The National Science Foundation (NSF) is funding this program in hopes to better understand how changes within our ecological, behavioral, physiological, and other systems have impacted transmission of infectious diseases.
MAXIMUM AWARD AMOUNT
“The categories of proposers eligible to submit proposals to the National Science Foundation are identified in the NSF Proposal & Award Policies & Procedures Guide (PAPPG), Chapter I.E. Unaffiliated individuals are not eligible to submit proposals in response to this solicitation”
PERIOD OF PERFORMANCE
Maximum of 5 years
November 24, 2021; November 16, 2022; Third Wednesday in November, Annually Thereafter. 5 p.m. submitter’s local time
EXPECTED NOTIFICATION DATE
PROJECT START DATE
For more information regarding this grant, visit the NSF website.
The Department of Energy Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in making research data and artificial intelligence (AI) models findable, accessible, interoperable, and reusable (FAIR1) to facilitate the development of new AI applications in SC’s congressionally authorized mission space, which includes the advancement of AI research and development. In particular, ASCR is interested in supporting FAIR benchmark data for AI; and FAIR frameworks for relating data and AI models
For this FOA, AI is inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). Data, in this context, are the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. An AI model is an inference method that can be used to perform a “task,” such as prediction, diagnosis, or classification. The model is developed using training data or other knowledge. An AI task is the inference activity performed by an artificially intelligent system.
Tools for training AI models on data are readily available and widely used. What is lacking, however, is a theoretical framework for understanding relationships between data and models. For example, given a specific data set and problem, we lack rigorous methods for identifying the best model, hyper-parameters, and training method to use. Given a specific data set and problem, which additional data would be helpful to include in the training set? What information about a dataset can be deduced from a model trained on the data? What attributes of the data can be reverse engineered from a model? What can we learn about model robustness and transfer learning by looking at relationships between data and models?
The primary focus of this FOA topic is to advance our understanding of the relationship between data and models by exploring relationships among them through the development of FAIR frameworks for relating data and models. Such frameworks should provide capabilities that advance our understanding of AI, provide new insights to help researchers with applications of AI techniques, and provide an environment where novel approaches to AI can be explored.
Proposed frameworks may focus on specific disciplines or sub-disciplines currently supported by SC’s programs in ASCR, Biological and Environmental Research (BER), Basic Energy Sciences (BES), Fusion Energy Sciences (FES), High Energy Physics (HEP), Nuclear Physics (NP), or may focus on particular aspects or sub-areas within AI.
The US Military Academy (USMA) at West Point’s mission is “to educate, train, and inspire the Corps of Cadets so that each graduate is a commissioned leader of character committed to the values of Duty, Honor, Country and prepared for a career of professional excellence and service to the Nation as an officer in the United States Army.” USMA executes research to enhance the education of cadets, develop the faculty professionally, and address important issues facing the Army and the Nation. In addition, the Academy conducts research and analysis in emerging fields that may realize novel or vastly improved Army capabilities.
At West Point, research is organized and administered through centers and institutes, most of which reside within academic departments. These centers and institutes, affiliated with each other through the Academy Research Council (ARC), coordinated and supported by the Academic Research Division (ARD), provide the infrastructure necessary to tackle the nation’s and the world’s most challenging problems. Our research centers and institutes bring context to the classroom, are central to our vibrant and pioneering faculty, and are one way West Point connects to the Army and to the Nation. In addition to applied research, there are centers and institutes at West Point that focus on other aspects of the USMA mission.
The USMA BAA identifies topics of interest to the USMA departments, directorates, and research centers and institutes. These groups focus on executing in-house research programs, with a significant emphasis on collaborative research with other organizations. The groups fund a modest amount of extramural research in certain specific areas, and those areas are described in this BAA.
The USMA BAA seeks proposals from institutions of higher education, nonprofit organizations, state and local governments, foreign organizations, foreign public entities, and for-profit organizations (i.e., large and small businesses) for research based on the following campaigns: Socio-Cultural; Information Technology; Ballistics, Weapons, and Protections; Energy and Sustainability; Materials, Measurements, and Facilities; Unmanned Systems and Space; Human Support Systems; and Artificial Intelligence, Machine Learning, and Quantum Technologies.
Proposals are sought for cutting-edge innovative research that could produce discoveries with a significant impact to enable new and improved Army technologies and related operational capabilities and related technologies. The specific research areas and topics of interest described in this document should be viewed as suggestive, rather than limiting.
