Location: National Programs
Project Number: 0500-00110-001-012-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 30, 2026
End Date: Sep 29, 2027
Objective:
Modern agricultural research is a highly multidisciplinary enterprise that increasingly requires sophisticated computational and statistical methods to analyze datasets that are rapidly expanding in size, scope, and complexity. As such, agricultural science benefits from a team approach that brings together deep domain expertise and strong data science and analytics skills. The overall goal of this partnership is to advance ARS research efforts and enhance the Cooperator’s student training by providing summer and spring fellowships that allow students with advanced data analytics skills to collaborate with ARS research teams working on real-world, data-intensive research problems.
Our specific objectives are four-fold. First, we will advance ARS research efforts by allowing graduate students in the Cooperator’s Applied Data Science Program to contribute their skills to ARS research projects. Second, we will enhance participating students’ educational experiences by providing paid, hands-on, real-world research opportunities, scientific domain expertise, and mentoring from ARS scientists. Third, we will boost the Cooperator’s Applied Data Science Program by increasing the breadth of training they are able to offer to their students. Finally, we will support future ARS workforce development efforts by increasing student awareness of ARS and agricultural research as a rewarding career path for data scientists.
Approach:
ARS has deep scientific expertise across a wide spectrum of agricultural research domains, spanning biological disciplines from landscape ecology to molecular biology and other research disciplines such as engineering, chemistry, and more. ARS’s SCINet initiative provides extensive scientific computing infrastructure that includes multiple high-performance computing (HPC) clusters, large-scale data storage systems, and a high-speed networking backbone. The Cooperator has strong academic programs in data science, including a 2-year, 36-credit Masters in Applied Data Science program. This program, offered by the Cooperator since 2015, includes various practical components such as a summer internship program and a full-semester spring Practicum program that allows graduate students to participate in real-word research and business projects that require the use of data science techniques with various (big) data sets. This collaboration will seek to recruit students from this MS program for summer internship as well as spring practicum opportunities with ARS researchers and research projects. Each component is typically administered under the supervision of a faculty advisor in collaboration with a participating organization, which will be ARS in this case. While students support scientific research at ARS with their data science knowledge and skills, the faculty member will work in an advising capacity to help ensure that the goals of each project are met.
The Cooperator and ARS already have a history of successful collaboration, through which they have developed a process to match graduate students who have strong skills in data science and related fields with ARS research units and ARS researchers who serve as mentors for the students during paid, 11-week summer internships or 13.5-week spring internships. During these internships, students will work with their ARS mentor (or mentors) to contribute their expertise to active ARS research projects while also learning new research skills and domain knowledge. Student interns will have access to scientific computing infrastructure and training resources provided by SCINet and the ARS AI Center of Excellence (AI-COE). Internships will follow a hybrid work model in which students receive 2 weeks of travel support to visit their assigned ARS research unit and mentor(s) and spend the remainder of their fellowship working remotely from their home or home institution.