Relevant bibliographies by topics / Data Interpretation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Interpretation'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Data Interpretation"

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Lee, James B., David Rowlands, Daniel A. James, Raymond I. Leadbetter, and Yuji Ohgi. "104 A Visual Method for Data Interpretation." Proceedings of the Symposium on sports and human dynamics 2012 (2012): 44–47. http://dx.doi.org/10.1299/jsmeshd.2012.44.

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Clayden, G. S. "Paediatric Data Interpretation." Archives of Disease in Childhood 64, no. 2 (February 1, 1989): 311. http://dx.doi.org/10.1136/adc.64.2.311.

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Peacock, S., and A. Reid. "Aeromagnetic data interpretation." Astronomy & Geophysics 54, no. 5 (September 18, 2013): 5.23. http://dx.doi.org/10.1093/astrogeo/att164.

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Mittal, Kundan, Prashant Kumar, Rajesh Mishra, and Vinayak Patki. "OSCE : Data interpretation." Journal of Pediatric Critical Care 5, no. 4 (2018): 79. http://dx.doi.org/10.21304/2018.0504.00415.

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Triggs, E. J. "Clinical interpretation of data: Guide to clinical interpretation of data." Medical Journal of Australia 147, no. 6 (September 1987): 299. http://dx.doi.org/10.5694/j.1326-5377.1987.tb133471.x.

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Sivarajah, Yathunanthan, Eun-Jung Holden, Roberto Togneri, and Michael Dentith. "Identifying effective interpretation methods for magnetic data by profiling and analyzing human data interactions." Interpretation 1, no. 1 (August 1, 2013): T45—T55. http://dx.doi.org/10.1190/int-2013-0002.1.

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Geoscientific data interpretation is a highly subjective and complex task because human intuition and biases play a significant role. Based on these interpretations, however, the mining and petroleum industries make decisions with paramount financial and environmental implications. To improve the accuracy and efficacy of these interpretations, it is important to better understand the interpretation process and the impact of different interpretation techniques, including data processing and display methods. As a first step toward this goal, we aim to quantitatively analyze the variability in geophysical data interpretation between and within individuals. We carried out an experiment to analyze how individuals interact with magnetic data during the process of identifying prescribed targets. Participants performed two target spotting exercises where the same magnetic image was presented at different orientations. The task was to identify the magnetic response from porphyry-style intrusive systems. The experiment involved analyzing the data observation pattern during the interpretation process using an eye tracker system that captures the interpreter’s eye gaze motion and the target-spotting performance. The time at which targets were identified was also recorded. Fourteen participants with varying degrees of experience and expertise participated in this study. The results show inconsistencies within and between the interpreters in target-spotting performance. The results show a correlation between a systematic data observation pattern and target-spotting performance. Improved target-spotting performance was obtained when the magnetic image was observed from multiple orientations. These findings will help to identify and quantify the effective interpretation practices, which can provide a roadmap for the training of geoscientific data interpreters and contribute toward the understanding of the uncertainties in the data interpretation process.
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Nam, Myung Jin. "A Review on Nuclear Magnetic Resonance Logging: Data Interpretation." Journal of the Korean Society of Mineral and Energy Resources Engineers 50, no. 1 (2013): 144. http://dx.doi.org/10.12972/ksmer.2013.50.1.144.

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Drummond, G. B., and S. L. Vowler. "Data interpretation: using probability." Journal of Physiology 589, no. 10 (May 13, 2011): 2433–35. http://dx.doi.org/10.1113/jphysiol.2011.208793.

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DIAS, S. "Data Interpretation in Paediatrics." Archives of Disease in Childhood 81, no. 2 (August 1, 1999): 195. http://dx.doi.org/10.1136/adc.81.2.e195.

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Cologne, John. "Probabilistic Interpretation of Data." Health Physics 105, no. 6 (December 2013): 576–77. http://dx.doi.org/10.1097/hp.0b013e3182a2a73f.

