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Journal articles on the topic 'Alignement de data elements'

Author: Grafiati

Published: 25 January 2025

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1

Hamzić, Adis, and Dina Kamber Hamzić. "Dam Movement Modeling by Using Multiple Linear Regression and Arima Models." Geodetski glasnik, no. 51 (December 31, 2020): 49–64. http://dx.doi.org/10.58817/2233-1786.2020.54.51.49.

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Structural health monitoring of the large infrastructural objects (high buildings, bridges, tunnels, dams, etc.) is in the domain of civil and geodetic engineers who use different methods and instruments for this task. Dam movement is influenced by various factors among which the most important are: thermal variations, hydrostatic pressure and dam ageing. This research investigates influence of thermal variations on dam crest movement by using statistical methods: autoregressive integrated moving average (ARIMA) and multiple linear regression. Dam crest movement data is obtained by using optical alignement method on the concrete gravity dam HP Salakovac. In the first part of this research correlation between dam crest movement and concrete temperature is determined, the second part deals with short term concrete temperature prediction and in the final part of this research previously fitted statistical models are used for dam movement prediction. The results showed that proposed model based on statistical methods can provide quality prediction of dam crest movement.

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2

Nahm, Meredith, Anita Walden, Brian McCourt, Karen Pieper, Emily Honeycutt, Carol D. Hamilton, Robert A. Harrington, et al. "Standardising clinical data elements." International Journal of Functional Informatics and Personalised Medicine 3, no. 4 (2010): 314. http://dx.doi.org/10.1504/ijfipm.2010.040213.

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3

Coleman, David E. "Elements of Graphing Data." Technometrics 29, no. 1 (February 1987): 116–18. http://dx.doi.org/10.1080/00401706.1987.10488193.

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4

Villere, Sarah. "Common Data Elements Repository." Medical Reference Services Quarterly 43, no. 2 (April 2, 2024): 182–90. http://dx.doi.org/10.1080/02763869.2024.2323896.

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5

Pipere, Anita, and Ilona Mičule. "Mathematical Identity for a Sustainable Future: An Interpretative Phenomenological Analysis." Journal of Teacher Education for Sustainability 16, no. 1 (June 1, 2014): 5–31. http://dx.doi.org/10.2478/jtes-2014-0001.

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Abstract Individual in-depth, semi-structured interviews with three mathematics teachers were conducted to investigate the dynamics of their life-long relationships with mathematics, synthesised as mathematical identity from different identity positions in the context of dialogical self. The qualitative data were scrutinised employing interpretive phenomenological analysis that displayed mostly positive instrumental relationships with mathematics and explicit connections between the teachersí life experiences and their disctinct identity voices that surfaced in interviews. Similarly, teachers appeared to be experts in different professional spheres: pedagogy, subject or didactics. The teachersí accounts contain various models of relationships between the other-motive and the self-motive as reflected in their pedagogical approaches. Emergent patterns resulting from the interaction of the teachersí mathematical identity and their perception of studentsí mathematical philia/phobia included the humanistic approach with an instrumental interpretation of mathematics and its teaching methods, self-actualisation in achieving success in mathematics through hard work and the issue of attribution of failure in mathematics either to external or internal factors. Moreover, these dialogical models and interactive patterns show alignement with one of the core competences for educators in education for sustainable development, that is, achieving transformation in what it means to be an educator, in teaching and learning, as well as in the entire education system. Practical implementation of findings and limitations of the study are outlined along with venues for future research.

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6

M., D. W., and William S. Cleveland. "The Elements of Graphing Data." American Journal of Psychology 99, no. 3 (1986): 443. http://dx.doi.org/10.2307/1422498.

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7

Srivastava, Rajendra, and William S. Cleveland. "The Elements of Graphing Data." Journal of Marketing Research 24, no. 4 (November 1987): 461. http://dx.doi.org/10.2307/3151402.

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8

Schnell, Gary D., and William S. Cleveland. "The Elements of Graphing Data." Systematic Zoology 34, no. 4 (December 1985): 471. http://dx.doi.org/10.2307/2413211.

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9

Waterhouse, J. M. "The Elements of Graphing Data." Journal of Arid Environments 12, no. 1 (January 1987): 83. http://dx.doi.org/10.1016/s0140-1963(18)31200-x.

