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Journal articles on the topic 'Data visualization tool'

Author: Grafiati

Published: 15 July 2021

Last updated: 1 February 2022

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1

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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CHUANG, LI-YEH, CHENG-HONG YANG, CHAO-CHING CHANG, WEN-SHYONG TZOU, and LI-CHENG JIN. "VSA-TOOL: A TOOL FOR DATA VISUALIZATION IN SEQUENCE ALIGNMENT." Biomedical Engineering: Applications, Basis and Communications 16, no. 02 (April 25, 2004): 68–72. http://dx.doi.org/10.4015/s1016237204000116.

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Sequence alignment is a fundamental and important tool for sequence data analysis in molecular biology. Many applications in molecular biology require the detection of a similarity pattern displayed by a number of DNA and protein sequences. Visual front-ends are useful for an intuitive viewing of alignment and help to analyze the structure, functions, and evolution of the DNA and protein. In this paper, we designed and implemented an interactive system for data visualization in DNA and proteins, which can be used in determining a sequence alignment, similarity search of sequence data, and function interference. Experimental results show that a user can easily operate the system after one hour's practice on the proposed system, which provides a clean output, easy identification of similarity and visualization of alignment data.

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Tang, Diane, Chris Stolte, and Robert Bosch. "Design Choices when Architecting Visualizations." Information Visualization 3, no. 2 (April 22, 2004): 65–79. http://dx.doi.org/10.1057/palgrave.ivs.9500067.

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In this paper, we focus on some of the key design decisions we faced during the process of architecting a visualization system and present some possible choices, with their associated advantages and disadvantages. We frame this discussion within the context of Rivet, our general visualization environment designed for rapidly prototyping interactive, exploratory visualization tools for analysis. As we designed increasingly sophisticated visualizations, we needed to refine Rivet in order to be able to create these richer displays for larger and more complex data sets. The design decisions we discuss in this paper include the internal data model, data access, semantic meta-data information the visualization can use to create effective visual encodings, the need for data transformations in a visualization tool, modular objects for flexibility, and the tradeoff between simplicity and expressiveness when providing methods for creating visualizations.

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Walsh, Kieran, Mircea A. Voineagu, Fatemeh Vafaee, and Irina Voineagu. "TDAview: an online visualization tool for topological data analysis." Bioinformatics 36, no. 18 (July 2, 2020): 4805–9. http://dx.doi.org/10.1093/bioinformatics/btaa600.

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Abstract Summary TDAview is an online tool for topological data analysis (TDA) and visualization. It implements the Mapper algorithm for TDA and provides extensive graph visualization options. TDAview is a user-friendly tool that allows biologists and clinicians without programming knowledge to harness the power of TDA. TDAview supports an analysis and visualization mode in which a Mapper graph is constructed based on user-specified parameters, followed by graph visualization. It can also be used in a visualization only mode in which TDAview is used for visualizing the data properties of a Mapper graph generated using other open-source software. The graph visualization options allow data exploration by graphical display of metadata variable values for nodes and edges, as well as the generation of publishable figures. TDAview can handle large datasets, with tens of thousands of data points, and thus has a wide range of applications for high-dimensional data, including the construction of topology-based gene co-expression networks. Availability and implementation TDAview is a free online tool available at https://voineagulab.github.io/TDAview/. The source code, usage documentation and example data are available at TDAview GitHub repository: https://github.com/Voineagulab/TDAview.

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Dragan, Dinu, Veljko Petrovic, Dusan Gajic, Zarko Zivanov, and Dragan Ivetic. "An empirical study of data visualization techniques in PACS design." Computer Science and Information Systems 16, no. 1 (2019): 247–71. http://dx.doi.org/10.2298/csis180430017d.

