Board # 78 : Training Students with T-shaped Interdisciplinary Studies in Predictive Plant Phenomics

Modern engineering and data analysis techniques make it feasible to develop methods to predict plant growth and productivity based on information about their genome and environment, however students trained with broader skillsets will be needed to unlock this potential. This paper describes the structure and activities of a National Science Foundation Graduate Research Traineeship (NRT) award focusing on Predictive Plant Phenomics (P3). Our program aims to increase agronomic output as highlighted by the National Plant Genome Initiative’s current fiveyear plan [NST, 2014]. Ph.D. training production levels and types are not always a good fit for addressing complex technical and societal problems such as these. To train these scientists, the P3 NRT is using the T-training model proposed by the American Society of Plant Biology (ASPB) and described in “Unleashing a Decade of Innovation in Plant Science: A Vision for 2015-2025”. This approach requires that students get broader exposure to multiple disciplines, work with industry and develop effective communication and collaboration skills without increasing the time to graduation. This paper describes how we are working towards meeting these challenges. Initial results show that the students have more contact with faculty across departments than single discipline graduate students and that the students are open to learning about new areas. However, we are still grappling with issues such as finding the best mechanism for balancing student skills as they start their program in leveling activities such as bootcamps and initial course training. Program Overview: The NSF Research Traineeship (NRT) Predictive Plant Phenomics (P3) Specialization implements the T-training model proposed by the American Society of Plant Biology (ASPB) [ASPB, 2013]. The goal of the Predictive Plant Phenomics (P3) program is to prepare graduate students with the understanding and tools to design and construct crops with desired traits that can thrive in a changing environment. Students with “T-shaped” experiences will differ from traditional STEM graduate programs that produce students with deep disciplinary knowledge in at least one area. This depth represents the vertical bar of the "T". The horizontal bar represents their ability to effectively collaborate across a variety of different disciplines [T-Summit, 2016], which is the focus of P3 as shown in Figure 1. This paper reports on the progress of the project to date and presents results on the first year’s project assessment on the effectiveness of the cross disciplinary training. The P3 program is preparing students for productive careers in plant phenomics research and development in academia and industry. The vertical stem of the T represents traditional research-based PhD training in an engineering, data science or plant biology based discipline. Adding horizontal skills, such as those shown across the top, prepares both masters and PhD students for a variety of career outcomes and reduces the frequency with which a tenure-track faculty position is seen as an all-or-nothing, overarching goal. Among the horizontal skills are those commensurate with data and Internet-driven science, in which programming, data mining, statistical analysis, visualization, and online collaboration are used to generate and execute research agendas [Miller, 2017]. Other horizontal components include entrepreneurial and private sector experiences. This approach is in contrast with most graduate training programs, which focus on training new members of the professoriate without actively taking into account that many PhD students in STEM fields ultimately find employment in the private sector, the federal government, or other non-academic settings [CGS, 2012]. Unfortunately, many professors lack the experience to provide students with specific skills required for these types of positions [CGS, 2010; CGS 2012], e.g., the ability to participate in team-based research and professional skills that involve communication, data analysis, and synthesis of new ideas and areas of exploration given results. Students may also be discouraged from participating in internship opportunities for fear that such activities could disrupt their PhD research activities. Thus, P3 training activities will not be solely focused on the students; instead all P3 graduate mentors will participate in a required workshop on transdisciplinary mentoring that will highlight the best practices in advising transdisciplinary students [Smith, 2014]. The overarching objective of P3 is to teach students and faculty how to use transdisciplinary research to improve the understanding of crop plant and agricultural production. Specific objectives are to: 1. Advance the science of predictive phenomics by developing sensors, imaging systems, and robotic systems to measure specific phenotypes in a high-throughput manner. 2. Apply data science to large, heterogeneous time-course data sets that link genomic and phenomic data. 3. Apply engineering design principles to modeling plant performance under a range of conditions.