First Pass!

.gitignore

@@ -5,3 +5,5 @@
 .Rhistory
 
 .Rproj.user
+
+**/*.quarto_ipynb

README.md

@@ -2,24 +2,65 @@
 
 ## About the Course
 
-[Insert course description]
+Data in the Wild introduces students to data science through the R programming language and a series of ecological problems set in Antarctica. Across the course, students use data to investigate food safety, wildlife tracking, habitat relationships, and sustainable fisheries management. Along the way, they learn skills in data wrangling, visualization, statistical analysis, modeling, and interpretation.
 
-## Course Materials
+The course is designed for students in the life sciences and assumes no prior programming experience. Lessons begin with the basics of coding in R and gradually build toward more advanced tools such as hypothesis testing, machine learning, regression, and population modeling.
 
-[Insert descriptions about modules]
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe, and has been taught at the University of Arizona as part of the general education curriculum.
 
-## Accessing the Material
+## Accessing Course Materials
 
-### Github
+### Website
 
-[About material in Github and how to navigate / use for class]
+The course website contains all modules, lessons, assignments, and their downloads:
 
-### Website
+<https://biodiversitydatasciencecorp.github.io/DataInTheWild_Website/>
+
+## Building the Website Locally
+
+This site is built with [Quarto](https://quarto.org) and requires R and several packages. To preview locally:
+
+### 1.  Install required software
+
+-   Install [R](https://cran.r-project.org)
+-   Install [Quarto](https://quarto.org/docs/get-started/)
+
+### 2.  Install Packages
+
+Install the required R packages by running the following in your R console (e.g., RStudio):
+```r
+install.packages(c(
+  "tidyverse",
+  "palmerpenguins",
+  "ggplot2",
+  "rmarkdown",
+  "knitr",
+  "caret",
+  "recipes",
+  "tseries",
+  "drc"
+), dependencies = TRUE)
+```
+
+The `dependencies = TRUE` ensures all required sub-packages are installed automatically.
+
+In powershell (making sure you're in the root directory), run:
+```
+quarto install tinytex
+```
+Sometimes Quarto will display an error indicating additional packages that need to be installed.
+
+### 3. Preview the Site
+
+In powershell, run:
+```
+quarto preview
+```
 
-[Link to website]
+## Support
 
-[About material in Github and how to navigate / use for class]
+This course was developed with support from the National Science Foundation under the Harnessing the Data Revolution (HDR) Data Science Corps (DSC) program, and the NSF-funded project "Building Capacity in Data Science through Biodiversity, Conservation, and General Education" (Awards [2122967](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122967) and [2122991](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122991)). Materials are openly available and designed to be adaptable for use at other institutions.
 
 ------------------------------------------------------------------------
 
-*Last updated: 19 January 2024, Maxine Cruz*
+2026, University of Arizona & Lewis & Clark College

_quarto.yml

@@ -1,85 +1,135 @@
 project:
-
   type: website
   output-dir: docs
 
-website:
+execute:
+  freeze: auto
 
+website:
   title: "Data In The Wild"
-  favicon: images/bdsc-logo-hex.png
   search: false
-  
-  navbar: 
+
+  navbar:
     logo: images/bdsc-logo-hex.png
-        
+
   sidebar:
-      logo: images/bdsc-logo-hex.png
-      background: $blanched-almond
-      align: center 
-      style: docked
-      collapse-level: 1
+    logo: images/bdsc-logo-hex.png
+    background: $blanched-almond
+    align: center
+    style: docked
+    collapse-level: 1
+
+    contents:
+      - text: "Home"
+        href: index.qmd
 
