Relevant bibliographies by topics / Data states

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data states'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Data states"

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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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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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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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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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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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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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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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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) states that panoramic images of streets are considered personal data. While Dutch case law judges that the data protection legislation does not apply if certain features are blurred and no link to an address is provided. The topographic datasets studied in the case studies do not contain personal data, according to the Dutch DPA, while the German DPA and the Belgian DPA judge that topographic maps of a large scale can contain personal data, and impose conditions on the processing of topographic maps. The UK DPA does consider this data outside of the scope of legal definition of personal data. The patchwork of differences in data protection legislation can be harmonised by using a traffic light model. This model focuses on the context in which the processing of the data takes place and has four categories of data: (1) sensitive personal data, (2) personal data, (3), data that can possibly lead to identification, and (4) non-personal data. For some geographic data, for example factual data that does not reveal sensitive information about a person, can be categorised in the third category giving room to opening up data under the INSPIRE Directive.
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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) states that panoramic images of streets are considered personal data. While Dutch case law judges that the data protection legislation does not apply if certain features are blurred and no link to an address is provided. The topographic datasets studied in the case studies do not contain personal data, according to the Dutch DPA, while the German DPA and the Belgian DPA judge that topographic maps of a large scale can contain personal data, and impose conditions on the processing of topographic maps. The UK DPA does consider this data outside of the scope of legal definition of personal data. The patchwork of differences in data protection legislation can be harmonised by using a traffic light model. This model focuses on the context in which the processing of the data takes place and has four categories of data: (1) sensitive personal data, (2) personal data, (3), data that can possibly lead to identification, and (4) non-personal data. For some geographic data, for example factual data that does not reveal sensitive information about a person, can be categorised in the third category giving room to opening up data under the INSPIRE Directive.
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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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Dissertations / Theses on the topic "Data states"

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Zöller, Gert. "Critical states of seismicity : modeling and data analysis." Thesis, Universität Potsdam, 2005. http://opus.kobv.de/ubp/volltexte/2006/742/.

