A vital part of the APS-CPS Joint Meeting, workshops on a variety of topics pertinent to plant science are scheduled for this year's meeting, offering even more educational value to meeting attendees.
Saturday, August 9 - 9:00 a.m. – 12:00 p.m.Organizers: Niklaus Grunwald, USDA ARS, Corvallis, OR, U.S.A.; Zhian Kamvar, Oregon State University, Corvallis, OR, U.S.A.; Sydney Everhart, Oregon State University, Corvallis, OR, U.S.A.Sponsoring Committee/Sponsors: Evolutionary Genetics and Genomics, EpidemiologyFee: $30
Analysis of population genetic data remains challenging. This session will focus on the kinds of analyses typically analyzed by plant pathologists. It will cover analyses of data from haploid and diploid populations with dominant or codominant marker systems applicable to a range of molecular genotyping techniques. Participants will gain hands-on experience with analysis in R using datasets provided by instructors.
Saturday, August 9 - 8:00 a.m. – 4:00 p.m. Organizers: Maya Hayslett, University of Wisconsin, Madison, WI, U.S.A.; Darin Eastburn, University of Illinois, Urbana, IL, U.S.A.; Dave Shew, North Carolina State University, Raleigh, NC, U.S.A. Sponsoring Committee/Sponsors: Office of Education, Teaching Committee Fee: $50
If you are thinking about starting an online course or are looking to improve a current online course, then this workshop is for you. Components and design principles of online courses will be presented including: synchronous vs. asynchronous class structures; recording on-line lectures, getting to know your students; exams; and peer to peer learning in an on-line environment. Participants will learn and discuss how to use these principles in courses they currently teach or are considering teaching.
Saturday, August 9 - 1:00 – 5:00 p.m. Organizers: Larry Madden, The Ohio State University, Wooster, OH, U.S.A.; Asimina Mila, North Carolina State University, Raleigh, NC, U.S.A. Sponsoring Committee/Sponsors: Epidemiology Fee: $25
Bayesian analysis is the statistical methodology that uses the tools of probability for updating existing knowledge with new information. Although computationally demanding, advances in computer software and hardware have made the methodology much more approachable for researchers. Participants will learn to use SAS for some basic Bayesian analyses, including the estimation of means, mean differences, and parameters of linear models. Participants need to bring a laptop with SAS 9.3 (or later) installed.