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Journal articles on the topic 'Data Interpretation'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Drummond, Gordon B., and Sarah L. Vowler. "Data interpretation: using probability." Clinical and Experimental Pharmacology and Physiology 38, no. 6 (May 23, 2011): 353–55. http://dx.doi.org/10.1111/j.1440-1681.2011.05522.x.

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12

Trissel, Lawrence A. "Alternative interpretation for data." American Journal of Health-System Pharmacy 49, no. 3 (March 1, 1992): 570. http://dx.doi.org/10.1093/ajhp/49.3.570a.

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13

Hoschka, Peter, and Willi Klösgen. "EXPLORA:content interpretation of data." Journal of Applied Statistics 18, no. 1 (January 1991): 87–97. http://dx.doi.org/10.1080/02664769100000008.

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14

Richter, Ellen W., and Roman Sniecinski. "Data Interpretation in Anesthesia." Anesthesia & Analgesia 127, no. 1 (July 2018): 297. http://dx.doi.org/10.1213/ane.0000000000003388.

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15

Gurman, S. J. "Interpretation of EXAFS Data." Journal of Synchrotron Radiation 2, no. 1 (January 1, 1995): 56–63. http://dx.doi.org/10.1107/s0909049594009179.

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16

Westbrook, G. K. "Data-Processing and Interpretation." Tectonophysics 144, no. 4 (December 1987): 355–56. http://dx.doi.org/10.1016/0040-1951(87)90305-2.

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17

Citerio, Giuseppe, Soojin Park, J. Michael Schmidt, Richard Moberg, Jose I. Suarez, and Peter D. Le Roux. "Data Collection and Interpretation." Neurocritical Care 22, no. 3 (April 7, 2015): 360–68. http://dx.doi.org/10.1007/s12028-015-0139-4.

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18

Riordan, Daniel Vincent, and Cameron Stark. "Interpretation of ecological data." British Journal of Psychiatry 197, no. 4 (October 2010): 332. http://dx.doi.org/10.1192/bjp.197.4.332.

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19

Drummond, G. B., and S. L. Vowler. "Data interpretation: using probability." Experimental Physiology 96, no. 6 (May 16, 2011): 561–63. http://dx.doi.org/10.1113/expphysiol.2011.058545.

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20

Drummond, GB, and SL Vowler. "Data interpretation: using probability." British Journal of Pharmacology 163, no. 5 (June 8, 2011): 887–90. http://dx.doi.org/10.1111/j.1476-5381.2011.01371.x.

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21

Miller, Martin R. "Lung Function Data Interpretation." Chest 141, no. 3 (March 2012): 832–33. http://dx.doi.org/10.1378/chest.11-2718.

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22

DRUMMOND, GORDON B., and SARAH L. VOWLER. "Data Interpretation: Using Probability." Microcirculation 18, no. 5 (July 2011): 358–60. http://dx.doi.org/10.1111/j.1549-8719.2011.00118.x.

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23

Drummond, Gordon B., and Sarah L. Vowler. "Data interpretation: using probability." Advances in Physiology Education 35, no. 2 (June 2011): 133–35. http://dx.doi.org/10.1152/advan.00023.2011.

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24

Irakarama, Modeste, Paul Cupillard, Guillaume Caumon, Paul Sava, and Jonathan Edwards. "Appraising structural interpretations using seismic data — Theoretical elements." GEOPHYSICS 84, no. 2 (March 1, 2019): N29—N40. http://dx.doi.org/10.1190/geo2018-0128.1.

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Structural interpretation of seismic images can be highly subjective, especially in complex geologic settings. A single seismic image will often support multiple geologically valid interpretations. However, it is usually difficult to determine which of those interpretations are more likely than others. We have referred to this problem as structural model appraisal. We have developed the use of misfit functions to rank and appraise multiple interpretations of a given seismic image. Given a set of possible interpretations, we compute synthetic data for each structural interpretation, and then we compare these synthetic data against observed seismic data; this allows us to assign a data-misfit value to each structural interpretation. Our aim is to find data-misfit functions that enable a ranking of interpretations. To do so, we formalize the problem of appraising structural interpretations using seismic data and we derive a set of conditions to be satisfied by the data-misfit function for a successful appraisal. We investigate vertical seismic profiling (VSP) and surface seismic configurations. An application of the proposed method to a realistic synthetic model shows promising results for appraising structural interpretations using VSP data, provided that the target region is well-illuminated. However, we find appraising structural interpretations using surface seismic data to be more challenging, mainly due to the difficulty of computing phase-shift data misfits.

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25

Islam, Annana, Yun-Ju Cho, Arif Ahmed, and Sung-Hwan Kim. "Data Interpretation Methods for Petroleomics." Mass Spectrometry Letters 3, no. 3 (September 20, 2012): 63–67. http://dx.doi.org/10.5478/msl.2012.3.3.63.

