{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:37:17Z","timestamp":1760243837879,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2011,10,11]],"date-time":"2011-10-11T00:00:00Z","timestamp":1318291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance\/Quality Control (QA\/QC) is necessary to ensure that the data collected is fit for purpose. Current automated QA\/QC approaches provide assessments based upon hard classifications of the gathered data; often as a binary decision of good or bad data that fails to quantify our confidence in the data for use in different applications. We propose a novel framework for automated data quality assessments that uses Fuzzy Logic to provide a continuous scale of data quality. This continuous quality scale is then used to compute error bars upon the data, which quantify the data uncertainty and provide a more meaningful measure of the data\u2019s fitness for purpose in a particular application compared with hard quality classifications. The design principles of the framework are presented and enable both data statistics and expert knowledge to be incorporated into the uncertainty assessment. We have implemented and tested the framework upon a real time platform of temperature and conductivity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. Results indicate that the error bars generated from the Fuzzy QA\/QC implementation are in good agreement with the error bars manually encoded by a domain expert.<\/jats:p>","DOI":"10.3390\/s111009589","type":"journal-article","created":{"date-parts":[[2011,10,11]],"date-time":"2011-10-11T12:52:32Z","timestamp":1318337552000},"page":"9589-9602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Automated Data Quality Assessment of Marine Sensors"],"prefix":"10.3390","volume":"11","author":[{"given":"Greg P.","family":"Timms","sequence":"first","affiliation":[{"name":"Tasmanian Information and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Hobart, TAS 7001, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0091-8925","authenticated-orcid":false,"suffix":"Jr.","given":"Paulo A.","family":"De Souza","sequence":"additional","affiliation":[{"name":"Tasmanian Information and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Hobart, TAS 7001, Australia"}]},{"given":"Leon","family":"Reznik","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rochester Institute of Technology, Rochester, NY 4623-5608, USA"}]},{"given":"Daniel V.","family":"Smith","sequence":"additional","affiliation":[{"name":"Tasmanian Information and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Hobart, TAS 7001, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2011,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1126\/science.1178927","article-title":"Research data in the digital age","volume":"325","author":"Kleppner","year":"2009","journal-title":"Science"},{"key":"ref_2","unstructured":"Committee on Science, Engineering, and Public Policy (2009). Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, National Academy of Sciences. Report of the Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age."},{"key":"ref_3","unstructured":"Potter, RW (2000). The Art of Measurement: Theory and Practice, Upper Saddle River, NJ, USA."},{"key":"ref_4","unstructured":"International Organization for Standardization (1995). Guide to the Expression of Uncertainty in Measurement, ISO."},{"key":"ref_5","unstructured":"National Conference of Standards Laboratories US Guide to the Expression of Uncertainty in Measurement, American National Standards Institute\/NCSL International. ANSI\/NCSL Z540-2-1997."},{"key":"ref_6","unstructured":"Mark, RJ, and Mark, RJ (1994). Fuzzy Logic Technology and Applications, IEEE Press. IEEE Technology Update Series."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S0263-2241(03)00021-6","article-title":"Fuzzy approach to the theory of measurement inexactness","volume":"34","author":"Urbanski","year":"2003","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TIM.2003.815993","article-title":"An innovative approach to the determination of uncertainty in measurements based on fuzzy variables","volume":"52","author":"Ferrero","year":"2003","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"ref_9","unstructured":"Abebe, AJ, Guinot, V, and Solomatine, DP (2000, January 23\u201327). Fuzzy Alpha-Cut vs. Monte Carlo Techniques in Assessing Uncertainty in Model Parameters. Iowa City, IA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/3516.928733","article-title":"Multisensor integration and fusion model that uses a fuzzy inference system","volume":"6","author":"Mahajan","year":"2001","journal-title":"IEEE\/ASME Trans. Mechatron"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1109\/19.893256","article-title":"Fuzzy modeling of measurement data acquired from physical sensors","volume":"49","author":"Mauris","year":"2000","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"ref_12","unstructured":"Wong, A, Keeley, R, and Carval, T Available online: http:\/\/www.argodatamgt.org\/content\/download\/341\/2650\/file\/argo-quality-control-manual.pdf (accessed February 2010)."},{"key":"ref_13","unstructured":"(2006, January 21\u201323). Quality Assurance of Real-Time Oceanographic Data, Final Report. Woods Hole Oceanographic Institution, Woods Hole, MA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Koziana, JV, Olson, J, Anselmo, T, and Lu, W (2008, January 15\u201318). Automated Data Quality Assurance for Marine Observations. Quebec City, QC, Canada.","DOI":"10.1109\/OCEANS.2008.5151904"},{"key":"ref_15","unstructured":"Faradjian, A, Gehrke, JE, and Bonnet, P (March, January 26). GADT: A Probability Space ADT for Representing and Querying the Physical World. San Jose, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/69.506705","article-title":"Current approaches to handling imperfect information in data and knowledge bases","volume":"8","author":"Parsons","year":"1996","journal-title":"Knowl. Data Eng"},{"key":"ref_17","unstructured":"Sanchez, E (1984). Fuzzy Information, Knowledge Representation and Decision Analysis, Pergamon Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.oceaneng.2006.01.011","article-title":"Data quality check procedures of an operational coastal ocean monitoring network","volume":"34","author":"Doong","year":"2007","journal-title":"Ocean Eng"},{"key":"ref_19","unstructured":"Bettencourt, LMA, Hagberg, AA, and Larkey, LB (2007, January 18\u201320). Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor Networks. Santa Fe, NM, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Timms, GP, McCulloch, JW, McCarthy, P, Howell, B, de Souza, PA, Dunbabin, MD, and Hartmann, K (2009, January 11\u201314). The Tasmanian Marine Analysis Network (TasMAN). Bremen, Germany.","DOI":"10.1109\/OCEANSE.2009.5278177"},{"key":"ref_21","unstructured":"Green, G, and Coughanowr, C (2003). State of the Derwent Estuary 2003: A Review of Pollution Sources, Loads and Environmental Quality Data from 1997\u20132003, Derwent Estuary Program, Department of Primary Industries, Water and the Environment."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/10\/9589\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:57:37Z","timestamp":1760219857000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/10\/9589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,10,11]]},"references-count":21,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2011,10]]}},"alternative-id":["s111009589"],"URL":"https:\/\/doi.org\/10.3390\/s111009589","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2011,10,11]]}}}