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Journal articles on the topic 'Data states'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Agodoa, Lawrence Y., and Paul W. Eggers. "Renal replacement therapy in the United States: Data from the United States renal data system." American Journal of Kidney Diseases 25, no. 1 (1995): 119–33. http://dx.doi.org/10.1016/0272-6386(95)90638-x.

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2

Levine-Clark, Michael. "United States Historical Census Data Browser." Journal of Government Information 28, no. 1 (2001): 119–22. http://dx.doi.org/10.1016/s1352-0237(01)00262-3.

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3

Pandey, Pankaj, Byom Kesh Jha, and Neelam Sinha. "Analyzing Cognitive States Using fMRI Data." Procedia Computer Science 90 (2016): 35–41. http://dx.doi.org/10.1016/j.procs.2016.07.007.

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4

Boyne, Shawn Marie. "Data Protection in the United States." American Journal of Comparative Law 66, suppl_1 (2018): 299–343. http://dx.doi.org/10.1093/ajcl/avy016.

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5

Fink, Katherine, and C. W. Anderson. "Data Journalism in the United States." Journalism Studies 16, no. 4 (2014): 467–81. http://dx.doi.org/10.1080/1461670x.2014.939852.

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6

Janoos, Firdaus, Raghu Machiraju, and Istvan A. Morocz. "Decoding brain states from fMRI data." International Journal of Psychophysiology 77, no. 3 (2010): 322–23. http://dx.doi.org/10.1016/j.ijpsycho.2010.06.244.

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7

Ahmad, Farida B., Jodi A. Cisewski, Arialdi Miniño, and Robert N. Anderson. "Provisional Mortality Data — United States, 2020." MMWR. Morbidity and Mortality Weekly Report 70, no. 14 (2021): 519–22. http://dx.doi.org/10.15585/mmwr.mm7014e1.

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8

de Jong, A. J., B. van Loenen, and J. A. Zevenbergen. "GEOGRAPHIC DATA AS PERSONAL DATA IN FOUR EU MEMBER STATES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 151–57. http://dx.doi.org/10.5194/isprsannals-iii-2-151-2016.

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The EU Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data aims at harmonising data protection legislation in the European Union. This should promote the free flow of products and services within the EU. This research found a wide variety of interpretations of the application of data protection legislation to geographic data. The variety was found among the different EU Member States, the different stakeholders and the different types of geographic data. In the Netherlands, the Data Protection Authority (DPA)

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9

de Jong, A. J., B. van Loenen, and J. A. Zevenbergen. "GEOGRAPHIC DATA AS PERSONAL DATA IN FOUR EU MEMBER STATES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 151–57. http://dx.doi.org/10.5194/isprs-annals-iii-2-151-2016.

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The EU Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data aims at harmonising data protection legislation in the European Union. This should promote the free flow of products and services within the EU. This research found a wide variety of interpretations of the application of data protection legislation to geographic data. The variety was found among the different EU Member States, the different stakeholders and the different types of geographic data. In the Netherlands, the Data Protection Authority (DPA)

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10

Gellman, Robert. "Data privacy law: A study of United States data protection." Government Information Quarterly 14, no. 2 (1997): 215–17. http://dx.doi.org/10.1016/s0740-624x(97)90026-5.

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11

Collins, Allan J., Robert Foley, Charles Herzog, et al. "United States Renal Data System 2007 Annual Data Report Abstract." American Journal of Kidney Diseases 51, no. 1 (2008): A6—A7. http://dx.doi.org/10.1053/j.ajkd.2007.11.001.

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12

Collins, Allan J., Robert N. Foley, Charles Herzog, et al. "United States Renal Data System 2008 Annual Data Report Abstract." American Journal of Kidney Diseases 53, no. 1 (2009): A6—A7. http://dx.doi.org/10.1053/j.ajkd.2008.10.005.

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13

Collins, Allan J., Bertram Kasiske, Charles Herzog, et al. "United States Renal Data System 2005 Annual Data Report Abstract." American Journal of Kidney Diseases 47 (January 2006): A5—A6. http://dx.doi.org/10.1053/j.ajkd.2005.12.001.

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14

Collins, Allan J., Bertram Kasiske, Charles Herzog, et al. "United States Renal Data System 2006 Annual Data Report Abstract." American Journal of Kidney Diseases 49 (January 2007): A6—A7. http://dx.doi.org/10.1053/j.ajkd.2006.11.019.

