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Добірка наукової літератури з теми "Quality control"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 5 жовтня 2024

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Статті в журналах з теми "Quality control"

1

Hron, J., and T. Macák. "Control of food products’ quality." Agricultural Economics (Zemědělská ekonomika) 55, No. 5 (June 1, 2009): 221–28. http://dx.doi.org/10.17221/28/2009-agricecon.

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The paper deals with one of the significant parts of the concept related to controlling production quality, which is the analysis of production quality stability done for the following production procedure regulation aimed at the food manufacturing industry. The proposed methodology (respecting the change of location and variability of the food quality characteristics) will be introduced on the milk quality regulation. Also, there is the frequent problem solved out here in the report that relates to food quality measuring – their self-regulation by using the statistical calculation of self-regulation higher classes’ coefficients and also using the functional statement taken from the differential description.

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2

Mazheyka, Olexsandr. "Improved quality control of welded joints." Collected Works of Kirovohrad National Technical University. Machinery in Agricultural Production, Industry Machine Building, Automation, no. 31 (2018): 90–95. http://dx.doi.org/10.32515/2409-9392.2018.31.90-95.

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3

Nassivera, E., and L. Nardin. "Quality control programme in mammography: second level quality controls." British Journal of Radiology 70, no. 834 (June 1997): 612–18. http://dx.doi.org/10.1259/bjr.70.834.9227255.

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4

Sutton, George W. "Quality Control." AIAA Journal 30, no. 1 (January 1992): 1–2. http://dx.doi.org/10.2514/3.59973.

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5

Trammell, Susan. "Quality Control." CFA Institute Magazine 25, no. 2 (March 2014): 29–33. http://dx.doi.org/10.2469/cfm.v25.n2.10.

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6

Ebsworth, Patricia. "Quality control." Nursing Standard 16, no. 15 (December 12, 2001): 24. http://dx.doi.org/10.7748/ns.16.15.24.s35.

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7

Eraut, Michael. "Quality Control." Management in Education 1, no. 3 (September 1987): 27–28. http://dx.doi.org/10.1177/089202068700100314.

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8

Singer, Jane B. "QUALITY CONTROL." Journalism Practice 4, no. 2 (April 2010): 127–42. http://dx.doi.org/10.1080/17512780903391979.

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9

Kell, Douglas. "Quality control." Physics World 7, no. 10 (October 1994): 21. http://dx.doi.org/10.1088/2058-7058/7/10/21.

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10

Smallridge, Rachel. "Quality control." Nature Reviews Molecular Cell Biology 6, no. 8 (July 15, 2005): 592. http://dx.doi.org/10.1038/nrm1707.

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Дисертації з теми "Quality control"

1

Jin, Ye. "Quality control of phytopharmaceuticals : assessment and quality control of traditional Chinese medicine." Thesis, Liverpool John Moores University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327675.

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2

Bush, Helen Meyers. "Nonparametric multivariate quality control." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/25571.

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3

PICCOLI, FLAVIO. "Visual Anomaly Detection For Automatic Quality Control." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241219.

