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dc.contributor.authorSkålvik, Astrid Marie
dc.contributor.authorSætre, Camilla
dc.contributor.authorFrøysa, Kjell Eivind
dc.contributor.authorBjørk, Ranveig Nygaard
dc.contributor.authorTengberg, Anders
dc.date.accessioned2023-11-16T07:47:36Z
dc.date.available2023-11-16T07:47:36Z
dc.date.created2023-05-16T10:49:11Z
dc.date.issued2023-04-06
dc.identifier.issn2296-7745
dc.identifier.urihttps://hdl.handle.net/11250/3102878
dc.description.abstractIn this paper we give an overview of factors and limitations impairing deep-sea sensor data, and we show how automatic tests can give sensors self-validation and self-diagnostic capabilities. This work is intended to lay a basis for sophisticated use of smart sensors in long-term autonomous operation in remote deep-sea locations. Deep-sea observation relies on data from sensors operating in remote, harsh environments which may affect sensor output if uncorrected. In addition to the environmental impact, sensors are subject to limitations regarding power, communication, and limitations on recalibration. To obtain long-term measurements of larger deep-sea areas, fixed platform sensors on the ocean floor may be deployed for several years. As for any observation systems, data collected by deep-sea observation equipment are of limited use if the quality or accuracy (closeness of agreement between the measurement and the true value) is not known. If data from a faulty sensor are used directly, this may result in an erroneous understanding of deep water conditions, or important changes or conditions may not be detected. Faulty sensor data may significantly weaken the overall quality of the combined data from several sensors or any derived model. This is particularly an issue for wireless sensor networks covering large areas, where the overall measurement performance of the network is highly dependent on the data quality from individual sensors. Existing quality control manuals and initiatives for best practice typically recommend a selection of (near) real-time automated checks. These are mostly limited to basic and straight forward verification of metadata and data format, and data value or transition checks against pre-defined thresholds. Delayed-mode inspection is often recommended before a final data quality stamp is assigned.en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleChallenges, limitations, and measurement strategies to ensure data quality in deep-sea sensorsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 the authorsen_US
dc.source.articlenumber1152236en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.3389/fmars.2023.1152236
dc.identifier.cristin2147773
dc.source.journalFrontiers in Marine Scienceen_US
dc.identifier.citationFrontiers in Marine Science. 2023, 10, 1152236.en_US
dc.source.volume10en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal