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Journal articles on the topic 'All-data'

To see the other types of publications on this topic, follow the link: All-data.

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Prathap, Jomma, and Ramavath Janu. "All Data Aggregation in Wireless Sensor Networks." International Journal of Innovative Research in Engineering & Management 4, no. 6 (November 2017): 757–60. http://dx.doi.org/10.21276/ijirem.2017.4.6.2.

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2

Harp, Nick. "All Data." Cream City Review 40, no. 1 (2016): 17. http://dx.doi.org/10.1353/ccr.2016.0041.

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3

McCall, Becky. "Data, data all around." Lancet Digital Health 3, no. 5 (May 2021): e284-e285. http://dx.doi.org/10.1016/s2589-7500(21)00063-7.

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4

Rosamond, Emily. "“All Data is Credit Data”." Paragrana 25, no. 2 (December 1, 2016): 112–24. http://dx.doi.org/10.1515/para-2016-0032.

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AbstractThis essay examines new means of measuring creditworthiness, reputation and character online and briefly considers the implications for contemporary art. New technologies for determining creditworthiness abound; for instance, companies in the so-called fintech (financial technology) industry, provide new methods for granting credit to the underbanked, using big data analytics and psychometric testing. Similarly, Rachel Botsman and others envision a future in which reputation becomes a kind of currency, following its bearers from platform to platform. Together, the world of consciously projected reputation-images online and the fintech industry’s inconspicuous measurement of creditworthiness form a conscious/ unconscious couplet of character measurement apparatuses. Character, in these data analytic worlds, acts as a lived fiction, a representation of futurity online that determines in advance one’s level of access to markets and social spheres. How might these emerging conditions change the ways in which artworks understand – and perhaps resist – the demand to be “good” characters online? Some possible artistic responses to this world of character measurement include questioning the correlative logics of measurement itself and testing the limits of creditworthy character traits, in order to demonstrate that credit must always rely on a set of locally shared assumptions as to what might be considered “desirable” behaviour.

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5

Mervis, Jeffrey. "Data for all?" Science 355, no. 6325 (February 9, 2017): 573. http://dx.doi.org/10.1126/science.355.6325.573.

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6

Turner, E. H. "All the relevant data?" BMJ 341, no. 16 4 (November 16, 2010): c6487. http://dx.doi.org/10.1136/bmj.c6487.

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7

Barbui, Corrado. "Sharing (all) clinical data." European Journal of Internal Medicine 33 (September 2016): e27. http://dx.doi.org/10.1016/j.ejim.2016.05.016.

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8

Schultz, John R. "Big Data Are, After All, Just Data." Performance Improvement 53, no. 5 (May 2014): 20–25. http://dx.doi.org/10.1002/pfi.21411.

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9

Tan, Hao, and Wojciech Golab. "Optimizing All-to-All Data Transmission in WANs." IEEE Transactions on Network and Service Management 18, no. 3 (September 2021): 3677–90. http://dx.doi.org/10.1109/tnsm.2021.3071025.

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10

Martens, Lennart, and Henning Hermjakob. "Proteomics data validation: why all must provide data." Molecular BioSystems 3, no. 8 (2007): 518. http://dx.doi.org/10.1039/b705178f.

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11

Aitken, Rob. "‘All data is credit data’: Constituting the unbanked." Competition & Change 21, no. 4 (June 20, 2017): 274–300. http://dx.doi.org/10.1177/1024529417712830.

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12

Peterson, Gordon, Geoff Boxshall, Neil Thomson, and Charles Hussey. "Where Are All the Data?" Oceanography 13, no. 3 (2000): 21–24. http://dx.doi.org/10.5670/oceanog.2000.04.

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13

K, Sathish, Aswinkumar R, Theresal T, and Dhanabal S. "ALL OPTICAL REVERSIBLE DATA DISTRIBUTOR." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 1–11. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.25.

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Nowadays, It became the fashion among the researchers about creating the New Reversible Gates. In the Reversible Literature, already many gates are proposed but it is the first time to propose a Gate for a decoder(Data Distributor). The proposed GLG (Garbage Less Gate) has No Garbage output which denotes its power efficiency. In this paper 2:4 reversible decoder is constructed using GLG. The proposed gate is also extended to N:2N decoder using the proposed GLG Gate and the Fredkin Gate. The theoretical proposition is verified through Optisystem & Modelsim Software. A comparison with existing reversible decoders is also included.

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14

Newby, Rob. "It's all about the data." Infosecurity 5, no. 4 (May 2008): 43. http://dx.doi.org/10.1016/s1754-4548(08)70063-9.

