Using a 3D heat map to explore the diverse correlations among elements and mineral species

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Applied Computing and Geosciences Pub Date : 2024-01-05 DOI:10.1016/j.acags.2024.100154
Jiyin Zhang , Xiang Que , Bhuwan Madhikarmi , Robert M. Hazen , Jolyon Ralph , Anirudh Prabhu , Shaunna M. Morrison , Xiaogang Ma
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Abstract

This paper presents an enhanced 3D heat map for exploratory data analysis (EDA) of open mineral data, addressing the challenges caused by rapidly evolving datasets and ensuring scientifically meaningful data exploration. The Mindat website, a crowd-sourced database of mineral species, provides a constantly updated open data source via its newly established application programming interface (API). To illustrate the potential usage of the API, we constructed an automatic workflow to retrieve and cleanse mineral data from it, thus feeding the 3D heat map with up-to-date records of mineral species. In the 3D heat map, we developed scientifically sound operations for data selection and visualization by incorporating knowledge from existing mineral classification systems and recent studies in mineralogy. The resulting 3D heat map has been shared as an online demo system, with the source code made open on GitHub. We hope this updated 3D heat map system will serve as a valuable resource for researchers, educators, and students in geosciences, demonstrating the potential for data-intensive research in mineralogy and broader geoscience disciplines.

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利用三维热图探索元素与矿物种类之间的多种关联性
本文介绍了一种用于开放矿物数据探索性数据分析(EDA)的增强型三维热图,以应对快速发展的数据集带来的挑战,并确保进行有科学意义的数据探索。Mindat 网站是一个矿物种类的众包数据库,通过其新建立的应用编程接口(API)提供了一个不断更新的开放数据源。为了说明 API 的潜在用途,我们构建了一个自动工作流程,从中检索和清理矿物数据,从而为三维热图提供最新的矿物种类记录。在三维热图中,我们结合现有矿物分类系统和矿物学最新研究的知识,开发了科学合理的数据选择和可视化操作。生成的三维热图已作为在线演示系统与大家分享,源代码已在 GitHub 上公开。我们希望这个更新的三维热图系统能成为地球科学研究人员、教育工作者和学生的宝贵资源,展示矿物学和更广泛的地球科学学科中数据密集型研究的潜力。
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来源期刊
Applied Computing and Geosciences
Applied Computing and Geosciences Computer Science-General Computer Science
CiteScore
5.50
自引率
0.00%
发文量
23
审稿时长
5 weeks
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