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A PyMOL snippet library for Jupyter to boost researcher productivity.

ABSTRACT: Snippets - code templates one line or longer - boost researcher productivity because they are faster to insert than writing the code from scratch and because they reduce debugging time. Several extensions support the use of snippets in Jupyter. We developed a Python version of the pymolsnips library and customized it for use in the jupyterlab-snippets-multimenus extension for JupyterLab. The extension provides access to the snippets by pull-down menus. Each snippet performs one task. Each task often requires many lines of code. This library's availability in Jupyter enables PyMOL users to run PyMOL efficiently inside Jupyter while storing the code and the associated molecular graphics images next to each other in one notebook document. This proximity of code and images supports reproducible research in structural biology, and the use of one computer file facilitates collaborations.

SUBMITTER: Mooers BHM

PROVIDER: S-EPMC8104305 | biostudies-literature | 2021 Mar-Apr

REPOSITORIES: biostudies-literature

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A PyMOL snippet library for Jupyter to boost researcher productivity.

Mooers Blaine H M BHM

Computing in science & engineering 20210216 2

Snippets - code templates one line or longer - boost researcher productivity because they are faster to insert than writing the code from scratch and because they reduce debugging time. Several extensions support the use of snippets in Jupyter. We developed a Python version of the pymolsnips library and customized it for use in the jupyterlab-snippets-multimenus extension for JupyterLab. The extension provides access to the snippets by pull-down menus. Each snippet performs one task. Each task o ...[more]

PMID: 33967632

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Project description:BackgroundJupyter 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.ApproachWe 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.ResultsOut 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.ConclusionsWe zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.

Dataset Information

A PyMOL snippet library for Jupyter to boost researcher productivity.

Publications

A PyMOL snippet library for Jupyter to boost researcher productivity.

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