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.

All material are freely available for you to use and reuse. We suggest the following citation:

[AUTHOR(S)]. Data Tutorial:[TUTORIAL NAME]. Accessed:[DATE OF ACCESS]. National Ecological Observatory Network, Battelle, Boulder, CO, USA. [URL]

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Hyperspectral Variation Uncertainty Analysis in Python

Learn to analyze the difference between rasters taken a few days apart to assess the uncertainty between days.

Mask a Raster Using Threshold Values in Python

In this tutorial, we will learn how to remove parts of a raster based on pixel values using a mask we create.

Classification of Hyperspectral Data with Ordinary Least Squares in Python

Learn to classify spectral data using the Ordinary Least Squares method.

Classification of Hyperspectral Data with Principal Components Analysis (PCA) in Python

Learn to classify spectral data using the Principal Components Analysis (PCA) method.

Git 07: Updating Your Repo by Setting Up a Remote

This tutorial covers how to set up a repository to update your local repo with any any changes from the central repo.

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