Название: Data Visualization For Scientific Discoveries: Technological Integration And Innovative Practices
Автор: Qian Liu, Wenling Li
Издательство: World Scientific Publishing
Год: 2027
Страниц: 377
Язык: английский
Формат: pdf (true)
Размер: 10.0 MB

Data Visualization for Scientific Discoveries provides innovative, practical methods, including dynamic graphics engines, interactive storytelling, and AI-generated content, to visualize multi-source, high-dimensional data more effectively. Authored by experts in art technology and educational psychology, the text presents data-validated methods for accelerating both discovery and public engagement. It offers a complete five-step workflow with case studies from reputable organizations like NASA and CERN, showcasing how solutions like Virtual Reality (VR) can boost anomaly detection and improve public climate awareness. This essential resource is ideal for researchers, data scientists, digital media professionals, and educators seeking a definitive guide for sophisticated scientific data visualization across various fields of study. It is also an excellent textbook for advanced undergraduate- and graduate-level courses on scientific data visualization, scientific information design/ management, and advanced applied computer graphics.

Traditional data visualization is often limited to static graphs and simple 2D presentations, and the complexity and diversity of modern ­scientific research pose a serious challenge to this traditional approach. The rapid development of Artificial Intelligence technology has brought revolutionary breakthroughs in data visualization.

In terms of technical practice, this book provides a detailed introduction to current state-of-the-art visualization tools and methods. From ­Web-based dynamic visualization libraries D3.js and Three.js, to professional scientific computing visualization tool ParaView; from the game engine Unity 3D in scientific visualization, to the latest advances in Artificial Intelligence. We focus on how these techniques can be applied to specific problems in science.