Data Quality Verification at STScI - Automated Assessment and Your Data

Details Data Quality Verification at STScI - Automated Assessment and Your Data Data Quality Verification at STScI - Automated Assessment and Your Data

Dec 1996

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996aas...189.4303d&link_type=abstract

American Astronomical Society, 189th AAS Meeting, #43.03; Bulletin of the American Astronomical Society, Vol. 28, p.1325

Astronomy and Astrophysics

Astronomy

Scientific paper

As satellite based observatories improve their ability to deliver wider varieties and more complex types of scientific data, so to does the process of analyzing and reducing these data. It becomes correspondingly imperative that Guest Observers or Archival Researchers have access to an accurate, consistent, and easily understandable summary of the quality of their data. Previously, at the STScI, an astronomer would display and examine the quality and scientific usefulness of every single observation obtained with HST. Recently, this process has undergone a major reorganization at the Institute. A major part of the new process is that the majority of data are assessed automatically with little or no human intervention. As part of routine processing in the OSS--PODPS Unified System (OPUS), the Observatory Monitoring System (OMS) observation logs, the science processing trailer file (also known as the TRL file), and the science data headers are inspected by an automated tool, AUTO_DQ. AUTO_DQ then determines if any anomalous events occurred during the observation or through processing and calibration of the data that affects the procedural quality of the data. The results are placed directly into the Procedural Data Quality (PDQ) file as a string of predefined data quality keywords and comments. These in turn are used by the Contact Scientist (CS) to check the scientific usefulness of the observations. In this manner, the telemetry stream is checked for known problems such as losses of lock, re-centerings, or degraded guiding, for example, while missing data or calibration errors are also easily flagged. If the problem is serious, the data are then queued for manual inspection by an astronomer. The success of every target acquisition is verified manually. If serious failures are confirmed, the PI and the scheduling staff are notified so that options concerning rescheduling the observations can be explored.

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