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

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

doi:https://doi.org/10.5194/gmd-10-3189-2017

Articles | Volume 10, issue 9

https://doi.org/10.5194/gmd-10-3189-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-10-3189-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 9

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

Model code and software

NEONScience/eddy4R: eddy4R-Docker 0.2.0 S. Metzger, D. Durden, C. Sturtevant, N. Pingintha-Durden, H. Luo, K. Xu, and A. Serafimovich https://w3id.org/smetzger/Metzger-et-al_2017_eddy4R-Docker/code/0.2.0

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.