Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3189-2017
https://doi.org/10.5194/gmd-10-3189-2017
Model description paper
 | 
31 Aug 2017
Model description paper |  | 31 Aug 2017

eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

Stefan Metzger, David Durden, Cove Sturtevant, Hongyan Luo, Natchaya Pingintha-Durden, Torsten Sachs, Andrei Serafimovich, Jörg Hartmann, Jiahong Li, Ke Xu, and Ankur R. Desai

Viewed

Total article views: 8,888 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,796 2,829 263 8,888 189 246
  • HTML: 5,796
  • PDF: 2,829
  • XML: 263
  • Total: 8,888
  • BibTeX: 189
  • EndNote: 246
Views and downloads (calculated since 01 Feb 2017)
Cumulative views and downloads (calculated since 01 Feb 2017)

Viewed (geographical distribution)

Total article views: 8,888 (including HTML, PDF, and XML) Thereof 8,393 with geography defined and 495 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 03 Jul 2025
Download
Short summary
We apply the development and systems operations software development model to create the eddy4R–Docker open-source, flexible, and modular eddy-covariance data processing environment. Test applications to aircraft and tower data, as well as a software cross validation demonstrate its efficiency and consistency. Key improvements in accessibility, extensibility, and reproducibility build the foundation for deploying complex scientific algorithms in an effective and scalable manner.
Share