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1. 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.

   https://qubeshub.org/qubesresources/publications/1221/?v=1

2. AVIDA exercise on the role of mutation in evolution

   21 May 2019 | Teaching Materials | Contributor(s):

   By William Beachly

   Hastings College

   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...

   https://qubeshub.org/qubesresources/publications/1224/?v=1

3. Exploring the population dynamics of wintering bald eagles through long-term data using R

   21 May 2019 | Teaching Materials | Contributor(s):

   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...

   https://qubeshub.org/qubesresources/publications/1158/?v=1

4. Investigating human impacts on local Hawaiian stream ecology

   20 May 2019 | Teaching Materials | Contributor(s):

   By pamela scheffler

   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...

   https://qubeshub.org/qubesresources/publications/1222/?v=1

5. 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...

   https://qubeshub.org/qubesresources/publications/1220/?v=1

6. 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...

   https://qubeshub.org/qubesresources/publications/1219/?v=1

7. How Many More Thymes & Herbivore Defense

   15 May 2019 | Teaching Materials | Contributor(s):

   By Sheryl Konrad¹, Angela Dassow¹

   Carthage College

   Adaptations to the How Many More Thymes? case study featuring terpene-based herbivore defense and pollinator attraction in Nicotiana plants.

   https://qubeshub.org/qubesresources/publications/1201/?v=1

8. Working with Datasets in R swirl

   15 May 2019 | Teaching Materials | Contributor(s):

   By Caitlin Hicks Pries

   Dartmouth College

   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...

   https://qubeshub.org/qubesresources/publications/1202/?v=1

9. 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

   Carthage College

   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...

   https://qubeshub.org/qubesresources/publications/1200/?v=1

10. 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...

    https://qubeshub.org/qubesresources/publications/1195/?v=1

11. Importing Data into R

    10 May 2019 | Teaching Materials | Contributor(s):

    By Rachel Hartnett

    Oklahoma State University

    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...

    https://qubeshub.org/qubesresources/publications/1166/?v=1

12. Discrete Math Modeling with Biological Applications (Course Materials)

    26 Apr 2019 | Teaching Materials | Contributor(s):

    By Carrie Diaz Eaton

    Bates College

    These are the materials for Math 214 offered at Rhodes College.

    https://qubeshub.org/qubesresources/publications/765/?v=1

13. Calling Bull in an Age of Big Data with R

    26 Apr 2019 | Teaching Materials | Contributor(s):

    By Carrie Diaz Eaton

    Bates College

    Use the calling bull course to introduce students to data, ethics, visualization, and R.

    https://qubeshub.org/qubesresources/publications/1150/?v=1

14. Calling Bull: Data Reasoning in a Digital World

    26 Apr 2019 | Teaching Materials | Contributor(s):

    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."

    https://qubeshub.org/qubesresources/publications/1149/?v=1

15. 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.

    https://qubeshub.org/qubesresources/publications/1142/?v=1

16. 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...

    https://qubeshub.org/qubesresources/publications/1138/?v=1

17. 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.

    https://qubeshub.org/qubesresources/publications/1137/?v=1

18. 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.

    https://qubeshub.org/qubesresources/publications/1102/?v=1

19. 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.

    https://qubeshub.org/qubesresources/publications/1092/?v=1

20. 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.

    https://qubeshub.org/qubesresources/publications/1024/?v=1