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Il controllo di qualità automatico nei processi di produzione è uno degli elementi chiave della quarta rivoluzione industriale che porterà alla creazione della cosiddetta industria 4.0. In questo contesto, un elemento fondamentale è il rilievo di difetti, anomalie o guasti del prodotto in tempo compatibile con quello di produzione. Questa tesi si focalizza esattamente su questo tema: il rilevamento delle anomalie per il controllo automatico di qualità, attraverso l'analisi di immagini raffiguranti il prodotto sotto ispezione. Questa analisi verrà fatta tramite l'utilizzo di tecniche di machine learning, in particolare tramite l'uso di reti neurali convoluzionali (CNN) che sono uno strumento molto potente utilizzato nell'analisi di immagini. In primo luogo questa tesi esegue uno studio estensivo sull'argomento per introdurre il lettore e propone una una sequenza di elaborazioni per il rilevamento automatico di anomalie. Le elaborazioni sono: 1) il miglioramento delle immagini per evidenziare i difetti; 2) il rilievo delle anomalie. La prima elaborazione viene risolta utilizzando una trasformazione colore globale in grado di rimuovere effetti di luce indesiderati ed aumentare il contrasto. Questa trasformazione è ottenuta grazie all'utilizzo di SpliNet, un metodo basato su CNN che viene presentato in questa tesi, che è in grado di migliorare le immagini di input inferendo i parametri di un insieme di spline. La seconda elaborazione, e cioè il rilievo di anomalie, è stata affrontata proponendo due diversi metodi. Il primo ha l'obiettivo di modellare la normalità imparando un dizionario ed utilizzandolo in fase di test per determinare il grado di abnormalità di una immagine incognita. Questo metodo è basato su CNNs, che notoriamente richiedono grandi quantità di dati per essere addestrate. Tuttavia l'algoritmo proposto è in grado di lavorare su un insieme di immagini di addestramento molto piccolo (nell'ordine delle cinque immagini). Il metodo presentato aumenta le performances rispetto allo stato dell'arte relativo al dataset delle nanofibre acquisite con microscopio SEM del 5%, ottenendo un'area sottesa alla curva di 97.4%. Il secondo metodo proposto usa un insieme di trasformazioni locali per restaurare le immagini di input. Specificamente, queste trasformazioni sono un insieme di polinomi di grado due, i cui parametri vengono determinati attraverso l'utilizzo di una rete neurale convoluzionale. Il metodo è progettato in maniera tale che è possibile, attraverso un parametro, modulare l'accuratezza e il tempo di calcolo in maniera tale da soddisfare le esigenze dell'utente finale. Per affrontare la mancanza di dati che affligge il campo del rilievo automatico di anomalie, è stato presentato un metodo innovativo di aumento dei dati basato su deep learning. Questo metodo è in grado di generare migliaia di nuovi campioni sintetici a partire da pochi dati reali e pertanto è particolarmente adatto per aumentare dataset di tipo long-tail. La qualità dei campioni sintetizzati è stata dimostrata misurando l'accuratezza delle performance di algoritmi di machine learning addestrati sul dataset aumentato. Questo metodo è stato utilizzato per espandere un dataset di immagini raffiguranti asfalti difettosi. In questo contesto, l'utilizzo del dataset aumentato ha portato ad un incremento delle performance medie sulla segmentazione di anomalie fino a 17.5 punti percentuali. Nel caso di classi aventi bassa cardinalità, l'aumento arriva fino a 54.5 punti percentuali. Tutti i metodi presentati sovraperformano lo stato dell'arte.
Automatic quality control is one of the key ingredients for the fourth industrial revolution that will lead to the development of the so called industry 4.0. In this context, a crucial element is a production-compatible-time detection of defects, anomalies or product failures. This thesis focuses exactly on this theme: anomaly detection for industrial quality inspection, ensured through the analysis of images depicting the product under inspection. This analysis will be done through the use of machine learning, and especially through the use of convolutional neural networks (CNNs), a powerful instrument used in image analysis. This thesis starts with an extensive study on the subject to introduce the reader and to propose a pipeline for automatic anomaly detection. This pipeline is composed by two steps: 1) the enhancement of the input images for highlighting defects; 2) the detection of the anomalies. The first step is addressed with the use of a global color transformation able to remove undesired light effects and to enhance the contrast. This transformation is inferred through the use of SpliNet, a new CNN-based method here presented, that is able to enhance the input images by inferring the parameters of a set of splines. In the context of anomaly detection, two methods are presented. The first one has the aim of modeling normality by learning a dictionary and using it in test time to determine the degree of abnormality of an inquiry image. This method is based on deep learning, which is known to be data-hungry. However, the proposed algorithm is able to work also on very small trainsets (in the order of five images). The presented method boosts the performances of 5% with respect to the state-of-the art for the SEM-acquired nanofibers dataset, achieving an area under curve of 97.4%. The second proposed algorithm is a generative method able to restore the input, creating an anomaly-free version of the inquiry image. This method uses a set of local transforms to restore the input images. Specifically, these transforms are sets of polynomials of degree two, whose parameters are determined through the use of a convolutional neural network. In this context, the method can be tuned with a parameter toward accuracy or speed, for matching the needs of the final user. To address the lack of data that is suffered in this field, a totally new method for data augmentation based on deep learning is presented. This method is able to generate thousands of new synthesized samples starting from a few and thus is particularly suitable for augmenting long-tail datasets. The quality of the synthesized samples is demonstrated by showing the increase in performance of machine learning algorithms trained on the augmented dataset. This method has been employed to enlarge a dataset of defective asphalts. In this context, the use of the augmented dataset permitted to increase the average performance on anomaly segmentation of up to 17.5 percentage points. In the case of classes having a low cardinality, the improvement is up to 54.5 percentage points. For all the methods here presented I show their effectiveness by analyzing the results with the respective state-of-the-art and show their ability in outperforming the existing methods.

