Welcome to BIOL 3140, Experimental Methods in Organismal Biology!!!

Organismal biology is the study of living systems of all scales that shape the structure, function, ecology, and evolution of individual organisms. Experiments that elucidate how organisms respond t0 biotic and abiotic environmental stimulus over broad time scales—from changes in behavior to adaptation-are crucial to understanding biological diversity. In this course we’ll explore the concepts and experimental and analytical tools that frame research in organismal biology. Through group projects and active learning exercises, students will make hypotheses concerning how organisms respond in time, space, and behavior to changes in environment and then design experiments and instrument prototypes that produce data to evaluate these hypotheses. Topics covered will include reconstructing phylogenetic history and the evolution of organismal form and function, evaluating form-function relationships, and the correlates of spatial and temporal distribution of organisms. In addition, the development of an analytical toolbox—specifically, learning the principles of data science and statistical analysis-is a central theme of this course.

This is a highly technical course and will surely challenge us all. BIOL 3140 is designed for folks with no coding or prototyping experience, so at many times in the course, a student will feel lost and confused. This is exactly where we want to be! This is where the learning happens.


Course and Instructor Deets

Delivery: In-person (M,W,F at 10:00 in Higgins 310) and self-guided (readings)

Instructor: Christopher P. Kenaley (Prof. K)

email: kenaley [ahht] bc.edu

Office hours: By appointment

Class Zoom Link: https://bccte.zoom.us/j/9533582156

Course materials

GitHub account

Why a GitHub account? GitHub is a handy place for those developing code and offers free hosting of websites produced and maintained from a desktop (that’s how this site was constructed). Our site is under the ``class” repository (i.e., a space where code resides) within the organization bcorgbio on GitHub. To access our organization, create a team, and use bcorgbio discussion pages, you’ll need an account. Once you have an account, I’ll be able to add you to our bcorgbio organization and the class-wide team for discussion purposes.

Course scope, goals, and objectives

To be an effective organismal biologist, one must prepare, analyze, visualize, and communicate data as we formulate answers to the questions we’re asking. Despite the importance of these technological skills to our discipline, students often see the acquisition and analysis of data as technological black boxes. Countless times in previous courses, instructors have no doubt demonstrated foundational principles and concepts through the presentation of data, usually through figures in papers or lecture slides. However, where these data come from and how they have answered a question often remain a mystery. In turn, students must rely on the authority of their instructors rather than their own grasp of quantitative skills and data collection to make meaningful connections.

The thrust of BIOL 3140 is to develop and hone meaninful quantitative skills relevant to organismal biology.

As was mentioned at the top, students will often feel adrift, unsure of how to proceed. This is by design and a model of how experimental work and data analysis in biology unfolds. Nonetheless, students may worry about their grade in the course and how to do well when challenges emerge at every turn. Fear not! For each assignment in the course (e.g., module project reports) student or team engagement and effort will be assessed just as much as how well objective questions have been answered.


Course goals and objectives

  1. Learning how to import data into a computing environment.
  2. Understanding basic tidying and transforming (i.e., wrangling) operations of data to answer scientific questions.
  3. Developing competency in basic data visualization.
  4. Learning how to model data in the context of answering a scientific question.
  5. Executing the critical basics of communication in a data science framework.

All of these data-science learning goals will be realized in the framework of the R computing environment. Using R, students will open the black box of data analysis and pursue these specific objectives:

  1. Import, tidy, and transform sensor data so that specific scientific questions can be asked.
  2. Visualize sensor data in the R environment using ggplot, an elegant and versatile tool for data visualization.
  3. Evaluate a-priori scientific questions within a modeling framework, rejecting or accepting hypotheses according to model expectations.
  4. Communicate answers in concise reports, leveraging R’s fast and reproducible markdown capabilities.

Course format and expectations

Lectures

Our course will be a semi-flipped, partially self-guided experience. This will require students to read and explore outside of class and meet in pre-assigned smaller groups (i.e., teams) on their own to work through the material. We will use class time to introduce concepts, tackle sticky problems, and answer specific questions. In addition, I will be available during office hours.