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The occurrence of earthquakes is characterized by a high degree of spatiotemporal complexity. Although numerous patterns, e.g. fore- and aftershock sequences, are well-known, the underlying mechanisms are not observable and thus not understood. Because the recurrence times of large earthquakes are usually decades or centuries, the number of such events in corresponding data sets is too small to draw conclusions with reasonable statistical significance. Therefore, the present study combines both, numerical modeling and analysis of real data in order to unveil the relationships between physical mechanisms and observational quantities. The key hypothesis is the validity of the so-called "critical point concept" for earthquakes, which assumes large earthquakes to occur as phase transitions in a spatially extended many-particle system, similar to percolation models. New concepts are developed to detect critical states in simulated and in natural data sets. The results indicate that important features of seismicity like the frequency-size distribution and the temporal clustering of earthquakes depend on frictional and structural fault parameters. In particular, the degree of quenched spatial disorder (the "roughness") of a fault zone determines whether large earthquakes occur quasiperiodically or more clustered. This illustrates the power of numerical models in order to identify regions in parameter space, which are relevant for natural seismicity. The critical point concept is verified for both, synthetic and natural seismicity, in terms of a critical state which precedes a large earthquake: a gradual roughening of the (unobservable) stress field leads to a scale-free (observable) frequency-size distribution. Furthermore, the growth of the spatial correlation length and the acceleration of the seismic energy release prior to large events is found. The predictive power of these precursors is, however, limited. Instead of forecasting time, location, and magnitude of individual events, a contribution to a broad multiparameter approach is encouraging.<br>Das Auftreten von Erdbeben zeichnet sich durch eine hohe raumzeitliche Komplexität aus. Obwohl zahlreiche Muster, wie Vor- und Nachbeben bekannt sind, weiß man wenig über die zugrundeliegenden Mechanismen, da diese sich direkter Beobachtung entziehen. Die Zeit zwischen zwei starken Erdbeben in einer seismisch aktiven Region beträgt Jahrzehnte bis Jahrhunderte. Folglich ist die Anzahl solcher Ereignisse in einem Datensatz gering und es ist kaum möglich, allein aus Beobachtungsdaten statistisch signifikante Aussagen über deren Eigenschaften abzuleiten. Die vorliegende Arbeit nutzt daher numerische Modellierungen einer Verwerfungszone in Verbindung mit Datenanalyse, um die Beziehung zwischen physikalischen Mechanismen und beobachteter Seismizität zu studieren. Die zentrale Hypothese ist die Gültigkeit des sogenannten "kritischen Punkt Konzeptes" für Seismizität, d.h. starke Erdbeben werden als Phasenübergänge in einem räumlich ausgedehnten Vielteilchensystem betrachtet, ähnlich wie in Modellen aus der statistischen Physik (z.B. Perkolationsmodelle). Es werden praktische Konzepte entwickelt, die es ermöglichen, kritische Zustände in simulierten und in beobachteten Daten sichtbar zu machen. Die Resultate zeigen, dass wesentliche Eigenschaften von Seismizität, etwa die Magnitudenverteilung und das raumzeitliche Clustern von Erdbeben, durch Reibungs- und Bruchparameter bestimmt werden. Insbesondere der Grad räumlicher Unordnung (die "Rauhheit") einer Verwerfungszone hat Einfluss darauf, ob starke Erdbeben quasiperiodisch oder eher zufällig auftreten. Dieser Befund zeigt auf, wie numerische Modelle genutzt werden können, um den Parameterraum für reale Verwerfungen einzugrenzen. Das kritische Punkt Konzept kann in synthetischer und in beobachteter Seismizität verifiziert werden. Dies artikuliert sich auch in Vorläuferphänomenen vor großen Erdbeben: Die Aufrauhung des (unbeobachtbaren) Spannungsfeldes führt zu einer Skalenfreiheit der (beobachtbaren) Größenverteilung; die räumliche Korrelationslänge wächst und die seismische Energiefreisetzung wird beschleunigt. Ein starkes Erdbeben kann in einem zusammenhängenden Bruch oder in einem unterbrochenen Bruch (Vorbeben und Hauptbeben) stattfinden. Die beobachtbaren Vorläufer besitzen eine begrenzte Prognosekraft für die Auftretenswahrscheinlichkeit starker Erdbeben - eine präzise Vorhersage von Ort, Zeit, und Stärke eines nahenden Erdbebens ist allerdings nicht möglich. Die genannten Parameter erscheinen eher vielversprechend als Beitrag zu einem umfassenden Multiparameteransatz für eine verbesserte zeitabhängige Gefährdungsabschätzung.
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Zöller, Gert. "Critical states of seismicity modeling and data analysis /." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980582830.

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Paige, Bonnie E. "Open data portals in northern New England states." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62894.

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As the United States transitions from the Obama administration’s engagement with open government data to the Trump administration’s more closed information strategies, the future support for federal open government data is uncertain. An alternative target for open data initiatives is state-level open government data portals. This study provides preliminary information on state level open data, illustrating challenges faced by small, rural states in supporting an open data portal. The research investigates the current condition of state open data portals: whether their current form and the laws supporting them are sufficient to support their intended use. This study also explores whether the effects of the national political climate can be seen on state portals. This research uses a case study approach, focusing on the northern New England states: Maine, New Hampshire, and Vermont. The case studies use four main methods of investigation: content analysis to determine the goals of the portal, consideration of the policies and context influencing the portal based on the Open Data Policy Framework, inventorying of the data based on the Open Data Barometer, and a review of saved copies of the portals using the Internet Archive. Based on these methods, we found that these portals fall short of supporting their stated goals. Problems with ambiguous licensing, unclear information organization, unclear project ownership, lack of support for data users, and minimal advertisement of the portal’s existence may have contributed to low citizen engagement with the portals. Portal data is vulnerable as none of the states currently have laws that ensure data will be open and proactively provided, although Vermont is considering such legislation. National politics may have an influence on state open data, as Maine’s portalceased updates two days before the federal election. There is potential for those in the field of library and information science to contribute to state level portals through the provision of support for the knowledge organization and information literacy aspects of the portal that are currently lacking. This study also suggests that evaluative tools more specifically attuned to the state open data context would considerably strengthen the analysis of future research.<br>Arts, Faculty of<br>Library, Archival and Information Studies (SLAIS), School of<br>Graduate
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Bookheimer, William R. "Predicting naval aviator attrition using economic data." Thesis, access online version, 1995. http://handle.dtic.mil/100.2/ADA307513.

