search

cct-datascience / organization

Initial repository for organizing group activities
1 stars 0 forks source link

USDA AFRI proposal resubmission #473

Closed jessicaguo closed 3 years ago

jessicaguo commented 3 years ago

Initial steps are to:

jessicaguo commented 3 years ago

New program name is Data Science for Food and Agricultural Systems (DSFAS). Application deadline is Thursday, July 29 by 5 pm Eastern. No LOI required for a standard grant. Titles must begin "DSFAS: [full title]" Grant must not exceed $650,000 total, including indirect costs Project periods between 3-5 years

From the call:

Program Area Priority: This program area priority focuses on data science to enable systems and communities to effectively utilize data, improve resource management, and integrate new technologies and approaches to further U.S. food and agriculture enterprises. The program encourages universitybased research as well as public and private partnerships. Many challenges are associated with data in agriculture and food production and processing systems. NIFA stakeholders identified at least a dozen issues that are critical to address including: data infrastructure and management; applications and use of data; entities affected by data; creation, collection, provenance, and characteristics of data; training, programs, student, and knowledge needs around data; principles and protocols associated with data; team, community, and public/private aspects of data; data producers, engineers, scientists, and researchers of data; roles of public, corporate, and commercial entities in data; privacy, security, confidentiality, and quality data; biological and interoperable data systems; bibliometrics, altmetrics, text and data mining; and data sharing, repositories, and analysis. This program area priority will support projects that examine the value of data for small and large farmers, as well as the agricultural and food industries, and gain an understanding of how data can impact the agricultural and food supply chain, reduce food waste and loss, improve consumer health, environmental and natural resource management, affect the structure of U.S. food and agriculture sectors, and increase U.S. competitiveness. The most competitive proposals will be equally well grounded in agricultural science and data science.

Applications for research and integrated research projects must address one or more of the following data science priorities in relation to food and agricultural systems:

  • Analysis of Agricultural Data
  • Develop data-integration and data-quality algorithms and tools to improve analytic capability.
  • Design, validate and implement new algorithms and methods for depicting and leveraging massive data.
  • Connect Multi-scale, Multi-domain or Multi-format Agricultural Data
  • Bridge real-time distributed and parallel data systems;
  • Create new methodologies and frameworks for tracking and processing data; and/or
  • Identify new approaches to data archiving and sharing that support Findable, Accessible, Interoperable, and Re-usable (FAIR) standards.
  • Agricultural Applications and Human-Technology-Data Interactions
  • Examine new scientific implications and practical aspects of how agricultural data and computer systems are accessed, designed, and used to improve human-human, human-technology, and human-decision experiences;
  • Integrate visualization with statistical methods and other analytic techniques in order to support discovery and analysis;
  • Engage students and professionals, teams, universities, and the public and private sectors; and /or
  • Develop decision-support tools that use diverse data sources and Big Data analytics modeling of short-term impacts of various factors to create best value to the U. S. agricultural enterprise.

Within the project description, all applications must include a sustainability plan explaining how project products and services will be accessible during and after the funding period. Projects that include development of tools and platforms are strongly encouraged to provide a detailed software development plan and build upon existing tools and platforms such as R/Python and the national cyberinfrastructure (e.g. XSEDE, Science Gateways). Proposals that include development of tools and platforms should include details of software development practices such as testing and validation plans, and plans for governance, development and support of user and developer communities. Implementation of innovative and effective methods for participation of stakeholders in tools and platform development priority-setting and testing is strongly encouraged.