Using NEON’s eddy4R eddy-covariance software and data products | AGU 2017

This workshop further explores the open-source “eddy4R” packages in the R language for eddy-covariance statistical computing. eddy4R intends to provide raw-data processing capabilities that complement existing high-level functionalities of, for example, the “REddyProc” R package. Together they are wrapped into a reproducible, shareable and extensible eddy-covariance processing environment based on Docker containerization technology.

Workshop motivation and purpose

• To bring together eddy-covariance (EC) software and data product developers, users and technical working group members both internal and external to the National Ecological Observatory Network (NEON).
• To provide a tutorial for (prospective) eddy4R users on how to interact with NEON EC data and software.
• To provide community-feedback from (prospective) eddy4R users to eddy4R developers for incorporation into data-generating NEON code.

Registration

Please see the workshop event page for more information. 

Before attending the workshop, please read about the eddy4R package:

Metzger, S., Durden, D., Sturtevant, C., Luo, H., Pingintha-Durden, N., Sachs, T., Serafimovich, A., Hartmann, J., Li, J., Xu, K., and Desai, A. R.: eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5, Geosci. Model Dev., 10, 3189-3206, doi:10.5194/gmd-10-3189-2017, 2017.

Schedule

Location: Gardenia Room, Hilton Garden Inn

Date: 19:00-22:00 CST 2017-12-12

Instructors

• Stefan Metzger - National Ecological Observatory Network
• Ankur Desai - University of Wisconsin-Madison
• David Durden - National Ecological Observatory Network
• Natchaya Pingintha-Durden - National Ecological Observatory Network
• Cove Sturtevant- National Ecological Observatory Network
• Ke Xu - University of Wisconsin-Madison

If you have questions about the workshop materials, please contact one of the instructors.