Relevant bibliographies by topics / Data visualization tool

Contents

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

Academic literature on the topic 'Data visualization tool'

Author: Grafiati
Published: 15 July 2021
Last updated: 1 February 2022

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Journal articles on the topic "Data visualization tool"

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Matzen, Laura E., Michael J. Haass, Kristin M. Divis, Zhiyuan Wang, and Andrew T. Wilson. "Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations." IEEE Transactions on Visualization and Computer Graphics 24, no. 1 (January 2018): 563–73. http://dx.doi.org/10.1109/tvcg.2017.2743939.

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Venier, Marta, Daniel Lehman, Amina Salamova, and Ronald A. Hites. "The IADN data visualization tool." Science of The Total Environment 645 (December 2018): 1617–19. http://dx.doi.org/10.1016/j.scitotenv.2018.07.191.

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Archambault, Susan Gardner, Joanne Helouvry, Bonnie Strohl, and Ginger Williams. "Data visualization as a communication tool." Library Hi Tech News 32, no. 2 (April 7, 2015): 1–9. http://dx.doi.org/10.1108/lhtn-10-2014-0098.

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Purpose – This paper aims to provide a framework for thinking about meaningful data visualization in ways that can be applied to routine statistics collected by libraries. Design/methodology/approach – An overview of common data display methods is provided, with an emphasis on tables, scatter plots, line charts, bar charts, histograms, pie charts and infographics. Research on “best practices” in data visualization design is presented; also provided is a comparison of free online data visualization tools. Findings – Different data display methods are best suited for different quantitative relationships. There are rules to follow for optimal data visualization design. Ten free online data visualization tools are recommended by the authors. Originality/value – Evidence-based libraries collect and use data to affect change and to support departmental and institutional accreditation standards. Proper data visualization allows libraries to communicate their message in a more compelling and interesting way, while assisting in the understanding of complex data.
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Jofre, Ana, Steve Szigeti, and Sara Diamond. "Materializing data." DAT Journal 1, no. 2 (December 27, 2016): 2–14. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p2-14.

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The visualization of data elucidates trends and patterns in the phenomena that the data represents, and opens accessibility to understanding complicated human and natural processes represented by data sets. Research indicates that interacting with a visualization amplfies cognition and analysis. A single visualization may show only one facet of the data. To examine the data from multiple perspectives, engaged citizens need to be able to construct their own visualizations from a data set. Many tools for data visualization have responded to this need, allowing non-data experts to manipulate and gain insights into their data, but most of these tools are restricted to the computer screen, keyboard, and mouse. Cognition and analysis may be strengthened even more through embodied interaction with data, whether through data sculpture or haptic and tangible interfaces. We present here the rationale for the design of a tool that allows users to probe a data set, through interactions with graspable (tangible) three-dimensional objects, rather than through a keyboard and mouse interaction. We argue that the use of tangibles facilitates understanding abstract concepts, and facilitates many concrete learning scenarios. Another advantage of using tangibles over screen-based tools is that they foster collaboration, which can promote a productive working and learning environment.
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ARAI, Naoki. "Development of Weather Data Visualization Tool." Journal of the Visualization Society of Japan 34, no. 135 (2014): 16–21. http://dx.doi.org/10.3154/jvs.34.16.

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Batt, Steven, Tara Grealis, Oskar Harmon, and Paul Tomolonis. "Learning Tableau: A data visualization tool." Journal of Economic Education 51, no. 3-4 (August 20, 2020): 317–28. http://dx.doi.org/10.1080/00220485.2020.1804503.

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Zhu, Yuan, Sami Demiroluk, Kaan Ozbay, Kun Xie, Hong Yang, and Di Sha. "SAVE-T: Safety Analysis Visualization and Evaluation Tool." Journal of Advanced Transportation 2021 (July 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/5545117.

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Traffic crashes are one of the biggest issues which constitute a threat to lives of the motorists and disrupt operations of the transportation system. To reduce the number of crashes and alleviate their impacts, it is necessary to scrutinize the factors contributing to the risk of traffic crashes. Lately, visual analytics tools become very popular for data exploration and obtaining insights from the data. In this paper, a new web-based data visualization tool called Safety Analysis Visualization and Evaluation Tool (SAVE-T) was introduced. This tool enables users to interactively create queries and visually explore the results. By utilizing an online crash database, it offers various innovative functionalities for analysis and visualization of the crash data such as custom query development module and a subway-like map for easily visualizing the accident on the roadway segments. This tool provides an effective and efficient way to transportation agencies and professionals for traffic safety analyses and visualizations.
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TAGUCHI, Nobuyuki. "Reference analysis and data visualization tool “RefViz”." Journal of Information Processing and Management 47, no. 5 (2004): 370–73. http://dx.doi.org/10.1241/johokanri.47.370.