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15

Pérez-Enciso, Miguel, José R. Quevedo, and Antonio Bahamonde. "Genetical genomics: use all data." BMC Genomics 8, no. 1 (2007): 69. http://dx.doi.org/10.1186/1471-2164-8-69.

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16

Hamilton, D. Kirk. "It's All About the Data." HERD: Health Environments Research & Design Journal 7, no. 4 (July 2014): 96–99. http://dx.doi.org/10.1177/193758671400700408.

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17

Solomon, Martin D. "It's All About the Data." Information Systems Management 22, no. 3 (June 2005): 75–80. http://dx.doi.org/10.1201/1078/45317.22.3.20050601/88747.9.

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18

Barrett, Gerald V. "Empirical data say it all." American Psychologist 49, no. 1 (1994): 69–71. http://dx.doi.org/10.1037/0003-066x.49.1.69.

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19

Berg, Tamara L., Alexander Sorokin, Gang Wang, David Alexander Forsyth, Derek Hoiem, Ian Endres, and Ali Farhadi. "It's All About the Data." Proceedings of the IEEE 98, no. 8 (August 2010): 1434–52. http://dx.doi.org/10.1109/jproc.2009.2032355.

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20

Morse, Janice M. "All Data Are Not Equal." Qualitative Health Research 25, no. 9 (August 12, 2015): 1169–70. http://dx.doi.org/10.1177/1049732315597655.

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21

Baggerly, Keith. "Disclose all data in publications." Nature 467, no. 7314 (September 2010): 401. http://dx.doi.org/10.1038/467401b.

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22

Owens, Brian. "Data sharing: Access all areas." Nature 533, no. 7602 (May 2016): S71—S72. http://dx.doi.org/10.1038/533s71a.

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23

Priestly, Jennifer. "We're all data geeks now." IEEE Spectrum 52, no. 8 (August 2015): 29. http://dx.doi.org/10.1109/mspec.2015.7164398.

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24

Smith, R. "All data and all diagnoses deserve a critical eye." European Journal of Public Health 23, no. 3 (April 18, 2013): 351–52. http://dx.doi.org/10.1093/eurpub/ckt043.

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25

Tegtmeyer, Ken. "Data, Data, Data—But How to Keep Track of It All." American Journal of Lifestyle Medicine 1, no. 2 (March 2007): 144–45. http://dx.doi.org/10.1177/1559827606297324.

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26

Martin, Erika G., and Grace M. Begany. "Transforming Government Health Data Into All-Star Open Data." Journal of Public Health Management and Practice 24, no. 6 (2018): E23—E25. http://dx.doi.org/10.1097/phh.0000000000000799.

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27

Richardson, Alice, and Tony Badrick. "All about love and big data." Journal of Laboratory and Precision Medicine 3 (September 2018): 78. http://dx.doi.org/10.21037/jlpm.2018.09.03.

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28

Palme, Jacob, and Gunborg Palme. "Can Stored Data Answer All Questions." International Journal of Reliable and Quality E-Healthcare 5, no. 2 (April 2016): 65–79. http://dx.doi.org/10.4018/ijrqeh.2016040104.

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A common statement is that 80% of the questions can be answered by a small set of perhaps 20% of all answers. To investigate this statement, a set of 630 questions, which were sent to an on-line service, were analyzed. All the questions were about eating disorders and obesity. The conclusion is that the statement, which was investigated, was not true. There was a large variation of different questions, and even though some questions did appear more than once, it was much less than 80% of all questions. The paper offers insight into different types of questions posted on-line in the area of eating disorders. From the study follows that to provide satisfactory answers to a large part of the questions, it is necessary to have humans answering the questions.

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29

Dodge, David A. "It all starts with the data." Statistical Journal of the United Nations Economic Commission for Europe 20, no. 2 (February 17, 2004): 155–61. http://dx.doi.org/10.3233/sju-2003-20210.

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30

Wisman, T. "Introduction: Data Protection in All Directions." European Data Protection Law Review 3, no. 3 (2017): 399–401. http://dx.doi.org/10.21552/edpl/2017/3/17.

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31

van den Hoven, John. "Data Warehousing: Bringing it All Together." Information Systems Management 15, no. 2 (March 1998): 92–96. http://dx.doi.org/10.1201/1078/43184.15.2.19980301/31127.16.

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32

Astone, P., M. Bassan, P. Bonifazi, K. M. Borkowski, R. J. Budzyński, A. Chincarini, E. Coccia, et al. "All-sky search of NAUTILUS data." Classical and Quantum Gravity 25, no. 18 (September 2, 2008): 184012. http://dx.doi.org/10.1088/0264-9381/25/18/184012.