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Gao, Xuyang. "Search for high mass resonances in electron-electron and electron-muon final states with CMS data and study of exotic states with BESIII data." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/288251/3/contents.pdf.

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We present a search for lepton flavor violation processes at eμ final states with the data collected by the CMS experiment at the LHC at a center-of-mass energy of 13 TeV in 2016 corresponding to the integrated luminosity of 35.9 fb^(-1). No evidence for physics beyond the Standard Model is observed in the eμ invariant mass spectrum, the results are interpreted in terms of three different models, an R-parity violating SUSY model (RPV), a heavy Z’ gauge boson model, and a quantum black hole model (QBH). The lower mass limits at 95\% confidence level (C.L.) are found.We also present a search for new high-mass resonances decaying into electron pair with CMS 2016 data. No evidence of signature beyond the Standard Model prediction in the ee invariant mass spectrum observed, therefore the lower mass limits at 95\% C.L. are found for predictions of sequential standard model, grand unify theory, Randall-Sundrum model.Then we update the results of lower mass limits with the combination of 2016 data in ee and μμ channels and 2017 data in ee channel.Further to these, a study of the vector charmonium-like state Y(4220) is performed by using a combined fit on the cross sections of e^+ e^-→ωχ_c0, e^+ e^-→π^+ π^- h_c, e^+ e^-→π^+ π^- J/ψ and e^+ e^-→D^0 D^(*-) π^++c.c. measured by the BESIII experiment. The mass and width of the Y(4220) are obtained, We find the lower limit of its leptonic decay width to be 30 eV. We also estimate its partial decay width to π^+ π^- J/ψ in different scenarios. These results can be compared with the theoretical expectations of different models, and help the understanding of the nature of the Y(4220) state.<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Vyzas, Elias. "Recognition of emotional and cognitive states using physiological data." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9795.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999.<br>Includes bibliographical references (leaves 77-79).<br>This thesis presents the application of several pattern recognition techniques on phys­iological data as a means to provide useful information about human emotional or cognitive states. As these states may be correlated with the well-being and per­formance of subjects, knowledge of these states could improve the human-computer interaction, increase productivity, and reduce accidents. We first focus on a method for recognizing the emotional state of a person who is deliberately expressing one of eight emotions. Four physiological signals were mea­sured and six features of each of these signals were extracted. We investigated three methods for the recognition: (1) Sequential floating forward search (SFFS) feature selection with K-nearest neighbors classification, (2) Fisher Projection (FP) on struc­tured subsets of features with MAP classification, and (3) A hybrid SFFS-FP method. Each method was evaluated on the full set of eight emotions as well as on several sub­sets. The day-to-day variations within the same class often exceeded between-class variations on the same day. We present a way to take account of the day information, resulting in an improvement to the Fisher-based methods. The SFFS attained a rate of 88% for a trio of emotions, while the Fisher Projection attained the best perfor­mance on the full set of emotions, 81.25%. We extend the previous study by building an online classifier so that it can be used for real-time applications. The performance is comparable to that of the offiine version. These findings demonstrate that there is significant information in physiological signals for classifying the affective state of a person who is deliberately expressing a small set of emotions. We then look into cognitive load under different driving conditions. Subjects are asked to drive in a driving simulator around several curves. Messages appear on the screen prompting the driver to either brake immediately to a standstill or to continue driving. In parts of the experiment the driver is asked to perform a simple mathe­matical task on the phone. Several measures of the subjects' behavior are recorded, including driving parameters such as lane deviation, distance and time to lane cross­ing, steering entropy, and braking delay, mistakes in addition, and physiological data (EMG, BVP, GSR, HR, Respiration). Results show that although the majority of braking delays (irrespective of the phone task) lay between -0.5 and +0.5 seconds of the average no-phone delay, there were a few cases in which subjects pressed the brakes significantly later (0.5-2.5 seconds after the average no-phone delay). Out of 315 messages prompting subjects to brake while they were not engaged on a phone task, only twice did their breaking delay exceed the average; out of 642 messages prompting subjects to brake while they were engaged on a phone task, the delay ex­ceeded the average 41 times. The effect of the mathematical task can also be seen in a 10% higher mean reaction time and a four times larger variance when subjects were on the phone compared to when they were not on the phone. Furthermore, people were on the phone in 9 out of the 10 cases that subjects mistakenly pressed the brake pedal while the message prompted them to continue driving, as well as in 6 out of the 7 cases that subjects did not show any reaction while the message prompted them to brake. We separated the responses into 2 classes, a normal and a slow one. Using the physiological data and similar pattern recognition techniques as mentioned above we predicted the class of the next delay with 65% success for an individual subject. These results indicate that the existence of specific secondary tasks while driving may adversely affect the reaction time of the driver, while use of physiological data may help in predicting such potentially dangerous situations.<br>by Elias Vyzas.<br>M.Eng.
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Hu, Lejuan. "Smart Fault Tracing: Learning with Diagnostic Data for Predicting Vehicle States." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396988.