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Dissertations / Theses on the topic "Data Interpretation"

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Miller, Susan L. M. "Multicomponent seismic data interpretation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq20841.pdf.

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Amarasinghe, Angodage Don Upul Shantha. "Interpretation of paste extrusion data." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285008.

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Matthews, Adrian. "Interpretation of spectral data from tokamaks." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314124.

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Napier, Ian. "Ontological interpretation of network monitoring data." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/15762.

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Interpreting measurement and monitoring data from networks in general and the Internet in particular is a challenge. The motivation for this work has been to in- vestigate new ways to bridge the gap between the kind of data which are available and the more developed information which is needed by network stakeholders to support decision making and network management. Specific problems of syntax, semantics, conflicting data and modeling domain-specific knowledge have been identified. The methods developed and tested have used the Resource Descrip- tion Framework (rdf) and the ontology languages of the Semantic Web to bring together data from disparate sources into unified knowledgebases in two discrete case studies, both using real network data. Those knowledgebases have then been demonstrated to be usable and valuable sources of information about the networks concerned. Some success has been achieved in overcoming each of the identified problems using these techniques, proving the thesis that taking an ontological ap- proach to the processing of network monitoring data can be a very useful technique for overcoming problems of interpretation and for making information available to those who need it.
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Russ, Keith Mitchell. "Visual data acquisition and computer interpretation /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487687485811057.

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St-Onge, Annie. "Interpretation of dairy data using interactive visualization." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18207.

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The agricultural domain is faced with the challenge of increasing amounts of collected data, multiple sources of external information and various summary reports, all tending to exist in a non-structured fashion. The formats and locations of these various data sources almost certainly differ, but profitable decision-making often depends on using and interpreting all of these inputs accurately. Based on the hypothesis that the interpretation of dairy-herd data can be aided using interactive visualization, the main goal of this research was to improve the understanding of the data-interpretation process, using such techniques, and by taking full advantage of them in the context of the Quebec dairy farm. The challenge was one of designing a system that would integrate interactive visualization techniques while also accounting for the diversity of information involved, the clientele of the dairy industry, and the technologies available. A methodological framework was developed to support the design of such an interactive visualization system, and a software prototype – HerdLine – subsequently developed. This prototype focused on the planning of a dairy-herd composition, based on the evolution of its performance over time via genetic and economic profiles. The framework helped the prototype development by better matching the characteristics of the targeted users with the data involved in the software. The result was a tool that provides an overall picture of the dairy-herd, as well as rapid, incremental, and reversible views of information contained within. An informal evaluation process was performed in order to assess the appropriateness of the prototype from a user’s point of view. Four participants, representing diverse sections of the dairy industry, downloaded, installed and tested the prototype. They subsequently completed a survey regarding their impressions to such an approach, as well as their actual experience with the prototype. Both quantitative and
Le domaine agricole fait face au défi d’un nombre croissant de données collectées, de rapports et de sources d’information. De plus, ils tendent tous à exister de manière non structurée. Le format et l’emplacement de ces multiples sources de données diffèrent, mais les prises de décision importantes à la ferme dépendent souvent d’une interprétation juste de toutes celles-ci. L’objectif principal de cette recherche était de présenter une approche qui améliorait l’interprétation des données par l’emploi de la visualisation interactive comme support technique en maximisant leur potentiel dans le contexte des fermes laitières québécoises. Le défi était d’effectuer le design d’un système qui intégrerait ces techniques de visualisation interactive tout en tenant compte du caractère diversifié de l’information qui serait à l’étude par ce système, ainsi que des utilisateurs potentiels et des technologies disponibles. Un cadre méthodologique a d’abord été conçu dans le but de supporter le développement d’un système de visualisation interactive et un prototype informatique - HerdLine - a ensuite été développé. Ce prototype avait pour domaine d’application la planification de la composition d’un troupeau laitier, se basant sur l’évolution de ses performances et son rendement, en plus de ses profils génétique et économique. Le cadre méthodologique a aidé au processus de développement en rejoignant mieux les caractéristiques des utilisateurs visés et des données impliquées aux caractéristiques que le logiciel devait posséder. Il en a résulté un outil capable de fournir une image globale d’une ferme laitière, en plus d’offrir des vues rapides, cumulatives et réversibles de l’information. Un processus d'évaluation auprès d’utilisateurs potentiels a été créé et exécuté dans le but d’évaluer si le prototype et l’approche utilisée pour son développement étaient appropri
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Qiu, Chenchen. "Model for interpretation of pipeline survey data." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0002315.

