1. Course outline and schedule
**Productivity and Reproducibility in Ecology & Evolution** #### Instructors: - Jason Pither (UBC, Okanagan campus) - jason <dot> pither [at] ubc <dot> ca - postdocs from the Living Data Project **Course Description** This course aims to transform the way that ecologists and evolutionary biologists work: to increase productivity, improve accountability, and meet new requirements for research reproducibility. Trainees will learn how to integrate open science best practices into their individual and collaborative research workflows, and use digital platforms and tools to facilitate collaboration, ensure transparency, enable pre-registrations, and implement version control and provenance tracking. **Meeting Times:** - 8:00 am - 9:30 am PDT (British Columbia) - 9:00 am - 10:30 am CST (Saskatchewan) - 11:00 am - 12:30 pm EDT (Ontario/Quebec) **Week 1**: Tues Sept. 07, Thurs Sept. 09 **Week 2**: Tues Sept. 14, Thurs Sept. 16 **Week 3**: Tues Sept. 21, Thurs Sept. 23 **Week 4**: Tues Sept. 28, Thurs Sept. 30 **Pre-requisites** - Graduate-level thesis in Ecology, Evolutionary Biology, or related Environment Science degree - Introductory R programming experience (i.e. base R) **Delivery format** - 7 sessions, 1.5 hours per session - Sessions alternate between primarily lecture/discussion based and primarily hands-on training **Online Resources:** All materials for this and associated courses will be available on coures Canvas websites, supplemented by wiki pages the [OSF site](https://osf.io/uwzx5/wiki/home/) for the Living Data Project. **Assessment** - Assignment (30%) - Group project (70%) **Workload** We anticipate the outside class time commitment to be in line with other 1 credit courses. This will include readings and some preparatory activities in advance of sessions (1 hr per session). Outside work on assignments and group project may take an additional 15-25 h total. **Required materials** - Personal computer with videoconferencing ability - Internet access **NOTE**: We focus on the use of open-source and free software and tools to maximize accessibility Participation in this course requires adherence to the Living Data Project [Code of Conduct](https://osf.io/u2abx/wiki/home/) *** >The approximate schedule of session topics for **FALL 2021** can be viewed at this [google document](https://docs.google.com/document/d/1nIDw7hF01B4CQPHHWhcLbOuJOXy3quNLbUzgwOzT9Ng/edit?usp=sharing). Note that the official schedule is available on the course Canvas site. *** #### **Assignment:** **Due date**: Friday Sept. 24, midnight Pacific time **Worth**: 30% [**Rubric**](https://osf.io/a2w78/) Pick one article in your field of study, and: 1. Provide a brief summary of its purpose and main findings. 2. Determine whether the journal in which the article is published has any form of checklist for authors with respect to transparency and reproducibility. If yes, provide a link. 3. Evaluate the article with respect to the 10 questions in this [checklist](https://static-content.springer.com/esm/art%3A10.1038%2Fs41559-018-0545-z/MediaObjects/41559_2018_545_MOESM1_ESM.pdf). Your answers to each question should be brief, i.e. yes / no if appropriate, or a few sentences at most. Use R Markdown to author your report, and include a bibliography at the end of your document (the bibliography should include at least one reference). Submit a rendered PDF document, and be sure to include a link to OSF or Github for access to the RMD file. #### **Group project:** **Due date**: Wednesday Oct. 6, midnight Pacific time **Worth**: 70% [**Rubric**](https://osf.io/9yfe2/) **OPTION 1:** Collaborate to prepare and submit a pre-registration for a replication study in your field of interest. The proposed statistical methods should be demonstrated using simulated / made-up data (with properties informed by published data), with supporting figures included in the pre-registration, and associated analysis script available for download. **OPTION 2:** Using the skills you've learned, collaborate to co-author a brief, mock manuscript that uses published data to address a question of your choice. It can be exploratory or confirmatory. The study should include at least one table and one figure, a bibliography, it should be computationally reproducible, and all aspects should be accessible online.