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4

Sepúlveda, Ariel. "The Minimax control chart for multivariate quality control." Diss., Virginia Tech, 1996. http://hdl.handle.net/10919/30230.

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5

Kenerson, Jonathan E. "Quality Assurance and Quality Control Methods for Resin Infusion." Fogler Library, University of Maine, 2010. http://www.library.umaine.edu/theses/pdf/KenersonJE2010.pdf.

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6

Crossman, S. H. "Quality control in developing epithelia." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10044689/.

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In the fruit-fly Drosophila melanogaster, extensive apoptosis is observed throughout the embryonic epidermis upon the mutation of many essential patterning genes. The molecular basis of cell elimination in this context is poorly understood, although previous studies have suggested the existence of a cell-autonomous quality control mechanism, which detects cells unable to adopt an appropriate terminal fate and removes them through apoptosis. This hypothetical system is thought to protect against patterning errors in order to preserve the integrity of the developing epidermis. To identify factors required for apoptosis in mis-patterned cells, I performed a targeted genetic screen, which identified a potential role for the EGFR signalling pathway in this process. Excess EGFR signalling was shown to rescue the cell death phenotype of the archetypal patterning mutant fushi tarazu (ftz), whilst EGFR null alleles triggered extensive epidermal apoptosis. Upon further experimentation, I was able to show that patterning mutant embryos fail to express the major EGFR activating ligands in the correct spatial pattern. This causes local troughs in EGFR signalling, which trigger transcriptional upregulation of the pro-apoptotic gene hid and subsequent cell death. These results argue against a cell-autonomous mechanism of cell elimination in mis-patterned embryos and instead suggest that the tissue-wide landscape of EGFR activity is responsible for coordinating cell fate and cell survival in the embryonic epidermis. Building on these observations, I have been able to show that the EGFR pathway also regulates apoptosis during normal development, where it specifies the maximum dimensions of embryonic segments. Taken together, these findings provide a novel link between early patterning events, cell viability and compartment size in the developing Drosophila embryo.

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7

Lennard, Nicola S. "Quality control for carotid endarterectomy." Thesis, University of Leicester, 2004. http://hdl.handle.net/2381/29469.

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The aims of this study are to assess whether the introduction of a rigorous quality control method could produce a sustained reduction in the intraoperative stroke rate in this unit and whether it was feasible and practical to implement such a programme. The second part of this study will assess the incidence of sustained embolisation in the early post-operative period and investigate whether the antiplatelet agent Dextran 40 can help stop this embolisation, potentially preventing carotid artery thrombosis.;A prospective audit of all patients undergoing carotid endarterectomy was performed. The ability to monitor intraoperatively with TCD and perform completion angioscopy was assessed, as was the impact that these quality control techniques had on influencing the surgery. Patients were monitored postoperatively with TCD and any patient who developed sustained embolisation was commenced on an infusion of Dextran 40.;91% had continuous intraoperative TCD monitoring and 94% underwent successful completion angioscopy, a technical error was identified in 5% of angioscopic assessments. The intraoperative stroke rate was 0% during this study. Postoperative monitoring revealed that 5% of patients develop significant embolisation following CEA, Dextran 40 appeared to stop this embolisation. The overall 30-day stroke or death rate following CEA has fallen from 6% prior to 1992 to 2.2% in 1998.;It is possible to implement a quality control programme for CEA and this has been associated with a fall in the overall 30-day death and any stroke rate.

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8

Hughes, Anthony. "Quality control in radionuclide imaging." Thesis, University of Aberdeen, 1990. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU601994.