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Belyaev, Nikolay. "DATA VISUALIZATION: INFOGRAPHICS AS A MARKETING TOOL." Bulletin of South Ural State University series "Economics and management" 9, no. 4 (2015): 125–30. http://dx.doi.org/10.14529/em090417.

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Rogé, Xavier, and Xuegong Zhang. "RNAseqViewer: visualization tool for RNA-Seq data." Bioinformatics 30, no. 6 (November 8, 2013): 891–92. http://dx.doi.org/10.1093/bioinformatics/btt649.

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Dissertations / Theses on the topic "Data visualization tool"

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Bengtsson, Christoffer, and Roger Hemström. "Warehouse3D : A graphical data visualization tool." Thesis, Karlstads universitet, Fakulteten för ekonomi, kommunikation och IT, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-6946.

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Automated warehouses are frequently used within the industry. SQL databases are often used for storing various kinds of information about stored items, including their physical positions in the warehouse with respect to X, Y and Z positions. Benefits of this includes savings in working time, optimization of storage capability and – most of all – increased employee safety. IT services company Sogeti’s office in Karlstad has been looking into a project on behalf of one of their customers to implement this kind of automated warehouse. In the pilot study of this project, ideas of a three-dimensional graphic visualization of the warehouse and its stored contents have come up. This kind of tool would give a warehouse operator a clear overview of what is currently in store, as well as quick access to various pieces of information about each and every item in store. Also, in a wider perspective, other types of warehouses and storage areas could benefit from this kind of tool. During the course of this project, a graphical visualization tool for this purpose was developed, resulting in a product that met a significant part of the initial requirements.
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Ehsanibenafati, Aida. "Visualization Tool for Sensor Data Fusion." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5677.

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In recent years researchers has focused on the development of techniques for multi-sensor data fusion systems. Data fusion systems process data from multiple sensors to develop improved estimate of the position, velocity, attributes and identity of entities such as the targets or entities of interest. Visualizing sensor data from fused data to raw data from each sensor help analysts to interpret the data and assess sensor data fusion platform, an evolving situation or threats. Immersive visualization has emerged as an ideal solution for exploration of sensor data and provides opportunities for improvement in multi sensor data fusion. The thesis aims to investigate possibilities of applying information visualization to sensor data fusion platform in Volvo. A visualization prototype is also developed to enables multiple users to interactively visualize Sensor Data Fusion platform in real-time, mainly in order to demonstrates, evaluate and analyze the platform functionality. In this industrial study two research methodologies were used; a case study and an experiment for evaluating the results. First a case study was conducted in order to find the best visualization technique for visualizing sensor data fusion platform. Second an experiment was conducted to evaluate the usability of the prototype that has been developed and make sure the user requirement were met. The visualization tool enabled us to study the effectiveness and efficiency of the visualization techniques used. The results confirm that the visualization method used is effective, efficient for visualizing sensor data fusion platform.
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Huang, Yunshui Charles. "A prototype of data analysis visualization tool." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12125.

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Engelbrektsson, Martin, and Marcus Lilja. "Data Driven Visualization Tool for Game Telemetry." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143871.

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This thesis describes and evaluates the implementation of a telemetry tool prototype for the game engine Stingray. Arbitrary data can be chosen from a database and visualized in an interactive 3D viewport in the program. The implemented visualization method is a scatter plot with optional color mapping. A MongoDB server communicates with the editor via a native DLL written in C/C++ which in turn can send data to the rendering engine via JavaScript-Lua communication. Several implemented and continuously improved pipelines are discussed and analyzed throughout the report. The tool is designed to be data driven. Advantages and disadvantages of doing so are discussed. In the final chapter future improvements and ideas of expanding the plug-in are discussed.
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Yu, Tamara H. (Tamara Hui). "ComBo : a visualization tool for comparative genomic data." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33393.

