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Data Storytelling in Data Visualisation: Does it Enhance the Efficiency and Effectiveness of Information Retrieval and Insights Comprehension? (2402.12634v2)

Published 20 Feb 2024 in cs.HC

Abstract: Data storytelling (DS) is rapidly gaining attention as an approach that integrates data, visuals, and narratives to create data stories that can help a particular audience to comprehend the key messages underscored by the data with enhanced efficiency and effectiveness. It has been posited that DS can be especially advantageous for audiences with limited visualisation literacy, by presenting the data clearly and concisely. However, empirical studies confirming whether data stories indeed provide these benefits over conventional data visualisations are scarce. To bridge this gap, we conducted a study with 103 participants to determine whether DS indeed improve both efficiency and effectiveness in tasks related to information retrieval and insights comprehension. Our findings suggest that data stories do improve the efficiency of comprehension tasks, as well as the effectiveness of comprehension tasks that involve a single insight compared with conventional visualisations. Interestingly, these benefits were not associated with participants' visualisation literacy.

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References (105)
  1. A taxonomy for Learning Teaching and Assessing.
  2. Jessie B Arneson and Erika G Offerdahl. 2018. Visual literacy in Bloom: Using Bloom’s taxonomy to support visual learning skills. CBE—Life Sciences Education 17, 1 (2018), ar7. https://doi.org/10.1187/cbe.17-08-0178
  3. Simplifying climate change communication: An application of data visualisation at the regional and local scale. GeoSpatial visualisation (2013), 119–136. https://doi.org/10.1007/978-3-642-12289-7_6
  4. Narrative Design Patterns for Data-Driven Storytelling. CRC Press (Taylor & Francis), 107–133. https://doi.org/10.1201/9781315281575-5
  5. Learning Tableau: A data visualization tool. Journal of Economic Education 51 (2020). Issue 3-4. https://doi.org/10.1080/00220485.2020.1804503
  6. Bloom taxonomy of educational objectives. In Allyn and Bacon. Pearson Education London.
  7. Mark Blythe. 2017. Research Fiction: Storytelling, Plot and Design. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 5400–5411. https://doi.org/10.1145/3025453.3026023
  8. Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors. Information Visualization 15, 3 (2016), 198–213. https://doi.org/10.1177/1473871615594652
  9. Reflections on visualization for broad audiences. Foundations of data visualization (2020), 297–305. https://doi.org/10.1007/978-3-030-34444-3_16
  10. Storytelling in Information Visualizations: Does It Engage Users to Explore Data?. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 1449–1458. https://doi.org/10.1145/2702123.2702452
  11. Showing people behind data: Does anthropomorphizing visualizations elicit more empathy for human rights data?. In Proceedings of the 2017 CHI conference on human factors in computing systems, Vol. 2017-May. 5462–5474. https://doi.org/10.1145/3025453.3025512
  12. A principled way of assessing visualization literacy. IEEE Transactions on Visualization and Computer Graphics 20 (2014). Issue 12. https://doi.org/10.1109/TVCG.2014.2346984
  13. Virginia Braun and Victoria Clarke. 2012. Thematic analysis. In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological., D. L. Long A. T. Panter D. Rindskopf & K. J. She H. Cooper, P. M. Camic (Ed.). APA handbooks in psychology., Vol. 2. American Psychological Association, Washington, DC, US, 57–71. https://doi.org/10.1037/13620-004
  14. Andrea Bravo and Anja M Maier. 2020. Immersive visualisations in design: Using augmented reality (AR) for information presentation. In Proceedings of the Design Society: DESIGN Conference, Vol. 1. Cambridge University Press, 1215–1224. https://doi.org/10.1017/dsd.2020.33
  15. How to evaluate data visualizations across different levels of understanding. Proceedings - 8th Evaluation and Beyond: Methodological Approaches for Visualization, BELIV 2020. https://doi.org/10.1109/BELIV51497.2020.00010
  16. Vetria Byrd. 2019. Using Bloom’s Taxonomy to Support Data Visualization Capacity Skills. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2019, Saul Carliner (Ed.). Association for the Advancement of Computing in Education (AACE), New Orleans, Louisiana, United States, 1039–1053. https://www.learntechlib.org/p/212809
  17. Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors. Information Visualization 15 (2016). Issue 3. https://doi.org/10.1177/1473871615594652
  18. I Bernard Cohen. 1984. Florence nightingale. Scientific American 250, 3 (1984), 128–137. https://doi.org/10.1038/scientificamerican0384-128
