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.
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 episodes 1-4, which focus on geospatial concepts and project management. Similarly, learners who have prior experience with the
Pythonprogramming language may wish to skip the Plotting and Programming in Python lesson.
To get started, follow the directions in the Setup tab to get access to the required software and data for this workshop.
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:
- Harvard Forest (HARV) - Massachusetts, USA - fieldsite description
- San Joaquin Experimental Range (SJER) - California, USA - fieldsite description
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 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
|Lesson Starting Points||Overview|
|Episode 1: Introduction to Raster Data||Understand data structures and common storage and transfer formats for spatial data. Start here if you want to understand fundamental geospatial concepts like coordinate reference systems, rasters, and vectors.|
|Plotting and Programming in Python||Import data into Python, calculate summary statistics, create publication-quality graphics. Start here if you have an understanding of geospatial concepts but want to learn Python fundamentals.|
|Episode 5: Intro to Raster Data in Python||Open, work with, and plot vector and raster-format spatial data in Python. Start here if you already have a good grasp of geospatial concepts and a working knowledge of Python.|