Teams and team responsibilities

To facilitate our team approach, I’ll be assigning each student to a team of 3 students for group projects. The teams will be number, i.e., “Team 1”, “Team2”, etc.

Upon being assigned, the team must do three things for each project:

  1. Pick a team nickname. Puns are welcome and encouraged (e.g., “R-tful Coders”)
  2. Have a single team member create a github repository titled ProjectNumber_TeamNumber, e.g., “Project2_Team2” (directions here). Note: only one person in the team should create a project repo.
  3. Add Professor Kenaley (@ckenaley) and other team members as collaborator to this repository (directions here)
  4. A team representative must submit a question to our discussion board and include the team handle in the question title.
  5. A team representative must answer a question on the discussion board and include the team handle in the response.

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Expectations for Students

A semi-flipped course, especially one of this technical nature, will require sublime concentration and focus. Therefore, we must be clear about our expectations of one another.

  • You, the student, are responsible for reading and adhering to all aspects of the course syllabus as outlined here on this site.
  • You are responsible for organizing and completing all readings, discussions, exercises, and projects.
  • You are responsible for becoming familiar with course-related software (R, RStudio, Arduino, etc.).
  • You must be aware of your own progress in the course.
  • It is up to you to pursue help and guidance when you feel you need it.

Expectations for the Instructor

  • I will challenge students intellectually to develop data analysis and experimental skills.
  • I will make expectations clear in the form of this site, syllabus, and course announcements made here and over email.
  • I will be consistent and quick in grading assignments.
  • I will be available to help and discuss topics during scheduled class time, arranged office hours, and on our class discussion board .

Discussion Board

Most weeks, a representative from your team you will be asked to submit at least one question and answer another on our class discussion board. This is hosted on GitHub, just like our course site and will certainly be an important resource as you work through course material. When you post a question, you should:

  • Be polite and concise.
  • Ask a question that has not be asked before.
  • Post the question with a short descriptive title (e.g.., ``Problems with temp sensor wiring” )

When responding to a question, you should:

  • Be polite and concise
  • Provide unique feedback rather than a duplicated answer.

Feel free to post reactions (via the smiley-face icon above the text box), but this won’t count as an answer.

A team representative is welcome to add another answer to a question that has already been answered as long as it expands upon the previous answers. I’ll be moderating and commenting myself, issuing feedback at the end of each week.

Any team for any project may create their own discussion board. If your team does so, please add me to the team as well so that I can monitor questions and provide feedback. Any Q&A on a team discussion board DOES NOT satisfy the general discussion requirement. You can learn more about making team discussion boards on GitHub here.

Course Outline


Phase I


The Basics
Phase II


Developing a Toolbox
Phase III


Independent projects


Phase I: Learning the Basics

In this phase, we’ll learn the basics of data analysis by working through 4 learning modules devoted to instrument design and data analysis. This will most likely be the most challenging part of the course in that many, if not most of you will not have ever designed and built instruments and data acquisition systems, let alone analyzed data from them. There might be a lot of head banging, encountering hurdle after hurdle. Rest assured, your experience will be curated, sticking to the bare bones of data science. This introductory phase will set the groundwork for Phase II, the development of a data analytics toolbox.

Modules for Phase I include:

  1. Data Pirates Code with. . . . R, Introduction to the Basics
  2. Data Wrangling: Introduction to Tools of the Trade in Data Analysis
  3. The Whiz and Viz Bang of Data: The Basics of Data Visualization and Modeling
  4. Making Messes Pretty: Leveraging Markdown to Produce Pleasing Reports

Phase II: Developing a Toolbox

In Phase II, we’ll use our new-found code-development skills to address key questions in organismal biology. These will be largely guided explorations of data analysis meant to challenge you to put these skills to good use in answering, for example, how size and phylogeny matters in physiological systems a changing climate impacts the distribution of vertebrates. Within this phase we’ll break up into teams of 3 students to focus on 3 modules devoted to semi-independent analysis and/or modeling.

