| 朝乐门,张晨.数据故事化:从数据感知到数据认知[J].中国图书馆学报,2019,45(5):61~78 |
| 数据故事化:从数据感知到数据认知 |
| Data Storytelling:From Data Perception to Data Cognition |
| 投稿时间：2019-04-17 修订日期：2019-06-02 |
| DOI： |
| 中文关键词: 数据科学 数据故事化 数据可视化 数据感知 数据认知 |
| 英文关键词: Data science Data storytelling Data visualization Data perception Data cognition |
| 基金项目: |
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| 摘要点击次数: 2299 |
| 全文下载次数: 1326 |
| 中文摘要: |
数据呈现是大数据时代的新课题。通常,数据呈现的主要途经有两个,即数据的可视化和数据的故事化,二者的区别在于,数据可视化主要解决的是数据感知问题,而数据故事化更加关注的是如何将数据感知转换为数据认知。数据故事化涉及三个基本要素:数据、视觉效果和叙述。从数据故事的创作者和受众之间的信息交流模式看,可将数据故事化分为创作者驱动和受众驱动两种不同模式。目前,数据故事化中常用的结构有三种:马提尼酒杯结构、互动演示幻灯结构和向下钻取事结构。数据故事化的主要活动包括理解数据、明确目的、了解受众、确定关键数据、选择故事模型以及故事叙述。数据故事化是数据科学的主要研究内容之一,也是数据科学区别于其他学科的重要特征。图6。表4。参考文献60。
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| 英文摘要: |
There are two main methods for data presentation:data visualization and data storytelling. The difference between them is that data visualization mainly solves the problem of data perception, while data storytelling focuses more on how to transform data perception into data cognition.
Data storytelling involves three basic elements: data, visuals, and narrative. Combining data and visuals can enlighten the audience to gain data insights; applying narrative into data can explain the data phenomena; coupling visuals with narrative can entertain the audience. The main activity flow of data story telling is divided into six steps: 1) The author must understand the data and make clear the meaning it represents. 2) The author who makes data stories needs to have a clear purpose. 3) Identifying the audiences as novices, generalists, managemers, experts or executives helps author create different data stories for different audiences. 4) The next step is that author identifies key data and uses the most effective data to describe the data story. 5) Then the author chooses the story model, chooses the appropriate chart, and describes the information presented by the story according to people's visual characteristics. 6) Providing background narration and guiding the audience according to the plot effectively synthesizes and organizes data stories. In addition, the following three special activities may be involved in the actual data storytelling project: 1) Data story telling experiment and pre investigation. 2) Continuous improvement of data storytelling. 3) Separation between the author and the narrator of the data story.
It is worth mentioning that the transformation from data to story and the presentation of data story are two different stages of data storytelling. The author of data story needs to convert the data into a story model beforehand, and then the narrator takes a special way to present the story. There are two presentation forms of data stories: 1)The narrator tells the story and the audience listens to the story. 2) The narrator shows the data story to the audience, and the audience can see the story.
Data storytelling is one of the main research contents of data science, and it is also an important feature that distinguishes data science from other disciplines. The application of data storytelling in data science is mainly reflected in four aspects. First, data storytelling solves the “last mile” problem of data science, which plays a crucial role in the success of data science projects; second, data storytelling is an important means of obtaining insights from big data; third, data storytelling can transform data insights into data actions; fourth, data storytelling is an important activity of data product development, such as data journalism.
Finally, this paper summarizes five main characteristics of the current research: 1) Foreign research is more than domestic research. 2) Research articles published informally (such as blogs) are more than the officially published academic papers. 3) Theoretical research lags behind the practice application. 4) There is more research on concept level than the realization of concrete technology. 5) The visualization tools for data story are more than specialized tools of data storytelling. In view of these characteristics, some recommended topics in the following research of data storytelling are proposed: 1) Improving the theoretical system of data storytelling. 2) Studying the evaluation method of data storytelling, optimizing specific data story projects. 3) Strengthening interdisciplinary research and further broadening the research perspective and theoretical basis of data storytelling. 4) Exploring new algorithms and models for data storytelling. 5) Developing special tools for data storytelling. 6 figs. 4 tabs. 60 refs. |
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