English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Jupyter notebooks allow to bundle executable code with its documentation andoutput in one interactive environment, and they represent a popular mechanismto document and share computational workflows, including for researchpublications. Here, we analyze the computational reproducibility of 9625Jupyter notebooks from 1117 GitHub repositories associated with 1419publications indexed in the biomedical literature repository PubMed Central.8160 of these were written in Python, including 4169 that had theirdependencies declared in standard requirement files and that we attempted tore-run automatically. For 2684 of these, all declared dependencies could beinstalled successfully, and we re-ran them to assess reproducibility. Of these,396 notebooks ran through without any errors, including 245 that producedresults identical to those reported in the original. Running the othernotebooks resulted in exceptions. We zoom in on common problems and practices,highlight trends and discuss potential improvements to Jupyter-relatedworkflows associated with biomedical publications.

Computational reproducibility of Jupyter notebooks from biomedical publications