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 most script based tutorials can be downloaded at the end of the tutorial. Source files can also be found on GitHub. If you are interested in contributing a tutorial to this collection, please reach out using the Contact Us form, and we can guide you through the process of submitting resources to the GitHub repository.

All materials are freely available for you to use and reuse. We suggest the following citation for tutorials:  [AUTHOR(S), NEON (National Ecological Observatory Network)]. Data Tutorial: [TUTORIAL NAME]. [URL] (accessed [DATE OF ACCESS]). See Citation Guidelines for examples, and for guidance in citing data and code.

Tutorials

Download a NEON Teaching Data Subset & Set A Working Directory In R

30 minutes

This tutorial explains how to set a working directory in R. The working directory points to a directory or folder on the computer where data that you wish to work with in R is stored.

Download and Explore NEON Data

1 - 2 hours

Tutorial for downloading data from the Data Portal and the neonUtilities package, then exploring and understanding the downloaded data

Download and work with NEON Aquatic Instrument Data

1.5 hours

Download NEON AIS data using the neonUtilities package and explore and interpret the downloaded data

Estimating Bird Occupancy and Detection Using NEON Observational Data

2 hrs

Use NEON breeding landbird data and the RPresence package to build detection histories, fit occupancy models, and incorporate site and survey covariates.

Explore and work with NEON biodiversity data from aquatic ecosystems

1 Hour

Download and explore NEON macroinvertebrate data. This includes instruction on how to convert to long and wide tables, as well as an exploration of alpha, beta, and gamma diversity from Jost (2007).

Explore biodiversity with NEON algae data

1 Hour

Download and explore NEON algae data. This includes instruction for using

Explore Field Spectra Data - Scaling Ground and Airborne Observations

1 Hour

Explore the field spectra data product, link field spectra to airborne surface reflectance data.

Explore NEON biodiversity data using ecocomDP

1.5 Hours

Download and explore NEON and LTER aquatic macroinvertebrate data with

Exploring diel carbon flux cycles

1 hour

Download and extract NEON eddy flux data to evaluate diel carbon flux cycles

Exploring NEON AOP remote sensing and GBIF occurrence data in Google Earth Engine Python (geemap) to assess the impacts of a wildfire

1 hour

Explore AOP reflectance along with

Exploring sample availability at the NEON Biorepository

30 minutes

Get a list of available samples at the NEON Biorepository based on data availability and research criteria.

Exploring Uncertainty in Lidar Raster Data using Python

30 minutes

Assess lidar raster uncertainty by comparing data from lidar acquisitions collected a few days apart.

Exploring Uncertainty in Lidar Raster Data using R

1 Hour

Analyze the difference between lidar rasters to assess uncertainty.

Extracting Timeseries from Images using the xROI R Package

0.5 hrs

Delineate Region of Interests (ROIs) and Extract Time-Series Data from Digital Repeat Photography Images using xROI

Filter, Piping, and GREPL Using R DPLYR - An Intro

1.0 - 1.5 Hours

Learn how to use the filter, group_by, and summarize functions with piping

Forecast Beetle Richness and Abundance and Submit Forecasts to the NEON Ecological Forecast Challenge

2 Hours

Generate forecasts of beetle richness and abundance that are submitted to the Ecological Forecasting Initiative NEON forecasting challenge.

Functions in Google Earth Engine (GEE)

30 minutes

Write functions to add AOP reflectance data as Map layers and apply cloud-masking

Get Started with NEON Data: A Series of Data Tutorials

This series contains tutorials to guide you through how to access and navigate NEON data, and introduce you to techniques and resources for working with specific types of NEON data.

Series

14 part series

Getting Started with the R Programming Language

30 minutes

This tutorial presents the basics of using R.

Git 01: Intro to Git Version Control

30 minutes

This tutorial introduces the importance of version control in scientific workflows.

Git 02: GitHub.com - Repos & Forks

30 minutes

This tutorial teaches you how to fork, or copy, an existing GitHub repository.

Git 03: Git Clone - Work Locally On Your Computer

30 minutes

This tutorial teaches you how to clone or copy a

Git 04: Markdown Files

30 minutes

This tutorial covers the basics of writing a document using the markdown language.

Git 05: Git Add Changes - Commit

30 minutes

This tutorial covers how to edit a local version of a Git repository and then commit changes to it to be tracked in the Git version control system.

Git 06: Sync GitHub Repos with Pull Requests

30 minutes

This tutorial covers how to submit a pull request to a repository that you don't have direct push access to in order to suggest changes to content.

Git 07: Updating Your Repo by Setting Up a Remote

30 minutes

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

Going On The Grid -- An Intro to Gridding & Spatial Interpolation

30 minutes

An overview of the basics needed to begin to exploring converting point data into raster or gridding format.

HDFView: Exploring HDF5 Files in the Free HDFview Tool

0.5 Hours

Explore HDF5 files and the groups and datasets contained within, using the free HDFview tool. See how HDF5 files can be structured and explore metadata. Explore both spatial and temporal data stored in HDF5!

Hierarchical Data Formats - What is HDF5?

0.25 - 0.5 Hours

An brief introduction to the Hierarchical Data Format 5 (HDF5) file/data model. Learn about how HDF5 is structured and the benefits of using HDF5.

Hyperspectral Variation Uncertainty Analysis in Python

0.5 hour

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