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Journal articles on the topic 'Data Importance'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Hejazi, Aylin, Neda Abdolvand, and Saeedeh Rajaee Harandi. "Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area." International Journal of Trade, Economics and Finance 8, no. 2 (April 2017): 102–8. http://dx.doi.org/10.18178/ijtef.2017.8.2.547.

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2

Brown, Renée F. "The Importance of Data Citation." BioScience 71, no. 3 (March 1, 2021): 211. http://dx.doi.org/10.1093/biosci/biab012.

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3

Spetz, Joanne. "The Importance of Good Data." Annual Review of Nursing Research 28, no. 1 (December 2010): 1–18. http://dx.doi.org/10.1891/0739-6686.28.1.

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In 1977, the federal government launched the nation's largest and most significant program to collect data on the registered nurse (RN) workforce of the United States—the National Sample Survey of Registered Nurses (NSSRN). This survey is conducted by the U.S. Health Resources and Services Administration, first in 1977 and then every 4 years since 1980. This article offers the history of the NSSRN and a review of the ways in which the NSSRN data have been used to examine education, demographics, employment, shortages, and other aspects of the RN workforce. The influence this body of research has had on policymaking is explored. Recommendations for future research are offered, in the hope that future waves of the NSSRN will continue to be used to their fullest potential.

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4

Nickels, Liz. "The importance of big data." Metal Powder Report 74, no. 4 (July 2019): 181–83. http://dx.doi.org/10.1016/j.mprp.2019.05.003.

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5

Wold, Svante, and Paul Geladi. "The importance of raw data." Chemometrics and Intelligent Laboratory Systems 22, no. 1 (January 1994): 1–2. http://dx.doi.org/10.1016/0169-7439(93)e0046-7.

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6

Samuelson, Frank, Brandon D. Gallas, Kyle J. Myers, Nicholas Petrick, Paul Pinsky, Berkman Sahiner, Gregory Campbell, and Gene A. Pennello. "The Importance of ROC Data." Academic Radiology 18, no. 2 (February 2011): 257–58. http://dx.doi.org/10.1016/j.acra.2010.10.016.

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7

Jang, Kyoung-Ae, and Woo-Je Kim. "Component Development and Importance Weight Analysis of Data Governance." Journal of the Korean Operations Research and Management Science Society 41, no. 3 (August 31, 2016): 45–58. http://dx.doi.org/10.7737/jkorms.2016.41.3.045.

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8

Richardson, Julie, and Diane Hoffman-Kim. "The Importance of Defining ‘Data’ in Data Management Policies." Science and Engineering Ethics 16, no. 4 (September 19, 2010): 749–51. http://dx.doi.org/10.1007/s11948-010-9231-5.

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9

Chaoli Wang, Hongfeng Yu, and Kwan-Liu Ma. "Importance-Driven Time-Varying Data Visualization." IEEE Transactions on Visualization and Computer Graphics 14, no. 6 (November 2008): 1547–54. http://dx.doi.org/10.1109/tvcg.2008.140.

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10

Jollis, James G. "Coronary Revascularization: Importance of Observational Data." Mayo Clinic Proceedings 71, no. 10 (October 1996): 1016–17. http://dx.doi.org/10.1016/s0025-6196(11)63780-4.

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11

Callaghan, Sarah. "On the Importance of Data Transparency." Patterns 1, no. 4 (July 2020): 100070. http://dx.doi.org/10.1016/j.patter.2020.100070.

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12

Ooi, Hong-Kean, and Ching-I. Chen. "The importance of updating epidemiological data." Parasitology Today 13, no. 1 (January 1997): 40. http://dx.doi.org/10.1016/s0169-4758(97)81615-8.

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13

Seixas, T. M., and M. A. Salgueiro da Silva. "The Importance of Measurement Data Spacing." Physics Teacher 53, no. 6 (September 2015): 356–57. http://dx.doi.org/10.1119/1.4928351.

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14

Fadiya, Samson, and Arif Sari. "The importance of big data technology." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 485. http://dx.doi.org/10.14419/ijet.v7i4.5.21139.

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The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectiveness at a business level. Apache Hadoop is an example of such a framework. This report discusses big data, it origins, opportunities and challenges that it presents, big data analytics and the application of big data using existing big data tools or frameworks. It also discusses Apache Hadoop as a big data framework and provides a basic overview of this technology from technological and business perspectives.