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15

Holtfreter, Robert E., and Adrian Harrington. "Data breach trends in the United States." Journal of Financial Crime 22, no. 2 (2015): 242–60. http://dx.doi.org/10.1108/jfc-09-2013-0055.

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Purpose – The main purpose of this paper is to analyze the trends of various types of data breaches and their compromised records in the USA using a new model recently developed by the authors. Design/methodology/approach – The 2,280 data breaches and over 512 million related compromised records tracked by the Privacy Rights Clearinghouse from 2005 through 2010 were analyzed and classified into four external, five internal and one non-traceable data breach categories, after which trends were determined for each. Findings – The findings indicate that although the trends for the annual number of

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16

Zini, G., A. Di Mario, M. Garzia, M. Bianchi, and G. d'Onofrio. "Reticulocyte population data in different erythropoietic states." Journal of Clinical Pathology 64, no. 2 (2011): 159–63. http://dx.doi.org/10.1136/jcp.2010.082669.

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17

Daly, Rich. "Many States Hoard Medicaid Data, Shun Collaboration." Psychiatric News 43, no. 1 (2008): 14. http://dx.doi.org/10.1176/pn.43.1.0014.

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18

Groisman, Pavel Ya, and David R. Legates. "The Accuracy of United States Precipitation Data." Bulletin of the American Meteorological Society 75, no. 2 (1994): 215–27. http://dx.doi.org/10.1175/1520-0477(1994)075<0215:taousp>2.0.co;2.

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19

Moore, Kathleen, Robb Stanley, and Graham Burrows. "Profile of Mood States: Australian Normative Data." Psychological Reports 66, no. 2 (1990): 509–10. http://dx.doi.org/10.2466/pr0.1990.66.2.509.

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The Profile of Mood States was administered to 90 Australian women, 30 depressed, 30 anxious, and 30 nonpsychiatric controls. Both clinical groups scored higher than the McNair, Lorr, and Droppleman (1971) normative samples on the negative mood states and scored lower on Vigor. The means for these groups are presented and compared with the 1971 normative data of McNair, Lorr, and Droppleman.

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20

Durkin, Allison, Brandon Willmore, Caroline Nobo Sarnoff, and David Hemenway. "The Firearms Data Gap." Journal of Law, Medicine & Ethics 48, S4 (2020): 32–38. http://dx.doi.org/10.1177/1073110520979399.

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The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun violence and promote productive conversation about gun policy. In the absence of federal leadership in firearms data systems improvement, motivated states may take proactive steps to stitch gaps in data systems. We propose that states evaluate the gaps in their systems, expand data collection, and improve data presentation and availability.

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21

Jensen, Jennifer M., and Thad Beyle. "Of Footnotes, Missing Data, and Lessons for 50-State Data Collection: The Gubernatorial Campaign Finance Data Project, 1977–2001." State Politics & Policy Quarterly 3, no. 2 (2003): 203–14. http://dx.doi.org/10.1177/153244000300300204.

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This article serves three purposes. First, we present a new source of campaign finance data for the 50 states. This includes annual data on candidate expenditures in gubernatorial races from 1977 to 2001, as well as triennial and biennial data on campaign finance laws affecting gubernatorial races during this period. Second, we review the various sources of campaign finance data for the 50 states. Third, we examine the challenges of collecting and compiling data across the 50 states, since these data can vary so much in availability and measurement. We hope to provide some general lessons to r

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22

Fkih, Fethi, and Mohamed Nazih Omri. "Hidden data states-based complex terminology extraction from textual web data model." Applied Intelligence 50, no. 6 (2020): 1813–31. http://dx.doi.org/10.1007/s10489-019-01568-4.

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23

Kundu, Sourav, and Rajshekhar Singhania. "Forecasting the United States Unemployment Rate by Using Recurrent Neural Networks with Google Trends Data." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 135–40. http://dx.doi.org/10.18178/ijtef.2020.11.6.679.

