Workshop: Using NEON’s eddy4R eddy-covariance software and data products

eddy4R-Docker demo/workshop details

This workshop will take place at the Garden Hilton Inn near the AGU 2017 meeting from 7pm - 10pm on Dec 12, 2017. NEON has begun to add to the family of eddy-covariance software a set of open-source “eddy4R” packages in the R language for statistical computing. eddy4R intends to provide raw-data processing capabilities that complement existing high-level functionalities of e.g. the “REddyProc” R-package. Together they are wrapped into a reproducible, shareable and extensible eddy-covariance processing environment based on Docker containerization technology.

In short: on the basis of pre-provided templates an eddy-4R Docker user can easily construct end-to-end workflows from raw data to hypothesis testing in single processing environment. In case this sounds interesting, you can find more information here:

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.

For accessing, installing and operating the processing environment please see section 2.6 “Installation and operation” (page 8) and the “Code and data availability” section (page 16). Please be aware that out of six eddy4R packages in development this release includes only the first two packages (basic statistical and QA/QC functionalities). More information on the complete range of capabilities can be found in section 2.1 “The eddy4R family of R packages” (page 4).

For comments, questions, or in case you are interested in contributing to eddy4R-Docker, please feel free to write us at eddy4R.info@gmail.com.

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

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.

The eddy4R-Docker demo/workshop during AGU in New Orleans is full. Registration is closed at this time.

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.