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15

Aitken, S. A., J. D. Hutchison, M. M. McQueen, and C. M. Court-Brown. "The importance of epidemiological fracture data." Bone & Joint Journal 96-B, no. 7 (July 2014): 863–67. http://dx.doi.org/10.1302/0301-620x.96b7.34023.

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16

Abbasi, Kamran. "Moral distress and the importance of data." Journal of the Royal Society of Medicine 115, no. 8 (July 28, 2022): 283. http://dx.doi.org/10.1177/01410768221115275.

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17

Kumar, Mayank, Shivam Tiwari, and Sansar Singh Chauhan. "Importance of Big Data Mining: (Tools, Techniques)." Journal of Big Data Technology and Business Analytics 1, no. 2 (August 22, 2022): 32–36. http://dx.doi.org/10.46610/jbdtba.2022.v01i02.005.

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We are in that era in which a substantial amount of data is being generated all the time. The need for tools and techniques is increasing day by day to evaluate and maintain this data correctly. Data analytics is beneficial in extracting important information because this data contains many types of information. There are many types of tools, techniques and algorithms for data mining, with the help of which we do data mining easily. These tools and algorithms can handle considerable data easily and also give important information from it at the time of need. Given the increasing order of data day by day, new tools and techniques are coming, with the help of which mining of data is becoming easier. Sometimes with the help of some good tools, we can only visualize the data which will be useful in future, which also saves our time and expens

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18

Song, Sang Wook, and Yoosoo Chang. "Importance of Weighted Analysis for Sampled Data." Korean Journal of Family Medicine 30, no. 10 (2009): 759. http://dx.doi.org/10.4082/kjfm.2009.30.10.759.

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19

Sidharthan, Rashmi. "Importance of quality in clinical data management." International Journal of Medical Engineering and Informatics 1, no. 3 (2009): 298. http://dx.doi.org/10.1504/ijmei.2009.022641.

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20

&NA;. "The Importance of Data in Holistic Care." Holistic Nursing Practice 25, no. 5 (2011): 223. http://dx.doi.org/10.1097/hnp.0b013e31822b3123.

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21

Roland, D., M. W. Shahzad, and F. Davies. "The importance of currency in data trends." Archives of Disease in Childhood 98, no. 7 (April 27, 2013): 568–69. http://dx.doi.org/10.1136/archdischild-2013-304182.

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22

Ross, Joseph S., Richard Lehman, and Cary P. Gross. "The Importance of Clinical Trial Data Sharing." Circulation: Cardiovascular Quality and Outcomes 5, no. 2 (March 2012): 238–40. http://dx.doi.org/10.1161/circoutcomes.112.965798.

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23

Signorini, David F., Ian R. Piper, Patricia A. Jones, and Timothy P. Howells. "Importance of textual data in multimodality monitoring." Critical Care Medicine 25, no. 12 (December 1997): 2048–50. http://dx.doi.org/10.1097/00003246-199712000-00024.

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24

Sørlie, Therese, Eldrid Borgan, Simen Myhre, Hans Kristian Vollan, Hege Russnes, Xi Zhao, Gro Nilsen, Ole Christian Lingjærde, Anne-Lise Børresen-Dale, and Einar Rødland. "The importance of gene-centring microarray data." Lancet Oncology 11, no. 8 (August 2010): 719–20. http://dx.doi.org/10.1016/s1470-2045(10)70174-1.

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25

Jamieson, W. R. Eric. "Bioprosthetic durability assessment: Importance of complete data." Journal of Thoracic and Cardiovascular Surgery 141, no. 2 (February 2011): 596. http://dx.doi.org/10.1016/j.jtcvs.2010.09.058.

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26

Isaacson, Keith, and Stephen L. Corson. "The Importance of Transparency in Data Reporting." Journal of Minimally Invasive Gynecology 22, no. 7 (November 2015): 1123. http://dx.doi.org/10.1016/j.jmig.2015.09.009.

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27

Black, C. "Importance of thermophysical data in process simulation." International Journal of Thermophysics 7, no. 4 (July 1986): 987–1002. http://dx.doi.org/10.1007/bf00503853.

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28

Agrawal, Alka, and Nawaf Rasheed Alharbe. "Need and Importance of Healthcare Data Integrity." International Journal of Engineering and Technology 11, no. 4 (August 31, 2019): 854–59. http://dx.doi.org/10.21817/ijet/2019/v11i4/191104033.

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29

Redberg, Rita F. "The Importance of Postapproval Data for Dabigatran." JAMA Internal Medicine 175, no. 1 (January 1, 2015): 25. http://dx.doi.org/10.1001/jamainternmed.2014.6265.