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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 62-64).
Propelled by advances in sequencing technology, the advent of the genomic era brings the challenge of managing and interpreting massive amount of genomic data. A number of single-genome browsers have been developed for data display. However, they are unsuitable for comparative analysis. We present a dynamic and interactive visualization tool called ComBo that allows users to quickly navigate sequences, browse their similarities in a dot plot format, display sequence annotations in feature maps, and compare annotations in corresponding regions. ComBo allows users to directly control the display. They can compare any two regions in the sequences at any resolution, from an entire chromosome down to the residue level. Users can also retrieve detailed information on any displayed sequence feature or alignment. ComBo can import data from the Calhoun annotation database or flat files. Developed in Java, ComBo has a flexible infrastructure allowing the easy addition of functionality in the future.
by Tamara H. Yu.
M.Eng.and S.B.
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Tenev, Tichomir Gospodinov. "SpreadCube--a visualization tool for exploratory data analysis." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43924.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (p. 153-154).
by Tichomir Gospodinov Tenev.
M.Eng.
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Udd, Krister. "Advanced Building Energy Data Visualization." Thesis, Högskolan Dalarna, Grafisk teknik, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3540.

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Advanced Building Energy Data Visualization is a way to detect performance problems in commercialbuildings. By placing sensors in a building that collects data from example, air temperature and electricalpower, then makes it possible to calculate the data in Data Visualization software. This softwaregenerates visual diagrams so the building manager or building operator can see if for example thepower consumption is to high.A first step (before sensors are installed in a building) to see how the energy consumption is in abuilding can be to use a Benchmarking Tool. There is a number of Benchmarking Tools that is availablefor free on the Internet. Each tool have a bit different approach, but they all show how much energyconsumption there is in a building compared to other similar buildings.In this study a new web design for the benchmarking tool CalARCH has been developed. CalARCHis developed at the Berkeley Lab in Berkeley, California, USA. CalARCH uses data collected only frombuildings in California, and is only for comparing buildings in California with other similar buildingsin the state.Five different versions of the web site were made. Then a web survey was done to determine whichversion would be the best for CalARCH. The results showed that Version 5 and Version 3 was the best.Then a new version was made, based on these two versions. This study was made at the LawrenceBerkeley Laboratory.
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Best, Daniel. "GWVis a tool for comparative ground-water data visualization /." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Thesis/Spring2009/D_Best_032909.pdf.

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Thesis (M.S. in computer science)--Washington State University, May 2009.
Title from PDF title page (viewed on June 11, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 46-47).
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DeVaney, Jonah E. "tidyTouch: An Interactive Visualization Tool for Data Science Education." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/honors/529.

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Accessibility and usability of software define the programs used for both professional and academic activities. While many proprietary tools are easy to grasp, some challenges exist in using more technical resources, such as the statistical programming language R. The creative project tidyTouch is a web application designed to help educate any user in basic R data visualization and transformation using the popular ggplot2 and dplyr packages. Providing point-and-click interactivity to explore potential modifications of graphics for data presentation, the application uses an intuitive interface to make R more accessible to those without programming experience. This project is in a state of continual development and will expand to cover introductory data science topics relevant to academics and professionals alike. The code for tidyTouch and this document can be found at https://github.com/devaneyJE/tidyTouch_thesis (see ui.R and server.R files for application code).
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Dickson, Emil. "Building an interactive visualization tool for athletes’ performance data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280842.

