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ATTENTION This is an experimental test of The Carpentries Workbench lesson infrastructure. It was automatically converted from the source lesson via the lesson transition script.

If anything seems off, please contact Zhian Kamvar [email protected]

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain.

Interested in teaching these materials? We have an onboarding video available to prepare Instructors to teach these lessons. After watching this video, please contact [email protected] so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Geospatial workshops.

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Getting Started

Data Carpentry's teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow. To most effectively use these materials, please make sure to download the data and install everything before working through this lesson.

This workshop assumes no prior experience with the tools covered in the workshop. However, learners with prior experience working with geospatial data may be able to skip the Geospatial Project Organization and Management lesson. Similarly, learners who have prior experience with the R programming language may wish to skip the Introduction to R for Geospatial Data lesson.

To get started, follow the directions in the Setup tab to get access to the required software and data for this workshop.

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Data

The data and lessons in this workshop were originally developed through a hackathon funded by the National Ecological Observatory Network (NEON) - an NSF funded observatory in Boulder, Colorado - in collaboration with Data Carpentry, SESYNC and CYVERSE. NEON is collecting data for 30 years to help scientists understand how aquatic and terrestrial ecosystems are changing. The data used in these lessons cover two NEON field sites:

There are four data sets included, all of which are available on Figshare under a CC-BY license. You can download all of the data used in this workshop by clicking this download link. Clicking the download link will download all of the files as a single compressed (.zip) file. To expand this file, double click the folder icon in your file navigator application (for Macs, this is the Finder application).

These data files represent the teaching version of the data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught.

Dataset File name Description
Site layout shapefiles NEON-DS-Site-Layout-Files.zip A set of shapefiles for the NEON's Harvard Forest field site and US and (some) state boundary layers.
Meteorological data NEON-DS-Met-Time-Series.zip Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest site
Airborne remote sensing data NEON-DS-Airborne-RemoteSensing.zip LiDAR data collected by the NEON Airborne Observation Platform (AOP) and processed at NEON including a canopy height model, digital elevation model and digital surface model for NEON's Harvard Forest and San Joaquin Experimental Range field sites.
Landstat 7 NDVI raster data NEON-DS-Landsat-NDVI.zip 2011 NDVI data product derived from Landsat 7 and processed by USGS cropped to NEON's Harvard Forest and San Joaquin Experimental Range field sites

More information on this dataset

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Workshop Overview

Lesson Overview
Introduction to Geospatial Concepts Understand data structures and common storage and transfer formats for spatial data.
Introduction to R for Geospatial Data Import data into R, calculate summary statistics, and create publication-quality graphics.
Introduction to Geospatial Raster and Vector Data with R Open, work with, and plot vector and raster-format spatial data in R.