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Caves, Ronald George. "Automatic matching of features in Synthetic Aperture Radar data to digital map data." Thesis, University of Sheffield, 1993. http://etheses.whiterose.ac.uk/1788/.

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The large amounts of Synthetic Aperture Radar (SAR) data now being generated demand automatic tools for image interpretation. Where available, map data provides a valuable aid for visual interpretation and it should aid automatic interpretation. Automatic map based interpretation will be heavily dependent on methods for matching image and map features, both for defining the initial registration and for comparing image and map. This thesis investigates methods for carrying out this matching. Before beginning to develop image map matching methods, a full understanding of the nature of SAR data is first required. The general theory of SAR imaging, the effects of speckle and texture on image statistics, multi-look image statistics, and parameter estimation, are all discussed before addressing the main subject matter. Initially the feasibility of directly matching map features to SAR image features is investigated. Simulations based on a simple image model produce promising results. However, the results of matching features in real images are disappointing. This is due to the limitations of the image model on which matching is based. Possible extensions to include texture and correlation are considered to be computationally too expensive. Rather, it is concluded that pre-processing is needed to structure the image prior to matching. Structuring using edge detection and segmentation are investigated. Among operators for detecting edges in SAR an operator based on intensity ratios is identified as the most suitable. Its performance is fully analysed. Segmentation using an iterative edge detection/segment growing algorithm developed at the Royal Signals and Radar Establishment is investigated and various improvements are suggested. The output of segmentation is structured to a higher level than the output of edge detection. Thus the former is the more suitable candidate for map matching. Approaches to matching segmentations to map data are discussed.
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Lahouar, Samer. "Development of Data Analysis Algorithms for Interpretation of Ground Penetrating Radar Data." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/11051.

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According to a 1999 Federal Highway Administration statistic, the U.S. has around 8.2 million lane-miles of roadways that need to be maintained and rehabilitated periodically. Therefore, in order to reduce rehabilitation costs, pavement engineers need to optimize the rehabilitation procedure, which is achieved by accurately knowing the existing pavement layer thicknesses and localization of subsurface defects. Currently, the majority of departments of transportation (DOTs) rely on coring as a means to estimate pavement thicknesses, instead of using other nondestructive techniques, such as Ground Penetrating Radar (GPR). The use of GPR as a nondestructive pavement assessment tool is limited mainly due to the difficulty of GPR data interpretation, which requires experienced operators. Therefore, GPR results are usually subjective and inaccurate. Moreover, GPR data interpretation is very time-consuming because of the huge amount of data collected during a survey and the lack of reliable GPR data-interpretation software. This research effort attempts to overcome these problems by developing new GPR data analysis techniques that allow thickness estimation and subsurface defect detection from GPR data without operator intervention. The data analysis techniques are based on an accurate modeling of the propagation of the GPR electromagnetic waves through the pavement dielectric materials while traveling from the GPR transmitter to the receiver. Image-processing techniques are also applied to detect layer boundaries and subsurface defects. The developed data analysis techniques were validated utilizing data collected from an experimental pavement system: the Virginia Smart Road. The layer thickness error achieved by the developed system was around 3%. The conditions needed to achieve reliable and accurate results from GPR testing were also established.
Ph. D.
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Pascua, Daniel E. "Data interpretation algorithms for sensor networks in buildings." Thesis, Colorado School of Mines, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1541892.