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Software applications, as the control centre of vehicle functionality, is becoming much important in recent years. The failure of downloading software applications can cause the loss of vehicle functionality. A tool that effectively estimates the software downloading statuses and locates root causes when failure happens, is highly required. The thesis investigates supervised learning methods, proposes a quantitative and data-driven approach to estimate software downloading statuses and measure the effect of software parts on software downloading process. The goal is to find out if classification models can be used to predict software downloading statuses, and help locate the part numbers that cause the failures in software downloading process. The experiment results indicate that the classification models can help predict the statuses with high prediction performance, and can help locate the causes. The trained models can be used to predict upcoming failures on other vehicles that have the same ECUs. The proposed method also gives a hint that classification tools can help evaluate the statuses of other components of the vehicle system, and help suggest vehicle maintenance planning. A tool that automatically analyses vehicle statuses will be helpful, which can be future work.
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Tadisetty, Srikanth. "Prediction of Psychosis Using Big Web Data in the United States." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169.

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Qudus, Abdul. "Automated Validation of User Equipment Connection States." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113889.

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Telecom today has become an essence of life. Everywhere we see people using their smart phones for calling, checking email or accessing internet. To handle all these kinds of services without any intrusion is a very challenging task. This study deals with software testing which helps to ensure the quality of service to the end user. Software testing is an essential part in the software development process. Software development for telecom domain might not look as safety critical as of an airplane or nuclear reactor but it is arguably more complex. The main focus of this study is to provide automation to the unit testing of different types of radio connections that can be assigned to the end user based on the requested service and capacity of the 3G network. This research is sponsored by Ericsson to improve the testing of User Equipment Radio Connection Handling system that controls multiple possible radio connection configurations. This research attempts to identify and test all possible transitions between radio connection states. This will improve the existing manual state testing system, where changes in connection states cause dramatic impacts on test fixtures. As a solution, an automatic test case executor is proposed that generates possible transitions, which are later executed and verified automatically.
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Rivard, Donald T., Martin M. Karpiscak, K. James DeCook, Glenn W. France, and Donald E. Osborn. "Water Contamination Sites in the Southwest: Compiling a Data Base." Arizona-Nevada Academy of Science, 1988. http://hdl.handle.net/10150/296421.

