Guidelines for instructors

Here, we provide guidelines to help instructors implement the tutorials in their courses. We designed the tutorials to be pre-packaged, portable, and ready to use. To implement them in the classroom, simply share the website link with students so that they can dive-in directly and start learning!

Integration into a lesson

Time to complete one tutorial : 1 to 3 hours.

Recommendations : The time to complete a tutorial varies between tutorials and should depend on the student’s skill and experience level. We suggest that instructors plan on choosing one tutorial per lesson as concepts do not overlap across tutorials. This would include a short introduction to the tutorial and dataset so that students are acquainted with the ecological theory, concepts, and ideas behind the data.

Below are a series of prompting questions with directions for instructors to embark on. We offer these simply as entry points, but there is no limit for where to take them.

1. How should I use these tutorials?

• As inspiration for teaching data science and discipline-specific concepts
• As a lesson plan for an entire class focused on having different student groups explore all the tutorials and teach concepts back to one another (more student led and student taught teaching methods employed, perhaps 3rd and 4th year undergrads)
• As an activity part of a broader lesson plan centered around a specific ecological topic featured in one of the tutorials
• As a catalyst for discussion on how we can make better use of publicly available data in the Age of Data

2. How should I integrate these tutorials into my lesson plan?

• Utilize small groups to explore the tutorials and foster small group discussions
• Link concepts in the tutorial with popular science or recent journal articles that bridge the gap from the data to the scientific community
• Employ the content as supplementary teaching materials to complement case studies or topics covered in previous lectures

3. How can I accommodate the tutorials to other skill levels or topics?

Our objective was to provide instructors with tutorials spanning a wide-range of skill levels so that they can implement them at different undergraduate levels (1^st, 2^nd, and 3^rd year students).

One way in which instructors can easily ensure that their class starts with similar foundations in open science principles is to :

• Include an introduction to reproducible workflows and open science practices (e.g. Open Science Education) before students dive in the tutorials
• Include an introduction to well-known databases of publicly available data (e.g. Global Biodiversity Information Facility)

Instructors can also reuse our tutorials under our license terms to adapt the content to their specific needs. This requires to download the .Rmd file of a specific tutorial hosted on GitHub to modify it locally in Rstudio for example. Please refer to the README on our GitHub repository for instructions. Once the .Rmd files are successfully downloaded, instructors can adapt the content to include data that is more relevant to their course topics. This also provides the option to extend the concepts introduced into larger modelling or research frameworks.

If you have any suggestions on improvements, you can clone our website repository locally, implement your changes, and create a pull request.

60 minute lesson plan example

Time	Activity
10 min	Overview of topics covered and/or referenced in the tutorial
Optional 45 min	To make this a longer discussion for a larger course, you could include examples of recent papers (2-3 papers) drawing important conclusions about the topic at hand based on skills taught in the tutorials
25 min	Group exploration of the concepts via tutorials, with the guiding questions:
	1. What was the dataset being explored in this tutorial
	2. What concepts did this tutorial explore with the data? Are there other concepts you think could have been explored with this dataset?
	3. Recreating a given plot or calculation referenced in this tutorial with a new, publicly available dataset. What did you explore and why? Prepare a 2 min presentation with your group to share our how you applied concepts in this tutorial to new data
25 min	Presentation of new figures/metrics by student groups, closing remarks.

Open science topics to explore with students

After students have worked through their tutorial, you may ask them to further explore the importance of open science practices by discussing as a class, discussing in small groups, or completing written reflections around the following questions:

• What is the purpose of open science?
• What are the benefits and challenges of open science?
• What are the benefits and challenges of sharing data?
• How might a reproducible workflow contribute to practicing open science?
• What are some steps you could take to practice open science during your undergraduate degree?
• Identify one aspect of your tutorial that utilizes open science practices.

Technical resources for instructors

R programming

• R for Data Science: an e-book diving into R topics, language, and additional formats (e.g. R scripts, RMarkdown, etc.)
• RStudio Education: teaching resource with specific learning modules and tutorial for students, instructors, and trainers

Git and GitHub

• Get started with GitHub: a general resource from GitHub developers on how to use the platform
• GitHub in Ecology and Evolution: a paper published in Methods in Ecology and Evolution by Pereira Braga et al. (2023)
• Git and GitHub for ecologists tutorial: a tutorial on how to use Git and GitHub for version control which may help instructors on how to navigate our GitHub and reuse our tutorials
• Setting up a GitHub repository tutorial: a tutorial on how to setup one’s own GitHub repository