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As standards in best practices in data quality assurance and quality control evolve, methods for discovery and transport of information relating to these practices must also be developed. An observation's history, from sensor descriptions, processing methods, parameters and quality control tests to data quality flags and sensor alert flags, must be accessible through standards-based web services to enable machine-to-machine interoperability. This capability enables a common understanding and thus an underlying trust in the expanding world of ocean observing systems. For example, a coastal observatory conducts several tests to evaluate and improve the quality of in situ time series data (e.g. velocity) and then generate an oceanic property (e.g. wave height). Using content-rich webenabled services, a data aggregation center will be able to determine which tests were conducted, interpret data quality flags and provide value added services, such as comparing the parameter with those from near-by observations. These additional processing steps may also be documented and sent along with the data to other participating ocean observing systems throughout the world. By utilizing standards-based protocol (Open Geospatial Consortium (OGC) frameworks) and welldefined community adopted QA/QC (Quality Assurance/Quality Control) tests and best-practices (Quality Assurance in Real-Time Oceanographic Data -QARTOD), information about the system provenance, sensor and data processing history needn't be lost.Are data providers ready, willing and able to describe sensors and processing history? And can we transport the information using a framework that offers semantic and syntactic interoperability? The group developing this community white paper has demonstrated that it can be and is being done. A project called Q2O, QARTOD to OGC (Open Geospatial Consortium), bridges the QARTOD community with the OGC community to demonstrate and document best practices in the implementation of QA/QC within the OGC Sensor Web Enablement (SWE) framework. This paper describes this demonstration project and documents the existence of parallel related efforts. With adequate funding to enable the strengthening and broadening of these communities, a solid foundation for ocean observing systems will be built with the assurance that best-practices of data quality are communicated in a meaningful way.

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Sensor technologies and capabilities have an effect on observational data quality. Typically, data management begins, at best, when a data manager obtains the data and needs to describe it sufficiently to data consumers. Often, the sensing methods are not adequately described and the data manager does not know the appropriate questions to ask or where to direct questions about sensors, their configuration, and the deployment. Consequently, knowledge often remains buried in sensor manuals and field operator logs. Thus, most metadata requirements have been simplified to accommodate this gap in knowledge. When information is captured where it is best understood and tools are created to easily capture this knowledge, machine-actionable descriptions can be provided to adequately describe the processes taken in generating observations. The information can be associated with the data and thus be accessible, discoverable and used in data quality control by data providers and in data quality assessment by the data consumers. Here, we define actors and actions to promote role-based creation of fully-described, standards-based documents. These documents can be created in SensorML (OGC SWE) that includes links to resolvable term definitions (W3C Semantic Web), enabling the creation of associated mappings and ontologies to extend and resolve the meaning of each term.

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