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Computational reproducibility of Jupyter notebooks from biomedical publications.

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Τίτλος: Computational reproducibility of Jupyter notebooks from biomedical publications.
Συγγραφείς: Samuel, Sheeba, Mietchen, Daniel
Στοιχεία εκδότη: Oxford University Press (OUP)
Έτος έκδοσης: 2024
Συλλογή: Digital Library Thüringen
Θεματικοί όροι: article, ScholarlyArticle, ddc:510, ddc:600, Records, Reproducibility of Results, Reproduction, Documentation, Workflow, Python, Pubmed Central, Github, Computational Reproducibility, Jupyter Notebooks, Dependency Decay, Workflow Documentation
Περιγραφή: Background: Jupyter notebooks facilitate the bundling of 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. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications. Approach: We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article's full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion. Results: Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions. ...
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: 23 Seiten
Γλώσσα: English
Relation: GigaScience; https://doi.org/10.1093/gigascience/giad113; https://europepmc.org/articles/PMC10783158; https://europepmc.org/articles/PMC10783158?pdf=render
DOI: 10.1093/gigascience/giad113
DOI: 10.1093/gigascience/giad113/55444771/giad113.pdf
Διαθεσιμότητα: https://doi.org/10.1093/gigascience/giad113
https://www.db-thueringen.de/receive/dbt_mods_00061752
https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00064153/giad113.pdf
https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giad113/55444771/giad113.pdf
https://europepmc.org/articles/PMC10783158
https://europepmc.org/articles/PMC10783158?pdf=render
Rights: https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
Αριθμός Καταχώρησης: edsbas.15C8850E
Βάση Δεδομένων: BASE
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  – Url: https://doi.org/10.1093/gigascience/giad113#
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  Data: Computational reproducibility of Jupyter notebooks from biomedical publications.
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  Data: Background: Jupyter notebooks facilitate the bundling of 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. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications. Approach: We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article's full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion. Results: Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions. ...
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  Data: GigaScience; https://doi.org/10.1093/gigascience/giad113; https://europepmc.org/articles/PMC10783158; https://europepmc.org/articles/PMC10783158?pdf=render
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  Data: https://doi.org/10.1093/gigascience/giad113<br />https://www.db-thueringen.de/receive/dbt_mods_00061752<br />https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00064153/giad113.pdf<br />https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giad113/55444771/giad113.pdf<br />https://europepmc.org/articles/PMC10783158<br />https://europepmc.org/articles/PMC10783158?pdf=render
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