Abstract
Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs, and is available as a numerical quality score that enables uniform comparison of datasets, and increases community’s trust in derived data.
Users
Please
log in to take part in the discussion (add own reviews or comments).