Relevant bibliographies by topics / Data Analysis and Visualization

Contents

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

Academic literature on the topic 'Data Analysis and Visualization'

Author: Grafiati
Published: 13 February 2024

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Journal articles on the topic "Data Analysis and Visualization"

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Keswani, Hrishikesh, Krishit Shah, Hritik Hassani, Moses Gadkar, and Er Manoj Kavedia. "Data Visualization and Analysis of COVID-19 Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 1328–37. http://dx.doi.org/10.22214/ijraset.2022.47179.

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Abstract: During the COVID-19 pandemic, many data visualizations were created to alert the public to the rapidly growing threat. Statistics on the spread of COVID-19 have been displayed on data dashboards, a mechanism for sharing information throughout the pandemic, which has aided in this process. When developing the visuals for COVID-19, the majority of time was spent on the technical aspects of designing and evaluating various visualization methods. Little is understood about the inner workings of visualization production processes due to the complex sociotechnical environments in which they are embedded. However, such ecological data is necessary for identifying the particulars and tendencies of visualization design practices in the wild and generating insights into how artists learn to perceive and approach visualization design on their terms and for their contextual aims. We conducted in-depth interviews with dashboard designers from federal and state health departments, major news media outlets, and other firms that created (often widely used) COVID-19 dashboards to gain insight into the following areas. What kind of problems, disagreements, and conflicts arose during making the COVID-19 dashboard because of the participation of visualization creators? The trajectory of design practices—from genesis to expansion, maintenance, and termination—is determined by the complex interconnections between design goals, design tools and technologies, labour, emerging crisis circumstances, and public participation. We zeroed in on these procedures' tensions between designers and the general public. Conflicts frequently arose due to a chasm between public demands and prevailing policies. They typically centred on the types and amounts of information that should be visualized and how public perceptions shape and are shaped by visualization design. The strategies used to deal with (potential) misinterpretations and misuse of visualizations. Our findings and takeaways offer fresh viewpoints on visualization design by highlighting the bundled activities typically linked with human and nonhuman participation along the entire trajectory of design practice
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Chin, George, Mudita Singhal, Grant Nakamura, Vidhya Gurumoorthi, and Natalie Freeman-Cadoret. "Visual Analysis of Dynamic Data Streams." Information Visualization 8, no. 3 (January 25, 2009): 212–29. http://dx.doi.org/10.1057/ivs.2009.18.

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For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as Abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension of high-volume, real-time data. The general strategy of the project is to develop and evolve visual contexts that will organize and orient high-volume dynamic data in conceptual and perceptive views. The goal is to allow users to quickly grasp dynamic data in forms that are intuitive and natural without requiring intensive training in the use of specific visualization or analysis tools and methods. Thus far, the project has prototyped five different visualization prototypes that represent and convey dynamic data through human-recognizable contexts and paradigms such as hierarchies, relationships, time and geography. We describe the design considerations and unique features of these dynamic visualization prototypes as well as our findings in the exploration and evaluation of their use.
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Dessiaming, Takdir Zulhaq, Siska Anraeni, and Suwito Pomalingo. "COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION." Jurnal Teknik Informatika (Jutif) 3, no. 5 (October 24, 2022): 1203–12. http://dx.doi.org/10.20884/1.jutif.2022.3.5.310.

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Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive process of analyzing academic data at universities to display them into interactive data visualizations, so that they can retrieve the information in it and make strategic decisions. The method used is a data visualization technique, which allows users to easily see the insights or insights contained in the data. The results obtained are data that has passed the preprocessing stage, can prepare data before being analyzed and processed to be used to make data visualization, so that the information obtained is more varied. This information can be used as a reference by academic managers to make strategic decisions.
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O'Donoghue, Seán I., Benedetta Frida Baldi, Susan J. Clark, Aaron E. Darling, James M. Hogan, Sandeep Kaur, Lena Maier-Hein, et al. "Visualization of Biomedical Data." Annual Review of Biomedical Data Science 1, no. 1 (July 20, 2018): 275–304. http://dx.doi.org/10.1146/annurev-biodatasci-080917-013424.

