CyVerse–NEON Airborne Observation Platform Workshop
November 19, 2021 | Virtual
The National Ecological Observatory Network (NEON) is changing the way we do science. The scope and scale of the NEON's remote sensing data from its Airborne Observation Platform (AOP) require larger data storage and compute than you may be used to working with on your laptop or desktop computer.
CyVerse, a research cyberinfrastructure for all life sciences, is by design capable of enabling data-intensive scientific discovery, providing services and resources for storing, analyzing, visualizing, and sharing your research using AOP data.
Join us for an intensive one-day, hands-on, remote workshop and learn how to create, use, deploy, and share analyses of NEON AOP data using the CyVerse platform.
You will learn how to use the NEON Data API and work with hands-on exercises using AOP remote sensing data in Project Juypter (Python) notebooks. We will also cover basic visualization of NEON AOP data in Google Earth Engine using the Python API. Basic understanding of scientific computer programming languages (e.g., Python, R) and command line tools (bash shell, and version control with GitHub) are useful but not required.
Click to register for this free workshop. You will need to login to your CyVerse account (or sign up for a free account if you don’t have one already). Once logged in, you may need to click the ‘Register’ button again to prompt the enrollment process. If you do not see the 'Register' button, click the calendar icon in the left sidebar and then click on the workshop.
Who should attend?
Any researcher seeking to work with NEON data and make computational analyses easier and more reproducible. Must enjoy a collaborative, innovative environment. Preference will be given to NSF BIO-funded PIs and early career researchers. Registration is limited to the first 50 enrollees.
What will I learn?
- How to download data using the NEON Data API
- How to work with AOP data in Python Notebooks
- How to visualize remote sensing data in Google Earth Engine through Python
- Course participants are encouraged to bring their own data and analytical workflows for use with the NEON data.
Recommended experience for this workshop
- Linux command line, shell commands
- Basic programming experience in Python or R
- Familiarity with GitHub version control
Research Assistant Professor of Geoinformatics, UAZ
Research Scientist, Remote Sensing, NEON