This was the 2021 CompSciBio Workshop Schedule. This page will be updated once we have our courses for Summer 2022.
Students will take a total of 4 classes (listed above) during the workshop, with the ability to choose between multiple courses depending on their interests. Each class will have approximately 15 students, allowing for one-on-one interaction with instructing faculty. WHEN CHOOSING CLASSES- each color represents ONE CLASS held over a two-day period.
The conclusion of the workshop will be presentations of the cumulative findings from the courses; many projects which will result in publications in peer-reviewed journals. Following lunch the final day, students will network with faculty, graduate students, and industry biologists.
Mon/Tues Morning Class (Orange)
- Introduction to R (part I): a true introduction to the coding language that is vital to most biologists, absolutely no previous experience required. (fun biological practice problems guaranteed!)
- Function-Valued Trait Modeling: Time-series data (for example, organismal growth, disease progression, or biomolecule accumulation including proteins and gene transcripts) are common in biology, but can be statistically complicated. In this module, you will learn easy ways to leverage computers to handle complex developmental trajectories in what is called a “function-valued trait” approach. Although we will focus on developmental processes, this approach can be applied to diverse data such as responses to increasing temperature, pathogen load, herbivore abundance, or changing community composition metrics.
- Introduction to GIS: Learn the basics of mapping and analyzing species in our intro to Geographic Information Systems (GIS). (How to look at distribution maps/animal movement etc). You’ll learn the foundational concepts of GIS as well as usage of GIS software through examples with real biological data (no previous experience required)
- Intro to Excel: The Skill you didn’t know you need for everything: Excel is the program that none of us thought we’d need after our freshman year of college. Probably more often than you’d like, in research or at work you’ll be given an Excel sheet full of new data that you’ve never worked with before. In this course you’ll learn how to get a huge dataset on video game reviews into a useable state, key functions for transforming, searching, and organizing the data, and some beginner tips to visualize and graph your data. No experience in excel, data management, or visualization needed. Beginners welcome. (Crosslisted course from our OU partners)
Mon/Tues Afternoon Class (Green)
- Introduction to Python (part I): a true introduction to Python the coding language. No previous experience required, fun biological practice problems guaranteed!
- Loops and Functions in R and Python: This is a more advanced class for biologists that are looking to better code using loops and functions! (R or Python experience required)
- Ecological Niche Modeling (on Pikas!!): Why are species where they are? Some previous experience in R suggested
- Data Visualization in R: For that more experienced biologist that wants to improve their ability to convey their data visually (basic R experience required)
- Ecological Niche Modeling Beetles: Ecological niche models use information on where a species has been found (for example, yearly temperatures or summer rainfall) to build maps of species ranges and predict where they might occur in the future. We will build ecological niche models for five beetles, an ideal group for ENMs because beetles comprise approximately 1/4 of all known species on Earth, yet, for most, we know very little about where they live or other aspects of their biology. Some previous experience in R is suggested to set up and run the models; however, even R novices will be able to generate a map by the end of the session. (Crosslisted course from our OU partners)
Wed/Thurs Morning Class (Blue)
- Introduction to R (Part II): The sequel to part I, and a great refresher for that more advanced biologist that wants to review the basics. Once again, fun bio practice problems!
- Genes & Evolution: An Introduction to working with genetic data– work with real gene sequences in aligning and understanding their evolutionary history.
- Introduction to HPC (Miami Students Only): How to use the linux shell and introduction to Miami University’s High Performance Computing Cluster: Redhawk. Prerequisites: Familiarity with R, Python, Matlab, or Shell, and understanding of file paths in your computer
- Introduction into Ecological Population and Community Models: In ecology, mathematical models are often used to explore the dynamics of species population growth, as well as species interactions like competition and predation. In this course module, we will learn how to model logistic population growth, and Lotka-Volterra competition and predator-prey equations. These equations taught in most undergraduate Ecology courses. Here we will learn how to use R software to code them and manipulate parameter values to learn fundamental ecological principles. (Crosslisted course from our OU partners)
Wed/Thurs Afternoon Class (Yellow)
- Introduction to Python (Part II): The sequel to part I, and a great refresher for that more advanced biologist that wants to review the basics. Once again, fun bio practice problems!
- Advanced GIS: Learn advanced spatial analysis using open source GIS software. This course is meant to build on the Intro to GIS course, but attendance of the Intro course is not a requirement
- R-Markdown: Intro-mid level course (basic R or Python experience is required). You’ll learn what Markdown is and how it can be integrated with R to make reproducible documents. You’ll also learn how to combine R and Python code in the same document.
- Advanced Python w/Tensorflow (Image classification): In this advanced python course you’ll learn how to use artificial intelligence methods to classify images of animals in two categories: Salamanders vs Lizards. (Crosslisted course from our OU partners)
- Image Analysis: In this course, you will learn how to extract data from still images and video using the R programming environment. We will start with some background about how images and video are rendered and compressed, then transition to importing images and video into R to extract information. By the end of this course, you should be able to extract numeric pixel data from still images and video frames, automatically identify and isolate objects in images, apply various filters to simplify complex images, and track objects over time in videos. (Crosslisted course from our OU partners)
- Data management in R, how to navigate complex data sheets: This course is a continuation of “Data Management in Excel,” but instead we show you how to use R to perform similar, but more sophisticated techniques. Here you’ll get the basics of navigating data management in a coding software using a biological practice data set. Then, you’ll learn how to get the final file you create in “Data Management in Excel” into R, how to write code to navigate your data, and compare methods of visualizing data with R and Excel. Do not need to have taken Data Management in Excel but it will help (no experience in Excel needed) and some experience in R will also help but is again, is not mandatory. Beginners welcome. By the end of these two courses, you’ll be able to choose which software best suits your style. (Crosslisted course from our OU partners)