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10

Ziegel, Eric R., and William S. Cleveland. "The Elements of Graphing Data." Technometrics 39, no. 2 (May 1997): 237. http://dx.doi.org/10.2307/1270929.

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11

Chatterjee, Samprit, and W. S. Cleveland. "The Elements of Graphing Data." Journal of Business & Economic Statistics 4, no. 2 (April 1986): 276. http://dx.doi.org/10.2307/1391328.

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12

Weiss, Robert E., and William S. Cleveland. "The Elements of Graphing Data." Journal of the American Statistical Association 90, no. 432 (December 1995): 1488. http://dx.doi.org/10.2307/2291543.

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13

Upton, G. J. G., and W. S. Cleveland. "The Elements of Graphing Data." Biometrics 44, no. 1 (March 1988): 315. http://dx.doi.org/10.2307/2531926.

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14

Rubin, Daniel L., and Charles E. Kahn. "Common Data Elements in Radiology." Radiology 283, no. 3 (June 2017): 837–44. http://dx.doi.org/10.1148/radiol.2016161553.

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15

Wright, Sue Ellen, and Gerhard Budin. "Data elements in terminological entries." Terminology 1, no. 1 (January 1, 1994): 41–59. http://dx.doi.org/10.1075/term.1.1.05wri.

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Differing theoretical and methodological views and working-group needs have spawned a wide diversity in the content, layout and internal structure of terminological entries in database environments, which in turn complicates standardization and data interchange. Major criticisms lodged against the data element list provided in ISO 6156 (MATER) prompted the authors to conduct an empirical examination of over thirty existing databases to ascertain which data elements are truly used in practice (as opposed to those which are espoused or rejected in theory). Their results reveal that designation of data elements, like other terminological products, are subject to the vagaries of polysemy and synonymy. They conclude that, given the widespread differences in approach evidenced in existing databases, the most practical approach to data element concerns during interchange is to compile an open-ended dictionary of common data element types for use as a mapping device during the data preparation stage.

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16

Raines, Gary L. "Elements of spatial data quality." Computers & Geosciences 23, no. 1 (February 1997): 135. http://dx.doi.org/10.1016/s0098-3004(97)87525-5.

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17

Cleveland, William S., and Glenn M. Schmieg. "The Elements of Graphing Data." American Journal of Physics 55, no. 8 (August 1987): 767. http://dx.doi.org/10.1119/1.15021.

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18

Esterby, S. R. "The elements of graphing data." Science of The Total Environment 61 (January 1987): 270–72. http://dx.doi.org/10.1016/0048-9697(87)90385-8.

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19

Zhang, Zhang, Shuhui Song, Jun Yu, Wenming Zhao, Jingfa Xiao, and Yiming Bao. "The Elements of Data Sharing." Genomics, Proteomics & Bioinformatics 18, no. 1 (February 2020): 1–4. http://dx.doi.org/10.1016/j.gpb.2020.04.001.

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20

Howard, Laura. "The Elements of Graphing Data." Journal of Quality Technology 19, no. 3 (July 1987): 169–70. http://dx.doi.org/10.1080/00224065.1987.11979059.

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21

Nelson, Lloyd S. "The Elements of Graphing Data,." Journal of Quality Technology 27, no. 2 (April 1995): 176–77. http://dx.doi.org/10.1080/00224065.1995.11979585.

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22

Patterson, H. D. "The elements of graphing data." Endeavour 10, no. 4 (January 1986): 214. http://dx.doi.org/10.1016/0160-9327(86)90104-3.

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23

McPhie, Peter. "The elements of graphing data." Analytical Biochemistry 156, no. 1 (July 1986): 263–64. http://dx.doi.org/10.1016/0003-2697(86)90183-1.

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24

Lovie, Pat, and William S. Cleveland. "The Elements of Graphing Data." Applied Statistics 36, no. 3 (1987): 375. http://dx.doi.org/10.2307/2347799.

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25

Simon, Gary, and William S. Cleveland. "The Elements of Graphing Data." Journal of the American Statistical Association 82, no. 397 (March 1987): 348. http://dx.doi.org/10.2307/2289184.

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26

Lodge, James P. "The elements of graphing data." Atmospheric Environment (1967) 20, no. 3 (January 1986): 609–10. http://dx.doi.org/10.1016/0004-6981(86)90109-5.

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27

Fujishiro, Issei. "The elements of graphing data." Visual Computer 2, no. 1 (January 1986): 54. http://dx.doi.org/10.1007/bf01890988.

