Recent studies have documented the overall failure of undergraduate programs to prepare students for the complex, professional lives that lie ahead for them. This project addresses a number of these shortfalls, training students to navigate the more complex and uncertain professional terrain associated with interdisciplinary scholarship. The project will launch a unique data sciences program at Virginia Tech (coordinating organization), Morehouse College (HBCU for men, Georgia, implementing organization), Bennett College (HBCU for women, North Carolina, implementing organization), and Hampden-Sydney College (all-male college, Virginia, implementing organization). Our ultimate goal is to provide interdisciplinary education and research opportunities in data and decision science for undergraduate students who are experts in a core discipline of engineering or biology, but who are also proficient in the alternate discipline. Undergraduates from biology and engineering will take classes and conduct research in data science at the engineering/biology interface. A new collaborative, multi-university capstone course "Data and Decisions at the Engineering/Biology Interface" will be launched simultaneously at all four universities. This new course will be driven by the needs of stakeholders from agriculture, conservation, search and rescue, water quality, transportation in inclement weather, and global health and emergency relief.
The program will provide unique research opportunities in data and decision science for at least 75 students over 3 years, with about half coming from the two HBCUs. Multi-university student teams (comprised of biologists and engineers) will work together to identify broad social, global, economic, cultural and technical needs/constraints, and determine ways in which their complementary technical skills contribute to addressing complex data science grand challenges at the engineering/biology interface. The teams will submit their data science challenge ideas using sensor-based assets and computational-based assets, competing for slots to participate in a coordinated field campaign in which they will collect data, and learn to make decisions from these data. Team projects will be developed in response to stakeholder needs, using sensor assets available from the participating universities and stakeholders. Students will become well-grounded in the language and tools of computational modeling and data analytics, including machine learning, data-driven discovery of equations and causality, clustering, and neural networks. Students will learn to communicate effectively with fellow students, policymakers, and the public. Following their data sciences experiences, the students are expected to: (1) be conversant with data science research in a second discipline, open to its methods, culture, and perspectives; (2) be able to integrate the second discipline into sustainable new data science research; and (3) conduct interdisciplinary data science research with team members from other fields. The program will provide insights into the attitudes of students towards interdisciplinary data science research, and explore how conceptions of collaboration and career path are affected by their participation in the program.
NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
