Data Quality: Yet Another Chapter in the Metadata Story

As our LTER site datasets flood in from the field, we Information Managers, as the 'humans-in-the-loop, ' remain continually challenged by the basic yet broad issue of standardization for data integration. Equally important for integration efforts as attribute-level metadata is standardization of data quality assurance (QA) and control (QC), necessary as similar datasets are brought together at sites and across the community. While data quality has been coming into its own as an issue independent of, although related to, metadata and data access, it remains largely unexplored and underdeveloped. At an international level, quality standardization issues have been discussed for years by organizations like the International Organization for Standards (ISO) and the Organization for Economic Cooperation and Development (OECD). Recently, the Digital Library movement has begun development of sensor network data assessment tools (Wallis et al.). However, there remain many unique data quality 'quirks ' associated with dynamic, local science that remain undocumented. To address these, a few grassroots communities have formed with the intent of creating community best practices and data quality standards, e.g. the LTER IM Data Quality Working Group. One more formal example of such an effort is Quality Assurance of Real-Time Oceanographic Data (QARTOD