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The paper presents an empirical study of multidimensional visualization techniques. The study is motivated by the problem of decision making in PACS (Picture Archiving and Communications System) design. A comprehensive survey of visualizations used in literature is performed and these survey results are then used to produce the final set of considered visualizations: tables (as control), scatterplots, parallel coordinates, and star plots. An electronic testing tool is developed to present visualizations to three sets of experimental subjects in order to determine which visualization technique allows users to make the correct decision in a sample decision making problem based on real-world data. Statistical analysis of the results demonstrates that visualizations show better results in decision support than tables. Further, when number of dimensions is large, 2D parallel coordinates show the best results in accuracy. The contribution of the presented research operates on two levels of abstraction. On the object level, it provides useful data regarding the relative merits of visualization techniques for the considered narrow use-case, which can then be generalized to other similar problem sets. On the meta level above, it contributes an enhanced methodology to the area of empirical visualization evaluation methods.

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Nölle, A., G. Pfister, G. Seckmeyer, H. Wilhelms, M. L. Richards, and G. K. Hartmann. "DUST: An interactive data visualization tool for selected atmospheric data." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 25, no. 8 (January 2000): 635–38. http://dx.doi.org/10.1016/s1464-1895(00)00097-1.

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Martínez-Martínez, José M., Pablo Escandell-Montero, Emilio Soria-Olivas, José D. Martín-Guerrero, and Antonio J. Serrano-López. "A new visualization tool for data mining techniques." Progress in Artificial Intelligence 5, no. 2 (January 18, 2016): 137–54. http://dx.doi.org/10.1007/s13748-015-0079-4.

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Bouveresse, Delphine Jouan-Rimbaud, Christophe Malabat, and Douglas N. Rutledge. "Polar coordinates projection: A simple data visualization tool." TrAC Trends in Analytical Chemistry 24, no. 9 (October 2005): 839–42. http://dx.doi.org/10.1016/j.trac.2005.04.014.

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Cignoni, P., C. Montani, and R. Scopigno. "Magicsphere: an insight tool for 3D data visualization." Computer Graphics Forum 13, no. 3 (August 1994): 317–28. http://dx.doi.org/10.1111/1467-8659.1330317.

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Morris, J. A., J. C. Randall, J. B. Maller, and J. C. Barrett. "Evoker: a visualization tool for genotype intensity data." Bioinformatics 26, no. 14 (May 27, 2010): 1786–87. http://dx.doi.org/10.1093/bioinformatics/btq280.

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Shankar, Ganesh, Michael R. Rossi, Devin E. Mcquaid, Jeffrey M. Conroy, Daniel G. Gaile, John K. Cowell, Norma J. Nowak, and Ping Liang. "aCGHViewer: A Generic Visualization Tool for aCGH Data." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200023.

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Array-Comparative Genomic Hybridization (aCGH) is a powerful high throughput technology for detecting chromosomal copy number aberrations (CNAs) in cancer, aiming at identifying related critical genes from the affected genomic regions. However, advancing from a dataset with thousands of tabular lines to a few candidate genes can be an onerous and time-consuming process. To expedite the aCGH data analysis process, we have developed a user-friendly aCGH data viewer (aCGHViewer) as a conduit between the aCGH data tables and a genome browser. The data from a given aCGH analysis are displayed in a genomic view comprised of individual chromosome panels which can be rapidly scanned for interesting features. A chromosome panel containing a feature of interest can be selected to launch a detail window for that single chromosome. Selecting a data point of interest in the detail window launches a query to the UCSC or NCBI genome browser to allow the user to explore the gene content in the chromosomal region. Additionally, aCGHViewer can display aCGH and expression array data concurrently to visually correlate the two. aCGHViewer is a stand alone Java visualization application that should be used in conjunction with separate statistical programs. It operates on all major computer platforms and is freely available at http://falcon.roswellpark.org/aCGHview/ .

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Zhang, R. R., B. J. Xiao, Y. Liu, F. Yang, G. Q. Li, Y. L. Qin, and W. T. Chai. "A visualization tool for EAST diagnostic profiles data." Fusion Engineering and Design 128 (March 2018): 149–53. http://dx.doi.org/10.1016/j.fusengdes.2018.01.068.