  19. Jacob Cohen. 2013. Statistical power analysis for the behavioral sciences. Academic press.
  20. Mohammad Kamel Daradkeh. 2021. An empirical examination of the relationship between data storytelling competency and business performance: The mediating role of decision-making quality. Journal of Organizational and End User Computing 33 (2021). Issue 5. https://doi.org/10.4018/JOEUC.20210901.oa3
  21. Evaluating data storytelling strategies: A case study on urban changes. IARIA Cognitive (2014), 250–255.
  22. Hollaback! The Role of Storytelling Online in a Social Movement Organization. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (San Antonio, Texas, USA) (CSCW ’13). Association for Computing Machinery, New York, NY, USA, 477–490. https://doi.org/10.1145/2441776.2441831
  23. David Donohoe and Eamon Costello. 2020. Data visualisation literacy in higher education: An exploratory study of understanding of a learning dashboard tool. International Journal of Emerging Technologies in Learning 15 (2020). Issue 17. https://doi.org/10.3991/ijet.v15i17.15041
  24. Brent Dykes. 2015. Data storytelling: What it is and how it can be used to effectively communicate analysis results. Applied Marketing Analytics 1, 4 (2015), 299–313.
  25. Towards data storytelling to support teaching and learning. ACM International Conference Proceeding Series. https://doi.org/10.1145/3152771.3156134
  26. Exploratory versus Explanatory Visual Learning Analytics: Driving Teachers’ Attention through Educational Data Storytelling. Journal of Learning Analytics 5 (2018). Issue 3. https://doi.org/10.18608/jla.2018.53.6
  27. Annotating Line Charts for Addressing Deception. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (¡conf-loc¿, ¡city¿New Orleans¡/city¿, ¡state¿LA¡/state¿, ¡country¿USA¡/country¿, ¡/conf-loc¿) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 80, 12 pages. https://doi.org/10.1145/3491102.3502138
  28. Anna Feigenbaum and Aria Alamalhodaei. 2020. The data storytelling workbook. Routledge. https://doi.org/10.4324/9781315168012
  29. Storytelling with learner data: Guiding student reflection on multimodal team data. IEEE Transactions on Learning Technologies 14, 5 (2021), 695–708. https://doi.org/10.1109/tlt.2021.3131842
  30. Stephen Few. 2004. Show me the numbers:Designing Tables & Graphs to Enlighten. (2004).
  31. Ana Figueiras. 2014. Narrative visualization: A case study of how to incorporate narrative elements in existing visualizations. Proceedings of the International Conference on Information Visualisation. https://doi.org/10.1109/IV.2014.79
  32. Interactive visualization literacy: The state-of-the-art. Information Visualization 21, 3 (mar 2022), 285–310. https://doi.org/10.1177/14738716221081831
  33. Storytelling: Branding in practice. Springer. https://doi.org/10.1007/b138635
  34. Robert B. Frary. 1988. Formula Scoring of Multiple‐Choice Tests (Correction for Guessing). Educational Measurement: Issues and Practice 7 (1988). Issue 2. https://doi.org/10.1111/j.1745-3992.1988.tb00434.x
  35. Yu Fu and John Stasko. 2022. Supporting Data-Driven Basketball Journalism through Interactive Visualization. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 598, 17 pages. https://doi.org/10.1145/3491102.3502078
  36. Simone Garlandini and Sara Irina Fabrikant. 2009. Evaluating the effectiveness and efficiency of visual variables for geographic information visualization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5756 LNCS. https://doi.org/10.1007/978-3-642-03832-7_12
  37. Nahum Gershon and Ward Page. 2001. What storytelling can do for information visualization. Commun. ACM 44, 8 (2001), 31–37. https://doi.org/10.1145/381641.381653
  38. Validity and Reliability (Credibility and Dependability) in Qualitative Research and Data Analysis. SAGE Publications, Inc., Thousand Oaks, 79–103. https://doi.org/10.4135/9781483384436
  39. Jeffrey Heer and Ben Shneiderman. 2012. Interactive Dynamics for Visual Analysis: A Taxonomy of Tools That Support the Fluent and Flexible Use of Visualizations. Queue 10, 2 (feb 2012), 30–55. https://doi.org/10.1145/2133416.2146416
  40. Martin Hicks. 2009. Perceptual and Design Principles for Effective Interactive Visualisations. Springer London, London, 155–174. https://doi.org/10.1007/978-1-84800-269-2_7
  41. Jamie Hoelscher and Amanda Mortimer. 2018. Using Tableau to visualize data and drive decision-making. Journal of Accounting Education 44 (2018). https://doi.org/10.1016/j.jaccedu.2018.05.002
  42. A deeper understanding of sequence in narrative visualization. IEEE Transactions on Visualization and Computer Graphics 19 (2013). Issue 12. https://doi.org/10.1109/TVCG.2013.119
  43. Noah Iliinsky and Julie Steele. 2011. Designing data visualizations: Representing informational Relationships. ” O’Reilly Media, Inc.”.