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The world of competitive sports is becoming more and more interested in data analysis. Using sensors to track actions and trajectories produces large datasets that could be hard to draw conclusions from at face value. Visualizations can be used to make the process of data analysis easier and possibly allow for more advanced insights. The question is how visualizations should be implemented and what the possibilities are in doing so. Described in this paper is a project for designing an interactive visualization tool for olympic kayaking. Available research and theories of information visualization was combined with related research regarding visual data analysis for sports, and applied to the project. The process followed a problem-driven approach during a period of continuous feedback with domain experts. Qualitative data was collected throughout feedback sessions in an iterative process of developing a concept for a web-based tool. The results indicate that visualizations indeed do support and enable intuitive insight generation. In summary, the concept could improve data analysis, both making it more sophisticated and more accessible. Furthermore, it could improve collaborations between different user roles. However, needing to tailor the concept to a specific sport may affect the transferability to other sports, and the multidimensionality of the dataset produced a number of challenges to consider in future work. A number of improvements were suggested for the concept, and it was deemed promising for future research, development and implementation, which should be done in collaboration with domain experts.
Sportvärlden blir mer och mer intresserad av att tillämpa dataanalys. Användandet av sensorer for att fa information om händelser och rörelser i olika riktningar genererar stora datamängder, vilka till synes kan vara svara att dra några direkta slutsatser av. Visualiseringar kan användas för att underlätta analysprocessen och möjliggöra mer avancerade insikter. Frågan ar dock hur dessa visualiseringar skall implementeras samt vilka möjligheter som finns med detta. Denna rapport beskriver ett projekt for att designa ett interaktivt visualiseringsverktyg for olympisk kanot. Befintlig forskning och teorier kring informationsvisualisering kombinerades med relaterad forskning inom visuell dataanalys for sport och tillämpades i projektet. Arbetsprocessen följde en problemdriven metod under en kontinuerlig period av återkoppling med ämnesexperter. Kvalitativ data insamlades genom feedback-aktiviteter under en iterativ utvecklingsprocess av ett koncept for ett webbaserat analysverktyg. Resultaten antyder att visualiseringar understödjer och möjliggör skapandet av insikter på ett intuitivt satt. Sammanfattningsvis kan detta koncept förbättra dataanalys, genom att göra den mer sofistikerad och samtidigt mer tillgänglig. Det kan aven förbättra samarbetet mellan olika användarroller i analysprocessen. Överförbarheten till andra sporter påverkades dock av behovet av att designa verktyget specifikt for kanot. Därtill medförde multi-dimensionaliteten hos det aktuella datasetet ett antal utmaningar att ta hänsyn till i framtida forsknings- och utvecklingsarbete. Ett antal förbättringar blev föreslagna for konceptet och det bedömdes vara lovande for framtida forskning, utveckling och implementation, vilket också bor göras i samarbete med ämnesexperter.
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Books on the topic "Data visualization tool"

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Parsaye, Kamran. Intelligent database tools & applications: Hyperinformation access, data quality, visualization, automatic discovery. New York: Wiley, 1993.

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S, Hankin, and Pacific Marine Environmental Laboratory (U.S.), eds. FERRET: A computer visualization and analysis tool for gridded data. Seattle, Wash: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Pacific Marine Environmental Laboratory, 1992.

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U.S. Fish and Wildlife Service, ed. Geospatial application: Aquatic habitat analysis and visualization tool. [Washington, D.C.?: U.S. Dept. of the Interior, Fish and Wildlife Service, 1995.

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Measuring Intelligence, Surveillance, and Reconnaissance Effectiveness at the United States Central Command: Data Visualization Tool Documentation. RAND Corporation, 2021. http://dx.doi.org/10.7249/tl358.

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Makela, Susanna, Yajuan Si, and Andrew Gelman. Graphical Visualization of Polling Results. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.12.

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This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.
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Pfeffer, Jürgen. Visualization of Political Networks. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.13.

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Network visualization and political networks have a long history, and some of the earliest and most effective network visualizations have been about power and influence. Now as in the past, network visualization is one of the most effective tools for both exploratory analysis and the communication of scientific results. This chapter discusses the rhetorical, technical, and aesthetic principles that underlie successful network visualizations. The chapter covers automated layout algorithms as well as layouts resulting from the substance of the network. Aspects of visualizing multivariate network data are also discussed. The use of additional visual elements such as color and size is deliberated. The various topics of this chapter are contrasted with issues resulting from human perception and with frequently encountered visualization challenges, such as those encountered when working with dense networks.
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Dodge, Martin E. Geographic Visualization: Concepts, Tools and Applications. Wiley & Sons, Incorporated, John, 2011.

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Dodge, Martin E. Geographic Visualization: Concepts, Tools and Applications. Wiley & Sons, Incorporated, John, 2008.

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Dodge, Martin E. Geographic Visualization: Concepts, Tools and Applications. Wiley & Sons, Incorporated, John, 2011.

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E, Bowie Jack, ed. Data visualization in molecular science: Tools for insight and innovation. Reading, Mass: Addison-Wesley Pub. Co., 1995.

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Book chapters on the topic "Data visualization tool"

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Yang, Chaozhou, Xin Wang, Qiang Xu, and Weixi Li. "SPARQLVis: An Interactive Visualization Tool for Knowledge Graphs." In Web and Big Data, 471–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96890-2_41.

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Auchère, F., E. Soubrié, K. Bocchialini, and F. LeGall. "FESTIVAL: A Multiscale Visualization Tool for Solar Imaging Data." In Solar Image Analysis and Visualization, 3–14. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-98154-3_2.