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Cherukuru, Nihanth W., and Tim Scheitlin. "Visual Comparator: An Interactive Tool for Dynamic Spatiotemporal Comparative Visualizations." Bulletin of the American Meteorological Society 101, no. 10 (October 1, 2020): E1861—E1869. http://dx.doi.org/10.1175/bams-d-19-0266.1.

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AbstractVisualizations enable us to detect patterns, time-evolving features, and trends in complex datasets that might not be obvious by looking at the raw data. The visual exploration process often requires comparisons between multiple visualizations, either from the same dataset or a different one, to identify relationships and patterns. This visualization process, referred to as comparative visualization, is valuable for analyzing multivariate, multispectral, or multidimensional data. The existing tools that facilitate visual comparisons do this by three means: juxtaposition (placing visuals side by side), superposition (overlaying visuals), and explicit encoding (visualizing a derived quantity corresponding to the relationship being studied). While superposition is ideal for static, geospatial datasets, where spatialization is a key component of the data, the spatiotemporal nature of Earth science datasets presents a challenge with comparative visualizations. Visual Comparator is an interactive, cross-platform (desktops, kiosks, and web), open-source application, developed to address this shortcoming. The application is used to superimpose and compare up to three synchronized, animated visualizations, and a transition between the visualizations is provided through a slider-based interface. This form of visualization has the advantage of drawing the viewers’ attention to changes between the datasets, enabling comparisons of scale, and reducing the clutter caused by having multiple variables in one visual. This article provides an overview of the project, a brief review of literature pertaining to human perception research and comparative visualizations, and a guide to accessing this application.

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S., Shreedhar, and Manju Naik. "Data visualization using Viewshare." Library Hi Tech News 34, no. 6 (August 7, 2017): 1–10. http://dx.doi.org/10.1108/lhtn-02-2017-0009.

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Purpose This paper aims to introduce the use of data visualization tool, the Viewshare, in library environment. Design/methodology/approach Viewshare has been successfully adopted in various institutions for depicting cultural heritage collections. Findings An attempt is made here to explore the possibility of the use of Viewshare in academic libraries. Social implications The details of e-journals being subscribed by the Kuvempu University are presented in an attractive and interactive environment using Viewshare to provide clear insights to library users. Originality/value The steps followed for creating different views have been explained to make other professionals feel confident and motivate them to use Viewshare in library environment.

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Koppler, Rainer, Siegfried Grabner, and Jens Volkert. "Visualization of Distributed Data Structures for High Performance Fortran-Like Languages." Scientific Programming 6, no. 1 (1997): 115–26. http://dx.doi.org/10.1155/1997/504101.

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This article motivates the usage of graphics and visualization for efficient utilization of High Performance Fortran's (HPF's) data distribution facilities. It proposes a graphical toolkit consisting of exploratory and estimation tools which allow the programmer to navigate through complex distributions and to obtain graphical ratings with respect to load distribution and communication. The toolkit has been implemented in a mapping design and visualization tool which is coupled with a compilation system for the HPF predecessor Vienna Fortran. Since this language covers a superset of HPF's facilities, the tool may also be used for visualization of HPF data structures.

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Willis, Melinda B. N., Sue Hay, Fred G. Martin, Michelle Scribner-MacLean, and Ivan Rudnicki. "Probability with Collaborative Data Visualization Software." Mathematics Teacher 109, no. 3 (October 2015): 194–99. http://dx.doi.org/10.5951/mathteacher.109.3.0194.

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Endsley, K. A., and M. G. Billmire. "Distributed visualization of gridded geophysical data: the Carbon Data Explorer, version 0.2.3." Geoscientific Model Development 9, no. 1 (January 29, 2016): 383–92. http://dx.doi.org/10.5194/gmd-9-383-2016.