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28

Dinsdale, A. T. "SGTE data for pure elements." Calphad 15, no. 4 (October 1991): 317–425. http://dx.doi.org/10.1016/0364-5916(91)90030-n.

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29

Dootson, Alistair. "Tracing data elements through a standard data flow." Pharmaceutical Programming 4, no. 1-2 (December 2011): 59–69. http://dx.doi.org/10.1179/175709311x13166801334352.

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30

Sipkás, Vivien, and Gabriella Vadászné Bognár. "Failure data evaluation for structural elements." Multidiszciplináris tudományok 11, no. 4 (2021): 31–41. http://dx.doi.org/10.35925/j.multi.2021.4.3.

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31

Uchida, Craig D., and William R. King. "Police Employee Data: Elements and Validity." Justice Research and Policy 4, no. 1-2 (December 2002): 11–19. http://dx.doi.org/10.3818/jrp.4.1.2002.11.

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The Federal Bureau of Investigation's (FBI) Uniform Crime Reports (UCR) Police Employee data, collected from U.S. police agencies annually since 1930, provide information on various aspects of police organizations (such as the number of employees and assaults on officers). Such data, spanning 72 years, offer researchers a potentially rich data set. This article provides a brief history of the Police Employee data, describes the various data elements, and tentatively addresses the validity and reliability of these data. Finally, suggested improvements (as well as possible uses) for these data are offered.

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32

Palestrant, Zelma G., and Elizabeth W. Carter. "Library Statistics and Conspectus Data Elements." Acquisitions Librarian 4, no. 7 (August 25, 1992): 115–23. http://dx.doi.org/10.1300/j101v04n07_09.

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33

Sterling, N. C., and M. C. Witthoeft. "Atomic data for neutron-capture elements." Astronomy & Astrophysics 529 (April 22, 2011): A147. http://dx.doi.org/10.1051/0004-6361/201116718.

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34

Sterling, N. C. "Atomic data for neutron-capture elements." Astronomy & Astrophysics 533 (August 29, 2011): A62. http://dx.doi.org/10.1051/0004-6361/201117471.

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35

Sterling, N. C., and P. C. Stancil. "Atomic data for neutron-capture elements." Astronomy & Astrophysics 535 (November 2011): A117. http://dx.doi.org/10.1051/0004-6361/201117584.

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36

Rush, James E., and Robert S. Tannehill. "Bibliographic Data Elements for Computer Programs." Library Hi Tech 4, no. 2 (February 1986): 79–93. http://dx.doi.org/10.1108/eb047649.

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37

Beyea, Suzanne C. "Perioperative Data Elements: Interventions and Outcomes." AORN Journal 71, no. 2 (February 2000): 344–52. http://dx.doi.org/10.1016/s0001-2092(06)62114-5.

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38

Dmitriev, A. S., and A. I. Ryzhov. "Digital data transmission between neuronlike elements." Journal of Communications Technology and Electronics 55, no. 4 (April 2010): 429–34. http://dx.doi.org/10.1134/s106422691004008x.

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39

Warner, Janet, Megan Johnston, Lawrence Korngut, Nathalie Jette, and Tamara Pringsheim. "Common Data Elements for Neurological Registries." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 40, S2 (July 2013): S62—S63. http://dx.doi.org/10.1017/s0317167100017212.

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40

Frederickson, Greg N. "Implicit data structures for weighted elements." Information and Control 66, no. 1-2 (July 1985): 61–82. http://dx.doi.org/10.1016/s0019-9958(85)80013-9.

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41

Clifford, Theresa L., Pamela E. Windle, and Linda Wilson. "ASPAN Perianesthesia Data Elements—The Model." Journal of PeriAnesthesia Nursing 23, no. 1 (February 2008): 49–52. http://dx.doi.org/10.1016/j.jopan.2007.11.002.

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42

Hinich, V. "Refined Dynkin Data for Nilpotent Elements." Advances in Mathematics 104, no. 2 (March 1994): 157–81. http://dx.doi.org/10.1006/aima.1994.1028.

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43

Suparmadi and Nuriadi Manurung. "Implementasi Google Data Studio Pada Visualisasi Data Bola Bass Ball dalam Bentuk Dashboard." RJOCS (Riau Journal of Computer Science) 10, no. 1 (January 31, 2024): 76–81. http://dx.doi.org/10.30606/rjocs.v10i1.2404.

