Data Tutorials

Looking to improve your data skills using tools like R or Python? Want to learn more about working with a specific NEON data product? NEON develops online tutorials to help you improve your research. These self-paced tutorials are designed for you to used as standalone help on a single topic or as a series to learn new techniques.

Code for all script based tutorials can be downloaded at the end of the tutorial. Original files can also be found on GitHub.


Quantifying The Drivers and Impacts of Natural Disturbance Events – The 2013 Colorado Floods

This teaching module demonstrates ways that scientists identify and use data that they use to study disturbance events. Further, it encourages students to think about why we need to quantify change and different types of data needed to quantify the change. The focus is on flooding as a natural disturbance event with impacts on the local human populations. Specifically, it focuses on the causes and impacts of flooding that occurred in 2013 throughout Colorado with an emphasis on Boulder county.

Data Activity: Visualize Elevation Change using LiDAR in R to Better Understand the 2013 Colorado Floods

This tutorial teaches how to use Digital Terrain Models derived from LiDAR data to create Digital Elevation Models of Differences that allow us to measure the change in elevation of an area after a disturbance event.

Data Activity: Visualize Stream Discharge Data in R to Better Understand the 2013 Colorado Floods

This lesson walks through the steps need to download and visualize USGS Stream Discharge data in R to better understand the drivers and impacts of the 2013 Colorado floods.

Data Activity: Visualize Palmer Drought Severity Index Data in R to Better Understand the 2013 Colorado Floods

This tutorial walks through how to download and visualize Palmer Drought Severity Index data in R. The data specifically downloaded for this activity allows one to to better understand a driver of the 2013 Colorado floods.

Image Raster Data in R - An Intro

This tutorial explains the fundamental principles, functions and metadata that you need to work with raster data, in image format, in R. Topics include raster stacks, raster bricks, plotting RGB images and exporting an RGB image to a GeoTIFF.


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