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The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.
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Heer, Jeffrey, and Joseph M. Hellerstein. "Data visualization and social data analysis." Proceedings of the VLDB Endowment 2, no. 2 (August 2009): 1656–57. http://dx.doi.org/10.14778/1687553.1687621.

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Cruz, António, Joel P. Arrais, and Penousal Machado. "Interactive and coordinated visualization approaches for biological data analysis." Briefings in Bioinformatics 20, no. 4 (March 26, 2018): 1513–23. http://dx.doi.org/10.1093/bib/bby019.

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AbstractThe field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein–protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.
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Kullman, Kaur, and Don Engel. "Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity." Journal of Defence & Security Technologies 4, no. 1 (January 2022): 37–52. http://dx.doi.org/10.46713/jdst.004.03.

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Interactive Data Visualizations (IDV) can be useful for cybersecurity subject matter experts (CSMEs) while they are exploring new data or investigating familiar datasets for anomalies, correlating events, etc. For an IDV to be useful to a CSME, interaction with that visualization should be simple and intuitive (free of additional mental tasks) and the visualization’s layout must map to a CSME's understanding. While CSMEs may learn to interpret visualizations created by others, they should be encouraged to visualize their datasets in ways that best reflect their own ways of thinking. Developing their own visual schemes makes optimal use of both the data analysis tools and human visual cognition. In this article, we focus on a currently available interactive stereoscopically perceivable multidimensional data visualization solution, as such tools could provide CSMEs with better perception of their data compared to interpreting IDV on flat media (whether visualized as 2D or 3D structures).
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Kim, Min Jung, and Eun Ryung Hyun. "Analysis of User Empathy Levels Based on Types of Data Visualization." Korea Institute of Design Research Society 8, no. 4 (December 31, 2023): 256–66. http://dx.doi.org/10.46248/kidrs.2023.4.256.

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This study aims to explore techniques for eliciting emotional empathy in data visualization. To achieve this, it utilizes preceding research to derive tools for measuring levels of empathy, and analyzes the impact of different types of data visualization on empathy and charitable behaviors to develop a humanism-based data design strategy. The methodology encompasses both literature review and empirical research, reviewing 11 previous studies to identify the types of data visualization and tools for empathy measurement. For empirical analysis, four types of visualizations were created and subjected to an online survey with 95 Korean adults between April 10 to 15, 2023. The analysis revealed that the type of data visualization significantly influences the viewer's emotional response and charitable actions. Notably, realistic illustration elicited the highest level of empathy, while metaphorical infographics induced moderate levels of empathy and donations. This research provides valuable insights for establishing data visualization strategies grounded in humanism.
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Kresh, J. Yasha, and Arthur D'Adamo. "Cardiovascular data visualization and analysis." Journal of the American College of Cardiology 17, no. 2 (February 1991): A14. http://dx.doi.org/10.1016/0735-1097(91)91023-8.

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Orner, Sylvia. "Data Visualization for Collection Analysis." Pennsylvania Libraries: Research & Practice 11, no. 1 (September 27, 2023): 34–44. http://dx.doi.org/10.5195/palrap.2023.278.

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Due to the increasingly digital nature of library resources and collections, it is sometimes difficult to envision a library’s unified holdings and to understand how they have changed over time. Conducting a collection analysis and applying data visualization techniques can be an excellent way to get a top-down view of the collection as a whole. This article outlines the author’s process for a collection analysis of the Weinberg Memorial Library’s entire catalog of print and electronic resources. It explores the rationale behind some key collection analysis decisions and discusses approaches for data extraction and clean-up as well as visualization using the Tableau software.
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Dissertations / Theses on the topic "Data Analysis and Visualization"

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Furuhashi, Takeshi. "Data Visualization for Kansei Analysis." 日本知能情報ファジィ学会, 2010. http://hdl.handle.net/2237/20694.

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SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan
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Cheong, Tat Man. "Money laundering data analysis and visualization." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492978.

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Yang, Di. "Analysis guided visual exploration of multivariate data." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-050407-005925/.