  44. Mohieddin Jafari and Naser Ansari-Pour. 2019. Why, when and how to adjust your P values? Cell Journal 20 (2019), 604–607. Issue 4. https://doi.org/10.22074/cellj.2019.5992
  45. Steven Johnson. 2006. The ghost map: The story of London’s most terrifying epidemic–and how it changed science, cities, and the modern world. Penguin.
  46. Slava Kalyuga. 2009. The expertise reversal effect. In Managing cognitive load in adaptive multimedia learning. IGI Global, 58–80. https://doi.org/10.4018/978-1-60566-048-6.ch003
  47. Cole Nussbaumer Knaflic. 2015. Storytelling with data : a data visualization guide for business professionals.
  48. Frames and Slants in Titles of Visualizations on Controversial Topics. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3174012
  49. Understanding Visual Cues in Visualizations Accompanied by Audio Narrations. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300280
  50. Robert Kosara and Jock MacKinlay. 2013. Storytelling: The next step for visualization. Computer 46 (2013). Issue 5. https://doi.org/10.1109/MC.2013.36
  51. Randy Krum. 2013. Cool infographics: Effective communication with data visualization and design. John Wiley & Sons.
  52. Automatic Annotation Synchronizing with Textual Description for Visualization. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376443
  53. Negative Emotions, Positive Outcomes? Exploring the Communication of Negativity in Serious Data Stories. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 28, 14 pages. https://doi.org/10.1145/3491102.3517530
  54. More Than Telling a Story: Transforming Data into Visually Shared Stories. IEEE Computer Graphics and Applications 35 (2015). Issue 5. https://doi.org/10.1109/MCG.2015.99
  55. How do people make sense of unfamiliar visualizations?: A grounded model of novice’s information visualization sensemaking. IEEE transactions on visualization and computer graphics 22, 1 (2015), 499–508. https://doi.org/10.1109/TVCG.2015.2467195
  56. VLAT: Development of a Visualization Literacy Assessment Test. IEEE Transactions on Visualization and Computer Graphics 23 (2017). Issue 1. https://doi.org/10.1109/TVCG.2016.2598920
  57. Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling. arXiv preprint arXiv:2304.08366 (2023).
