This course is taught as a set of modules. Each module utilizes long-term, high-frequency, and sensor-based datasets from sources like the Global Lakes Ecological Observatory Network, the United States Geological Survey, and the Long Term Ecological Research Network, and the National Ecological Observatory Network. Modules can be adapted for any skill level to enhance students’ understanding of macrosystems ecology, computational skills, and ability to conduct inquiry-based studies.
Live Modules include:
This is a short-course style learning opportunity covering statistics in the context of soil science. Topics covered include: data and R (tabular and spatial data), exploratory data analysis, sampling, numerical taxonomy, uncertainty & validation, linear regression, generalized linear models, and tree-based models. This course provides lecture notes, exercises and projects for the user to learn and practice these topics.
Click here to begin learning!
Statistical Inference via Data Science: A ModernDive into R and the tidyverse is an intro to stats textbook that teaches students how to wrangle data. It provides students the whole data analysis pipe line, incorporates contemporary, user-friendly R packages directly into text, and emphasizes models that prepare students for our multivariate world. R packages such as dplyr, tidyverse, ggplot2, and infer are focused on in this course.
Click here to begin!
Some videos we made to keep you occupied and to give you an idea of what our real proteomics 3d short courses are like. We will gradually add more and replace some of the audio when we get a better microphone. Sorry for the audio quality! For questions, updates and powerpoints register at our basecamp https://3.basecamp.com/3834907/join/HvxU2pWno7VZ
Click here to access on AggieVideo!
This website, developed by the University of New South Wales, offers a brief introduction to techniques for data organization, graphics, and analyses. Topics covered include: an introduction to R, data manipulation, coding skills, graphical techniques, and statistics.
To access these materials, click here.
To access the raw markdown files on GitHub, click here.
This repository contains many of the course materials (for free!) from the Bio720 course taught by Brian Golding and Ian Dworkin at McMaster University in 2018. While this course is structured for graduate students, anyone who has a working knowledge of basic molecular biology and genetics should be able to use this course. The goal of this course is to allow the user to develop fundamental computational skills that will prepare them to further develop skills appropriate for bioinformatics and genomics.
To learn more about this course, click here.
This is a set of lectures, exercises, and challenges using R. Topics covered include: an introduction to comparative methods and R, Brownian motion, PGLS, ancestral state reconstruction, trait evolution, diversification with BAMM and RPanda, state dependent diversification, Mk models and character correlations, and placing fossils/recently extinct taxa on a tree.
Arbor is an R software package that can be used to build workflows for phylogenetic comparative analysis. This tutorial set showcases how to use Arbor and how to run analyses using Arbor. Common analyses for which you can use Arbor include: phylogenetic signal, ancestral state reconstruction, independent contrasts, comparative model fitting, PGLS, fitting birth-death models, community phylogenetics, BiSSE/MuSSE/QuaSSE/GeoSSE, and cospeciation. Click here to begin!
This video series (~11 hours total) focuses on the use of R to build and test evolutionary models. It reviews basic aspects of quantitative genetic theory and illustrates how that theory can be tested with data , both from single species and with multiple-species phylogenies. To view the videos, select the “Videos” tab near the top right of the page.
Here is a collection of RStudio “Cheatsheets” that provide an at-a-glance look at R packages and their related functions. View cheatsheets for dplyr, ggplot2, RStudio IDE and more.