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The Living Data Tutorials: Data Packages for Teaching in Ecology and Evolution 

Group Leaders: Dr. Jennifer Sunday (McGill University) and Dr. Joey Bernhardt (McGill University)
 
Group Facilitators:  Dr. Jason Pither (UBC-Okanagan), Dr. Joey Burant (UdeM/McGill University), Dr. Gracielle Higino (UBC), Dr. Ellen Bledsoe (University of Arizona), Dr. Laura Pollock (McGill University)

Working group dates: April 4-8, 2022

Deadline: January 17, 2022

Apply here: closed

The Canadian Institute of Ecology and Evolution, as part of its NSERC-CREATE Living Data Project, is now accepting applications for graduate students who wish to participate in the following virtual working group April 4 - 8, 2022. Students will gain valuable experience in synthesis science, have the opportunity to co-author publications, and may be eligible for two course credits. Students must be currently registered in a graduate program in ecology, environmental science, evolution or a related discipline in a Canadian university. Students should either be enrolled in a CIEE or BIOS2 member university, or they or their supervisor must be a current CSEE member.  The highest priority will go to students who have already taken the Living Data project courses “Synthesis statistics for ecology and evolution” and “Scientific collaboration in ecology and evolution”. Together with these two courses, participation in this working group will fulfil most of the requirements for a CIEE Certificate in Synthetic and Collaborative Science.

The goal of this working group is to produce a series of curated datasets with accompanying R-based tutorials that can be used to teach/illustrate ecological and evolution concepts, based on Canadian-generated data sources. The datasets, with accompanying metadata, will be made available, discoverable, and advertised for use across a variety of platforms, and will form the basis of a science education publication. Tutorials will be designed to illustrate and support exploration on a variety of core concepts aimed for undergraduate courses in ecology and evolution. Participants will learn about pedagogical best practices for data-driven learning activities, and how to wrangle, clean and document data from diverse data sources using reproducible workflows. 

We will work in groups to create tutorial vignettes in R Markdown. For every dataset, we will workshop ecological concepts to which the data can be matched and develop vignettes for data visualization and analysis that illustrate core concepts and/or match learning objectives of undergraduate-level ecology and evolution courses.


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