  58. Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude. Computer Graphics Forum 39 (2020). Issue 3. https://doi.org/10.1111/cgf.13980
  59. Joy Lowe and Machdel Matthee. 2020. Requirements of data visualisation tools to analyse big data: A structured literature review. In Responsible Design, Implementation and Use of Information and Communication Technology: 19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020, Skukuza, South Africa, April 6–8, 2020, Proceedings, Part I 19. Springer, 469–480. https://doi.org/10.1007/978-3-030-44999-5_39
  60. Scientific storytelling using visualization. IEEE Computer Graphics and Applications 32 (2012). Issue 1. https://doi.org/10.1109/MCG.2012.24
  61. Data visualization literacy: Investigating data interpretation along the novice—expert continuum. Journal of College Science Teaching 45, 1 (2015), 84–90. https://doi.org/10.2505/4/jcst15_045_01_84
  62. From data to insights: A layered storytelling approach for multimodal learning analytics. In Proceedings of the 2020 chi conference on human factors in computing systems. 1–15. https://doi.org/10.1145/3313831.3376148
  63. Assessment of visualisation skills in biochemistry students. South African Journal of Science 112, 9-10 (2016), 1–8. https://doi.org/10.17159/sajs.2016/20150412
  64. Can anthropographics promote prosociality? a review and large-sample study. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–18. https://doi.org/10.1145/3411764.3445637
  65. Tamara Munzner. 2014. Visualization analysis and design. CRC press. https://doi.org/10.1201/b17511
  66. Designing Ambient Narrative-Based Interfaces to Reflect and Motivate Physical Activity. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376478
  67. Nadim Nachar. 2008. The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution. Tutorials in Quantitative Methods for Psychology 4 (2008). Issue 1. https://doi.org/10.20982/tqmp.04.1.p013
  68. Ifeanyi Glory Ndukwe and Ben Kei Daniel. 2020. Teaching analytics, value and tools for teacher data literacy: A systematic and tripartite approach. International Journal of Educational Technology in Higher Education 17, 1 (2020), 1–31. https://doi.org/10.1186/s41239-020-00201-6
  69. Adegboyega Ojo and Bahareh Heravi. 2018. Patterns in award winning data storytelling: Story types, enabling tools and competences. Digital journalism 6, 6 (2018), 693–718. https://doi.org/10.1080/21670811.2017.1403291
  70. Charlie and the Semi-Automated Factory: Data-Driven Operator Behavior and Performance Modeling for Human-Machine Collaborative Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–16. https://doi.org/10.1145/3544548.3581457
  71. Geographical Information Recognition and Visualization in Texts Written in Various Languages. In Proceedings of the 2004 ACM Symposium on Applied Computing (Nicosia, Cyprus) (SAC ’04). Association for Computing Machinery, New York, NY, USA, 1051–1058. https://doi.org/10.1145/967900.968115
  72. How Do Teachers Use Dashboards Enhanced with Data Storytelling Elements According to Their Data Visualisation Literacy Skills?. In LAK23: 13th International Learning Analytics and Knowledge Conference (Arlington, TX, USA) (LAK2023). Association for Computing Machinery, New York, NY, USA, 89–99. https://doi.org/10.1145/3576050.3576063
  73. Single or Multi-page Learning Analytics Dashboards? Relationships Between Teachers’ Cognitive Load and Visualisation Literacy. In Responsive and Sustainable Educational Futures, Olga Viberg, Ioana Jivet, Pedro J. Muñoz-Merino, Maria Perifanou, and Tina Papathoma (Eds.). Springer Nature Switzerland, Cham, 339–355.
  74. ChartAccent: Annotation for data-driven storytelling. In 2017 IEEE Pacific Visualization Symposium (PacificVis). 230–239. https://doi.org/10.1109/PACIFICVIS.2017.8031599
  75. Re-understanding of data storytelling tools from a narrative perspective. Visual Intelligence 1, 1 (2023), 11. https://doi.org/10.1007/s44267-023-00011-0
  76. Data-driven storytelling. CRC Press. https://doi.org/10.1201/9781315281575
  77. The Explanatory Visualization Framework: An Active Learning Framework for Teaching Creative Computing Using Explanatory Visualizations. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2018), 791–801. https://doi.org/10.1109/TVCG.2017.2745878
  78. Storied numbers: Supporting media-rich data storytelling for television. TVX 2014 - Proceedings of the 2014 ACM International Conference on Interactive Experiences for TV and Online Video. https://doi.org/10.1145/2602299.2602308
  79. Once upon a time in a land far away: guidelines for spatio-temporal narrative visualization. In 2019 23rd International Conference Information Visualisation (IV). IEEE, 44–49. https://doi.org/10.1109/IV.2019.00017
  80. Patrick Royston. 1992. Approximating the Shapiro-Wilk W-test for non-normality. Statistics and Computing 2 (1992). Issue 3. https://doi.org/10.1007/BF01891203
  81. L. Ryan. 2016. The Visual Imperative : Creating a Visual Culture of Data Discovery. Elsevier Science. https://doi.org/10.1016/c2015-0-00786-9
  82. Lindy Ryan. 2018. Visual data storytelling with tableau: story points, telling compelling data narratives. Addison-Wesley Professional.
  83. Evaluation of a financial portfolio visualization using computer displays and mixed reality devices with domain experts. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–9. https://doi.org/10.1145/3313831.3376556
  84. Towards a characterization of guidance in visualization. In Poster at IEEE Conference on Information Visualization (InfoVis), Vol. 2.
