Human – Centred Scientific Data – Robertas Damasevicius

Interactive tooltips are particularly effective in dynamic dash- boards or web-based visualizations, where users can explore the data at their own pace. Example of interactive tooltips in Python: import plotly.express as px data = { “Category”: [“A”, “B”, “C”, “D”], “Value”: [50, 30, 15, 5] } fig = px.pie(data, names=’Category’, values=’Value’, hole=0.4) fig.update_traces(textinfo=’percent+label’, hoverinfo=’label+value+percent’) ↩→ fig.update_layout(title=”Interactive Donut with Hover Tooltips”) fig.show() Design Tip Introduce interactivity using hover effects, tooltips, or clickable segments to keep your audience engaged and allow for deeper data exploration without cluttering the chart.

One of the main advantages of tooltips is that they can contain more detailed information than would be possible with static labels or callouts. In addition to the category name and value, tooltips can provide additional context such as percentages, comparisons to other data points, or even multimedia elements like images or links to further information.

This level of interactivity enhances user engagement by allowing the viewer to focus deeper on the data without overwhelming them with too much information upfront. However, it’s important to ensure that tooltips are implemented in a user-friendly way. Tooltips should appear instantly when the user interacts with the chart and should disappear smoothly when the user moves away. They should also be designed in a way that is consistent with the overall visual style of the chart, using appropriate fonts, colors, and layouts.

Design Tip Be consistent with your color assignments across charts. Assign fixed colors to recurring categories to help users recognize patterns across multiple visualizations. Labeling and annotations are essential for ensuring that pie and donut charts communicate data clearly and effectively. Whether using direct labels, callouts, legends, or interactive tooltips, designers must prioritize clarity and usability while avoiding clutter. By carefully selecting and placing labels and annotations, designers can create charts that are both informative and visually engaging.

6.5 Donut Charts for Modern Data Storytelling Donut charts have evolved into a powerful tool for modern data storytelling, com- bining the simplicity of traditional pie charts with added flexibility and functionality. Their distinct design, with a hollow center, opens up new possibilities for presenting information in a visually appealing and engaging way.

, Department of Computing, Imperial College London, London, Dexter C. Kozen, Department of Computer Science, Cornell University, Ithaca, USA Steven S. Skiena, Department of Computer Science, Stony Brook University Stony Brook, USA Joseph Migga Kizza, Engineering and Computer Science, University of Tennessee at Chattanooga, Chattanooga, USA Roy Crole, School of Computing and Mathematics Sciences, University of Leicester, Leicester, UK Elizabeth Scott, Department of Computer Science, Royal Holloway University of London, Egham, UK ‘Undergraduate Topics in Computer Science’ (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science.

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For advanced undergraduate textbooks that are likely to be longer and more expository, Springer continues to offer the highly regarded Texts in Computer Science series, to which we refer potential authors. Robertas Damaševiˇcius Human-Centred Scientific Data Visualisation Making Complex Data Accessible for Everyone Robertas Damaševiˇcius Department of Software Engineering Kaunas University of Technology Kaunas, Lithuania ISSN 1863-7310 ISSN 2197-1781 (electronic) Undergraduate Topics in Computer Science ISBN 978-3-032-01605-8 ISBN 978-3-032-01606-5 (eBook) https://doi.org/10.1007/978-3-032-01606-5 This work is subject to copyright.

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