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The purpose of this research was to create methods to determine important information from large amounts of data collected from a sensor network in a building. This project focused on data in the form of counts of people in areas of the building, with the goal of determining the movement that occurred. The project was also largely focused on the case when sensors cover the building completely, and the sensors' ranges do not overlap, with the use of this solution being the ability to resolve ambiguity that will exist in all versions of the problem, and the foundation that it provides for solutions to the complex scenarios. A method is given to solve the problem using linear programming and a cross prediction method. Three sources of ambiguity are identified: crossing, move extensions, and flow around cycles. A second approach using movement along predefined paths is given, and this approach is shown to be accurate when all people follow the predefined paths, as long as the paths selected meet certain conditions. An application of this method to the case when sensors do not cover the building is discussed. The algorithms were tested by using simulated people to generate movement data. Crossing prediction was shown to have high accuracy when people move at a constant speed of one node per time step, have less accuracy when people move at variable speeds slightly slower than one, and have very low accuracy when a wide range of speeds both below and above one are allowed. The linear programming method with and without crossing prediction was shown to handle cycle ambiguity well when the number of people in the building was not too large compared to the number of sensors.

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Books on the topic "Data Interpretation"

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Keller, H., and Ch Trendelenburg, eds. Data Presentation / Interpretation. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880.

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Simon, Lenton, and Gabriel Cynthia M, eds. Paediatric data interpretation. London: Butterworths, 1987.

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Smith, Roy Keith. Interpretation of organic data. Amsterdam, NY: Genium Pub. Corp., 2000.

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Raj, Tilak D., ed. Data Interpretation in Anesthesia. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2.

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MRCP, Finlay Fiona, ed. Data interpretation questions in pediatrics. Oxford: Blackwell Science, 1998.

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Bryon, Mike. How to Pass Data Interpretation Tests. London: Kogan Page Publishers, 2009.

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Hounslow, Arthur W. Water quality data: Analysis and interpretation. Boca Raton: Lewis, 1995.

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Interpretation of three-dimensional seismic data. 2nd ed. Tulsa, OK: American Association of Petroleum Geologists, 1986.

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Fraser, C. G. Interpretation of clinical chemistry laboratory Data. Oxford: Blackwell Scientific, 1986.

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Brown, Alistair R. Interpretation of three-dimensional seismic data. 2nd ed. Tulsa, Okla., U.S.A: American Association of Petroleum Geologists, 1988.

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Book chapters on the topic "Data Interpretation"

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Baker, Michael J. "Data interpretation." In Research for Marketing, 209–50. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-21230-9_9.

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Russ, John C. "Data Interpretation." In Practical Stereology, 103–20. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-3533-5_6.

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Fowler, John. "Data interpretation." In Fundamental Toxicology for Chemists, 43–52. Cambridge: Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847550941-00043.

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Vaudel, Marc, Julia Maria Burkhart, René Peiman Zahedi, Lennart Martens, and Albert Sickmann. "iTRAQ Data Interpretation." In Methods in Molecular Biology, 501–9. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-885-6_30.

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Akram, Jubran. "Microseismic Data Interpretation." In Understanding Downhole Microseismic Data Analysis, 153–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34017-9_5.

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Keller, H. "Data Presentation and Interpretation: Problems, Tools and Goals." In Data Presentation / Interpretation, edited by H. Keller and Ch Trendelenburg, 1–8. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880-004.

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Politser, P. E. "Chapter 2.1. How to Make Laboratory Information More Informative: Psychological and Statistical Considerations." In Data Presentation / Interpretation, edited by H. Keller and Ch Trendelenburg, 11–32. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880-005.

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Porth, A. J., C. Badke, S. Bothung, and M. Worzyk. "Chapter 2.2. Result Reports from Large Centralized Laboratories." In Data Presentation / Interpretation, edited by H. Keller and Ch Trendelenburg, 33–62. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880-006.