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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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Wu, Yingyu. "Using Text based Visualization in Data Analysis." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1398079502.

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Alam, Sayeed Safayet. "Analysis of Eye-Tracking Data in Visualization and Data Space." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3473.

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Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument visualizations that have open source code, and leverage real-time information about the layout of the rendered visual content, to automatically relate gaze-samples to visual objects drawn on the screen. Since such visual objects are shown in a visualization stand for data, the method would allow us to necessarily detect data that users focus on or Data of Interest (DOI). This dissertation has two contributions. First, we demonstrated the feasibility of collecting DOI data for real life visualization in a reliable way which is not self-evident. Second, we formalized the process of collecting and interpreting DOI data and test whether the automated DOI detection can lead to research workflows, and insights not possible with traditional, manual approaches.
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Song, Huaguang. "Multi-scale data sketching for large data analysis and visualization." Scholarly Commons, 2012. https://scholarlycommons.pacific.edu/uop_etds/832.

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Analysis and visualization of large data sets is time consuming and sometimes can be a very difficult process, especially for 3D data sets. Therefore, data processing and visualization techniques have often been used in the case of different massive data analysis for efficiency and accuracy purposes. This thesis presents a multi-scale data sketching solution, specifically for large 3D scientific data with a goal to support collaborative data management, analysis and visualization. The idea is to allow users to quickly identify interesting regions and observe significant patterns without directly accessing the raw data, since most of the information in raw form is not useful. This solution will provide a fast way to allow the users to choose the regions they are interested and save time. By preprocessing the data, our solution can sketch out the general regions of the 3D data, and users can decide whether they are interested in going further to analyze the current data. The key issue is to find efficient and accurate algorithms to detect boundaries or regions information for large 3D scientific data. Specific techniques and performance analysis are also discussed.
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Park, Joonam. "A visualization system for nonlinear frame analysis." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/19172.

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Schroeder, Michael Philipp 1986. "Analysis and visualization of multidimensional cancer genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/301436.

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Cancer is a complex disease caused by somatic alterations of the genome and epigenome in tumor cells. Increased investments and cheaper access to various technologies have built momentum for the generation of cancer genomics data. The availability of such large datasets offers many new possibilities to gain insight into cancer molecular properties. Within this scope I present two methods that exploit the broad availability of cancer genomic data: OncodriveROLE, an approach to classify mutational cancer driver genes into activating and loss of function mode of actions and MutEx, a statistical measure to assess the trend of the somatic alterations in a set of genes to be mutually exclusive across tumor samples. Nevertheless, the unprecedented dimension of the available data raises new complications for its accessibility and exploration which we try to solve with new visualization solutions: i) Gitools interactive heatmaps with prepared large scale cancer genomics datasets ready to be explored, ii) jHeatmap, an interactive heatmap browser for the web capable of displaying multidimensional cancer genomics data and designed for its inclusion into web portals, and iii) SVGMap, a web server to project data onto customized SVG figures useful for mapping experimental measurements onto the model.
El cancer és una malaltia complexa causada per alteracions somàtiques del genoma i epigenoma de les cèl•lules tumorals. Un augment d’inversions i l'accés a tecnologies de baix cost ha provocat un increment important en la generació de dades genòmiques de càncer. La disponibilitat d’aquestes dades ofereix noves possibilitats per entendre millor les propietats moleculars del càncer. En aquest àmbit, presento dos mètodes que aprofiten aquesta gran disponibilitat de dades genòmiques de càncer: OncodriveROLE, un procediment per a classificar gens “drivers” del càncer segons si el seu mode d’acció ésl'activació o la pèrdua de funció del producte gènic; i MutEx, un estadístic per a mesurar la tendència de les mutacions somàtiques a l’exclusió mútua. Tanmateix, la manca de precedents d’aquesta gran dimensió de dades fa sorgir nous problemes en quant a la seva accessibilitat i exploració, els quals intentem solventar amb noves eines de visualització: i) Heatmaps interactius de Gitools amb dades genòmiques de càncer a gran escala, a punt per ser explorades, ii) jHeatmap, un heatmap interactiu per la web capaç de mostrar dades genòmiques de cancer multidimensionals i dissenyat per la seva inclusió a portals web; i iii) SVGMap, un servidor web per traslladar dades en figures SVG customitzades, útil per a la transl•lació de mesures experimentals en un model visual.
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Walter, Martin Alan. "Visualization techniques for the analysis of neurophysiological data." Thesis, University of Plymouth, 2004. http://hdl.handle.net/10026.1/2551.

