NEON's Uncertainty Pipeline - Approach and Lessons Learned
Abstract
Understanding the causes and responses of ecological change at the continental scale is the goal of the National Ecological Observatory Network (NEON). To meet this goal, NEON integrated disciplines of science, engineering, metrology, cyber infrastructure and field operations and developed a standardized framework to provide traceability, transparency and reliability in the data. Here we will detail an aspect of this framework highlighting the integration of these disciplines: the uncertainty propagated from the truth used for the calibration, through the field measurements and data processing pipeline, to the final data product. Furthermore, we will describe approaches being taken to provide drift estimates on measurements, how drift ultimately affects the uncertainty of data products, and lessons learned. In NEON's design, uncertainty quantification provides a basis for meeting measurement requirements, delivers a level of confidence in the data, and informs automated QA/QC tests. With 47 terrestrial and 34 aquatic sites and 181 data products, the overarching philosophy relies on a standardized framework that accommodates a diverse and large volume of data while maintaining high quality.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMIN43C0920C
- Keywords:
-
- 0520 Data analysis: algorithms and implementation;
- COMPUTATIONAL GEOPHYSICSDE: 1904 Community standards;
- INFORMATICSDE: 1976 Software tools and services;
- INFORMATICSDE: 1978 Software re-use;
- INFORMATICS