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Connelly, D. P. "Chapter 2.3. Graphical Data Display and Medical Decision Making." In Data Presentation / Interpretation, edited by H. Keller and Ch Trendelenburg, 63–80. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880-007.

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Renn, W., R. Maulbetsch, and M. Eggstein. "Chapter 2.4. Digital and Analogous Presentation of Laboratory Results in Dependence of Time." In Data Presentation / Interpretation, edited by H. Keller and Ch Trendelenburg, 81–120. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880-008.

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Conference papers on the topic "Data Interpretation"

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Romantsov, A. S., A. M. Galechyan, and V. V. Kapsenkov. "Automation of LWD interpretation." In Data Science in Oil & Gas. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202054012.

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Alharbi, Raed, Minh N. Vu, and My T. Thai. "Learning Interpretation with Explainable Knowledge Distillation." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671988.

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Lampkins, Joshua, Darren Chan, Alan Perry, Sasha Strelnikoff, Jiejun Xu, and Alireza Esna Ashari. "Multimodal Road Sign Interpretation for Autonomous Vehicles." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020808.

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El Eraki, M. "Archaeological interpretation of magnetic data." In 4th EEGS Meeting. European Association of Geoscientists & Engineers, 1998. http://dx.doi.org/10.3997/2214-4609.201407218.

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Labson, V. F., and A. Becker. "Interpretation of tipper survey data." In 1985 SEG Technical Program Expanded Abstracts. SEG, 1985. http://dx.doi.org/10.1190/1.1892693.

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Bartel, D. C., and G. W. Hohmann. "Interpretation of Crone PEM data." In 1985 SEG Technical Program Expanded Abstracts. SEG, 1985. http://dx.doi.org/10.1190/1.1892887.

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Gola*, Andrea, and Giancarlo Bernasconi. "Quick qualitative CSEM data interpretation." In SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, 2014. http://dx.doi.org/10.1190/segam2014-1674.1.

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Reiser, C., T. Bird, and M. Whaley. "Added value of acquired broadband seismic for interpretation and quantitative interpretation: case studies review." In EAGE Workshop on Broadband Marine Seismic Data. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201412449.

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Yeruva, Vijaya Kumari, Mayanka Chandrashekar, Yugyung Lee, Jeff Rydberg-Cox, Virginia Blanton, and Nathan A. Oyler. "Interpretation of Sentiment Analysis with Human-in-the-Loop." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378221.

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Cui, Xiaojie, Xuehua Chen, Jian Zhou, and Dong Lin. "Transformer in image interpretation." In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), edited by Feng Wu, Jinping Liu, and Yanping Chen. SPIE, 2022. http://dx.doi.org/10.1117/12.2631151.

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Reports on the topic "Data Interpretation"

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Teskey, D. J. Statistical interpretation of aeromagnetic data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128046.

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Worcester, Peter F., James A. Mercer, and Robert C. Spindel. Ocean Acoustic Observatories: Data Analysis and Interpretation. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada628417.

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Worcester, Peter F., James A. Mercer, and Robert C. Spindel. Ocean Acoustic Observatories: Data Analysis and Interpretation. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada629597.

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Le Bas, T. P. Scaling of interpretation with OBIA with backscatter data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305883.

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Canavan, G. H. Example of scattering noise in radar data interpretation. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/434321.

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Nakhleh, C. W. Plausible inference and the interpretation of quantitative data. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/573294.

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Shaw, J., R. C. Courtney, and D. Beaver. Bonne Bay, Newfoundland: interpretation of multibeam bathymetry data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213389.

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Lowe, C. Interpretation of magnetic data, southwestern Atlin map area. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214768.

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Folds, Dennis J., Carl T. Blunt, and Raymond M. Stanley. Training for Rapid Interpretation of Voluminous Multimodal Data. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada480522.

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Huckaby, James L., Loni M. Peurrung, and Phillip A. Gauglitz. Gas Release During Saltwell Pumping: Interpretation of Operational Data. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/15002689.

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