From listmaster animalgenome.org Mon Mar 25 10:16:11 2019
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From: "Matukumalli, Lakshmi - NIFA" <lmatukumallinifa.usda.gov>
Postmaster: submission approved by list moderator
To: Members of AnGenMap <angenmapanimalgenome.org>
Subject: Joint NIFA-NSF effort on agricultural science, big
data, informatics,
Date: Mon, 25 Mar 2019 10:16:11 -0500
Dear Colleagues:
Building on NSF's history of investments in data and computational sciences
and USDA/NIFA's history of investments in agricultural science, NSF and
USDA/NIFA wish to notify the community of our intention to jointly fund
convergent research that combines methods in agricultural, biological, and
computer and information science and engineering to address pressing
challenges and opportunities in digital agriculture. This Dear Colleague
Letter (DCL) is aligned with NSF's Harnessing the Data Revolution Big Idea,
and aims to build capacity across disciplinary boundaries, in preparation
for larger scale investments at the intersection of computational,
agricultural, and biological sciences.
Motivated by the increasing volumes of data, faster computation, and
algorithmic advances, there is an opportunity to apply transformative,
data-driven research methods to the agriculture sector that are responsive
to and will yield meaningful insights for farmers, other stakeholders, and
society at large. Of interest for this DCL are applications focused on
economically important plants, animals, and their environments---in
particular food, fuel, feed, and health---and where research outcomes in a
particular application area may be transferable to, or informative for,
other agricultural application areas. Relevant stakeholders can be
integrated into the proposed research activities, including as partners in
the project, if appropriate for the project.
Specific topics of interest include, but are not limited to, the following:
* Methods for analyzing existing, large datasets, such as artificial
intelligence, machine learning, and computer vision, for example,
leveraging environmental, imaging, and genomic data;
* Models for genetic x environment x management x socioeconomic interactions
(G x E x M x S) in order to predict livestock, aquaculture, and plant
phenotypic outcomes and sustainability---such as yield, survivability,
resistance to environmental stressors, pest resistance, drought resistance,
and nutritional value;
* Data storage, management, and integration across a range of data types to
enable a systems-level approach, including integration of big data in real-
time systems;
* Wired and wireless networking challenges in rural settings, including
computation at the edge;
* Security, privacy, and management for access and sharing of farm and
community data; and
* Learning science innovations, which may include development of
computational skills for biological and agricultural science majors,
and communities of agricultural practice for a diverse and innovative
future workforce.
Principal Investigators may also consider the design of instructional
materials or workforce development pathways, combining computational and
agricultural expertise, in the broader impacts of proposals. The intention
is to encourage students in biological, agricultural and engineering
programs in two- or four-year colleges and universities, across all
education levels, to acquire data and/or computational science skills and,
vice versa, to expose students in data and/or computational science to
agricultural challenges. Additionally, activities could aim to improve
retention and capabilities of a region's agricultural workforce.
Proposals pursuant to this DCL may be submitted to one of the three
programs listed below:
* Cyber-Physical Systems (CPS) program
https://www.nsf.gov/...m.jsp?pims_id=503286
* Information Integration and Informatics (III) program;
https://www.nsf.gov/...mm.jsp?pims_id=13707
* Smart and Connected Communities (S&CC) program.
https://www.nsf.gov/...m.jsp?pims_id=505364
Proposals must follow the guidance contained in NSF's
https://www.nsf.gov/...mm.jsp?ods_key=pappg, the
corresponding solicitation and that is described here.
All proposals pursuant to this DCL must include the prefix "DATAg:"
following the title prefixes required in each solicitation, where
appropriate.
Additionally, researchers are encouraged to leverage existing agriculture
data sets. Data and code resulting from funded work is expected to be
adequately characterized, readily accessible and usable, and stored in a
safe environment with adequate measures taken for long-term preservation in
specific repositories and catalogs, as appropriate, as well as with
consideration for protection of confidentiality, personal privacy, and
proprietary interests.
For more information, including questions about this DCL, please contact:
* Sylvia Spengler, NSF/CISE, (703) 292-8930, mailto:sspenglensf.gov;
* David Corman, NSF/CISE, (703) 292-8754, mailto:dcormannsf.gov;
* Cliff Weil, NSF/BIO, (703) 292-8712, mailto:cweilnsf.gov; and
* Charlotte Kirk Baer, USDA/NIFA, (202) 445-3426, mailto:cbaernifa.usda.gov
Sincerely,
Jim Kurose
Assistant Director, Computer and Information Science and Engineering, NSF
Joanne S. Tornow Assistant Director, Biological Sciences, NSF
J. Scott Angle Director, National Institute of Food and Agriculture, USDA
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