来自生物医学出版物的计算机笔记本的计算再现性
Computational reproducibility of Jupyter notebooks from biomedical publications
摘要：
Yutter笔记本允许在一个交互式环境中将可执行代码与其文档和输出捆绑在一起，它们代表了一种流行的机制来记录和共享计算工作流，包括研究出版物。在这里，我们分析了来自1117个GitHub存储库的9625字节笔记本的计算再现性，这些存储库与生物医学文献存储库PubMed Central中索引的1419篇出版物相关。其中8160个是用Python编写的，包括4169个在标准需求文件中声明了依赖关系，我们试图自动重新运行。对于其中的2684个，所有声明的依赖项都可以成功安装，我们重新运行它们以评估可重复性。其中，396个笔记本没有任何错误，包括245个产生的结果与原始报告相同。运行其他笔记本会导致异常。我们放大常见问题和实践，突出趋势，并讨论与生物医学出版物相关的生物相关工作流程的潜在改进。
英文摘要：
Jupyter notebooks allow to bundle executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows, including for research publications. Here, we analyze the computational reproducibility of 9625 Jupyter notebooks from 1117 GitHub repositories associated with 1419 publications indexed in the biomedical literature repository PubMed Central. 8160 of these were written in Python, including 4169 that had their dependencies declared in standard requirement files and that we attempted to re-run automatically. For 2684 of these, all declared dependencies could be installed successfully, and we re-ran them to assess reproducibility. Of these, 396 notebooks ran through without any errors, including 245 that produced results identical to those reported in the original. Running the other notebooks resulted in exceptions. We zoom in on common problems and practices, highlight trends and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