-      contents: 
-        - section: "Introduction"
-          contents: 
-            - index.qmd
-            - structure.qmd
-            - text: "Contact Us"
-              href: https://github.com/BiodiversityDataScienceCorp
-
-        - section: "Module 1"
-          contents: 
-            - modules/module_1/module1_1.qmd
-            - modules/module_1/module1_2.qmd
-            - modules/module_1/module1_3.qmd
-
-        - section: "Module 2"
-          contents: 
-            - modules/module_2/module2_1.qmd
-            - modules/module_2/module2_2.qmd
-            - modules/module_2/module2_3.qmd
-            - modules/module_2/module2_4.qmd
-            - modules/module_2/module2_5.qmd
-            - modules/module_2/module2_6.qmd
-            - modules/module_2/module2_7.qmd
-
-        - section: "Module 3"
-          contents: 
-            - modules/module_3/module3_1.qmd
-            - modules/module_3/module3_2.qmd
-            - modules/module_3/module3_3.qmd
-            - modules/module_3/module3_4.qmd
-            - modules/module_3/module3_5.qmd
-
-        - section: "Module 4"
-          contents: 
-            - modules/module_4/module4_1.qmd
-            - modules/module_4/module4_2.qmd
-            - modules/module_4/module4_3.qmd
-
-        - section: "Resources"
-          contents: 
-            - resources.qmd
-
-  page-footer: 
+      - text: "Contact Us"
+        href: https://github.com/BiodiversityDataScienceCorp
+
+      - text: "For Instructors"
+        href: instructors.qmd
+
+      - text: "---"
+
+      - section: "Module 1"
+        contents:
+          - text: "Overview"
+            href: modules/module_1/module1_home.qmd
+          - modules/module_1/module1_1.qmd
+          - modules/module_1/module1_2.qmd
+          - modules/module_1/module1_3.qmd
+          - text: "Assignment 1"
+            href: modules/module_1/assignments/a1/m1a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_1/assignments/a2/m1a2.qmd
+
+      - section: "Module 2"
+        contents:
+          - text: "Overview"
+            href: modules/module_2/module2_home.qmd
+          - modules/module_2/module2_1.qmd
+          - modules/module_2/module2_2.qmd
+          - modules/module_2/module2_3.qmd
+          - modules/module_2/module2_4.qmd
+          - modules/module_2/module2_5.qmd
+          - text: "Assignment 1"
+            href: modules/module_2/assignments/a1/m2a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_2/assignments/a2/m2a2.qmd
+          - text: "Assignment 3"
+            href: modules/module_2/assignments/a3/m2a3.qmd
+
+      - section: "Module 3"
+        contents:
+          - text: "Overview"
+            href: modules/module_3/module3_home.qmd
+          - modules/module_3/module3_1.qmd
+          - modules/module_3/module3_2.qmd
+          - modules/module_3/module3_3.qmd
+          - text: "Assignment 1"
+            href: modules/module_3/assignments/a1/m3a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_3/assignments/a2/m3a2.qmd
+
+      - section: "Module 4"
+        contents:
+          - text: "Overview"
+            href: modules/module_4/module4_home.qmd
+          - modules/module_4/module4_1.qmd
+          - modules/module_4/module4_2.qmd
+          - modules/module_4/module4_3.qmd
+          - modules/module_4/module4_4.qmd
+          - text: "Assignment 1"
+            href: modules/module_4/assignments/a1/m4a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_4/assignments/a2/m4a2.qmd
+          - text: "Assignment 3"
+            href: modules/module_4/assignments/a3/m4a3.qmd
+          - text: "Assignment 4"
+            href: modules/module_4/assignments/a4/m4a4.qmd
+
+      - section: "Module 5"
+        contents:
+          - text: "Overview"
+            href: modules/module_5/module5_home.qmd
+          - modules/module_5/module5_1.qmd
+          - modules/module_5/module5_2.qmd
+          - modules/module_5/module5_3.qmd
+          - text: "Assignment 1"
+            href: modules/module_5/assignments/a1/m5a1.qmd
+          - text: "Assignment 2"
+            href: modules/module_5/assignments/a2/m5a2.qmd
+          - text: "Assignment 3"
+            href: modules/module_5/assignments/a3/m5a3.qmd
+
+      - text: "Final Project"
+        href: modules/final_project/FinalProject.qmd
+
+      - text: "---"
+
+      - text: "Resources"
+        href: resources.qmd
+
+  page-footer:
     background: $dark-blue
     foreground: $blanched-almond
-    left: "Copyright 2024, University of Arizona | Last modified: 12 June 2024"
-    right: 
+    left: "2026, University of Arizona & Lewis & Clark College"
+    right:
       - href: https://quarto.org/
         text: "Made with Quarto"
       - href: https://github.com/BiodiversityDataScienceCorp/DataInTheWild_Website
         icon: github
 
