| 朝乐门.数据故事的内涵、生成及应用研究[J].中国图书馆学报,2024,50(3):96~116 |
| 数据故事的内涵、生成及应用研究 |
| Data Story:Definition,Methods and Applications |
| 投稿时间：2023-01-03 |
| DOI： |
| 中文关键词: 数据故事 数据科学 数据洞见 数据分析 叙述 |
| 英文关键词: Data story Data science Data insight Data analysis Narrative |
| 基金项目: |
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| 摘要点击次数: 218 |
| 全文下载次数: 174 |
| 中文摘要: |
故事是古老的艺术和文学体裁,而数据故事是大数据时代新兴的一门科学与工程技术。数据故事的公式化定义揭示了数据故事已有定义之间的区别与联系,聚焦数据故事化中的主要矛盾,加深了对数据故事的理解层次,较好地支持数据故事的自动生成。数据故事的两个主要阶段、三类核心科学问题、四个基本特征以及五个关键要素的提出,进一步明确了数据故事的知识体系。数据故事的生成过程模型——DAIS的提出不仅明确了数据故事生成过程中的四个关键要素——数据、分析、洞见和故事,而且深入探讨了每个阶段的工作要点。数据故事具有体验、解释和启发三种主要功能,是现实世界和虚拟世界之间的桥梁。数据故事将成为元宇宙为代表的虚实结合型应用问题研究的关键课题之一。图8。表3。参考文献58。
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| 英文摘要: |
Stories are an ancient genre of art and literature,while data stories are a new technique of science and technology. As a brand new application area,data stories have attracted much attention in industry,but the academic community is also in dire need of groundbreaking research on its key issues.
The formulation of the definition of data stories unifies the differences among existing definitions of data stories,focuses on the key contradictions in data storytelling,deepens the understanding of data stories,and better supports the automatic generation of data stories. At the same time,the proposal of the two stage theory of data storytelling not only further explores the definition of data storytelling but also decomposes data storytelling into two relatively independent activities of generation and description. Usually,there are three basic scientific questions behind data stories and data storytelling:what if questions,why not questions,and how to questions,which focus on exploratory analysis,explanatory analysis,and instructive analysis,respectively. Data stories have four main features,namely data,story,business and science. These four features of data stories are proposed to better describe the differences between data stories and literary stories and data visualization works. A data story usually consists of five elements,namely the character,the event,the plot,the data insight and the business purpose. The proposal of the above five elements of data stories has eliminated the confusion of sources and components in previous research,corrected the misunderstanding of the one sided emphasis on the status of data visualization in data storytelling,and realized the creation and narration of data stories.
The DAIS model for data story generation clarifies not only the four key stages of the data story generation process—data,analysis,insights and story—but also the work content and procedures in each stage. From a methodological perspective,data story generation methods can be divided into four types:model agnostic global storytelling,model specific local storytelling,model agnostic global storytelling,and model specific local storytelling. Data storytelling has three main functions:to experience,to explain,and to enlighten. Data storytelling is currently used primarily in data analysis and model interpretation,metaverse application and linking virtual and real worlds,teaching and training,brand advertising and digital marketing,data driven management and decision making,and content creation and product design. Data stories bridge the real and virtual worlds,and the exploration of virtual real synthetic data stories is becoming an important topic in the study of the metaverse. 8 figs. 3 tabs. 58 refs.
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