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33

COLLOPY, B. T. "Data Bank — “1984 And All That”." International Journal for Quality in Health Care 9, no. 5 (1997): 327–28. http://dx.doi.org/10.1093/intqhc/9.5.327.

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34

Butler, Declan. "Google makes data free for all." Nature 438, no. 7067 (November 23, 2005): 400–401. http://dx.doi.org/10.1038/438400a.

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35

Service, R. F. "STRUCTURAL GENOMICS:Protein Data Justice for All." Science 288, no. 5468 (May 12, 2000): 939b—941. http://dx.doi.org/10.1126/science.288.5468.939b.

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36

Neill, Ushma S. "All data are not created equal." Journal of Clinical Investigation 119, no. 3 (March 2, 2009): 424. http://dx.doi.org/10.1172/jci38802.

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37

Heger, Michal. "Don't discount all curcumin trial data." Nature 543, no. 7643 (March 2017): 40. http://dx.doi.org/10.1038/543040c.

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38

Howe, Chanelle J., Lauren E. Cain, and Joseph W. Hogan. "Are All Biases Missing Data Problems?" Current Epidemiology Reports 2, no. 3 (July 12, 2015): 162–71. http://dx.doi.org/10.1007/s40471-015-0050-8.

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39

Jones, P. J. "Data users must observe all principles of Data Protection Act." BMJ 313, no. 7056 (August 31, 1996): 560. http://dx.doi.org/10.1136/bmj.313.7056.560a.

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40

Vingelmann, Péter, Janus Heide, Morten Videbæk Pedersen, Qi Zhang, and Frank H. P. Fitzek. "All-to-all data dissemination with network coding in dynamic MANETs." Computer Networks 74 (December 2014): 34–47. http://dx.doi.org/10.1016/j.comnet.2014.06.018.

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41

Guth, Joseph H., Richard A. Denison, and Jennifer Sass. "Require Comprehensive Safety Data for All Chemicals." NEW SOLUTIONS: A Journal of Environmental and Occupational Health Policy 17, no. 3 (November 2007): 233–58. http://dx.doi.org/10.2190/ns.17.3.g.

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42

Huang, Bo-Ji, Cheng-Ting Tsai, Yung-Hsiang Lin, Chih-Hsien Cheng, Huai-Yung Wang, Yu-Chieh Chi, Po-Han Chang, Chih-I. Wu, and Gong-Ru Lin. "SiGeC Waveguide for All-Optical Data Switching." ACS Photonics 5, no. 6 (March 22, 2018): 2251–60. http://dx.doi.org/10.1021/acsphotonics.8b00105.

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43

Mathieson, SA. "UK gets all-EU traffic data retention." Infosecurity Today 2, no. 4 (July 2005): 4. http://dx.doi.org/10.1016/s1742-6847(05)70291-3.

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44

Mathieson, SA. "UK seeks all-EU traffic data retention." Computer Fraud & Security 2005, no. 7 (July 2005): 1–2. http://dx.doi.org/10.1016/s1361-3723(05)70226-8.

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45

Astone, P., D. Babusci, M. Bassan, K. M. Borkowski, L. Brocco, E. Coccia, S. D'Antonio, et al. "An all-sky search of EXPLORER data." Classical and Quantum Gravity 22, no. 18 (September 6, 2005): S1243—S1254. http://dx.doi.org/10.1088/0264-9381/22/18/s38.

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46

Meliopoulos, A. P. Sakis, George J. Cokkinides, Floyd Galvan, Bruce Fardanesh, and Paul Myrda. "Delivering accurate and timely data to all." IEEE Power and Energy Magazine 5, no. 3 (May 2007): 74–86. http://dx.doi.org/10.1109/mpae.2007.365823.

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47

Braun, Steve, and Henri Hodara. "High Speed All-Optical Data Distribution Network." Fiber and Integrated Optics 36, no. 6 (November 2, 2017): 219–26. http://dx.doi.org/10.1080/01468030.2017.1408238.

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48

Gierasch, Lila M., Nicholas O. Davidson, Kerry-Anne Rye, and Alma L. Burlingame. "The Data Must Be Accessible to All." Molecular & Cellular Proteomics 19, no. 4 (February 18, 2020): 569–70. http://dx.doi.org/10.1074/mcp.e120.001985.

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49

Fearn, Tom. "Why High-Dimensional Data are All Outliers." NIR news 25, no. 7 (November 2014): 20–21. http://dx.doi.org/10.1255/nirn.1485.

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50

Andrews, C. "Is it really all about the data?" Engineering & Technology 11, no. 3 (April 1, 2016): 64–67. http://dx.doi.org/10.1049/et.2016.0330.

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