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.

Filter

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 in R's dplyr package. And combine these with grepl to select portions of character strings.

Introduction to HDF5 Files in R

1.0 - 1.5 Hours
Learn how to build a HDF5 file in R from scratch! Add groups, datasets and attributes. Read data out from the file.

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.

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.

Data Management using National Ecological Observatory Network’s (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis

In this lesson and accompanying teaching module, students use small mammal trapping data from the National Ecological Observatory Network to understand necessary steps of data management from field collected data to data analysis. Students explore this in the context of estimating small mammal population size using the Lincoln-Peterson model.

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