About the course

Welcome to Biol [4800|7800] - Open and Reproducible Research in Ecology and Evolution.

Course objectives

The content and structure of this course is designed to help you work towards the following objectives:

Understand the trends and tools for reproducible and open research practices in science generally, and in ecology/evolutionary biology specifically;
Develop and articulate your individual philosophy and workflow towards reproducible research;
Integrate openly available datasets and tools to reproduce classic result(s);
Envision and begin to implement the steps you will take towards ensuring reproducibility and robustness of your own research;
Build a community of practice¹ around reproducible and open research in ecology and evolution.

Who is this course for?

The target audience for this course is graduate students or advanced undergraduates with some past experience analyzing biological data with tools like R, python, or other programs called from the command line. The tools we will cover in this course are broadly applicable across fields, but many examples will refer to topics in ecology and evolution.

Pre-requisites

While there are no formal course pre-requisites, this course will likely be most valuable for participants who have a working knowledge of conducting data analysis/visualization in R (and/or Python) and executing commands from the command line. As a practical yardstick, if the material covered in chapters 1–5 of R for Data Science is not completely unfamiliar to you, then you should be able to complete all the exercises in this course.

If you have never before seen the material in the chapters mentioned above, you might be better served by signing up for LSU’s “Foundations for computational biology” course, which is more specifically designed as an introduction to using these tools.

If you are starting with zero prior experience in using R but want to take this course anyway, please meet with Dr. Kandlikar early in the semester to ensure that there a path for you to get the most out of this course.

Please come to class with a laptop capable of running R, RStudio, git, and other associated tools. LSU students can borrow a laptop for an entire semester through the LSU Library.

Course communication

Official communication about the course will occur over Moodle and LSU email. For informal communication (e.g. to seek help on a bug you are encountering, or to share a cool tool), I encourage all students to join the unofficial course discord server.

Important dates

Homework assignments will be due on the following dates: September 20th, October 4th, October 25th, and November 22nd.

Graduate students will present their semester projects in the final week of class, i.e. on Dec. 1 and 3.

Final semester projects for all students, including undergraduates, will be due on December 9th.

Grading

Letter grades will be determined through in-class participation (combination of quizzes and conversations; 50 points total), homework activities (four activities worth 25 points each) and semester project (worth a total of 100 points for graduate students and 50 points for undergraduates; see semester project for details). Your final grade out of 250 points will determine your letter grade.

A+ for earning 242 or more points over the semester;    
A  for earning 233-241 points over the semester;  
A- for earning 225-232 points over the semester;   
B+ for earning 217-224 points over the semester;  
B  for earning 208-216 points over the semester;   
B- for earning 200-207 points over the semester;   
C+ for earning 193-199 points over the semester;  
C  for earning 183-192 points over the semester;   
C- for earning 175-182 points over the semester;  
D+ for earning 168-174 points over the semester;  
D  for earning 158-167 points over the semester;
D- for earning 150-157 points over the semester

Assignment deadlines

All assignments except the final semester project submission come with a 24-hour grace period (i.e. you can submit the assignment for full credit without any prior discussion with me). If you are unable to complete an activity submission during this grace period, please get in touch with me to discuss alternatives.

Guidelines for using AI-generated code

The goal for this course is for you to think through the principles and practice of conducting ethical, open, and robust science. In my experience, the casual use generative AI tools is largely antithetical to these goals. So if you are here to genuinely learn how to think through crafting reproducible analytical workflows, I strongly discourage their use for classwork.

Instead, when your are stuck, consider turning to a human, whether it is through our unofficial course discord, a human–authored resource (books, blogs, package documentation, etc.), or forums like stack overflow. You are also welcome to come to Gaurav’s “office hours” (AKA hacky hours) to discuss any issues.

I don’t have the tools, capacity, or desire to monitor or penalize your use of AI tools in this course. But if I get the sense that you are relying on these sources to complete the coursework, I may ask for an individual meeting to discuss the extent to which your submissions reflect your own understanding of the material.

About this site

The source code of this website is available on gitlab: https://gitlab.com/gklab/teaching/rr-f26.

Footnotes

Communities of practices are “groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.” – ref ↩︎