Source:
National Ecological Observatory Network (NEON)
Below you will find a list of self-paced-data-tutorials that you can complete on your own time.
This tutorial provides an overview of finding spatial data to support a research question. It covers some of the key data sources, providers and places that one can use to find data.
This tutorial introduces the spatial data tool landscape. It covers the types of gui and non-gui tools that are available. The tutorial explores the benefits (and challenges) of using a non-gui (coding) approach to method development that supports clear documentation of methods.
This tutorial covers the basics of key data formats that may contain spatial information including shapefile, GeoTIFF and .csv. It also provides a brief overview of other formats that you may encounter when working with spatial data.
This tutorial covers what metadata are and why we need to work with metadata in the context of spatio-temporal data. It covers the three common metadata formats: text file format, web page format and Ecological Metadata Language (EML).
This lesson covers the key spatial attributes that are needed to work with spatial data including: Coordinate Reference Systems (CRS), Extent and spatial resolution.
This tutorial focuses on the Universal Trans Mercator (UTM) projected Coordinate Reference which divides the globe into zones to optimize projection results in each zone. It also briefly introduces the concept of a datum.
This lesson covers formats that CRS information may be in including proj4 and EPGS and how to work with them in R.
This tutorial covers spatial data cleaning - specifically dealing with missing (NA / NAN) values and bad values when working with spatial data in R.
This lesson covers the key spatial attributes that are needed to work with spatial data including: Coordinate Reference Systems (CRS), Extent and spatial resolution.
This lesson covers the key packages that support working with spatial data in R.