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9

Gordon, Kara Leigh. "TorsinA and protein quality control." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2708.

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DYT1 dystonia (DYT1) is a disabling inherited neurological disorder with juvenile onset. The genetic mutation in DYT1 leads to the deletion of a glutamic acid (E) residue in the protein torsinA. The function of torsinA and how the mutation leads to DYT1 is poorly understood. We hypothesize that how efficiently the disease-linked mutant protein is cleared may be critical for DYT1 pathogenesis. Therefore we explored mechanisms of torsinA catabolism, employing biochemical, cellular, and animal-based approaches. We asked if torsinA(wt) and torsinA(DE) are degraded preferentially through different catabolic mechanisms, specifically the ubiquitin proteasome pathway (UPP) and autophagy. We determined that torsinA(wt) is cleared by autophagy while torsinA(DE) is efficiently degraded by the UPP suggesting degradation processes can modulate torsinA(DE) levels. Proteins implicated in recognizing motifs on torsinA(DE) for targeting to the UPP represent candidate proteins that may modify DYT1 pathogenesis. We examined how removal of the hydrophobic domain and mutation of glycosylated asparagine residues on torsinA altered stability and catabolic mechanism. We found the glycosylation sites on torsinA are important for stability modulate its degradation through the UPP. F-box G-domain protein 1 (FBG1) has been implicated in degradation of glycosylated ER proteins. We hypothesized that FBG1 would promote torsinA degradation and demonstrated that FBG1 modulates levels of torsinA in a non-canonical manner through the UPP and autophagy. We examined if lack of FBG1 in a torsinA(DE) mouse model altered motor phenotypes. We saw no effect which suggests FBG1 does not alter DYT1 pathogenesis despite its promotion of torsinA(DE) degradation. In addition, we explored a potential mechanism for the previously described role of torsinA in modulating cytoplasmic protein aggregation. We hypothesized this endoplasmic reticulum (ER) resident protein would indirectly alter cytoplasmic protein aggregation through modulation of ER stress. We employed a poly-glutamine expanded repeat protein and pharmacological ER stressors to determine that torsinA does not alter poly-glutamine protein aggregation nor ER stress in a mammalian system. In summary, this thesis suggests proteins involved in the catabolism of torsinA(DE) may modify DYT1 pathogenesis and that torsinA and its DYT1-linked mutant are model proteins for investigating ER protein degradation by the UPP and autophagy.

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10

Cassady, Charles Richard. "Statistical quality control techniques using multilevel discrete product quality measures." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-151120/.

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Книги з теми "Quality control"

1

S, Stephens Kenneth, and Godfrey A. Blanton, eds. Modern methods for quality control and improvement. 2nd ed. New York: Wiley, 2002.

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2

Wadsworth, Harrison M. Modern methods for quality control and improvement. New York: Wiley, 1986.

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3

Wadsworth, Harrison M. Modern methods for quality control and improvement. New York: Wiley, 1986.

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4

Feigenbaum, A. V. Total quality control. 3rd ed. New York: McGraw-Hill, 1991.

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5

Evans, James R. The management and control of quality. St. Paul, MN: West Pub. Co., 1989.

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6

M, Lindsay William, ed. The management and control of quality. 3rd ed. Minneapolis/St. Paul: West Pub. Co., 1996.

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7

R, Evans James. The management and control of quality. 4th ed. Cincinnati, Ohio: South-Western College Pub., 1999.

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8

R, Evans James. The management and control of quality. 4th ed. St. Paul, MN: South-Western College, 1999.

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9

M, Lindsay William, ed. The management and control of quality. 5th ed. Australia: South-Western, 2002.

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10

M, Lindsay William, ed. The management and control of quality. 2nd ed. Minneapolis: West Pub. Co., 1993.

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Частини книг з теми "Quality control"

1

Jaeger, Richard F. "Control Quality Control." In A Collection of Papers Presented at the 1978, 1979, and 1980 Meetings of the Materials & Equipment/Whitewares: Ceramic Engineering and Science Proceedings, Volume 1, Issue 9/10, 815–17. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470291047.ch24.

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2

Minifie, Bernard W. "Quality Control." In Chocolate, Cocoa, and Confectionery, 771–805. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-5745-0_23.