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Fattibene, Enrico, Tiziana Ferrari, Luciano Gaido, Giuseppe Misurelli, and Peter Solagna. "HLRmon, a Tool for Advanced Visualization of Resource Utilization." In Data Driven e-Science, 187–98. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8014-4_15.

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Xu, Dawei, Lin Wang, Xin Wang, Dianquan Li, Jianpeng Duan, and Yongzhe Jia. "KG3D: An Interactive 3D Visualization Tool for Knowledge Graphs." In Advanced Data Mining and Applications, 886–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35231-8_67.

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Lotov, Alexander V., Alexander A. Kistanov, and Alexander D. Zaitsev. "Visualization-Based Data Mining Tool and Its Web Application." In Data Mining and Knowledge Management, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_1.

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Hutchens, Mark, Nikhil Krishnaswamy, Brent Cochran, and James Pustejovsky. "Jarvis: A Multimodal Visualization Tool for Bioinformatic Data." In Lecture Notes in Computer Science, 104–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60152-2_9.

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Bakardzhiev, Hristo, Marloes van der Burgt, Eduardo Martins, Bart van den Dool, Chyara Jansen, David van Scheppingen, Günter Wallner, and Michael Burch. "A Web-Based Eye Tracking Data Visualization Tool." In Pattern Recognition. ICPR International Workshops and Challenges, 405–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68796-0_29.

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Chen, Liangjian, Siyu Chen, Shengnan Guo, Yue Yang, and Jianqiu Xu. "A Mobile Phone Data Visualization Tool for People Flow Analysis." In Web and Big Data, 407–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26075-0_32.

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Shi, Chuan, Dan Zhou, Bin Wu, and Jian Liu. "VisNetMiner: An Integration Tool for Visualization and Analysis of Networks." In Advanced Data Mining and Applications, 611–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3_63.

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Liu, Yuan, Xin Wang, and Qiang Xu. "BioPW: An Interactive Tool for Biological Pathway Visualization on Linked Data." In Web and Big Data, 333–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63564-4_30.

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Conference papers on the topic "Data visualization tool"

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Derksen, Gerry, Stan Ruecker, Tim Causer, and Melissa Terras. "Demonstrating data using storyboard visualization tool." In the 6th International Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2493102.2493115.

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Baskaran, Kumaran, Jonathan Wedell, Eldon Ulrich, Jeffery Hoch, and John Markley. "PyBMRB: Data visualization tool for BioMagResBank." In Python in Science Conference. SciPy, 2021. http://dx.doi.org/10.25080/majora-1b6fd038-00a.

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Goecks, Jeremy, Kanwei Li, Dave Clements, The Galaxy Team, and James Taylor. "The Galaxy Track Browser: Transforming the genome browser from visualization tool to analysis tool." In 2011 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2011. http://dx.doi.org/10.1109/biovis.2011.6094046.

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Jauhar Ali. "A visualization tool for Data Structures course." In 2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccsit.2009.5234389.

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Yassien, Amal, Youssef Emad, and Slim Abdennadher. "CDVVAR: VR/AR Collaborative Data Visualization Tool." In 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2021. http://dx.doi.org/10.1109/vrw52623.2021.00182.

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Shengnan, Guo, Xu Jianqiu, and Sun Tao. "VTGeo: A Visualization Tool for Geospatial Data." In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE, 2020. http://dx.doi.org/10.1109/icmcce51767.2020.00510.

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Ouni, Slim, Loïc Mangeonjean, and Ingmar Steiner. "Visartico: a visualization tool for articulatory data." In Interspeech 2012. ISCA: ISCA, 2012. http://dx.doi.org/10.21437/interspeech.2012-510.

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Wang, Wenbo, Xi Luo, Liangfu Lu, and Youyi Zheng. "iHDViewer: A Visualization Tool for Tracking HD." In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018. http://dx.doi.org/10.1109/bigcomp.2018.00057.

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Freidlin, Raisa Z., Chikai J. Ohazama, Andrew E. Arai, Delia P. McGarry, Julio A. Panza, and Benes L. Trus. "NIHmagic: 3D visualization, registration, and segmentation tool." In 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, edited by William R. Oliver. SPIE, 2000. http://dx.doi.org/10.1117/12.384874.

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Wenskovitch, John, Jian Zhao, Scott Carter, Matthew Cooper, and Chris North. "Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure." In 2019 IEEE Visualization in Data Science (VDS). IEEE, 2019. http://dx.doi.org/10.1109/vds48975.2019.8973385.