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Abstract. Due to the proliferation of geophysical models, particularly climate models, the increasing resolution of their spatiotemporal estimates of Earth system processes, and the desire to easily share results with collaborators, there is a genuine need for tools to manage, aggregate, visualize, and share data sets. We present a new, web-based software tool – the Carbon Data Explorer – that provides these capabilities for gridded geophysical data sets. While originally developed for visualizing carbon flux, this tool can accommodate any time-varying, spatially explicit scientific data set, particularly NASA Earth system science level III products. In addition, the tool's open-source licensing and web presence facilitate distributed scientific visualization, comparison with other data sets and uncertainty estimates, and data publishing and distribution.

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Stukowski, Alexander. "Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool." Modelling and Simulation in Materials Science and Engineering 18, no. 1 (December 15, 2009): 015012. http://dx.doi.org/10.1088/0965-0393/18/1/015012.

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Fantham, Marcus, and Clemens F. Kaminski. "A new online tool for visualization of volumetric data." Nature Photonics 11, no. 2 (February 2017): 69. http://dx.doi.org/10.1038/nphoton.2016.273.

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Todt, I., H. Lamecker, H. Ramm, and A. Ernst. "A computed tomographic data-based vibrant bonebridge visualization tool." Cochlear Implants International 15, sup1 (May 2014): S72—S74. http://dx.doi.org/10.1179/1467010014z.000000000155.

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Agrahari, Amit, and Samir K. Srivastava. "A data visualization tool to benchmark government tendering process." Benchmarking: An International Journal 26, no. 3 (April 1, 2019): 836–53. http://dx.doi.org/10.1108/bij-06-2017-0148.

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PurposeGovernment e-Procurement systems are generating tender-level process event data which are not being analyzed much. The purpose of this paper is to present a data visualization tool to benchmark the government tendering process. This tool collects, collates and presents e-Procurement process data in a meaningful way that enables comparisons and benchmarking leading to insights for process improvements and identification of the best practices. This tool is accessible on the website of South Asia’s first public procurement observatory (www.procurementobservatoryup.com) founded by the authors.Design/methodology/approachThe authors started with an initial set of key performance indicators (KPIs) based on extant literature and existing practices. The authors got them verified by various relevant stakeholders through a series of formal and informal discussions. Some of them were dropped due to observed variations or their inability to offer insights to arrive at the final set of KPIs. In this paper, the authors use actual process-level data. Government of India’s National Informatics Center has implemented e-Procurement portals in various states and public enterprises (PEs) in India which provide tender-wise detailed e-tendering process data. The authors designed a web crawler that collects these data in a tabular format, which allows an easy analysis and comparison to measure and compare government tendering process performance for the last five years for the two large PEs. The authors also engaged in discussions with the procurement executives of the two PSEs to derive meaningful managerial insights from the results obtained.FindingsUsing the public procurement data visualization tool, the authors compare the procurement process of two of the largest Indian PEs, Coal India Limited and Indian Oil Corporation on the developed KPIs and draw insights. The results show significant difference in their procurement process performance due to different practices followed by them. Through interaction with the procurement managers of these two organizations, the authors identify few good procurement practices that can be applied to improve public procurement process.Research limitations/implicationsThis paper uses actual process-level data which can be used not just to identify improvement opportunities but also to gauge the impact of any process-level change. It presents a data visualization tool to generate insights from data generated by a procurement/tendering system. Such visualization and benchmarking can uncover better procurement practices and provide an impetus toward data-driven policy formulation. Apart from the two PEs as reported in this paper, this tool has also been applied on the public procurement data of eight Indian states.Practical implicationsThe KPIs presented in this paper are aligned with the various dimensions of public procurement’s objectives. The visualization tool presented in this paper is based on the Open Contract Data Standard and has a universal application.Social implicationsThe use of technology and open data sharing as the enablers of benchmarking and process improvements help in establishing a dynamic competitive environment leading to financial savings, better services to citizens and proper use of taxpayers’ money.Originality/valueThis paper presents an original work carried out under the aegis of South Asia’s first procurement observatory at IIM Lucknow. The benchmarking tool presented in this paper uses open contract data standard and can be applied in most of the public procurement processes. This paper takes the discussions on e-Procurement to the next level, where the concern is no longer restricted to only adoption and assimilation issues, but also on how to make use of the data that these e-Procurement systems generate.

