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Access NEON Data Using the Data Portal – A Visual Guide

This guide walks viewers through how to access data through the NEON data portal and a few related resources for working with NEON data.  The NEON data portal remains in dynamic development. This photographic guide will be regularly updated. If the data portal you are viewing does not match this guide, please check for an updated version on the NEONScience.org website and let Megan know so she can update it here.

The guide is laid out to be followed sequentially from starting on the NEONScience.org website to downloading data to your computer. After this sequence additional slides are provided that details on other pages or topics of interest to data users.

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NWBC Introductory Biology Learning Outcomes

Learning outcomes for the first year of biology compiled and consented to at the NWBC 2018 Winter Workshop.

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Core competency reference sheet

Reference sheet from the workshop. Adapted from 2011 Vision and Change report by Stasinos Stavrianeas.

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NW PULSE resources

Scroll down to find links to webinars on Dynamic Governance, Curriculum Mapping, and more.

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BioSkills Learning Outcomes

Coming soon! Full set will be posted after validation.

The BioSkills Guide is a validated set of essential learning outcomes for the six core competencies for graduating general biology majors.

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Slides from workshop

Powerpoint slides used during the SABER West 2019 "Scaffolding Core Competency Learning Outcomes across the Undergraduate Biology Curriculum" workshop.

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Northwest BioSciences Consortium

QUBES site for the NWBC group. To learn more about NWBC.

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Vision and Change in Undergraduate Biology: A Call to Action

The 2011 report. See pages 14-15 for core competencies.

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Test

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R for Data Science: Chapter 4: Communicate

So far, you’ve learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn’t matter how great your analysis is unless you can explain it to others: you need to communicate your results.

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R for Data Science: Chapter 4: Model

Now that you are equipped with powerful programming tools we can finally return to modelling. You’ll use your new tools of data wrangling and programming, to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you’ll learn a few basic tools that help you understand the variation within your models.

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R for Data Science: Chapter 3: Program

In this part of the book, you’ll improve your programming skills. Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper.

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R for Data Science: Chapter 2: Wrangle

In this part of the book, you’ll learn about data wrangling, the art of getting your data into R in a useful form for visualisation and modelling. Data wrangling is very important: without it you can’t work with your own data!

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R for Data Science: Chapter 1: Explore

The goal of the first part of this book is to get you up to speed with the basic tools of data exploration as quickly as possible. Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again. The goal of data exploration is to generate many promising leads that you can later explore in more depth.

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DataCamp Course: Data Visualization in R with lattice

Course Description

Visualization is an essential component of interactive data analysis in R. Traditional (base) graphics is powerful, but limited in its ability to deal with multivariate data. Trellis graphics is the natural successor to traditional graphics, extending its simple philosophy to gracefully handle common multivariable data visualization tasks. This course introduces the lattice package, which implements Trellis graphics for R, and illustrates its basic use.

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DataCamp Course: Data Visualization in R

Course Description

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.

After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.

Base Graphics Background
R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

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DataCamp Course: Visualization Best Practices in R

Course Description

This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets to cover different types of data (proportions, point-data, single distributions, and multiple distributions) and discuss the pros and cons of the most common visualizations. In addition, we will cover some less common alternatives visualizations for the data types and how to tweak default ggplot settings to most efficiently and effectively get your message across.

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DataCamp Course: Data Visualization with ggplot2 (Part 2)

Course Description

This ggplot2 tutorial builds on your knowledge from the first course to produce meaningful explanatory plots. We'll explore the last four optional layers. Statistics will be calculated on the fly and we’ll see how Coordinates and Facets aid in communication. Publication quality plots will be produced directly in R using the Themes layer. We’ll also discuss details on data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.

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DataCamp Track: Data Visualization with R

Communicate the most important features of your data by creating beautiful visualizations using ggplot2 and base R graphics.

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DataCamp Course: Data Visualization with ggplot2 (Part 1)

Course Description

The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. Here, we’ll examine the first three essential layers for making a plot - Data, Aesthetics and Geometries. By the end of the course you will be able to make complex exploratory plots.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Building Dashboards with shinydashboard

Course Description

Once you've started learning tools for building interactive web applications with shiny, this course will translate this knowledge into building dashboards. Dashboards, a common data science deliverable, are pages that collate information, often tracking metrics from a live-updating data source. You'll gain more expertise using shiny while learning to build and design these dynamic dashboards. In the process, you'll pick up tips to optimize performance as well as best practices to create a visually appealing product.

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Building Web Applications in R with Shiny: Case Studies

Course Description

After learning the basics of using Shiny to build web applications, this course will take you to the next level by putting your newly acquired skills into use. You'll get experience developing fun and realistic Shiny apps for different common use cases, such as using Shiny to explore a dataset, to generate a customized plot, and even to create a wordcloud. With all this practice and new knowledge, you should be inspired and well-equipped to develop Shiny apps for your own use!

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Building Web Applications in R with Shiny

Course Description

Shiny is an R package that makes it easy to build interactive web apps straight from R. Shiny combines the computational power of R with the interactivity of the modern web. This course will take you from R programmer to Shiny developer. If you want to take a fresh, interactive approach to telling your data story, let users interact with your data and your analysis, and do it all with R, dive in!

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DataCamp Course: Building Dashboards with flexdashboard

Course Description

Communication is a key part of the data science process. Dashboards are a popular way to present data in a cohesive visual display. In this course you'll learn how to assemble your results into a polished dashboard using the flexdashboard package. This can be as simple as adding a few lines of R Markdown to your existing code, or as rich as a fully interactive Shiny-powered experience. You will learn about the spectrum of dashboard creation tools available in R and complete this course with the ability to produce a professional quality dashboard.

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