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3

Wagner, Stefan. "Quality Control." In Software Product Quality Control, 111–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38571-1_4.

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4

Buxbaum, Engelbert. "Quality Control." In Biophysical Chemistry of Proteins, 427–34. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7251-4_47.

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5

Heath, Henry B., and Gary Reineccius. "Quality Control." In Flavor Chemistry and Technology, 406–29. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-09350-2_14.

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6

Grous, Ammar. "Quality Control." In Fracture Mechanics 3, 1–86. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118580158.ch1.

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7

Peters, William S. "Quality Control." In Springer Texts in Statistics, 151–62. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4638-1_16.

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8

Nahler, Gerhard. "quality control." In Dictionary of Pharmaceutical Medicine, 151–52. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1164.

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9

Li, Guoliang, Jiannan Wang, Yudian Zheng, Ju Fan, and Michael J. Franklin. "Quality Control." In Crowdsourced Data Management, 21–44. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7847-7_3.

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10

Black, Rod. "Quality Control." In Design and Manufacture, 174–83. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13429-8_11.

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Тези доповідей конференцій з теми "Quality control"

1

Forlizzi, Jodi, Carl DiSalvo, Jeffrey Bardzell, Ilpo Koskinen, and Stephan Wensveen. "Quality control." In the 2011 annual conference extended abstracts. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1979742.1979497.

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2

Hillman, Tomos, and Vincent Lizzi. "Self-generating quality control." In Declarative Amsterdam. Amsterdam: John Benjamins, 2020. http://dx.doi.org/10.1075/da.2020.hillman-lizzy.quality-control.

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This paper demonstrates how quality control infrastructure can be generated from a single requirements document. Taken from a recent project that is now being used in production at a large journal publisher, it discusses some of the challenges faced and techniques used when generating Schematron, XSpec tests, XML grammar checks, and documentation. The project set out to implement quality control requirements for journal articles using Schematron. In pursuing this objective, the project also created quality control infrastructure for Schematron itself that streamlines the process for incorporating iterative changes to requirements. The techniques used in this project and described in this paper may be generally applicable in other projects.

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3

Wan, au, au Marjanovic, and au Lennox. "Trajectory tracking of batch product quality using intermittent measurements and moving window estimation." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334640.

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4

Darker, A. "TOTAL QUALITY CONTROL." In 4th International Pacific Conference on Automotive Engineering. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1987. http://dx.doi.org/10.4271/871263.

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5

Bonati, Marco A., Peter Moore, and Diego Gomez. "Detector quality control." In Software and Cyberinfrastructure for Astronomy VI, edited by Juan C. Guzman and Jorge Ibsen. SPIE, 2020. http://dx.doi.org/10.1117/12.2556497.

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6

Zabelina, O. "Masonry quality control." In INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INNOVATIVE TECHNOLOGIES IN AGRICULTURE”. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0167943.

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7

Panjaitan, Seno D., Rudi Kurnianto, Bomo W. Sanjaya, and Matthew C. Turner. "Control of Parallel Inverters for High Power Quality and Sharing Accuracy in Single-Phase AC Microgrids." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516761.

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8

Nguyen, Xuan Hung. "Networked control systems quality of service aware quality of control adaptation." In Factory Automation (ETFA 2011). IEEE, 2011. http://dx.doi.org/10.1109/etfa.2011.6059154.

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9

Krause, Markus, Farhad Mohammad Afzali, Simon Caton, and Margeret Hall. "Is Quality Control Pointless?" In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.636.

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Al-Ameer, Hassan, Hijji Al-Alawi, and Abdulrahman Al-Balawi. "Air Quality Control Initiatives." In International Petroleum Technology Conference. International Petroleum Technology Conference, 2013. http://dx.doi.org/10.2523/iptc-16751-ms.

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Звіти організацій з теми "Quality control"

1

Crosier, Ronald B. Multivariate Quality Control Procedures. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada203305.

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2

Zambrano, Carlos, Vincent Drnevich, and Philippe Bourdeau. Advanced Compaction Quality Control. West Lafayette, IN: Purdue University, 2006. http://dx.doi.org/10.5703/1288284313408.