 format:
-
   html:
     theme:
       - styles.scss
     mainfont: "Raleway"
     toc: true
     page-layout: full
-  pdf: default
+    include-in-header:
+      text: |
+        <script src="/scripts/pdf-button.js"></script>
+        <script src="/scripts/toggle-view.js"></script>
 
 editor: visual
-

docs/scripts/pdf-button.js

@@ -0,0 +1,19 @@
+document.addEventListener("DOMContentLoaded", () => {
+  const tocSidebar = document.querySelector("#quarto-margin-sidebar");
+
+  if (!tocSidebar) return;
+
+  if (document.querySelector(".toc-pdf-button")) return;
+
+  const btn = document.createElement("a");
+  btn.href = "#";
+  btn.textContent = "PDF";
+  btn.className = "toc-pdf-button";
+
+  btn.addEventListener("click", (e) => {
+    e.preventDefault();
+    window.print();
+  });
+
+  tocSidebar.appendChild(btn);
+});

index.qmd

@@ -8,50 +8,43 @@ format:
 
 ![](images/penguins.jpg)
 
-Welcome! This course introduces students in the life sciences to data science through the application of quantitative reasoning and the R programming language to "real-world" problems. Below, you will find the overall narrative and material that will be covered. There are four modules that guide the student from their first steps on the Land of the Penguins, to building new roads for access to fishing sites. Each module will lead them through basic programming, data visualization, statistics, machine learning, and interpretation of quantitative concepts.
+*Data in the Wild* introduces students to data science through the R programming language and a series of ecological problems set in Antarctica. Across the course, students use data to investigate food safety, wildlife tracking, habitat relationships, and sustainable fisheries management. Along the way, they learn skills in data wrangling, visualization, statistical analysis, modeling, and interpretation.
 
-## The Narrative
-
-### **⊳** Module 1: *Mission Antarctica!*
-
-A new effort to establish a permanent, sustainable colony in Antarctica is being launched. Students are introduced to the field of data science, the application (RStudio, RMarkdown, Jupyter Notebooks), and programming language (R, Python) used in the course.
-
-### **⊳** Module 2: *Good Food Gone Bad*
+The course is designed for students in the life sciences and assumes no prior programming experience. Lessons begin with the basics of coding in R and gradually build toward more advanced tools such as hypothesis testing, machine learning, regression, and population modeling.
 
-There is a food poisoning outbreak among team members. Students use data visualization to determine where the problem lies (fish, not plants) and simulations to determine the root of the problem (fish tank density, not fish tank temperature).
+The course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe, and has been taught at the University of Arizona as part of the general education curriculum. Funding comes from the NSF-funded project "Building Capacity in Data Science through Biodiversity, Conservation, and General Education" (Awards [2122967](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122967) and [2122991](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122991)).
 
-### **⊳** Module 3: *Follow That Seal*
+## Learning Objectives
 
-The fish tanks need to be restocked, but we want to avoid fishing in places with high leopard seal density, so we track the seals. However, the radio collars on leopard seals are failing and there is a deadly conflict between fish collectors and seals. The collars come from two different manufacturers, but we need to tell how the collars are failing (days to recharge, not signal distance) and how to classify collars of unknown provenance.
+By the end of the course, students will be able to:
 
-### **⊳** Module 4: *March of the Penguins*
+1.  Import, organize, summarize, and work with ecological datasets in R.
+2.  Create and interpret appropriate data visualizations using `ggplot2`.
+3.  Apply statistical tools such as t-tests, ANOVA, and regression to ecological questions.
+4.  Combine datasets and use basic classification approaches to support inference and prediction.
+5.  Interpret population growth models, carrying capacity, and sustainable yield in an ecological management context.
+6.  Communicate quantitative findings clearly using code, figures, and written interpretation.
 