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From the Proceedings of the 1988 Meetings of the Arizona Section - American Water Resources Association and the Hydrology Section - Arizona-Nevada Academy of Science - April 16, 1988, University of Arizona, Tucson, Arizona<br>The University of Arizona, under a contract from the Solar Energy Research Institute (SERI), investigated water contamination problems in six Southwestern States -- Arizona, California, Colorado, New Mexico, Oklahoma, and Texas. A variety of surface and groundwater problems were encountered, including 1) high total dissolved solids (TDS) concentrations, 2) contamination by organic compounds, 3) contamination due to high concentrations of inorganic compounds, 4) biological contamination, 5) radioactive contamination, and 6) toxic and hazardous waste disposal. Literature and computer searches provided an overview of existing problems, but no central depository of information on water contamination problems was found to exist. Specific information was obtained from federal, state, and local government agencies concerned with water quality. Data were collected via telephone interviews, letters, and in- person office visits. Limitations inherent in these data collection methods included, 1) not knowing if all the correct contacts were made concerning a specific problem or site, 2) inability to ascertain whether all contacts were willing and /or able to supply complete, accurate, and updated information, 3) possible bypassing of important data sources, and 4) delays in receiving reports and materials by mail from telephone contacts. Findings indicate that many localities in the Southwest have water contamination problems in some form; more than sixty sites have been described to date.
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Books on the topic "Data states"

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Hoogeveen, Johannes, and Utz Pape, eds. Data Collection in Fragile States. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25120-8.

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R, Bowden Sara, and United States. International Trade Administration. Office of Trade Information and Analysis., eds. Understanding United States foreign trade data. U.S. Dept. of Commerce, International Trade Administration, Office of Trade and Investment Analysis, Trade Information and Analysis Trade Development, 1985.

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1961-, Reidenberg Joel R., ed. Data privacy law: A study of United States data protection. Michie, 1996.

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New Mexico. Legislature. Legislative Council Service. Congressional map and data. The Service, 1991.

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Darwin, William N. Forest industry data (FIND) system. Southern Region, USDA Forest Service, 1987.

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Office, General Accounting. IRS-Customs data systems exchange. The Office, 1993.

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Naval Education and Training Program Development Center. Data systems technician 3&2. Naval Education and Training Program Development Center], 1985.

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United States. General Accounting Office. RCED. Data on EPA's contracts. The Office, 1995.

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Overberghe, Albert G. Van. Data processing technician 3. The Activity, 1987.

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United States Army Soldier Support Institute. Automatic data processing management handbook. 4th ed. The Institute, 1985.

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Book chapters on the topic "Data states"

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Muendel, M. H., G. Wagner, J. Gea-Banacloche, and G. Leuchs. "Squeezed States of Light." In Gravitational Wave Data Analysis. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1185-7_10.

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Gather, Ursula, Roland Fried, and Michael Imhoff. "Online Classification of States in Intensive Care." In Data Analysis. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_34.

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Ghose, Subrata, Narayani Choudhury, S. L. Chaplot, and K. R. Rao. "Phonon Density of States and Thermodynamic Properties of Minerals." In Thermodynamic Data. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2842-4_11.

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Nalbantis, Georgios, and Tim Pawlowski. "Data." In The Demand for International Football Telecasts in the United States. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48075-6_4.

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Elango, B. "United States Service Sector Data." In Service Industry Databook. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19111-9_3.

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Schulz, Stefan. "Knowledge Representation of Pathological States." In Data Analysis and Information Systems. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80098-6_38.

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Nam, SungHun, and Hwang Chong-Sun. "Dynamic Remote Update Adapting Wireless Network Connection States." In Mobile Data Management. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44498-x_29.