  85. Edward Segel and Jeffrey Heer. 2010. Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics 16 (2010), 1139–1148. Issue 6. https://doi.org/10.1109/TVCG.2010.179
  86. Research on Data Storytelling Strategies for Cultural Heritage Transmission and Dissemination. In HCI International 2022 – Late Breaking Posters, Constantine Stephanidis, Margherita Antona, Stavroula Ntoa, and Gavriel Salvendy (Eds.). Springer Nature Switzerland, Cham, 344–353.
  87. Stephen R.J. Sheppard. 2005. Landscape visualisation and climate change: The potential for influencing perceptions and behaviour. Environmental Science and Policy 8 (2005). Issue 6. https://doi.org/10.1016/j.envsci.2005.08.002
  88. Dilruba Showkat and Eric PS Baumer. 2021. Where do stories come from? examining the exploration process in investigative data journalism. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–31. https://doi.org/10.1145/3479534
  89. Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2023), 1233–1243. https://doi.org/10.1109/TVCG.2022.3209383
  90. Data Work of Frontline Care Workers: Practices, Problems, and Opportunities in the Context of Data-Driven Long-Term Care. Proceedings of the ACM on Human-Computer Interaction 7 (2023), 1–28. Issue 1 CSCW. https://doi.org/10.1145/3579475
  91. Measuring Effects of Spatial Visualization and Domain on Visualization Task Performance: A Comparative Study. IEEE Transactions on Visualization and Computer Graphics 29 (2023). Issue 1. https://doi.org/10.1109/TVCG.2022.3209491
  92. Edward R Tufte. 2001. The visual display of quantitative information. Vol. 2. Graphics press Cheshire, CT. https://doi.org/10.4135/9781071812082.n670
  93. Teaching Data Visualization and Storytelling with Data Comic Workshops. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI EA ’19). Association for Computing Machinery, New York, NY, USA, 1–9. https://doi.org/10.1145/3290607.3299043
  94. Colin Ware. 2019. Information visualization: perception for design. Morgan Kaufmann. https://doi.org/10.1016/C2016-0-02395-1
  95. Benjamin Watson and Vidya Setlur. 2015. Emerging research in mobile visualization. MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. https://doi.org/10.1145/2786567.2786571
  96. Reflective Data Storytelling for Youth: The CODAP Story Builder. In Proceedings of the 20th Annual ACM Interaction Design and Children Conference (Athens, Greece) (IDC ’21). Association for Computing Machinery, New York, NY, USA, 503–507. https://doi.org/10.1145/3459990.3465177
  97. Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17. https://doi.org/10.1145/3544548.3581398
  98. Muhammad Faris Basheer B.Mohd Zanan and Madihah Sheikh Abdul Aziz. 2022. A Review On The Visual Design Styles In Data Storytelling Based On User Preferences And Personality Differences. Proceedings of the 2022 IEEE 7th International Conference on Information Technology and Digital Applications, ICITDA 2022. https://doi.org/10.1109/ICITDA55840.2022.9971409
  99. The influence of data storytelling on the ability to recall. CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval. https://doi.org/10.1145/3498366.3505755
  100. Yangjinbo Zhang. 2018. Converging data storytelling and visualisation. In Entertainment Computing–ICEC 2018: 17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018, Proceedings 17, Vol. 11112 LNCS. Springer, 310–316. https://doi.org/10.1007/978-3-319-99426-0_36
  101. Yangjinbo Zhang and Artur Lugmayr. 2019. Designing a user-centered interactive data-storytelling framework. In Proceedings of the 31st Australian Conference on Human-Computer-Interaction. 428–432. https://doi.org/10.1145/3369457.3369507
  102. A Visual Data Storytelling Framework. Informatics 9 (2022). Issue 4. https://doi.org/10.3390/informatics9040073
  103. Understanding partitioning and sequence in data-driven storytelling. In Information in Contemporary Society: 14th International Conference, iConference 2019, Washington, DC, USA, March 31–April 3, 2019, Proceedings 14, Vol. 11420 LNCS. Springer, 327–338. https://doi.org/10.1007/978-3-030-15742-5_32
  104. GameViews: Understanding and Supporting Data-Driven Sports Storytelling. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300499
  105. Ying Zhu. 2007. Measuring effective data visualization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4842 LNCS. Issue PART 2. https://doi.org/10.1007/978-3-540-76856-2_64
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