-A new road is needed to access fishing sites with low leopard seal density. There are several possible routes, but we want to avoid crossing through Gentoo penguin nesting grounds. Students will build models to determine predictors of nesting success, first with bootstrapping for confidence intervals, then with linear regression.
-
-## How to Navigate this Site
-
-Under Course Materials, you will find lessons taught in WFSC 223 at the University of Arizona as well as links download lectures, assignments, discussions, and other assets used for instruction.
-
-## Overall Learning Objectives
+## The Narrative
 
-By the end of the semester, you will be able to...
+### **Module 1: Mission Antarctica!**
 
-1.  Define, differentiate, and explain the nature and application of computational methods
+The team has arrived in Antarctica and needs to establish the technical foundation for all future data work. Students begin by learning how to use R and RStudio in Posit Cloud, including how to run code, create objects, work with vectors, understand data classes, subset data, and use functions. They are also introduced to the `tidyverse`, where they begin learning how to select, filter, summarize, group, and mutate data. By the end of the module, students are comfortable navigating their coding environment and working with simple ecological datasets.
 
-    for acquiring, managing, analyzing, visualizing, and sharing data as it relates to real-
+### **Module 2: Good Food Gone Bad**
 
-    world natural resource scenarios.
+A wave of food poisoning spreads through the Antarctic base, and the team needs to figure out why. Students investigate a potential food contamination event using descriptive statistics and data visualization to track down the culprit. Along the way, they are introduced to histograms, scatter plots, and box plots in both base R and `ggplot2`, and they develop an eye for what makes a visualization effective or misleading. By the end of the module, students have solved the problem and begun exploring a dataset of their own for the course's final project.
 
-2.  Associate, examine, and compare how to infer meaning and insight from data through
+### **Module 3: Follow That Seal**
 
-    written, visual, and verbal communication to multiple audiences.
+With the aquaponics system compromised, the team must turn to wild-caught fish to supplement their food supply, but fishing in Antarctic waters comes with risks. Leopard seals are apex predators in the area, and the radio collars used to track them have been failing unexpectedly. Students are tasked with figuring out which collar manufacturer is to blame by comparing means numerically, visually, and statistically - first using t-tests to compare two groups, then extending to ANOVA and post-hoc comparisons when more than two groups are involved. By the end of the module, students can determine whether differences between groups are statistically meaningful and apply these tools to new ecological contexts.
 
-3.  Summarize, implement, and appraise multiple perspectives and make meaningful
+### **Module 4: Roads and Regressions**
 
-    connections across disciplines and social positions, think conceptually and critically, and
+With fishing bays selected and collars in need of replacement, the team faces two new challenges: identifying the makers of a mystery batch of collars, and planning a road to the fishing sites that minimizes harm to Antarctica's fragile ecosystem - including avoiding habitat for Antarctic hair grass and penguin nesting sites. Students learn to combine datasets using joins and binds, get an introduction to machine learning through K-Nearest Neighbors classification, and dive into linear and multiple regression to model environmental relationships. By the end of the module, students can work with data from multiple sources, fit regression models, and interpret their results.
 
-    solve problems with data informed approaches.
+### **Module 5: Sustainable Fishing**
 
-## Footnotes (For Instructors)
+With fishing bays selected and roads planned, the team must now figure out how many fish they can sustainably harvest without depleting the population. Students are introduced to population growth models - exponential and logistic - and use them to estimate carrying capacities and maximum sustainable yield for Antarctic toothfish populations. They then learn to fit logistic growth curves to real data and compare populations statistically. Along the way, students also step outside of R to practice finding and citing scientific literature and writing a scientific abstract in preparation for the final project.
 
-This course was developed by Drs. Katy Prudic, Jeff Oliver, Keaton Wilson, and Ellen Bledsoe. This was taught in pilot form at the University of Arizona in Spring 2020 as *Settlers of Antarctica*, and it has undergone multiple revisions to better communicate and optimize the material to both students and instructors over the years. At the end of the day, with the help of funding from the NSF's Harnessing the Data Revolution, the material is designed to be adaptable to other disciplines and serve as a template for your own courses. R was used as the primary programming language here, but the course is modifiable to other languages such as Python.
+> **Note:** Module 5 is an example of how *Data in the Wild* can be extended or adapted to reflect a specific disciplinary focus. This version was developed by Dr. Ellen Bledsoe and draws on their background in ecology. The course can be taught with or without this module, and instructors are encouraged to develop alternatives that reflect their own expertise or research context.