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    Introduction to Earth Data Science Textbook
    Introduction to Earth Data Science is an online textbook for anyone new to open reproducible science and the Python programming language. There are no prerequisites for this material, and no prior...
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    The Language of Science
    This part focuses on the language of science
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    REMNet Tutorial, R Part 2: Importing Your Microbiome Data into R 5.3.19
    Video on importing data into R from the Research Experiences in Microbiomes Network
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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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    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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    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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    REMNet Tutorial, R Part 5: Normalizing Microbiome Data in R 5.2.19
    Video on normalizing microbiome data from the Research Experiences in Microbiomes Network
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    REMNet Tutorial, R Part 3: Creating Alpha Diversity Plots in R 5.2.19
    Video on creating alpha diversity plots in R from the Research Experiences in Microbiomes Network
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    DCS 105 Calling Bull F24 Schedule
    Schedule overview for Bates College Course DCS 105 Calling Bull
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    Data-Driven Decision-Making: Antarctic Fisheries
    This is an entire module for a general education data science and ecology course. The module was originally designed as the fourth and final section of the course; the full course is openly...
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    Module M.5 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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    Module M.8 Survival analysis
    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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    Module M.6 Generalized linear 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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    Module M.4 Simple linear regression analysis
    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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    Module M.3 Visualizing data with ggplot2
    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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    Module M.2 Descriptive statistics
    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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    Curvature and Maximum Permissible Speeds
    In this activity students will compute the curvature of a section of commuter rail and use it to determine a safe speed at which a train should can round the corner without tipping or derailing....
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    Resource list for inclusive teaching in STEM (HHMI)
    A resource collection created for HHMI BioInteractive to guide the development of teaching resources aligned with principles of equity and inclusion.
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    Writing Clean Code in R Workshop
    When working with data, you often spend the most amount of time cleaning your data. Learn how to write more efficient code using the tidyverse in R.
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