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Reports on the topic "Data visualization tool"

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Murphy, Joe J., Michael A. Duprey, Robert F. Chew, Paul P. Biemer, Kathleen Mullan Harris, and Carolyn Tucker Halpern. Interactive Visualization to Facilitate Monitoring Longitudinal Survey Data and Paradata. RTI Press, May 2019. http://dx.doi.org/10.3768/rtipress.2019.op.0061.1905.

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Surveys often require monitoring during data collection to ensure progress in meeting goals or to evaluate the interim results of an embedded experiment. Under complex designs, the amount of data available to monitor may be overwhelming and the production of reports and charts can be costly and time consuming. This is especially true in the case of longitudinal surveys, where data may originate from multiple waves. Other such complex scenarios include adaptive and responsive designs, which were developed to act on the results of such monitoring to implement prespecified options or alternatives in protocols. This paper discusses the development of an interactive web-based data visualization tool, the Adaptive Total Design (ATD) Dashboard, which we designed to provide a wide array of survey staff with the information needed to monitor data collection daily. The dashboard was built using the R programming language and Shiny framework and provides users with a wide range of functionality to quickly assess trends. We present the structure of the data used to populate the dashboard, its design, and the process for hosting it on the web. Furthermore, we provide guidance on graphic design, data taxonomy, and software decisions that can help guide others in the process of developing their own data collection monitoring systems. To illustrate the benefits of the dashboard, we present examples from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We also discuss features of the dashboard to be developed for future waves of Add Health.
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Miller, M. Pixar data visualization tools overview. Office of Scientific and Technical Information (OSTI), February 1990. http://dx.doi.org/10.2172/7083971.

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Miller, M. Pixar data visualization tools overview. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/6860368.

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Astrom, Richard L. Data Analysis Tools for Visualization Study. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ada625734.

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Williams, Dean N., and Claudio Silva. Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT). Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1328687.

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Williams, Dean. Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT). Office of Scientific and Technical Information (OSTI), July 2011. http://dx.doi.org/10.2172/1022945.

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Williams, Dean. Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Final Report. Office of Scientific and Technical Information (OSTI), May 2014. http://dx.doi.org/10.2172/1136155.

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Appleyard, Bruce, Jonathan Stanton, and Chris Allen. Toward a Guide for Smart Mobility Corridors: Frameworks and Tools for Measuring, Understanding, and Realizing Transportation Land Use Coordination. Mineta Transportation Institue, December 2020. http://dx.doi.org/10.31979/mti.2020.1805.

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The coordination of transportation and land use (also known as “smart growth”) has been a long-standing goal for planning and engineering professionals, but to this day it remains an elusive concept to realize. Leaving us with this central question -- how can we best achieve transportation and land use coordination at the corridor level? In response, this report provides a review of literature and practice related to sustainability, livability, and equity (SLE) with a focus on corridor-level planning. Using Caltrans’ Corridor Planning Process Guide and Smart Mobility Framework as guideposts, this report also reviews various principles, performance measures, and place typology frameworks, along with current mapping and planning support tools (PSTs). The aim being to serve as a guidebook that agency staff can use for reference, synergizing planning insights from various data sources that had not previously been brought together in a practical frame. With this knowledge and understanding, a key section provides a discussion of tools and metrics and how they can be used in corridor planning. For illustration purposes, this report uses the Smart Mobility Calculator (https://smartmobilitycalculator. netlify.app/), a novel online tool designed to make key data easily available for all stakeholders to make better decisions. For more information on this tool, see https://transweb.sjsu.edu/research/1899-Smart-Growth-Equity-Framework-Tool. The Smart Mobility Calculator is unique in that it incorporates statewide datasets on urban quality and livability which are then communicated through a straightforward visualization planners can readily use. Core sections of this report cover the framework and concepts upon which the Smart Mobility Calculator is built and provides examples of its functionality and implementation capabilities. The Calculator is designed to complement policies to help a variety of agencies (MPOs, DOTs, and local land use authorities) achieve coordination and balance between transportation and land use at the corridor level.
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Chaudhary, Aashish. Ultra-Scale Visualization Climate Data Analysis Tools (UV-CDAT) Final Technical Report. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1164136.

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Smith, Deborah K. Data Management for MG&G: Tools for Archiving, Analysis and Visualization. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada405770.

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