Integrating Quality Assurance and Quality Control into Open GeoSpatial Consortion Sensor Web Enablement

Abstract

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.

Q-SOS—A Sensor Observation Service for Accessing Quality Descriptions of Environmental Data

“…A related study in this direction is that of [31], who developed informal quality flag mappings between 15 widely-used flag standards in the oceanographic domain. Another work is by [11] , who suggested the use of ontology to link quality tests of marine data between different authorities.…”

“…The similarity between Q2O and our approach lies in the fact that both approaches have used the OGC's SWE specifications to capture information about data quality assessment. Q2O focuses on representing sensor components, processing chains and quality tests applied to in-situ oceanographic data through SensorML documents [11,27]. Our data model captures these aspects as well as other contextual information required for assessing the data (see Section 4.1).…”

“…For example, some approaches [7][8][9] primarily address quality measures and the uncertainties of observation data. Others [10][11][12] partially capture data assessment descriptions at a very general level or are limited to specific sensing applications. For more examples, see Section 2.3.…”

Developing Coordinated Communities of Autonomous Gliders for Sampling Coastal Ecosystems

“…As the number of gliders sensors increase in oceanography, establishing solid quality assurance and quality control procedures for sensors and data collected from observatory platforms, including gliders, is essential (Fredericks et al, 2009). This includes the production of standard operating procedures (SOPs), quality assurance procedures such as a Quality Assurance Project Plan (QAPP) and Quality Assurance in Real Time Oceanographic Data (QARTOD), validating data, applying quality control in real time, and developing a data analysis/data management system for future glider monitoring.…”

Ocean Data Dissemination: New Challenges for Data Integration

“…Another suite of standards (Sensor Web Enablement or SWE) defines a Sensor Observation Service (SOS) for sharing sensor data, a Sensor Planning Service for controlling or tasking sensors, Sensor Model Language (SensorML, based on XML) for describing sensors and processing (e.g. QC/QA tests [36]) surrounding the observation, and the Observations and Measurement schema for encoding the observation content and values. SWE not only encodes information about the sensor and sensor system, it also provides a framework for defining and/or describing processing components.…”