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Huh, Sunghyun, and Min‐Sik Kim. "A User‐Friendly Visualization Tool for Multi‐Omics Data." PROTEOMICS 20, no. 21-22 (September 3, 2020): 2000136. http://dx.doi.org/10.1002/pmic.202000136.

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Frantz, Terrill L. "Blockmap: an interactive visualization tool for big-data networks." Computational and Mathematical Organization Theory 24, no. 2 (April 5, 2017): 149–68. http://dx.doi.org/10.1007/s10588-017-9252-6.

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Jiang, H., F. Wang, N. P. Dyer, and W. H. Wong. "CisGenome Browser: a flexible tool for genomic data visualization." Bioinformatics 26, no. 14 (May 30, 2010): 1781–82. http://dx.doi.org/10.1093/bioinformatics/btq286.

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Best, Daniel M., and Robert R. Lewis. "GWVis: A tool for comparative ground-water data visualization." Computers & Geosciences 36, no. 11 (November 2010): 1436–42. http://dx.doi.org/10.1016/j.cageo.2010.04.006.

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Auchère, F., E. Soubrié, K. Bocchialini, and F. LeGall. "FESTIVAL: A Multiscale Visualization Tool for Solar Imaging Data." Solar Physics 248, no. 2 (March 28, 2008): 213–24. http://dx.doi.org/10.1007/s11207-008-9163-2.

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Perkhofer, Lisa Maria, Peter Hofer, Conny Walchshofer, Thomas Plank, and Hans-Christian Jetter. "Interactive visualization of big data in the field of accounting." Journal of Applied Accounting Research 20, no. 4 (December 9, 2019): 497–525. http://dx.doi.org/10.1108/jaar-10-2017-0114.

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Purpose Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue. Design/methodology/approach A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data. Findings The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption. Research limitations/implications Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages. Practical implications The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession. Originality/value A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.

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Sevilla, Javier, Pablo Casanova-Salas, Sergio Casas-Yrurzum, and Cristina Portalés. "Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage." Applied Sciences 11, no. 4 (February 11, 2021): 1636. http://dx.doi.org/10.3390/app11041636.

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Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.

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Oyarzun, Cristina Laura, Katrin Hartwig, Anna-Sophie Hertlein, Florian Jung, Jan Burmeister, Jörn Kohlhammer, Stefan Wesarg, and Guido Sauter. "Web-based Prostate Visualization Tool." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 563–66. http://dx.doi.org/10.1515/cdbme-2020-3144.

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AbstractProper treatment of prostate cancer is essential to increase the survival chance. In this sense, numerous studies show how important the communication between all stakeholders in the clinic is. This communication is difficult because of the lack of conventions while referring to the location where a biopsy for diagnosis was taken. This becomes even more challenging taking into account that experts of different fields work on the data and have different requirements. In this paper a web-based communication tool is proposed that incorporates a visualization of the prostate divided into 27 segments according to the PI-RADS protocol. The tool provides 2 working modes that consider the requirements of radiologist and pathologist while keeping it consistent. The tool comprises all relevant information given by pathologists and radiologists, such as, severity grades of the disease or tumor length. Everything is visualized using a colour code for better undestanding.

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Urzúa, Jonathan, Nelson Baloian, and José A. Pino. "Supporting Data Structures Visualization for Teachers and Learners." Proceedings 31, no. 1 (November 19, 2019): 8. http://dx.doi.org/10.3390/proceedings2019031008.

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Teachers of first courses in Computing face the problem of making slides showing data structures. Students must also show them in their assignments. Drawing them may not be the best choice. The Data Structures (DSTR) language proposed here is intended as an easy-to-use tool to describe those structures for visualization. A preliminary evaluation of a tool to interpret DSTR statements shows users’ acceptance.

