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    REMNet Tutorial, R Part 1: Getting Started in R with Microbiome Data 5.3.19
    Video on the basics of R software from the Research Experiences in Microbiomes Network
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    QB@CC Poster for NABT 2020
    This is a poster presentation at the 2020 NABT Annual PD Conference. This poster describes the goals of QB@CC project, progress in the first year and an update on the upcoming events.
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    Phenology Trends and Climate Change in Minnesota (Project EDDIE)
    In this module, students will practice answering a specific question about how climate change has affected flowering date in American elm trees. Students can then practice on a species of their own...
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    Earth Analytics in R Course
    Earth analytics is an advanced, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of...
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    Earth Analytics Bootcamp Course
    The Earth Analytics Bootcamp is a three-week introductory-level course taught by instructors in Earth Lab and is a part of the Professional Certificate in Earth Data Analytics - Foundations at CU...
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    Investigating human impacts on stream ecology: Intro to R
    This resource uses the Human Impact on Stream Ecology data set, background and questions and provides students an very general introduction to using R. Students perform basic summary statistics and...
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    A first lesson in mathematical modeling for biologists: Rocs
    Using a fictitious population of Rocs, a mythical species of giant raptor, students accomplish two broad objectives: 1) they undertake the process of building a first mathematical model of a...
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    Genome Hunters: A Quantitative Biology Course-Based Undergraduate Research Experience (CURE)
    Poster outlining the process of designing, implementing, and assessing a new quantitative biology CURE course series. Presented at the 2021 BIOME Institute.
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    Interactive, online modules to enhance quantitative skills in biology students
    Session presentation on the need for enhancing quantitative proficiency through a series of interactive, web‐based modules at the 2018 QUBES/BioQUEST Summer Workshop
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    Understanding the Quantitative and Computational Skills of Incoming Biology Students
    Presentation given as part of a Minisymposium at BEER 2015.
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    Getting students to reason with data: QUBES tools and teaching resources built for introductory biology
    Workshop presented at the 2019 Biology Learning Community Conference
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    Final Project for Calling Bull
    This is the final project for the calling bull course & it is to find 3 pieces of misinformation/BS and debunk them. They have to re-do at least one visualization, use R in at least one debunking,...
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    Introduction to Quantitative Biology - Fall 2014 Syllabus
    Syllabus for Introduction to Quantitative Biology taught at the College of William and Mary.

    Keywords: quant biosyllabus

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    QUBES: Building a community to promote undergraduate quantitative biology education
    QUBES is designed to improve communication among educators, assist faculty in understanding and implementing novel content and teaching strategies in their classrooms, and create an academic reward...

    Keywords: QUBES

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    2015 Quantitative Biology Education Summit
    Abstract for a meeting, 2015 Quantitative Biology Education Summit.
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    Developing Quantitative Skills Using HHMI BioInteractive Resources
    A workshop on using HHMI BioInteractive data activities to teach science content and quantitative skills
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    Quantitative Reasoning Learning Progression
    Three articles about the work of a project on quantitative reasoning in the context of environmental literacy.

    Keywords: quantitative

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    Module M.7 Generalized linear mixed-effects models
    Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis...
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    An Introduction to the R Programming Environment
    An online module introducing students and biologists to R, published in American Phytopathological Society
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    Checking Normality in R with swirl
    How to use the "three-prong" approach to check for normality
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