Workshops

Materials related to workshops, courses and data institutes hosted by the NEON project are posted to this section of the website as they become available. We also host workshop materials for NEON-related courses and workshops presented by collaborating institutions. While these materials are designed to be used by participants during a workshop, many of the materials are structured as self-guided, do-it-yourself tutorials for people to review and work with if they are unable to attend an event.

If you are interested in collaborating on a workshop or would like us to host your NEON-related workshop materials, please contact us

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Explore NEON Workshop

November 08, 2018 - November 09, 2018
NEON
Workshop for graduate students to learn about NEON and explore ways to use NEON in their graduate research.

Access and Work with NEON Data

August 05, 2018
Ecological Society of America Annual Meeting
This workshop will provide an introduction to discovering, accessing and preparing a variety of NEON data for your research, primarily using R.

NEON Data Institute 2018: Remote Sensing with Reproducible Workflows using Python

July 09, 2018 - July 14, 2018
National Ecological Observatory Network
The 2018 Institute focuses on remote sensing of vegetation using open source tools to promote reproducible science. The primary computing language of this Institute is Python. This Institute will be held Boulder, CO 9-14 July 2018.

Using NEON Data and Teaching Materials with Your Students

June 19, 2018
BioQuest Summer Workshop: Wicked Problems
The standardized data collection and delivery methods for over 180 different data products from NEON allow you to use public OERs or build your own materials for your classroom knowing that the same data will be available next year.

Using Observation Networks to Advance Earth System Understanding: State of the Art, Data-Model Integration, and Frontiers

February 13, 2018 - February 15, 2018
Joint CZO, LTER, NEON, & ISMC Workshop
Through a community process, this workshop will (i) identify the current state-of-the-science to model physical, chemical and ecological processes at and below the Earth’s surface, its strengths, limitations and frontiers; (ii) advance data integration into future model frameworks across networks...

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