一句话：通过定性研究考察科学家如何在 Jupyter Notebook 中清理代码，为自动化清理工具提供设计建议。
数据科学家常用 Jupyter notebooks 测试多个模型，但完成代码、找到理想模型后，仍需投入时间清理代码以便他人理解。本文对科学家在 Jupyter notebooks 中清理代码的方式进行定性研究，旨在为自动化该过程的工具提供设计依据。研究聚焦科学家的实际清理实践与潜在工具需求，最终目标是帮助工具构建者发现支持空白，为数据科学家提供辅助，使其减少日常繁琐清理工作，更专注于真正的研究。摘要未报告具体样本、编码或工具原型，贡献在于问题揭示与工具设计启示。
Data scientists commonly use computational notebooks because they provide a good environment for testing multiple models. However, once the scientist completes the code and finds the ideal model, the data scientist will have to dedicate time to clean up the code in order for others to understand it. In this paper, we perform a qualitative study on how scientists clean their code in hopes of being able to suggest a tool to automate this process. Our end goal is for tool builders to address possible gaps and provide additional aid to data scientists, who can then focus more on their actual work rather than the routine and tedious cleaning duties.