## 3 Automated Data Quality Control for Marine Sensors  
Despite a lack of data quality assessment in deployed near real-time marine sensors, there have been some notable exceptions. For example, the Argo float project has deployed over 3,000 profiling floats with satellite communications capabilities throughout the world's oceans, and could be regarded as one of the largest sensor networks in operation worldwide. A key aspect of the project is both automated and manual QC of the data collected by the floats [12]. In recent years, a series of workshops have also been held in the USA to address the quality assurance of real-time marine data. Whilst recommending quality assurance procedures to improve the raw data quality, the participants also recognized the importance of automated QC including range and gradient checks on data as well as routines to remove data spikes and to take into account the output of nearby sensors [13]. [PERSON] _et al._[14] describe their approach which also makes use of range and gradient checks on data. In all these cases, the outcome of the QC process is a data quality flag associated with each measurement; information on which quality checks have been passed or failed is often also made available. Whilst the provision of a data quality assessment is a significant improvement over its absence, it still leaves a challenge for the data user (who may not be a domain expert) in determining whether the data is fit for their purpose. For example, a swimmer or surfer looking at real-time water temperature data from local beaches may be content with uncertainties of 0.5 \({}^{\circ}\)C (0.9 \({}^{\circ}\)F) in the quoted data. But, can they rely on data flagged as \"probably good\"? In practice, the swimmer or surfer would need to consult a domain expert (impractical in most cases) to know whether data flagged \"probably good\" is fit for their purpose. In contrast, the work reported here seeks to quantify the uncertainty of individual measurements, effectively encoding domain expertise in the stored data themselves. In related work, [PERSON] _et al._[15] proposed abstract data types (ADT) and data structures for \"indexing\" noisy sensors. Data from the sensors are represented as probability density functions to take into account the uncertainty associated with each data point. However they do not generate the uncertainties but assume the measurement mean and standard deviation as inputs for their data types and algebra. A number of theoretical papers in the computer science literature have dealt with the challenge of dealing with incomplete and imprecise information. [PERSON] [16] provides a good review of this work in the research fields of databases and artificial intelligence. Of most relevance is the work of [PERSON] [17] who combines the use of possibility distributions to model uncertainty in the value of an attribute and fuzzy degrees of membership to model the degree of association between values. The work of [PERSON] _et al._[18] is the most closely related to the work presented here. They point out that \"Data quality control is based on both objective criteria and human experience\" and outline an automated data QC procedure for their coastal ocean monitoring network. A key difference in our approach is the use of fuzzy logic to result in a continuum in the data quality assessment and hence the ability to automatically assign error bars to data. This capability is crucial for data re-purposing, meaningful comparisons with models, prediction of sensor failure and scheduling of network maintenance. [PERSON] concludes that \"it would be both interesting and useful to study the imperfections in data that real systems come up against from the perspective of actually building such a real system rather than studying the problems of imperfect data in a theoretical vacuum.\" [PERSON] _et al._[19] demonstrate this maxim with a practical anomaly detection scheme for wireless networks and, in the present paper; we develop and apply our approach to data from the Tasmanian Marine Analysis Network.