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The nose knows: How tri-trophic interactions and natural history shape bird foraging behavior. Introduction to data visualization.
30 May 2019 | Teaching Materials | Contributor(s):
By pamela scheffler
Students investigate the role of olfaction and infochemicals on bird foraging behavior.. In this module students learn to analyze and present graphic data in ways that simplify interpretation.
AVIDA exercise on the role of mutation in evolution
21 May 2019 | Teaching Materials | Contributor(s):
By William Beachly
A tested exercise at the college freshmen level using AVIDA-Ed to generate and test a hypothesis about the relationship between mutation rate and the probability of evolving a useful function in...
Exploring the population dynamics of wintering bald eagles through long-term data using R
By Kristen Kaczynski
California State University - Chico
The purpose of this modification was to take the analyses that were done in Excel and have the students perform them using R. Students were not given any R code for this activity, as R code was...
Investigating human impacts on local Hawaiian stream ecology
20 May 2019 | Teaching Materials | Contributor(s):
Students learn about stream ecology on the island of Hawaii using data available through USGS and University of Hawaii websites and develop an understanding for potential stream changes due to...
Examining human impacts on tusk evolution in elephants using authentic research data
17 May 2019 | Teaching Materials | Contributor(s):
By Kaitlin Bonner
St. John Fisher College
In this activity students explore and analyze real, authentic research data paired with HHMI’s “Selection for Tuskless Elephants” video in a hands-on investigation of human impacts on elephant...
Sequence Similarity Resource Adaptation: Exploring Ebola Virus
16 May 2019 | Teaching Materials | Contributor(s):
By William Tapprich
University of Nebraska-Omaha
This adaptation of the sequence similarity resource provides biology students with a basic set of bioinformatics concepts and tools. These concepts and tools are used to explore relationships...
How Many More Thymes & Herbivore Defense
15 May 2019 | Teaching Materials | Contributor(s):
By Sheryl Konrad1, Angela Dassow1
Adaptations to the How Many More Thymes? case study featuring terpene-based herbivore defense and pollinator attraction in Nicotiana plants.
Working with Datasets in R swirl
By Caitlin Hicks Pries
The goal of this lesson is to learn how to import datasets into R, understand variable types, make adjustments to variables, perform basic calculations, and begin data visualization. The exercise...
The Polar Bear of the Salt Marsh? Warming Tolerance Limits of Local Species
13 May 2019 | Teaching Materials | Contributor(s):
By Angela Dassow1, Sheryl Konrad1
National Center for Case Study Teaching in Science- In the adaptation exercise, students explore connections between saltmarsh sparrow declines and declining populations of local species that live...
Sampling Distributions and Null Distributions: two swirl lessons in R
10 May 2019 | Teaching Materials | Contributor(s):
By Rachel Hartnett
Oklahoma State University
There are two complementary lessons. The first covers how sampling distributions are made and ID's their key properties. The second covers how null distributions and how the test statistic of a...
Importing Data into R
This short swirl lesson on importing data is designed to 1) get a basic understanding in how tables are read into R and some common issues and 2) develop an individual set of instructions for...
Discrete Math Modeling with Biological Applications (Course Materials)
26 Apr 2019 | Teaching Materials | Contributor(s):
By Carrie Diaz Eaton
These are the materials for Math 214 offered at Rhodes College.
Calling Bull in an Age of Big Data with R
Use the calling bull course to introduce students to data, ethics, visualization, and R.
Calling Bull: Data Reasoning in a Digital World
By Carl Bergstrom1, Jevin West1
University of Washington
"So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bull seeing through it, and combating it with effective analysis and argument."
Quantifying and Visualizing Campus Tree Phenology
18 Apr 2019 | Teaching Materials | Contributor(s):
By Nate Emery1, Caleb Trujillo1, Andrew Jarosz1, Tammy M Long1
Michigan State University
This lesson, published on CourseSource, enables collaborative teams to collect, quantify, and analyze observable seasonal changes in nature.
Data is the New Science
12 Apr 2019 | Teaching Materials | Contributor(s):
By Anna Monfils1, Debra Linton1, Libby Ellwood2, Molly Phillips3
1. Central Michigan University 2. iDigBio 3. iDigBio, Florida Museum of Natural History, University of Florida
In this module, students will be introduced to some emerging biodiversity data resources. They will be asked to think critically about the strengths and utility of these data resources and apply...
Following the Data
09 Apr 2019 | Teaching Materials | Contributor(s):
By Anna Monfils1, Debra Linton1, Molly Phillips2, Libby Ellwood3
1. Central Michigan University 2. iDigBio, Florida Museum of Natural History, University of Florida 3. iDigBio
The video and exercise provides insight into how researchers are using digital data resources to investigate biodiversity in prairie fen wetlands.
Exploring Data with BISON
14 Mar 2019 | Teaching Materials | Contributor(s):
By Erica Krimmel
Natural History Museum of Los Angeles County
A module to explore aggregated observation and collection information, through BISON, or Biodiversity Information Serving Our Nation (https://bison.usgs.gov), a tool for visualizing GBIF data.
Intro to Command Line Coding Genomics Analysis
28 Feb 2019 | Teaching Materials | Contributor(s):
By Rachael St. Jacques1, Max Maza1, Ray A Enke1
James Madison University
This resource is a fun computer-based intro to command line programming. The activity takes FASTQ NGS data files and runs a fun program called FASTQE.
Exploring the functionality of R
23 Jan 2019 | Teaching Materials | Contributor(s):
By Austin Happel
Colorado State University
Students work with example data to generate a length-weight plot and use lm() to obtain regression coefficients. Base plot functions are used to plot both the data and the regression line.