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Kim, Jiwoo, and Gunhak Lee. "Exploring Geovisualization of Flow Data Using a Web-based Data Visualization Tool." Journal of the Korean Cartographic Association 17, no. 1 (April 30, 2017): 25–39. http://dx.doi.org/10.16879/jkca.2017.17.1.025.

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Kim, Nayoung, Herin Park, Ningning He, Hyeon Young Lee, and Sukjoon Yoon. "QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data." Genomics & Informatics 10, no. 4 (2012): 263. http://dx.doi.org/10.5808/gi.2012.10.4.263.

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Xu, Shuangbin, Zehan Dai, Pingfan Guo, Xiaocong Fu, Shanshan Liu, Lang Zhou, Wenli Tang, et al. "ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data." Molecular Biology and Evolution 38, no. 9 (June 7, 2021): 4039–42. http://dx.doi.org/10.1093/molbev/msab166.

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Abstract We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.

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Zakharova, A. A., A. G. Podvesovskii, and A. V. Shklyar. "VISUAL AND COGNITIVE INTERPRETATION OF HETEROGENEOUS DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 243–47. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-243-2019.

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<p><strong>Abstract.</strong> The paper identifies conditions that allow the use of visualization means as tools for practical study of heterogeneous data. Effectiveness requirements, the amount of input data or uncertainty of the overall goal of data analysis should be considered complicating factors for such a study. Development and practical application of visualization tools allow to overcome these factors as a result of using the advantages of visual perception for the source data interpretation. Use of cognitive maps has been proposed as a way to compensate for the subjective aspects of visual perception, as well as a tool for verifying the results of visual analysis. Combined use of visualization tools and cognitive maps forms cognitive interpretation technology, which allows solving the problems of research of empirical data belonging to specialized subject areas. An example application of this technology for processing biomedical data is considered.</p>

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Han, Rey, Knaap, Kang, and Wolf. "Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents." ISPRS International Journal of Geo-Information 8, no. 11 (November 11, 2019): 509. http://dx.doi.org/10.3390/ijgi8110509.

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Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

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Avazpour, Iman, John Grundy, and Hai L. Vu. "Generating Reusable Visual Notations Using Model Transformation." International Journal of Software Engineering and Knowledge Engineering 25, no. 02 (March 2015): 277–305. http://dx.doi.org/10.1142/s0218194015400100.

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Visual notations are a key aspect of visual languages. They provide a direct mapping between the intended information and set of graphical symbols. Visual notations are most often implemented using the low level syntax of programming languages which is time consuming, error prone, difficult to maintain and hardly human-centric. In this paper we describe an alternative approach to generating visual notations using by-example model transformations. In our new approach, a semantic mapping between model and view is implemented using model transformations. The notations resulting from this approach can be reused by mapping varieties of input data to their model and can be composed into different visualizations. Our approach is implemented in the CONVErT framework and has been applied to many visualization examples. Three case studies for visualizing statistical charts, visualization of traffic data, and reuse of a Minard's map visualization's components, are presented in this paper. A detailed user study of our approach for reusing notations and generating visualizations has been provided. 80% of the participants in this user study agreed that the novel approach to visualization was easy and 87% stated that they quickly learned to use the tool support.

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Allen, Micah, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall, and Rogier A. Kievit. "Raincloud plots: a multi-platform tool for robust data visualization." Wellcome Open Research 4 (April 1, 2019): 63. http://dx.doi.org/10.12688/wellcomeopenres.15191.1.

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Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab (https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.

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Allen, Micah, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall, Jordy van Langen, and Rogier A. Kievit. "Raincloud plots: a multi-platform tool for robust data visualization." Wellcome Open Research 4 (January 21, 2021): 63. http://dx.doi.org/10.12688/wellcomeopenres.15191.2.

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Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab (https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.

