Principles of engineering, data analysis, and plant sciences and their interplay applied to predictive plant phenomics. Transport phenomena, sensor design, image analysis, graph models, network data analysis, fundamentals of genomics and phenomics. Multidisciplinary laboratory exercises. 

Text:

None.

Online:

Various resources are available. Use online dictionaries where jargon is prevalent.

Course Goals:

Enable students to speak across engineering, data science, and plant science disciplines. Understand how biological concepts fit with computational representations of the data.

Student Learning Outcomes:

1. Students will achieve fundamental understanding of how plant science, engineering, and data science can be used together for research.
2. Students will be able to formulate a hypothesis and then design computational experiments that test that hypothesis.
3. Students will be able to take information learned in one context and transfer/apply it to a different context.
4. Students will be able to articulate ideas outside their own primary disciplines and find others to work with where their skills are insufficient to pursue meaningful research.
5. Students will be able to graphically represent experimental data in a meaningful way.

Assessment:

Instructors can grade in any way they see fit.  One possible mechanism for assessment that was a part of the Iowa State University course was to require the submission of a paragraph describing the utility of the day’s lecture to the student’s own research or research interests and one lecture was delivered by each student in the class rather than an outside expert. The students selected a topic in keeping with their existing knowledge and expertise.

Suggested Order of Lectures:

(link to all recordings: https://vimeo.com/showcase/8796225 )

This material is based upon work supported by the National Science Foundation under DGE#1545463 and the United States Department of Agriculture-National Institute of Food and Agriculture Grant number 2020-68013-30934.