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In the field of sports, the use of information technology is used to support performance in a sport. Data visualization is the answer to simplifying complex data into a graphical format so that it's easier to understand the data. Data management for one sport is an important process that must be carried out by sports. With good data management, the company gets more value. This added value is like decision support information, in order to be able to increase operational efficiency and effectiveness. This research uses data from one of the sports branches, Bass ball, items obtained from the internet, namely 2426 data. The research was conducted with the help of Google Data Studio tools for making dashboards. The results obtained are that there are several elements that help in making it easier to read information, namely scorecard elements, Pie Chart elements, bar chart elements, geographic chart elements, and table elements. The Record count element displays the total number of records, the average number of players, On the Pie Chart displays product insights in percent, the average attendance is obtained. The bar chart element displays the total player attendance each month, the player with the highest attendance being in May

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44

O. Mahdi 1, Ghaith, and Murtadha M. Hamad. "Duplicate detection and elimination in xml data for a data warehouse." International Journal of Engineering & Technology 7, no. 4 (May 27, 2019): 6175–80. http://dx.doi.org/10.14419/ijet.v7i4.20419.

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Due to the significant increase in the volume of data in recent decades, the problem of duplicate data has emerged because of the multiplicity of resources where the data is collected in different formats. The presence of duplicates comes as a result of the existence of different formulas of data. Thus, it is necessary to clean the duplicate data to access a pure data set. The main concern of this study is to clean data which Known by its complex hierarchal structure in data warehouse. This can be achieved by detecting duplicate in large data bases in order to increase the efficiency of data mining. In the current study the proposed system of duplicate elements passes through three-stages. The first stage (Pre-processing stage) includes two parts: the first part is the elimination of the exact match which, in turn, works to eliminate many of the identical elements completely. This procedure saves a lot of time and effort by preventing the entrance of many elements to the processing stage which are usually known by its complexity. In the second part blocking technique is used based on Levenshtein distance to minimize the number of comparisons and to maximize the accuracy of blocking elements than the traditional ones. These processes are performed to improve the dataset. The second stage (Processing stage) is taken to compute the similarity ratios between each pair of elements within each block by using smith waterman similarity algorithm. The third stage is the classification stage of the elements in which an element is identified whether it is duplicate or non-duplicate. The Artificial Neural Network technique (Back-Propagation) is used to meet this purpose. The threshold 0.65 has been determined which is relied on the results obtained. The Artificial Neural Network (Back-Propagation) is used to classify the elements in to duplicate and non-duplicate. The efficiency of the proposed system is represented by the accuracy obtained which is closer to 100% through reducing the number of "false negatives" and "false positive" relative to the "true positive".Â Â

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45

Mayer, Craig S., and Vojtech Huser. "Learning important common data elements from shared study data: The All of Us program analysis." PLOS ONE 18, no. 7 (July 7, 2023): e0283601. http://dx.doi.org/10.1371/journal.pone.0283601.

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There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.

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46

Zuiderwijk, Anneke, Marijn Janssen, and Chris Davis. "Innovation with open data: Essential elements of open data ecosystems." Information Polity 19, no. 1,2 (June 12, 2014): 17–33. http://dx.doi.org/10.3233/ip-140329.

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47

Seedah, Dan P. K., Bharathwaj Sankaran, and William J. O'Brien. "Approach to Classifying Freight Data Elements across Multiple Data Sources." Transportation Research Record: Journal of the Transportation Research Board 2529, no. 1 (January 2015): 56–65. http://dx.doi.org/10.3141/2529-06.

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48

Duadi, Hamootal, and Zeev Zalevsky. "Special Diffractive Optical Elements for Data Encoding." Open Optics Journal 5, no. 1 (December 30, 2011): 46–50. http://dx.doi.org/10.2174/1874328501105010046.

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49

Singh, Harjit, and Ashish Oberoi. "Extracting Data Elements from Punjabi Language Query." International Journal of Computer Sciences and Engineering 6, no. 12 (December 31, 2018): 81–85. http://dx.doi.org/10.26438/ijcse/v6i12.8185.

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50

Petchenik, Barbara Bartz. "The Elements of Graphing Data (Book Review)." College & Research Libraries 47, no. 3 (May 1, 1986): 286. http://dx.doi.org/10.5860/crl_47_03_286.

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