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We study the problem of obtaining an accurate forecast of the unemployment claims using online search data. The motivation for this study arises from the fact that there is a need for nowcasting or providing a reliable short-term estimate of the unemployment rate. The data regarding initial jobless claims are published by the US Department of labor weekly. To tackle the problem of getting an accurate forecast, we propose the use of the novel Long Short-Term Memory (LSTM) architecture of Recurrent Neural Networks, to predict the unemployment claims (initial jobless claims) using the Google Tren

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24

Steele, Molly K., Mary E. Wikswo, Aron J. Hall, et al. "Characterizing Norovirus Transmission from Outbreak Data, United States." Emerging Infectious Diseases 26, no. 8 (2020): 1818–25. http://dx.doi.org/10.3201/eid2608.191537.

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25

Mattioli, Michael. "Data Policy in the United States: New Challenges." Zeitschrift für geistiges Eigentum 9, no. 3 (2017): 299. http://dx.doi.org/10.1628/186723717x15069451170883.

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26

Woodcock, Kathryn. "Amusement ride injury data in the United States." Safety Science 62 (February 2014): 466–74. http://dx.doi.org/10.1016/j.ssci.2013.10.003.

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27

Madsen, Wayne. "United States remains adamantly opposed to data protection." Computer Fraud & Security 1996, no. 12 (1996): 6–10. http://dx.doi.org/10.1016/s1361-3723(97)82569-9.

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28

SIMON, JULIAN L. "Basic Data Concerning Immigration into the United States." ANNALS of the American Academy of Political and Social Science 487, no. 1 (1986): 12–56. http://dx.doi.org/10.1177/0002716286487001002.

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29

Jayaraman, K. S. "India and United States to share satellite data." Nature 391, no. 6663 (1998): 109. http://dx.doi.org/10.1038/34236.

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30

KATZ, RICHARD S. "United States: Political development and data for 2017." European Journal of Political Research Political Data Yearbook 57, no. 1 (2018): 299–307. http://dx.doi.org/10.1111/2047-8852.12199.

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31

KATZ, RICHARD S. "United States: Political Developments and Data in 2018." European Journal of Political Research Political Data Yearbook 58, no. 1 (2019): 287–93. http://dx.doi.org/10.1111/2047-8852.12251.

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32

KATZ, RICHARD S. "United States: Political development and data for 2019." European Journal of Political Research Political Data Yearbook 59, no. 1 (2020): 386–92. http://dx.doi.org/10.1111/2047-8852.12282.

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33

BRINT, STEVEN. "Data on Higher Education in the United States." American Behavioral Scientist 45, no. 10 (2002): 1493–522. http://dx.doi.org/10.1177/0002764202045010004.

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Higher education is a mixed sector. It includes many public institutions as well as many independent colleges and universities. It also includes some for-profit enterprises. Data resources for the study of higher education are generally very good. This is particularly true for studies of students, faculty, institutional quality, and financial resources. This article provides a catalog of existing data resources, including comments about limitations in the quality of some data sources. The article also discusses data resources needs for the future. These needs will focus on key changes in highe

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34

Bigerelle, M., and A. Iost. "Characterisation of the diffusion states by data compression." Computational Materials Science 24, no. 1-2 (2002): 133–38. http://dx.doi.org/10.1016/s0927-0256(02)00191-x.

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35

Rüegger, Dominik, Mirjam Stieger, Marcia Nißen, Mathias Allemand, Elgar Fleisch, and Tobias Kowatsch. "How Are Personality States Associated with Smartphone Data?" European Journal of Personality 34, no. 5 (2020): 687–713. http://dx.doi.org/10.1002/per.2309.

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Smartphones promise great potential for personality science to study people's everyday life behaviours. Even though personality psychologists have become increasingly interested in the study of personality states, associations between smartphone data and personality states have not yet been investigated. This study provides a first step towards understanding how smartphones may be used for behavioural assessment of personality states. We explored the relationships between Big Five personality states and data from smartphone sensors and usage logs. On the basis of the existing literature, we fi

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36

Conception, Maria, I. Donoso, Bernard Le Mehaute, and Robert Bryan Long. "Data Base of Maximum Sea States During Hurricanes." Journal of Waterway, Port, Coastal, and Ocean Engineering 113, no. 4 (1987): 311–26. http://dx.doi.org/10.1061/(asce)0733-950x(1987)113:4(311).

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37

Currie, Robert G., and Douglas P. O'Brien. "Deterministic signals in Southeast United States precipitation data." Journal of Environmental Science and Health . Part A: Environmental Science and Engineering and Toxicology 27, no. 3 (1992): 827–41. http://dx.doi.org/10.1080/10934529209375763.

