Data QA/QC assessment identifies potential data quality issues. It is a secondary quality assessment that complements the primary check performed by the site team. Data QA/QC follows the methodology adopted for processing the FLUXNET2015 dataset (Pastorello et al. 2014, 2020) and includes additional checks based on data user feedback. AMP sends assessment results in a Data QA/QC Report to the site team.
After passing Format QA/QC, uploaded files are combined with, if any, previously published BASE file (1 in figure below). The automated Data QA/QC codes generate statistics and figures that AMP reviews (2). If the data Pass Data QA/QC, AMP notifies the site team of any corrections needed (3). Otherwise the data are queued for BASE Generation and bundled with BADM for publication as the BASE-BADM data product (4).
QA/QC Test Modules
Data QA/QC assesses units and sign conventions, timestamp alignments, trends, step changes, outliers based on site-specific historical ranges, multivariate comparisons, diurnal/seasonal patterns, USTAR (i.e., friction velocity) filtering, and variable availability. Read more details for the test modules:
During Data QA/QC, AMP synthesizes the identified issues into a concise and actionable report. The Data QA/QC Summary briefly explains the Data QA/QC results. Data issues and Explanatory Figures detail the identified issues and potential solutions. All generated figures and the Format QA/QC Report associated with the data are provided in Additional Links. AMP emails the Data QA/QC Report to the site team for clarification or correction.
Pastorello, G., et al. (2014), Observational data patterns for time series data quality assessment, paper presented at e-Science (e-Science), 2014 IEEE 10th International Conference on e-Science, Sao Paulo DOI:10.1109/eScience.2014.45
Pastorello, G., et al. (2020), The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7(1), 225, DOI:10.1038/s41597-020-0534-3