Data Quality Assessment will be performed on all products. An automated assessment will identify errors that can be determined
from limits checks and mean values. A visual product inspection will identify anomalies that can be missed by limits or other
automated checks. An example of such an anomaly would be noise or miss-analyzed data.
Each science team algorithm is required to perform quality assurance (QA) and consistency checks and output the results for operator review and, if necessary, action. Standard QA output will consist of a text-based display of the results of limit and consistency checks and quicklook image files.
The operator provides feedback to the responsible algorithm developer via email (minor anomalies) or by phone for serious anomalies (program error or data exceeding pre-determined limits).