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26

Balaji, SM. "Data interpretation and statistical significance." Indian Journal of Dental Research 30, no. 2 (2019): 163. http://dx.doi.org/10.4103/ijdr.ijdr_363_19.

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27

Kim, Se Hong. "Interpretation of Research Data, Discussion." Korean Journal of Family Practice 9, no. 1 (February 20, 2019): 1–2. http://dx.doi.org/10.21215/kjfp.2019.9.1.1.

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28

Nesvizhskii, Alexey I., and Ruedi Aebersold. "Interpretation of Shotgun Proteomic Data." Molecular & Cellular Proteomics 4, no. 10 (July 11, 2005): 1419–40. http://dx.doi.org/10.1074/mcp.r500012-mcp200.

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29

Johnson, B. David, and Michael A. Mayhew. "Interpretation of satellite magnetometer data." Exploration Geophysics 16, no. 2-3 (June 1985): 238–40. http://dx.doi.org/10.1071/eg985238.

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30

Cottis, R. A. "Interpretation of Electrochemical Noise Data." CORROSION 57, no. 3 (March 2001): 265–85. http://dx.doi.org/10.5006/1.3290350.

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31

Van Vuuren, J. A. J., R. O. Barnard, and A. S. Claassens. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2055–66. http://dx.doi.org/10.1080/00103620009370563.

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32

Csathó, Péter, Katalin Debreczeni, and Katalin Sárdi. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2067–79. http://dx.doi.org/10.1080/00103620009370564.

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33

Csathó, Péter, Katalin Debreczeni, and Katalin Sárdi. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2081–92. http://dx.doi.org/10.1080/00103620009370565.

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34

Levison, Philip W., Donald J. Eckert, and Jerry M. Bigham. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2093–100. http://dx.doi.org/10.1080/00103620009370566.

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35

George, T., R. Magbanua, B. Tubana, J. Quiton, A. Almendras, W. Khatib, F. Cox, and R. Yost. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2101–10. http://dx.doi.org/10.1080/00103620009370567.

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36

Huang, Longbin, Richard W. Bell, and Bernie Dell. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2111–23. http://dx.doi.org/10.1080/00103620009370568.

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37

Jászberényi, István, Béla Kovács, and Jakab Loch. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2125–34. http://dx.doi.org/10.1080/00103620009370569.

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38

Paz‐González, Antonio, and Ma Teresa Taboada. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2135–46. http://dx.doi.org/10.1080/00103620009370570.

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39

Ryan, John. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2147–54. http://dx.doi.org/10.1080/00103620009370571.

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40

Varsa, E. C., and S. A. Ebelharar. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2155–61. http://dx.doi.org/10.1080/00103620009370572.

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41

Warman, P. R., I. Y. Walsh, and A. V.Rodd. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2163–69. http://dx.doi.org/10.1080/00103620009370573.

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42

Zbíral, Jirí, and Pavel Němec. "Data presentation, interpretation, and communication." Communications in Soil Science and Plant Analysis 31, no. 11-14 (June 2000): 2171–82. http://dx.doi.org/10.1080/00103620009370574.

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43

Yamada, Ryo, Daigo Okada, Juan Wang, Tapati Basak, and Satoshi Koyama. "Interpretation of omics data analyses." Journal of Human Genetics 66, no. 1 (May 8, 2020): 93–102. http://dx.doi.org/10.1038/s10038-020-0763-5.

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AbstractOmics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

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44

Wennerberg, Leif. "Preliminary Interpretation of Aftershock Data." Earthquake Spectra 7, no. 1_suppl (October 1991): 17–26. http://dx.doi.org/10.1193/1.1585647.

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45

Farrell, E. J. "Visual interpretation of complex data." IBM Systems Journal 26, no. 2 (1987): 174–200. http://dx.doi.org/10.1147/sj.262.0174.

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46

Chinn, Clark A., and William F. Brewer. "Mental Models in Data Interpretation." Philosophy of Science 63 (September 1996): S211—S219. http://dx.doi.org/10.1086/289954.

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47

Bynum, T. Edward. "Clinical Interpretation of Recurrence Data." Journal of Clinical Gastroenterology 9 (1987): 31–33. http://dx.doi.org/10.1097/00004836-198709011-00007.

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48

Amarasinghe, A. D. U. S., and D. I. Wilson. "Interpretation of Paste Extrusion Data." Chemical Engineering Research and Design 76, no. 1 (January 1998): 3–8. http://dx.doi.org/10.1205/026387698524523.

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49

Beamish, David. "Quantitative 2D VLF data interpretation." Journal of Applied Geophysics 45, no. 1 (August 2000): 33–47. http://dx.doi.org/10.1016/s0926-9851(00)00017-3.

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50

Subramanian, S. V., Malavika A. Subramanyam, and George Davey Smith. "Discrepancy between data and interpretation." Preventive Medicine 52, no. 6 (June 2011): 468–69. http://dx.doi.org/10.1016/j.ypmed.2011.01.029.

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