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38

O'Brien, Carl M. "Data Analysis with Competing Risks and Intermediate States." International Statistical Review 84, no. 2 (2016): 319–20. http://dx.doi.org/10.1111/insr.12186.

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39

Simpson, Kathleen Rice. "Trends in Natality Data in the United States." MCN, The American Journal of Maternal/Child Nursing 45, no. 3 (2020): 192. http://dx.doi.org/10.1097/nmc.0000000000000623.

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40

Demo, Teresa L. "United States congressional districts and data, 1843–1883." Government Information Quarterly 5, no. 1 (1988): 89–90. http://dx.doi.org/10.1016/0740-624x(88)90065-2.

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41

Janz, G. J., T. Yamamura, and M. D. Hansen. "Corresponding-states data correlations and molten salts viscosities." International Journal of Thermophysics 10, no. 1 (1989): 159–71. http://dx.doi.org/10.1007/bf00500716.

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42

Vyhnanek, Louis. "United States Congressional districts and data, 1843–1883." Government Publications Review 13, no. 6 (1986): 804–5. http://dx.doi.org/10.1016/0277-9390(86)90085-3.

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43

Kerr, K. M., W. R. Auger, R. L. Benza, et al. "Preliminary Data from the United States CTEPH Registry." Journal of Heart and Lung Transplantation 36, no. 4 (2017): S19—S20. http://dx.doi.org/10.1016/j.healun.2017.01.039.

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44

McGuire, Lisa C. "DATA FOR ACTION: CDC’S BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM (BRFSS) COGNITIVE DECLINE AND CAREGIVING DATA." Innovation in Aging 3, Supplement_1 (2019): S366—S367. http://dx.doi.org/10.1093/geroni/igz038.1339.

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Abstract The Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance System (BRFSS) is the world’s largest ongoing health survey, administered in all 50 U.S. states, as well as the District of Columbia and the three U.S. territories, with data collected from more than 400,000 respondents. The BRFSS collects data on cognitive decline and caregiving as well as many health behaviors, annually. In 2015-2017, the 6-item cognitive decline module has been administered in 49 states, DC, and Puerto Rico, while the 9-item caregiving has been administered in 44 states, DC,

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45

McQuade, Brendan. "World Histories of Big Data Policing." Journal of World-Systems Research 27, no. 1 (2021): 109–35. http://dx.doi.org/10.5195/jwsr.2021.1033.

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Textbook presentations of U.S. policing name the present as new stage of professionalization: the homeland security era, where the application of “big data” promises “smarter” policing. Within this framework of gradual progress, liberal police scholarship has become the official criticism of big data policing to organize a project of liberal reform. Of course, this scholarship is being in written in the context of both militant social movements within the United States and the terminal decline of U.S. global hegemony. To clarify the stakes of this moment, this paper connects the Marxist anti-s

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46

Yang, Bijou, and David Lester. "Which States Have the Death Penalty: Data from 1980." Psychological Reports 65, no. 1 (1989): 185–86. http://dx.doi.org/10.2466/pr0.1989.65.1.185.

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47

Trestrail, John H. "Mushroom Poisoning in the United States-An Analysis of 1989 United States Poison Center Data." Journal of Toxicology: Clinical Toxicology 29, no. 4 (1991): 459–65. http://dx.doi.org/10.3109/15563659109025741.

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48

Van Moorter, Bram, Darcy R. Visscher, Christopher L. Jerde, Jacqueline L. Frair, and Evelyn H. Merrill. "Identifying Movement States From Location Data Using Cluster Analysis." Journal of Wildlife Management 74, no. 3 (2010): 588–94. http://dx.doi.org/10.2193/2009-155.

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49

Carroll, Stephanie Russo, Desi Rodriguez-Lonebear, and Andrew Martinez. "Indigenous Data Governance: Strategies from United States Native Nations." Data Science Journal 18, no. 1 (2019): 31. http://dx.doi.org/10.5334/dsj-2019-031.

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50

Rotenberg, Marc. "Data protection in the United States — A rising tide?" Computer Law & Security Review 14, no. 1 (1998): 38–40. http://dx.doi.org/10.1016/s0267-3649(97)88075-2.

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