Dataset
Προβολή σε EDS

Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications
Συγγραφείς: Sheeba Samuel, Daniel Mietchen
Στοιχεία εκδότη: Zenodo
Έτος έκδοσης: 2023
Συλλογή: Zenodo
Θεματικοί όροι: Jupyter Notebook, Computational reproducibility, biomedical publications, GitHub, PubMed Central, Python, workflow documentation, dependency decay
Περιγραφή: This repository contains the dataset for the study of computational reproducibility of Jupyter notebooks from biomedical publications. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories. Data Collection and Analysis We use the code for reproducibility of Jupyter notebooks from the study done by Pimentel et al., 2019 and adapted the code from ReproduceMeGit. We provide code for collecting the publication metadata from PubMed Central using NCBI Entrez utilities via Biopython. Our approach involves searching PMC using the esearch function for Jupyter notebooks using the query: ``(ipynb OR jupyter OR ipython) AND github''. We meticulously retrieve data in XML format, capturing essential details about journals and articles. By systematically scanning the entire article, encompassing the abstract, body, data availability statement, and supplementary materials, we extract GitHub links. Additionally, we mine repositories for key information such as dependency declarations found in files like requirements.txt, setup.py, and pipfile. Leveraging the GitHub API, we enrich our data by incorporating repository creation dates, update histories, pushes, and programming languages. All the extracted information is stored in a SQLite database. After collecting and creating the database tables, we ran a pipeline to collect the Jupyter notebooks contained in the GitHub repositories based on the code from Pimentel et al., 2019. Our reproducibility pipeline was started on 27 March 2023. Repository Structure Our repository is organized into ...
Τύπος εγγράφου: dataset
Γλώσσα: English
ISSN: 2047-217X
Relation: https://zenodo.org/records/8226725; oai:zenodo.org:8226725; https://doi.org/10.5281/zenodo.8226725
DOI: 10.5281/zenodo.8226725
Διαθεσιμότητα: https://doi.org/10.5281/zenodo.8226725
https://zenodo.org/records/8226725
Rights: Creative Commons Zero v1.0 Universal ; cc0-1.0 ; https://creativecommons.org/publicdomain/zero/1.0/legalcode
Αριθμός Καταχώρησης: edsbas.162B634D
Βάση Δεδομένων: BASE
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  Data: Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications
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  Data: <searchLink fieldCode="AR" term="%22Sheeba+Samuel%22">Sheeba Samuel</searchLink><br /><searchLink fieldCode="AR" term="%22Daniel+Mietchen%22">Daniel Mietchen</searchLink>
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  Data: 2023
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  Data: This repository contains the dataset for the study of computational reproducibility of Jupyter notebooks from biomedical publications. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories. Data Collection and Analysis We use the code for reproducibility of Jupyter notebooks from the study done by Pimentel et al., 2019 and adapted the code from ReproduceMeGit. We provide code for collecting the publication metadata from PubMed Central using NCBI Entrez utilities via Biopython. Our approach involves searching PMC using the esearch function for Jupyter notebooks using the query: ``(ipynb OR jupyter OR ipython) AND github''. We meticulously retrieve data in XML format, capturing essential details about journals and articles. By systematically scanning the entire article, encompassing the abstract, body, data availability statement, and supplementary materials, we extract GitHub links. Additionally, we mine repositories for key information such as dependency declarations found in files like requirements.txt, setup.py, and pipfile. Leveraging the GitHub API, we enrich our data by incorporating repository creation dates, update histories, pushes, and programming languages. All the extracted information is stored in a SQLite database. After collecting and creating the database tables, we ran a pipeline to collect the Jupyter notebooks contained in the GitHub repositories based on the code from Pimentel et al., 2019. Our reproducibility pipeline was started on 27 March 2023. Repository Structure Our repository is organized into ...
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        Value: 10.5281/zenodo.8226725
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      – Text: English
    Subjects:
      – SubjectFull: Jupyter Notebook
        Type: general
      – SubjectFull: Computational reproducibility
        Type: general
      – SubjectFull: biomedical publications
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      – SubjectFull: GitHub
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      – SubjectFull: PubMed Central
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      – SubjectFull: workflow documentation
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      – SubjectFull: dependency decay
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      – TitleFull: Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications
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              Y: 2023
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