  1. Rolly Polly Fish Heads: Head shape evolution in the Actinopterygii
  2. Spatial Awareness: Historical Changes in New England Mammal Distribution.
  3. Birds of a Feather Migrate Together . . . Sometimes: Using EBird data to Explore the Phenology of Passerine Migration

For each module of Phase II, you’ll be asked to read one or two important topic papers to set the stage and provide crucial context and then move on to data acquisition, analysis, and synthesis. The end product of each module will be a short, 3-4 page markdown that includes some background, description of methods, results, and discussion within the context of the topic papers and other relevant research.

Phase III: Independent Project

In Phase III, you’ll be tasked with quickly developing a project of your own design that requires the fabrication of a new or modified data acquisition system. The goal of this project is the use your sensor to answer a question personal and interesting to your team (assembled during Phase II). The question should be new within the context of the course and reflect a comparative focus (i.e., not simply a human-based project). This project will require you to first delve into the the scientific literature to set the stage and then leverage your new data-analysis and report-writing skills in the synthesis of 4-5 page markdown. This final project report should follow the same format and requirements of the reports produced in Phase II, but with expanded content.

Course Assessment

Your grade will be based on five assignment components: Asking and answering questions on our discussion board, many in-class coding exercises, and a series of project reports: 4 for Phase I projects, 4 for Phase II projects, and one for the Phase III project.

Grade Breakdown

Points
Discussion Q&A 50
Wednesday Code Review ( WCRs) 12x10*
Phase I Projects 4x25
Phase II Projects 3x50
Phase III Project 1x100
total 500

Note: *the lowest two of the 12 WCRs will be dropped, for a total of 100 pts.

The discussion portion of your grade will be calculated by determining the percentage of times your team both asked and answered a question when assigned to (usually once per week) and multiplying this by 50. That is, if you are asked to submit 15 questions and answers over the course of the semester and post and answer 12, your discussion points will equal 40. A discussion assignment will be considered complete if and only if a question is posted AND another answered.

As the name implies, on Wednesday of most weeks, we’ll have Wednesday Code Reviews (WCRs). These short exercises will assess your aptitude in scripting, data visualization, and modeling.

More details about the specific requirements of Phase I, II, and III projects can be found under our “Projects” tab at the top of this page.

As a reminder, assessment for project reports will incorporate an evaluation of an individual’s or team’s effort and engagement in addition to more objective components (i.e., answers, conclusions, etc.). To do well, the formula is simple: students and their teams should remain persistent, ask for guidance, and make clear conclusions using the skills and tools they learn in the course.


Accommodation and Accessibility

Boston College is committed to providing accommodations to students, faculty, staff and visitors with disabilities. Specific documentation from the appropriate office is required for students seeking accommodation in Woods College courses. Advanced notice and formal registration with the appropriate office is required to facilitate this process. There are two separate offices at BC that coordinate services for students with disabilities:

Find out more about BC’s commitment to accessibility at www.bc.edu/sites/accessibility.

Scholarship and Academic Integrity

Students in this course, as all others at BC, must produce original work and cite references appropriately. Failure to cite references is plagiarism. Academic dishonesty includes, but is not necessarily limited to, plagiarism, fabrication, facilitating academic dishonesty, cheating on exams or assignments, or submitting the same material or substantially similar material to meet the requirements of more than one course without seeking permission of all instructors concerned. Scholastic misconduct may also involve, but is not necessarily limited to, acts that violate the rights of other students, such as depriving another student of course materials or interfering with another student’s work. Please see the Boston College policy on academic integrity for more information.

Email and Office Hours Policy

If you write Professor Kenaley with a question, he’ll try to get back to you within 24 hours. Rather than write Prof. Kenaley with questions related to course mechanics and expectations, please post these in our discussion board, It’s probably the case that others have the same questions.

Office hours can be arranged by appointment via email and will take place over Zoom or in person (Higgins 535). Office hours may involve more more than student or team, so please come equipped with poignant questions and expect that other students have questions as urgent as yours.

 

Cobbled together by Chris Kenaley