While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely confined to standalone applications and offer limited support for transitioning their visual analytics solutions into interactive web environments. As a result, data analysis pipelines are difficult to share, embed, and adapt into user-facing analytical tools. We present Orange Lab, a web-based collaborative environment for visual data analytics. At its core, Orange Lab enables users to visually construct machine learning workflows from modular components, where interactions in any component propagate seamlessly through the workflow, turning static pipelines into dynamic, reactive systems that support exploration and data-driven storytelling. Our key contribution is component exposition, a paradigm that allows authors to embed selected workflow components, or parts of their interfaces, into arbitrary web contexts, creating synchronized, interactive interfaces while hiding underlying workflow complexity. This enables the development of tailored analytical views and narrative-driven experiences that integrate data analysis directly into online materials. We demonstrate the approach through deployments in data literacy education, where embedded components guide students in hands-on exploration of machine learning concepts without requiring knowledge of the underlying system, showing that Orange Lab effectively lowers barriers to entry and supports the democratization of data science.

翻译：尽管数据分析工作流的可视化编程已成为数据科学民主化的重要工具，此类系统仍主要局限于独立应用程序，且对其将可视化分析方案迁移至交互式网络环境的支持有限。这导致数据分析管道难以共享、嵌入并适配为用户端的分析工具。我们提出Orange Lab——一个基于网络的协作式可视化数据分析环境。其核心在于，用户可通过模块化组件直观构建机器学习工作流，其中任何组件的交互都会无缝传播至整个工作流，将静态管道转化为支持探索与数据驱动叙事的动态响应系统。我们的关键贡献在于“组件暴露”（component exposition）范式：该范式允许作者将选定的工作流组件或其界面片段嵌入任意网页环境，在隐藏底层工作流复杂性的同时创建同步的交互式界面。这一特性使得开发者能够构建定制化分析视图与叙事驱动体验，将数据分析直接集成至在线素材中。我们通过在数据素养教育领域的部署验证了该方法——嵌入式组件引导学生亲自动手探索机器学习概念而无需了解底层系统，证明Orange Lab有效降低了入门门槛，并推动了数据科学的民主化。