An Interdisciplinary Graduate Course for Engineers, Plant Scientists, and Data Scientists in the Area of Predictive Plant Phenomics

Theodore J. Heindel; Carolyn J. Lawrence-Dill; Julie A. Dickerson; Patrick S. Schnable

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Conference: 2017 ASEE Annual Conference & Exposition
Location: Columbus, Ohio
Publication Date: June 24, 2017
Start Date: June 24, 2017
End Date: June 28, 2017
Conference Session: Developing and Establishing Graduate Study Programs - Graduate Studies Division Technical Session 2
Tagged Division: Graduate Studies
Page Count: 10
DOI: 10.18260/1-2--27577
Permanent URL: https://peer.asee.org/27577
Download Count: 453

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Paper Authors

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Theodore J. Heindel Iowa State University orcid.org/0000-0002-8142-9938

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Theodore (Ted) Heindel is currently the Bergles Professor of Thermal Science in the Department of Mechanical Engineering at Iowa State University; he also holds a courtesy professor appointment in the Department of Chemical and Biological Engineering. He directs the Experimental Multiphase Flow Laboratory at ISU, which houses a unique instrument for performing X-ray visualization studies of large-scale complex fluid flows. This instrument can also be used to visualize root systems for phenotyping. Ted’s teaching emphasis is in the area of thermal science (thermodynamics, fluid dynamics, and heat/mass transfer) and measurement and instrumentation. He has also developed two new graduate-level courses: "ME 531: Advanced Energy Systems and Analysis"” and "ME 585: Fundamentals of Predictive Plant Phenomics." He has been recognized for his teaching efforts through the College of Engineering’s Superior Engineering Teacher of the Year Award, and was twice selected by graduating seniors as mechanical engineering’s Professor of the Year. He has co-authored one book and published over 75 peer-reviewed journal papers and over 220 conference papers, abstracts, and technical reports. Ted received his B.S. from the University of Wisconsin – Madison and his M.S. and Ph.D. from Purdue University, all in mechanical engineering with an emphasis in the thermal sciences

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Carolyn J. Lawrence-Dill Iowa State University

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Carolyn Lawrence-Dill has devoted the last 20 years to developing computational systems/solutions that support the plant research community. Her work enables the use of existing and emerging knowledge to establish common standards and methods for data collection, integration, and sharing. Such efforts help to eliminate redundancy, improve the efficiency of current and future projects, and increase the availability of data and data analysis tools for plant biologists working in diverse crops across the world. Carolyn led the USDA’s maize model organism database MaizeGDB (http://maizegdb.org/) for a decade, currently coordinates the development of the information platform for the US maize Genomes to Fields Initiative (http://www.genomes2fields.org/), and is an active member of the community working to put in place methods for phenotype data access, analyses, and re-use. To learn more about her contributions to plant biology and information access, visit https://scholar.google.com/citations?user=bHQPmtEAAAAJ&hl=en. In addition to research and development efforts, Lawrence-Dill is a coPI on the NSF-funded grant "NRT: Plant Predictive Phenomics," which supports the development of mechanisms to train the next generation of scientists and engineers to work together on shared problems that involve plant biology, data sciences, and engineering.

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Julie A. Dickerson Iowa State University

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Julie Dickerson is a Professor at Iowa State University in the Department of Electrical and Computer Engineering (ECpE). She served as a program officer at the National Science Foundation in the Advances in Biological Informatics Program and the Postdoctoral Research Fellowships in Biology Program in the Biology Directorate as the lone engineer. She has also served as the Chair of the Bioinformatics and Computational Biology program at Iowa State University. She holds a B.S. degree in electrical engineering from the University of California, San Diego. She received her master's degree and Ph.D. in electrical engineering from the University of Southern California. She designed radar systems for Hughes Aircraft Company and Martin Marrietta while getting her Ph.D. Her current research activities are in systems biology, bioinformatics, bioinformatics education, and data visualization. She was a Carver Fellow in the Virtual Reality Applications Center and a member of the Baker Center for Bioinformatics in the Plant Sciences Institute and the Human-Computer Interaction Program. Dr. Dickerson has over 120 peer-reviewed publications in journals, book chapters, and conference proceedings and supervises research projects funded by the National Science Foundation, ARDA, and the United States Department of Agriculture.