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30

Celeux, Gilles, Jean-Michel Marin, and Christian P. Robert. "Iterated importance sampling in missing data problems." Computational Statistics & Data Analysis 50, no. 12 (August 2006): 3386–404. http://dx.doi.org/10.1016/j.csda.2005.07.018.

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31

Menaspà, Paolo. "Building evidence with flawed data? The importance of analysing valid data." British Journal of Sports Medicine 51, no. 15 (February 21, 2017): 1173. http://dx.doi.org/10.1136/bjsports-2016-097029.

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32

Czetwertyński, Sławomir. "Importance of big data in statistics and dynamics of economic efficiency." Zeszyty Naukowe Uniwersytetu Szczecińskiego. Studia Informatica 45 (2017): 5–14. http://dx.doi.org/10.18276/si.2017.45-01.

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33

McCullough, Dale R. "Importance of population data in forest management planning." Forestry Chronicle 70, no. 5 (October 1, 1994): 533–37. http://dx.doi.org/10.5558/tfc70533-5.

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Recent shifts in emphasis to preservation of biological diversity and establishment of large nature reserves have resulted in downgrading of the perceived importance of data at the population level. These shifts in emphasis, while laudable, should not result in loss of focus on the fundamental importance of population information. Suitable habitat, although a prime necessity, is not sufficient for wildlife preservation. Species may not occupy otherwise suitable habitat because of internal (predation, interspecific competition, disease) or external (adverse climate, exploitation, disturbance, impact of exotic species, pollution and toxicants) factors. Historical impacts may persist for many years, and may require active restoration. Forest management plans must consider population data on a metapopulation scale. Data for common species can be at the reconnaissance level whereas for threatened or sensitive species they need to be more detailed. To evaluate sustainability, population data must be integrated with forest stand data in projection models to predict habitat-population mosaics at various times in the future. These models must be developed and improved over time in an adaptive management approach.

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34

R.B, Bhise. "“Importance of Data Mining in Higher Education System”." IOSR Journal of Humanities and Social Science 6, no. 6 (2013): 18–21. http://dx.doi.org/10.9790/0837-0661821.

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35

Ziembińska, Paulina. "The importance of data revisions for statistical inference." Wiadomości Statystyczne. The Polish Statistician 66, no. 2 (February 26, 2021): 7–24. http://dx.doi.org/10.5604/01.3001.0014.7387.

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The aim of the study is a quantitative analysis of revisions conducted by means of a new, real-time macroeconomic dataset for Poland, designed on the basis of the Statistical bulletin (Biuletyn statystyczny) published by Statistics Poland, covering the period from as early as 1995 until 2017. Polish data have positively verified a number of hypotheses concerning the impact of data revisions on the modelling process. Procedures assessing the properties of time series can yield widely discrepant results, depending on the extent to which the applied data have been revised. A comparison of the fitted ARIMA models for series of initial and final data demonstrates that the fitted models are similar for the majority of variables. In the cases where the form of the model is identical for both series, the coefficients retain their scale and sign. Most differences between coefficients result from a different structure of the fitted model, which causes differences in the autoregressive structure and can have a considerable impact on the ex ante inference. A prognostic experiment confirmed these observations. For a large number of variables, the total impact of revisions on the forecasting process exceeds 10%. Extreme cases, where the impact goes beyond 100%, or situations where data have a direct impact on the forecast sign, are also relatively frequent. Taking these results into account by forecasters could significantly improve the quality of their predictions. The forecast horizon has a minor impact on these conclusions. The article is a continuation of the author's work from 2017.

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36

Poirier, Ryan. "Volatility Managed Indexes: The Importance of Intraday Data." Journal of Index Investing 12, no. 3 (November 12, 2021): 15–31. http://dx.doi.org/10.3905/jii.2021.1.112.

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37

Priedols, Valdis, and Armands Grickus. "Importance of data acquisition in problem based learning." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 155. http://dx.doi.org/10.17770/etr2015vol3.176.

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This research paper demonstrates untraditional learning and teaching method that is developed from combination of experimentation, usage of computer simulations and problem based learning. Taking all previously mentioned methods together there can be created very successful learning environment which provides students to master electromagnetism more effectively. Research focuses on proper use of data acquisition modules and computer simulations in PBL teaching method. Based on the results of the research experimental PBL in various cases provides better learning outcomes, but there are also a few occasions where the results aren’t so pleasing. Overall PBL provides results that are at least as promising as results of other teaching and learning methods. Therefore this method will be utilized in Liepaja University to teach physics, especially electromagnetism.