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In order to understand the diverse and complex functions of the Human brain, the temporal relationships of vast quantities of multi-dimensional spike train data must be analysed. A number of statistical methods already exist to analyse these relationships. However, as a result of expansions in recording capability hundreds of spike trains must now be analysed simultaneously. In addition to the requirements for new statistical analysis methods, the need for more efficient data representation is paramount. The computer science field of Information Visualization is specifically aimed at producing effective representations of large and complex datasets. This thesis is based on the assumption that data analysis can be significantly improved by the application of Information Visualization principles and techniques. This thesis discusses the discipline of Information Visualization, within the wider context of visualization. It also presents some introductory neurophysiology focusing on the analysis of multidimensional spike train data and software currently available to support this problem. Following this, the Toolbox developed to support the analysis of these datasets is presented. Subsequently, three case studies using the Toolbox are described. The first case study was conducted on a known dataset in order to gain experience of using these methods. The second and third case studies were conducted on blind datasets and both of these yielded compelling results.
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Books on the topic "Data Analysis and Visualization"

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MATLAB: Data analysis and visualization. Singapore ; NJ: World Scientific, 2008.

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1957-, Blasius Jörg, and Greenacre Michael J, eds. Visualization of categorical data. San Diego: Academic Press, 1998.

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Engebretsen, Martin. Data Visualization in Society. Amsterdam: Amsterdam university Press, 2020.

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Tierny, Julien. Topological Data Analysis for Scientific Visualization. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71507-0.

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Embarak, Dr Ossama. Data Analysis and Visualization Using Python. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4109-7.

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McCormick, Keith, and Jesus Salcedo. SPSSreg Statistics for Data Analysis and Visualization. Indianapolis, Indiana: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119183426.

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Pascucci, Valerio, Xavier Tricoche, Hans Hagen, and Julien Tierny, eds. Topological Methods in Data Analysis and Visualization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15014-2.

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Mirkin, Boris. Core Data Analysis: Summarization, Correlation, and Visualization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00271-8.

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Nieuwstadt, F. T. M. 1946-, ed. Flow visualization and image analysis. Dordrecht: Kluwer Academic Publishers, 1993.

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Francisco, Azuaje, and Dopazo Joaquín, eds. Data analysis and visualization in genomics and proteomics. Chichester, West Sussex, England: John Wiley, 2005.

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Book chapters on the topic "Data Analysis and Visualization"

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Fries, Karsten, Jörg Meyer, Hans Hagen, and Bernd Lindemann. "Correspondence Analysis." In Data Visualization, 53–66. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_4.

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Eick, Stephen G. "Ebusiness Click Stream Analysis." In Data Visualization, 185–99. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_13.

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Marron, J. S., and Ian L. Dryden. "Data Visualization." In Object Oriented Data Analysis, 97–124. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351189675-6.

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Embarak, Ossama. "Data Visualization." In Data Analysis and Visualization Using Python, 293–342. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4109-7_7.

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Musa, Kamarul Imran, Wan Nor Arifin Wan Mansor, and Tengku Muhammad Hanis. "Data Visualization." In Data Analysis in Medicine and Health using R, 37–72. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003296775-4.

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Divya Zion, G., and B. K. Tripathy. "Comparative Analysis of Tools for Big Data Visualization and Challenges." In Data Visualization, 33–52. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2282-6_3.

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Bennett, Janine, Attila Gyulassy, Valerio Pascucci, and Peer-Timo Bremer. "Large Scale Data Analysis." In Mathematics and Visualization, 339–51. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6497-5_27.