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3

Landon-Browne, A. R. R., R. D. Knight, B. A. Kjarsgaard, and H. A. J. Russell. Quality control of pXRF spectrometry. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/299726.

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4

Shepherd, S. Jeff, Pei-Jan Paul Lin, John M. Boone, Dianna D. Cody, Jane R. Fisher, G. Donald Frey, Hy Glasser, et al. Quality Control in Diagnostic Radiology. AAPM, 2002. http://dx.doi.org/10.37206/73.

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5

Jayachandran, Toke, and Richard Franke. Quality Control of Meteorological Observations. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada242694.

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6

Pfeil, Benjamin. SOCAT Quality Control (QC) procedures. EuroSea, 2022. http://dx.doi.org/10.3289/eurosea_d4.7.

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Анотація:

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with annual updates. SOCAT aims to provide data with the highest possible quality for carbon data – consistent quality control (QC) is essential in achieving this primary goal of SOCAT. Currently there are various steps of quality control, and within this task of EuroSea we aimed to develop an operational implementation of QC as a showcase for data within SOCAT from the European Research Infrastructure Integrated Carbon Observing System. The aim within EuroSea is to increase the Technology Readiness Level (TRL) from 5 (Technology validated in relevant environment) to 7 (system prototype demonstration in operational environment) for relevant ICOS data for direct submission to SOCAT. This was achieved by creating automated quality control into the ICOS state-of-art-software QuinCe, a web-based tool for processing and quality control of data from in situ sensors and underway instruments that is used for first and second level quality control for operational ICOS stations. One important aspect of SOCAT is the assessment of data quality, to ensure that all published data is fit for purpose and manual eyes-on QC is currently essential to lower uncertainties. Currently, this assessment consists of evaluating the metadata of each dataset to ensure that the correct Standard Operational Procedures (SOPs) have been followed during data collection, that the system setup is correct, instruments are calibrated and in addition examining data to ensure they are of good quality. SOCAT consists of three steps of QC: 1.) QC while data is being ingested; 2.) Eyes-on QC by regional experts and 3.) QC for the entire dataset defining the uncertainty based upon the submitted metadata and within this task it has been shown that certain parts of this QC process can be automated while other levels bear challenges if a higher level of TRL is aimed for. (EuroSea Deliverable D4.7)

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7

Davila, Xabier. GLODAP Quality Control (QC) procedures. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.12.

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The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort that provides high-quality, quality-controlled ocean biogeochemical bottle data with annual-updates, playing a crucial role in advancing our understanding of the Earth's oceans and their complex biogeochemical processes. This deliverable covers the GLODAP annual updates under the EuroSea funding, as well as the automatization of the quality control process of the data. Under the EuroSea funding, GLODAP has received three updates (GLODAPv2.2020, GLODAPv2.2021 and GLODAPv2.2022) with a total number of 245 cruises added, and in addition, a new version release (GLODAPv3) is planned. These updates were possible as a result of the large degree of automatization of the quality control process that ensures the accuracy of the data. The core of the quality control process is the crossover analysis that is currently performed via the 2nd QC Matlab toolbox from Lauvset and Tanhua (2015). However, following Eurosea’s vision of a user-focused, truly interdisciplinary, and responsive European ocean observing and forecasting system, this deliverable aims to migrate from the Matlab toolbox to an online web application based on the open-source software Django and Python. This will allow the user to simply upload the data file to be quality controlled and the web application performs the secondary quality control through the deep water crossover analysis just as in Matlab, and offers similar graphics for visualization. Because the crossover analysis is partially automated on this online tool, the users do not need to possess any programming knowledge in order to quality control their data. In addition, this online tool can be part of a fully automated GLODAP quality control process, without need for manual intervention. (EuroSea Deliverable, D4.12)

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8

Kane, J. S. Quality control and reference sample databases. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/193261.

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Desai, Jal, and Cory Chovanec. Quality Control Inspector Job Task Analysis. Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2005573.

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Ong, Ghim Ping (Raymond), Samy Noureldin, and Kumares Sinha. Automated Pavement Condition Data Collection Quality Control, Quality Assurance, and Reliability. West Lafayette, Indiana: Purdue University, 2011. http://dx.doi.org/10.5703/1288284314288.

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