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Workshop data |
Available on FigShare: NEON Spatio-temporal Teaching Data Subset.
CITATION: Data Skills Teaching Data Subsets, NEON; Wasser, Leah; Jones, Megan A. (2016): NEON Spatio-temporal Teaching Data Subset. figshare. Fileset. https://doi.org/10.6084/m9.figshare.2009586.v10
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 our aquatic and terrestrial ecosystems are changing. The data used in these lessons cover two NEON field sites:
- Harvard Forest (HARV) - Massachusetts, USA - fieldsite description
- San Joaquin Experimental Range (SJER) - California, USA - fieldsite description
You can download all of the data used in this workshop by clicking
this download link.
Clicking the download link will automatically download all of the files to your default download directory 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 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 |
These vector data provide information on the site characterization and infrastructure at the National Ecological Observatory Network's Harvard Forest field site. The Harvard Forest shapefiles are from the Harvard Forest GIS & Map archives. US Country and State Boundary layers are from the US Census Bureau.
The data used in this lesson were collected at the National Ecological Observatory Network's Harvard Forest. These data are proxy data for what will be available for 30 years on the NEON data portal for the Harvard Forest and other field sites located across the United States.
This data was collected at the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range field sites.
Data are collected during peak greenness at each field site and are processed to provide useful data products to the community. The following NEON data products are used in these lessons:
Data Created from Discrete Return (point clouds) Lidar Data:
- DSM (Digital Surface Model; full mosaic)
- DTM (Digital Terrain Model; full mosaic)
- CHM (Canopy Height Model; full mosaic; Harvard Forest site only) and
- RGB imagery (Harvard Forest site only) derived from the RGB Camera
Additional information about airborne remote sensing data, including other data types for these and other sites can be found on NEON's Airborne Data page.
The imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range field sites. The imagery was created by the U.S. Geological Survey (USGS) using a multispectral scanner on a Landsat Satellite. The data files are Geographic Tagged Image-File Format (GeoTIFF).