*'Comparative Analysis of 3D Plotting Techniques for Multidimensional Data in Scientific Computing: Performance Metrics, Human Perception, and Cross-Domain Applications'* ; Alternative Titles: 1. 'From Static to Interactive: Evaluating 3D Visualization Tools for Scientific Data in Python' 2. 'Seeing Beyond the Plane: A Systematic Study of 3D Data Representation in Computational Research' 3. 'Benchmarking 3D Plotting Libraries: A Multidisciplinary Perspective on Matplotlib, Plotly, and PyVista' 4. 'The Art of Multidimensional Storytelling: How 3D Visualization Techniques Impact Scientific Insight' ; Subtitles: • *'Quantifying the Trade-offs: Speed vs. Interactivity in 3D Scientific Visualization'* • 'Color, Depth, and Cognition: Designing Effective 3D Plots for Human Interpretation' • *'From Physics to Genomics: Domain-Specific Best Practices for 3D Data Representation'* • 'Beyond the Benchmark: How Tool Selection Influences Research Outcomes in Computational Science'
Gespeichert in:
| Titel: | *'Comparative Analysis of 3D Plotting Techniques for Multidimensional Data in Scientific Computing: Performance Metrics, Human Perception, and Cross-Domain Applications'* ; Alternative Titles: 1. 'From Static to Interactive: Evaluating 3D Visualization Tools for Scientific Data in Python' 2. 'Seeing Beyond the Plane: A Systematic Study of 3D Data Representation in Computational Research' 3. 'Benchmarking 3D Plotting Libraries: A Multidisciplinary Perspective on Matplotlib, Plotly, and PyVista' 4. 'The Art of Multidimensional Storytelling: How 3D Visualization Techniques Impact Scientific Insight' ; Subtitles: • *'Quantifying the Trade-offs: Speed vs. Interactivity in 3D Scientific Visualization'* • 'Color, Depth, and Cognition: Designing Effective 3D Plots for Human Interpretation' • *'From Physics to Genomics: Domain-Specific Best Practices for 3D Data Representation'* • 'Beyond the Benchmark: How Tool Selection Influences Research Outcomes in Computational Science' |
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| Autoren: | geruganti, sudhakar, orcid:0009-0000-0039- |
| Verlagsinformationen: | Zenodo |
| Publikationsjahr: | 2025 |
| Bestand: | Zenodo |
| Beschreibung: | Detailed Description This study provides a three-tier evaluation framework for 3D plotting techniques in scientific computing: Technical Performance Computational efficiency (rendering time, memory use) across Matplotlib (static), Plotly (interactive), and PyVista (GPU-accelerated) Scalability tests on datasets from 10³ to 10⁶ points Human-Factor Analysis Controlled user study (n=30) measuring interpretation accuracy for surface vs. parametric plots Accessibility guidelines for colorblind-friendly palettes and depth perception enhancement Domain Applications Physics: Orbital trajectories (Plotly’s interactivity preferred) Biology: Protein structures (PyVista’s mesh handling optimal) Engineering: Stress distributions (Matplotlib’s vector exports favored) Innovation Spotlight: First study to correlate tool selection with research efficiency in computational science Open-source benchmark suite for reproducible testing |
| Publikationsart: | report |
| Sprache: | unknown |
| Relation: | https://zenodo.org/records/16760411; oai:zenodo.org:16760411; https://doi.org/10.5281/zenodo.16760411 |
| DOI: | 10.5281/zenodo.16760411 |
| Verfügbarkeit: | https://doi.org/10.5281/zenodo.16760411 https://zenodo.org/records/16760411 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Dokumentencode: | edsbas.CD5CD67D |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Detailed Description This study provides a three-tier evaluation framework for 3D plotting techniques in scientific computing: Technical Performance Computational efficiency (rendering time, memory use) across Matplotlib (static), Plotly (interactive), and PyVista (GPU-accelerated) Scalability tests on datasets from 10³ to 10⁶ points Human-Factor Analysis Controlled user study (n=30) measuring interpretation accuracy for surface vs. parametric plots Accessibility guidelines for colorblind-friendly palettes and depth perception enhancement Domain Applications Physics: Orbital trajectories (Plotly’s interactivity preferred) Biology: Protein structures (PyVista’s mesh handling optimal) Engineering: Stress distributions (Matplotlib’s vector exports favored) Innovation Spotlight: First study to correlate tool selection with research efficiency in computational science Open-source benchmark suite for reproducible testing |
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| DOI: | 10.5281/zenodo.16760411 |