  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.