Dear Colleagues: We are pleased to announce the availability of both NSF-approved formats for the Biographical Sketch and Current and Pending Support sections of National Science Foundation (NSF) proposals that fall under the revised Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 20-1) (see the February 6, 2020 webinar for complete details on all revisions to the PAPPG).
Although use of an NSF-approved format for submission of these proposal sections is not required until implementation of the revised PAPPG (NSF 20-1) on June 1, 2020, NSF is encouraging proposers to begin using the NSF-approved formats now. NSF values the feedback from the research community, and we would like to hear about your experience with the new NSF-approved formats. Information about how to provide feedback is included below.
Use of an NSF-approved format aims to reduce administrative burden and improve efficiencies by providing proposers with a compliant and reusable way to maintain this information for subsequent proposal submissions to NSF, while also ensuring that the information is submitted in a standard and searchable composition.
SciENcv: NSF has partnered with the National Institutes of Health (NIH) to use SciENcv: Science Experts Network Curriculum Vitae as an NSF-approved format for use in preparation of both the Biographical Sketch and Current and Pending Support sections of an NSF proposal. SciENcv will produce an NSF-compliant PDF version of the documents which proposers can save and submit as part of their proposals via FastLane, Research.gov or Grants.gov. Additional information about the NSF-approved SciENcv formats is available on the NSF biographical sketch and current and pending support websites.
The SciENcv tool integrates with ORCID, enabling proposers to populate their Biographical Sketches by importing data directly from their ORCID records rather than having to manually enter all the required information. Additionally, Biographical Sketch data maintained in SciENcv can be quickly and easily updated on an ongoing basis for subsequent proposal submissions.
NSF Fillable PDF: NSF is also providing a fillable PDF as an NSF-approved format for use to prepare both the Biographical Sketch and Current and Pending Support sections of an NSF proposal. Proposers can download the respective fillable PDF form from the NSF biographical sketch and current and pending support websites and then submit the completed forms as part of their proposals via FastLane, Research.gov or Grants.gov. Note that the NSF fillable PDF for the Biographical Sketch does not integrate with ORCID.
It is important to note that beginning June 1, 2020, proposers will be required to use one of the NSF-approved formats for both the Biographical Sketch and Current and Pending Support sections of NSF proposals. Proposals submitted via FastLane, Research.gov and Grants.gov will be compliance checked to ensure that the documents were prepared in accordance with this new policy.
Although not required for proposal submission until June 1, 2020, we hope that you will start using the NSF-approved formats for Biographical Sketch and Current and Pending Support as soon as possible. If you have any feedback that would help us make improvements to the two formats in the future, please let us know. Feedback may be submitted by email to email@example.com or via the Research.gov Feedback page (select “Biographical Sketch” or “Current & Pending Support” under the Site Area dropdown menu).
To assist the community about these new requirements and to start using SciENcv now, NSF and NIH are planning to conduct a joint webinar that will include a walk-through of how to prepare the Biographical Sketch and Current and Pending Support documents in SciENcv. Information will be provided as soon as it is available, and we encourage you to sign up for notifications.
We also invite you to participate in the next NSF Electronic Research Administration (ERA) Forum on May 14, 2020 at 1:00PM – 2:30PM EDT where we will discuss the NSF-approved format requirements, as well as the new capability to prepare and submit separately submitted collaborative proposals in Research.gov. To sign up for ERA Forum notifications including registration availability for the May 14 event, please send a blank email to NSF-ERA-FORUMfirstname.lastname@example.org and you will be automatically enrolled.
The following training resources are now available, and NSF will continue to keep the community informed as additional resources are released.
FAQs addressing policy questions related to the PAPPG (NSF 20-1) clarifications to the current and pending support coverage, as well as questions regarding use of an NSF-approved format for current and pending support
Questions? Policy-related questions should be directed to email@example.com. If you have technical or IT system-related questions, please contact the NSF Help Desk at 1-800-673-6188 (7:00 AM – 9:00 PM ET; Monday – Friday except federal holidays) or via firstname.lastname@example.org.
At EERE, we understand that due to the coronavirus outbreak (COVID-19), many of us have had to make adjustments to our business operations and practices in order to safeguard the health and safety of our communities. Due to the extraordinary circumstances in which we now find ourselves, EERE is issuing an extension of 14 calendar days to respond to FOA 2197. The due date for full application submissions to this FOA is now April 28, 2020.
All questions and answers related to this FOA will be posted on EERE Exchange at: https://eere-exchange.energy.gov. Please note that you must first select this specific FOA Number in order to view the questions and answers specific to this FOA. Thank you, applicants, for your continued efforts during this uncertain time. We hope that you and your loved ones are well and we look forward to hearing from you.