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Patrick S. Schnable Iowa State University

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Abstract

This paper describes the development and first offering of a new graduate course entitled "Fundamentals of Predictive Plant Phenomics," which is part of a recently awarded National Science Foundation Graduate Research Traineeship (NRT) award to Iowa State University. The focus of this particular NRT award is to train engineering, plant science, and data science graduate students in the area of predictive plant phenomics (P3), with the goal to develop researchers who can design and construct crops with desired traits to meet the needs of a growing population and that can thrive in a changing environment. To meet this goal, the P3 NRT program will train next generation crop scientists to have broad technical skillsets as well as strong "soft skills" in communication and collaboration. A companion paper (Dickerson et al., 2017) provides an overview of the P3 NRT program, whereas this paper focuses on a new course developed as part of the P3 NRT.

One of the challenges associated with providing the students in the P3 NRT program with the needed multidisciplinary skills to thrive is to ensure that all students have a common knowledge base in engineering, plant sciences, and data sciences, no matter their background. The goal is to get all students communicating in the same language. The course "Fundamentals of Predictive Plant Phenomics" was developed to meet this challenge. The course planning took nearly one year and incorporated input from faculty with various disciplinary backgrounds. The actual course is coordinated by an engineering faculty member and taught through a series of guest lecturers covering various plant science, data science, and engineering topics over a 15-week period. In addition to the three 50-minute lectures per week, a 3-hour laboratory each week provides an experiential learning opportunity where students can apply the knowledge they learn in the lectures. The first offering of this course occurred in fall 2016, with 16 enrolled students, 7 from engineering disciplines, and 9 from plant and data science programs. Lessons learned from the first offering of this course are summarized in this paper. The course is providing the needed background so students can develop a successful research topic in the area of predictive plant phenomics and communicate with others in this broad multidisciplinary field. Because the course is a leveling or survey of three disciplines, and each student has a good background in at least one of the three, it has been challenging to keep all students interested and engaged for all lectures (but not labs). To address this challenge, expanding the application of Inquiry-Based Learning approaches during the lecture period in future years is proposed.

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Heindel, T. J., & Lawrence-Dill, C. J., & Dickerson, J. A., & Schnable, P. S. (2017, June), An Interdisciplinary Graduate Course for Engineers, Plant Scientists, and Data Scientists in the Area of Predictive Plant Phenomics Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27577

TY - CPAPER
AB - This paper describes the development and first offering of a new graduate course entitled &quot;Fundamentals of Predictive Plant Phenomics,&quot; which is part of a recently awarded National Science Foundation Graduate Research Traineeship (NRT) award to Iowa State University. The focus of this particular NRT award is to train engineering, plant science, and data science graduate students in the area of predictive plant phenomics (P3), with the goal to develop researchers who can design and construct crops with desired traits to meet the needs of a growing population and that can thrive in a changing environment. To meet this goal, the P3 NRT program will train next generation crop scientists to have broad technical skillsets as well as strong &quot;soft skills&quot; in communication and collaboration. A companion paper (Dickerson et al., 2017) provides an overview of the P3 NRT program, whereas this paper focuses on a new course developed as part of the P3 NRT.

One of the challenges associated with providing the students in the P3 NRT program with the needed multidisciplinary skills to thrive is to ensure that all students have a common knowledge base in engineering, plant sciences, and data sciences, no matter their background. The goal is to get all students communicating in the same language. The course &quot;Fundamentals of Predictive Plant Phenomics&quot; was developed to meet this challenge. The course planning took nearly one year and incorporated input from faculty with various disciplinary backgrounds. The actual course is coordinated by an engineering faculty member and taught through a series of guest lecturers covering various plant science, data science, and engineering topics over a 15-week period. In addition to the three 50-minute lectures per week, a 3-hour laboratory each week provides an experiential learning opportunity where students can apply the knowledge they learn in the lectures. The first offering of this course occurred in fall 2016, with 16 enrolled students, 7 from engineering disciplines, and 9 from plant and data science programs. Lessons learned from the first offering of this course are summarized in this paper. The course is providing the needed background so students can develop a successful research topic in the area of predictive plant phenomics and communicate with others in this broad multidisciplinary field. Because the course is a leveling or survey of three disciplines, and each student has a good background in at least one of the three, it has been challenging to keep all students interested and engaged for all lectures (but not labs). To address this challenge, expanding the application of Inquiry-Based Learning approaches during the lecture period in future years is proposed.
AU - Theodore J. Heindel
AU - Carolyn J. Lawrence-Dill
AU - Julie A. Dickerson
AU - Patrick S. Schnable
CY - Columbus, Ohio
DA - 2017/06/24
PB - ASEE Conferences
TI - An Interdisciplinary Graduate Course for Engineers, Plant Scientists, and Data Scientists in the Area of Predictive Plant Phenomics
UR - https://peer.asee.org/27577
DO - 10.18260/1-2--27577
ER -