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38

Halcomb, Elizabeth. "Understanding the importance of collecting qualitative data creatively." Nurse Researcher 23, no. 3 (January 22, 2016): 6–7. http://dx.doi.org/10.7748/nr.23.3.6.s2.

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39

Wolski, Malcolm, Louise Howard, and Joanna Richardson. "The importance of tools in the data lifecycle." Digital Library Perspectives 33, no. 3 (August 14, 2017): 235–52. http://dx.doi.org/10.1108/dlp-11-2016-0042.

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Purpose This paper aims to outline principal implications for institutions, particularly universities, in supporting the increasingly complex tools which are used in the data lifecycle. Design/methodology/approach The discussion paper draws upon the experience of authors in this domain at the institutional, national and international levels. Findings Support for research tools by universities has high-level implications, ranging from financial, strategic and compliance through to capacity, capability and connectivity. The large number of existing tools highlights the need to evaluate them against standardised checklists to determine suitability and levels of resources required for support. Librarians and other information professionals need to expand their current support for research tools beyond the discovery phase to the entire data lifecycle. Practical implications Universities can use this paper to assess their maturity in supporting tools in the data lifecycle. Librarians, in particular, can broaden their knowledge of the various categories of tools which support specific aspects of that lifecycle. Originality/value While much attention is currently being focused on supporting researchers with their data management requirements, there is a general lack of literature on how to support tools as a critical element in enhancing research outcomes.

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40

Drazen, Jeffrey M., Stephen Morrissey, Debra Malina, Mary Beth Hamel, and Edward W. Campion. "The Importance — and the Complexities — of Data Sharing." New England Journal of Medicine 375, no. 12 (September 22, 2016): 1182–83. http://dx.doi.org/10.1056/nejme1611027.

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41

Ceresio, C., O. Cabellos, J. S. Martínez, and C. J. Diez. "Importance of Nuclear Data Uncertainties in Criticality Calculations." EPJ Web of Conferences 27 (2012): 00004. http://dx.doi.org/10.1051/epjconf/20122700004.

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42

Sharma, Vikash, Bhavna Pandey, and Vipin Kumar. "Importance of Big Data in financial fraud detection." International Journal of Automation and Logistics 2, no. 4 (2016): 332. http://dx.doi.org/10.1504/ijal.2016.080339.

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43

Herselman, M., and L. Du Plessis. "The importance of access to food intake data." South African Journal of Clinical Nutrition 24, no. 1 (January 2011): 7–8. http://dx.doi.org/10.1080/16070658.2011.11734344.

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44

Isobe, Noriko. "Emerging importance of big data in neuroimmunological research." Clinical and Experimental Neuroimmunology 9, no. 1 (February 2018): 3. http://dx.doi.org/10.1111/cen3.12451.

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45

Larson, Kelly, Kelly Henning, and Margie Peden. "The Importance of Data for Global Road Safety." Traffic Injury Prevention 13, sup1 (March 2012): 3–4. http://dx.doi.org/10.1080/15389588.2012.671737.

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46

Zink, Richard C., and Geoffrey Mann. "On the Importance of a Single Data Standard." Drug Information Journal 46, no. 3 (May 2012): 362–67. http://dx.doi.org/10.1177/0092861512441101.

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47

Raheja, Kavita, Ajay Dubey, and Rahul Chawda. "Data Analysis and its Importance in Health Care." International Journal of Computer Trends and Technology 48, no. 4 (June 25, 2017): 176–80. http://dx.doi.org/10.14445/22312803/ijctt-v48p132.

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48

HARPER, ROBERT A., and BARNABY C. REEVES. "Glaucoma Screening: The Importance of Combining Test Data." Optometry and Vision Science 76, no. 8 (August 1999): 537–43. http://dx.doi.org/10.1097/00006324-199908000-00022.

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49

Laird, Trevor. "The Importance of Experimental Data and Record Keeping." Organic Process Research & Development 6, no. 1 (January 2002): 1. http://dx.doi.org/10.1021/op015517p.

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50

Contri, Enrico, Antonella Brancaglione, Immacolata Romeo, Manuela Guerci, Vito Sgromo, Fabrizio Canevari, Alessandra Palo, and Paolo Galimberti. "Telephone CPR improvement: the importance of sharing data." Resuscitation 142 (September 2019): e107-e108. http://dx.doi.org/10.1016/j.resuscitation.2019.06.259.

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