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Ha, Hyoji, Jihye Lee, Hyunwoo Han, Sungyun Bae, Sangjoon Son, Changhyung Hong, Hyunjung Shin, and Kyungwon Lee. "Dementia Patient Segmentation Using EMR Data Visualization: A Design Study." International Journal of Environmental Research and Public Health 16, no. 18 (September 16, 2019): 3438. http://dx.doi.org/10.3390/ijerph16183438.

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(1) Background: The Electronic Medical Record system, which is a digital medical record management architecture, is critical for reliable medical research. It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis of multidimensional datasets via a combination of 3-dimensional radial coordinate visualization (3D RadVis) and many-objective optimization (e.g., Parallel Coordinates). Also, we propose a user-driven research design to facilitate visualization. We followed a design process to (1) understand the demands of domain experts, (2) define the problems based on relevant works, (3) design visualization, (4) implement visualization, and (5) enable qualitative evaluation by domain experts. (3) Results: This study provides clinical insight into dementia based on EMR data via visual analysis. Results of a case study based on questionnaires surveying daily living activities indicated that daily behaviors influenced the progression of dementia. (4) Conclusions: This study provides a visual analytical tool to support cluster segmentation. Using this tool, we segmented dementia patients into clusters and interpreted the behavioral patterns of each group. This study contributes to biomedical data interpretation based on a visual approach.

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Shee, Kevin, Sumanta K. Pal, J. Connor Wells, Jose Manuel Ruiz-Morales, Kenton Russell, Shaan Dudani, Toni K. Choueiri, Daniel Y. Heng, John L. Gore, and Anobel Y. Odisho. "Interactive Data Visualization Tool for Patient-Centered Decision Making in Kidney Cancer." JCO Clinical Cancer Informatics, no. 5 (August 2021): 912–20. http://dx.doi.org/10.1200/cci.21.00050.

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PURPOSE Patients and providers often lack clinical decision tools to enable effective shared decision making. This is especially true in the rapidly changing therapeutic landscape of metastatic kidney cancer. Using the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria, a validated risk prediction tool for patients with metastatic renal cell carcinoma, we created and user-tested a novel interactive visualization for clinical use. METHODS An interactive visualization depicting IMDC criteria was created, with the final version including data for more than 4,500 patients. Usability testing was performed with nonmedical lay-users and medical oncology fellow physicians. Subjects used the tool to calculate median survival times based on IMDC criteria. User confidence was surveyed. An iterative user feedback implementation cycle was completed and informed revision of the tool. RESULTS The tool is available at CloViz—IMDC. Initially, 400 lay-users and 15 physicians completed clinical scenarios and surveys. Cumulative accuracy across scenarios was higher for physicians than lay-users (84% v 74%; P = .03). Eighty-three percent of lay-users and 87% of physicians thought the tool became intuitive with use. Sixty-eight percent of lay-users wanted to use the tool clinically compared with 87% of physicians. After revisions, the updated tool was user-tested with 100 lay-users and 15 physicians. Physicians, but not lay-users, showed significant improvement in accuracy in the updated version of the tool (90% v 67%; P = .008). Seventy-two percent of lay-users and 93% of physicians wanted to use the updated tool in a clinical setting. CONCLUSION A graphical method of interacting with a validated nomogram provides prognosis results that can be used by nonmedical lay-users and physicians, and has the potential for expanded use across many clinical conditions.

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Contassot-Vivier, Sylvain. "A Load Balanced Parallel Ground Visualization Tool." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 07 (November 1997): 1113–27. http://dx.doi.org/10.1142/s0218001497000512.

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We propose in this paper, a parallel implementation of a ground visualization algorithm. Our input data consist in a Digital Elevation Model (DEM) covering a rectangular region, together with a raster image of the same area (the texture). The goal of the algorithm is to compute in parallel, images of the DEM from any point of view while mapping the texture onto the surface. The main originality of our approach concerns the distribution of the data, leading to a load-balanced and scalable parallel algorithm. We use a workload estimation to partition the output image, and then redistribute the input data according to this division. Special attention is paid on the data structures used for minimizing the cost of communications.

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