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Bittner, Roman, Karel Hána, Lubomír Poušek, Pavel Smrka, Petr Schreib, and Petr Vysoký. "Detecting of Fatigue States of a Car Driver." In Medical Data Analysis. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39949-6_32.

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Walters, Robert, and Marko Novak. "The United States." In Cyber Security, Artificial Intelligence, Data Protection & the Law. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1665-5_14.

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Etang, Alvin, and Johannes Hoogeveen. "Tracking Displaced People in Mali." In Data Collection in Fragile States. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25120-8_4.

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Conference papers on the topic "Data states"

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Rodriguez, Alexander, Bijaya Adhikari, Andres D. Gonzalez, Charles Nicholson, Anil Vullikanti, and B. Aditya Prakash. "Mapping Network States using Connectivity Queries." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378355.

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Reut, V. S., A. B. Mikhalychev, and D. S. Mogilevtsev. "Data Pattern Tomography of Entangled States." In Quantum Information and Measurement. OSA, 2017. http://dx.doi.org/10.1364/qim.2017.qt6a.34.

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Neish, Andrew, Todd Walter, and Ignacio Fernandez-Hernandez. "Receiver States for SBAS Data Authentication." In 2020 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2020. http://dx.doi.org/10.33012/2020.17197.

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Yonezawa, Takuya, Ismail Arai, Toyokazu Akiyama, and Kazutoshi Fujikawa. "Proposal of classification method of bus operation states using sensor data." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258569.

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Almoqbil, Abdullah, Brian O'Connor, Rich Anderson, et al. "The correlation between substance abuse and crime in the United States." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378197.

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Shi, Fangzhou, Shan You, and Chang Xu. "Reinforced Molecule Generation with Heterogeneous States." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00065.

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Losavio, Michael, Deborah W. Keeling, Adel Elmaghraby, George Higgins, and John Shutt. "Network Forensics: Network Data and State Seizures in the United States." In 2008 3rd International Workshop on Systematic Approaches to Digital Forensic Engineering (SADFE). IEEE, 2008. http://dx.doi.org/10.1109/sadfe.2008.15.

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Dong, Kun, Austin R. Benson, and David Bindel. "Network Density of States." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2019. http://dx.doi.org/10.1145/3292500.3330891.

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Kitson, Thomas, Paula Olaya, Elizabeth Racca, et al. "Data analytics for modeling soil moisture patterns across united states ecoclimatic domains." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258536.

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Balan, Shilpa, Nishant Shristiraj, Vrunda Shah, and Anusha Manjappa. "Big data analysis of youth tobacco smoking trends in the United States." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258522.

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Reports on the topic "Data states"

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Cook, Paul, David Legare, David Hummel, John Malowicki, David H. Hughes, and Reinhard Erdmann. Investigating Quantum Data Encrypted Modulation States. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada615446.

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Bird, L., and J. Sumner. Green Power Marketing in the United States: A Status Report (2009 Data). Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/991052.

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Bird, Lori, Claire Kreycik, and Barry Friedman. Green Power Marketing in the United States. A Status Report (2008 Data). Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1219267.

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Bird, Lori, and Jenny Sumner. Green Power Marketing in the United States. A Status Report (2009 Data). Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1219268.

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Bird, L., C. Kreycik, and B. Friedman. Green Power Marketing in the United States: A Status Report (2008 Data). Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/968195.

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Shehabi, Arman, Sarah Smith, Dale Sartor, et al. United States Data Center Energy Usage Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1372902.

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Szunyogh, Istvan. Coupled Global-Regional Data Assimilation Using Joint States. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada574025.

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Szunyogh, Istvan. Coupled Global-Regional Data Assimilation Using Joint States. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada598015.

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Wiser, Ryan, Ryan Wiser, Galen Barbose, et al. Renewable Portfolio Standards in the United States - A Status Report with Data Through 2007. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/927151.

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Guldmann, J. Penetration of gas delivery systems in the United States: A state-level data analysis. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7262938.

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