Parallel visualization of large-scale multifield scientific data

Cao, Yi; Mo, Zeyao; Ai, Zhiwei; Wang, Huawei; Xiao, Li; Zhang, Zhe

doi:10.1007/s12650-019-00591-4

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It details the current state of the art in the subject and provides a glimpse into the future of scientific data visualization and exploration.In the paper [11] author, examines the most current developments in multivariate data visualization. Methods and tools for visualizing and analyzing multivariate data are presented, and the benefits and drawbacks of working with this type of data are discussed.In the paper [12] author,we'll look at how data visualization can help withscientific investigation by showcasing methods and tools for visualizing and probing scientific data. The need of visualization in scientific study is emphasized, and the difficulties of working with complex scientific data are revealed.In the paper [13] author, we look at the cutting-edge methods now available for depicting fields of expertise.…”

Section: Literature Reviewmentioning

confidence: 99%

Visualization and Exploration of Complex Scientific Data

2023

Turkish Online Journal of Qualitative Inquiry

View full text Add to dashboard Cite

Researchers can acquire insights from complex data sets and better share their findings with others when they employ data visualization and exploration tools. We discuss existing techniques and tools for visualizing and exploring complex scientific data, identify the key concepts and principles of data visualization, and propose a new technique to address some of the limitations of existing approaches to data exploration and visualization. Our suggested method utilizes cloud computing and ML to deliver dynamic, interactive visualizations in real-time, regardless of data size or complexity. Our research provides a unique method for overcoming the limits of existing data visualization and exploration tools in the context of scientific inquiry, in addition to a detailed overview of existing methodologies and tools. We hope our results will stimulate greater investigation into this vital area, leading to improved methods and tools for visualizing and navigating intricate scientific data

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Visualization and Exploration of Complex Scientific Data

2023

Turkish Online Journal of Qualitative Inquiry

View full text Add to dashboard Cite

show abstract

“…为欧洲海水盐度数据, D5 和 D6 为不同视角无人机电磁辐射数据 [23] , D7 和 D8 分别为肺部正电子发射 (3) 实验数据生成过程. 基于以上数据集和颜色图, 通过分别采用本文方法、 Tominski 等 [6] 方法、…”

Section: 用户实验unclassified

Adaptive Colormap Optimization Based on Inserting Colors

Zhao¹,

Qiong²,

Wang³

et al. 2021

J Comput-Aided Design Comput Graphics

View full text Add to dashboard Cite

Traditional automatic color optimization methods face the challenge of expressing global features in dynamic data ranges. To solve this problem, an adaptive colormap optimization method based on inserting colors is proposed, which includes a process of estimating color inserting position and an inserting color optimization procedure. Firstly, color inserting positions are selected based on color discriminability and data histogram distribution. By keeping the color inserting positions, corresponding embedding colors are estimated through a novel energy optimization equation under the guidance of visual discriminability and the consistency to the original colormap. On the basis of the algorithm, an interactive visual data exploratory system is provided, which includes supporting global data perception and local ROI analysis. The effectiveness and applicability of the algorithm is evaluated via a user study and a case study, based on 6 colormaps with different color features and 8 datasets with different data distributions. The results demonstrate that proposed method can produce high quality visual data information compared with other algorithms, providing a condition for further data analysis.

show abstract

Parallel visualization of large-scale multifield scientific data

Cited by 2 publications

References 20 publications

Visualization and Exploration of Complex Scientific Data

Visualization and Exploration of Complex Scientific Data

Adaptive Colormap Optimization Based on Inserting Colors

Contact Info

Product

Resources

About