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Umadevi, K. S., and D. Geraldine Bessie Amali. "Data Visualization and Analysis for Air Quality Monitoring Using IBM Watson IoT Platform." In Data Visualization, 15–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2282-6_2.

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Chellam, Aditya, Ayush Chaturvedi, and L. Ramanathan. "Data Visualization: Visualization of Social Media Marketing Analysis Data to Generate Effective Business Revenue Model." In Data Visualization, 75–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2282-6_5.

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Zamora Saiz, Alfonso, Carlos Quesada González, Lluís Hurtado Gil, and Diego Mondéjar Ruiz. "Visualization." In An Introduction to Data Analysis in R, 111–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48997-7_4.

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Conference papers on the topic "Data Analysis and Visualization"

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Ferro, Daniela P., Ugo Becciani, Vincenzo Antonuccio-Delogu, Angela Germaná, and Claudio Gheller. "Astrophysical Data Analysis and Visualization Toolkit." In ADA-III - Astronomical Data Analysis III Conference. BCS Learning & Development, 2004. http://dx.doi.org/10.14236/ewic/ada-iii2004.9.

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Li, Mingzhe, Sourabh Palande, Lin Yan, and Bei Wang. "Sketching Merge Trees for Scientific Visualization." In 2023 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2023. http://dx.doi.org/10.1109/topoinvis60193.2023.00013.

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Hereld, Mark, and Michael E. Papka. "The data analysis computing hierarchy." In 2008 Workshop on Ultrascale Visualization. IEEE, 2008. http://dx.doi.org/10.1109/ultravis.2008.5154058.

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Singh, Kiran, and Rakhi Wajgi. "Data analysis and visualization of sales data." In 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave). IEEE, 2016. http://dx.doi.org/10.1109/startup.2016.7583967.

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Li, Jixian, Daniel Van Boxel, and Joshua A. Levine. "Autoencoder-Aided Visualization of Collections of Morse Complexes." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00009.

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"Author Index." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00019.

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"Title Page iii." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00002.

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Nigmetov, Arnur, and Dmitriy Morozov. "Fast Merge Tree Computation via SYCL." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00007.

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Elkin, Yury, and Vitaliy Kurlin. "Counterexamples expose gaps in the proof of time complexity for cover trees introduced in 2006." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00008.

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Sharma, Mohit, and Vijay Natarajan. "Jacobi Set Driven Search for Flexible Fiber Surface Extraction." In 2022 Topological Data Analysis and Visualization (TopoInVis). IEEE, 2022. http://dx.doi.org/10.1109/topoinvis57755.2022.00012.

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Reports on the topic "Data Analysis and Visualization"

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Shen, Han-Wei. Scalable Data Management, Analysis, and Visualization. Office of Scientific and Technical Information (OSTI), October 2017. http://dx.doi.org/10.2172/1406107.

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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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Choudhary, Alok. Scalable Data Management, Analysis, and Visualization Institute. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1567859.

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Ahrens, James P. Visualization and Data Analysis at the Exascale. Office of Scientific and Technical Information (OSTI), January 2011. http://dx.doi.org/10.2172/1011053.

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Zhan, Peng. Statistical Analysis and Data Visualization in R. Instats Inc., 2022. http://dx.doi.org/10.61700/dizyg5iq1mqj5469.

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Abstract:
This seminar provides a gentle introduction to R for those looking to use it for applied statistics and visualisation, covering a range of essential topics on basic data manipulation, regression analysis, and visualizing data and results. Examples from multiple fields are used to demonstrate the applications. An official Instats certificate of completion is provided at the conclusion of the seminar.
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Ma, Kwan-Liu. Scalable Data Management, Analysis, and Visualization (SDAV) Institute. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1498620.

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Bethel, E. Wes. Query-Driven Network Flow Data Analysis and Visualization. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/888963.

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Ma, Kwan-Liu. Interactive Correlation Analysis and Visualization of Climate Data. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1325752.

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Sewell, Christopher Meyer. Visualization and Data Analysis for High-Performance Computing. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1329547.

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SAMATOVA, Nagiza Faridovna. Scalable Data Management, Analysis, and Visualization (SDAV) Institute. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1502382.

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