML models FAIR: findable, accessible, interoperable, and reusable.
What does FAIR mean in this announcement?
FAIR refers to practices that make data and models findable, accessible, interoperable, and reusable, so others can discover them, obtain them, integrate them with other resources, and reuse them reliably.
How does DOE/ASCR expect FAIR practices to help the scientific community?
By pushing the community toward FAIR data and model practices, ASCR aims to speed up the creation of new AI applications that support DOE Office of Science mission areas and strengthen AI research and development more broadly.
What are the two main focus areas of the opportunity?
The FOA highlights two primary needs: (1) FAIR benchmark data for AI, and (2) FAIR frameworks that connect data and AI models.
What is meant by "FAIR benchmark data for AI"?
FAIR benchmark data refers to well-documented and well-curated datasets that can be used reliably to train and evaluate AI systems.
Why are benchmark datasets emphasized?
Benchmarks are important because they allow researchers to compare methods fairly, reproduce results, and understand where models succeed or fail.
What is meant by "FAIR frameworks that connect data and AI models"?
These are approaches, standards, or infrastructure that capture and expose the relationship between training data, model development, and model use during inference.
What kinds of relationships between data and models does the FOA want to capture?
The FOA emphasizes the ability to understand what data produced a model, how the data was processed, what task the model performs, and how the model should be applied (or combined with additional data) when making predictions or classifications.
How does the FOA describe "data" in the context of AI?
Data are defined as the digital artifacts used to generate AI models and/or used alongside models during inference.
How does the FOA define an "AI model"?
An AI model is defined as an inference method used to perform a task such as prediction, diagnosis, or classification.
What is an "AI task" under this announcement?
An AI task is the inference activity carried out by an intelligent system.
How broadly does the FOA use the term "AI"?
"AI" is used broadly and includes machine learning, deep learning, neural networks, computer vision, and natural language processing.
What parts of the AI lifecycle is this funding intended to improve?
The FOA is aimed at improving the full lifecycle around AI in science, including producing trustworthy datasets, creating models tied to clear provenance and documentation, and enabling others to discover, access, integrate, and reuse those assets without redoing foundational work.
What type of funding instrument was used?
This was a discretionary funding opportunity issued as a cooperative agreement.
What does it mean that the award is a cooperative agreement?
A cooperative agreement indicates DOE expected substantial involvement during the project period, rather than a fully hands-off grant structure.
What activity category does this opportunity fall under?
It fell under the "Science and Technology and other Research and Development" activity category.
What is the CFDA number associated with this opportunity?
The CFDA number is 81.049.
Who was eligible to apply?
Eligibility was described as broadly open to any type of applicant entity, subject to any additional eligibility details in the full announcement.
When was the FOA released?
The FOA was released on April 2, 2020.
What was the original closing date?
The original closing date was May 15, 2020.
How many awards did DOE anticipate making?
DOE anticipated making about seven awards.
What was the maximum award size?
The maximum award size (ceiling) was $750,000 per project.
What is the overarching goal behind making data and models FAIR?
The goal is to make it easier for the community to discover and reuse high-quality datasets and AI/ML models, improve reproducibility and fair comparison of methods, and reduce the need for researchers to redo foundational dataset curation and model development work.
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