Data science workflows are human-centered processes involving programming and analysis. While programmable and interactive interfaces embedded within computational notebooks called widgets are increasingly prevalent within these workflows, they lack robust state management capabilities and do not support user-defined customizations. The absence of such capabilities hinders workflow reusability and transparency while limiting the scope of exploration of data practitioners. In response, we developed MAGNETON, a framework for authoring interactive widgets within computational notebooks that enables transparent, reusable, and customizable data science workflows. The framework enhances widgets to support fine-grained interaction history management, reusable states, and user-defined customizations. A persistent interaction history alleviates unnecessary execution of previous interactions and enables users to revisit their previous exploration states, thereby ensuring transparency and reusability. User-defined widget customization enables users to plug in arbitrary exploration objectives within their workflows and removes the dependency on widget developers. We conducted three case studies in a real-world knowledge graph construction and serving platform to evaluate the effectiveness of these widgets. The framework enables interesting future research on building more expressive widget authoring frameworks, exploring optimization strategies to deal with large-scale data when rendering widgets, and designing plastic widgets with cross-platform capabilities that can accommodate users of varying roles and degrees of expertise.
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