A Roadmap for Enriching Jupyter Notebooks Documentation with Kaggle Data

Recent advancements in AI and data science have led to the increased use of Jupyter notebooks. As such, various AI-Based automated tools have been also developed to automatically document notebooks. However, a key challenge is the absence of suitable datasets for training AI models. In this paper, we outline a valuable roadmap for developing a dataset of (markdown, code) pairs centered on functions in Jupyter notebooks. The roadmap encompasses four high-level steps: structural filtering, structural processing, conceptual filtering, and conceptual processing. Our proposed roadmap leads to providing a quality dataset for training AI models on Jupyter notebooks.