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Related projects and alternative approaches

Other ways to share notebooks and scientific tools

Sharing a notebook does not guarantee that it runs on someone else's computer. Several open-source projects address this with different approaches: desktop applications, containers, notebooks in the cloud and browser-based apps. LabConstrictor turns Jupyter notebooks into installable desktop applications that run locally in their own software environment, and it builds its installers with conda constructor. The projects below take other routes. Which one fits depends on where the notebook has to run, on the data and on the people who will use it.

Some of the people who wrote LabConstrictor are also authors of publications about DL4MicEverywhere, ZeroCostDL4Mic and BiaPy. We describe every project here from its own documentation and link to its sources.

  • Album

    Runs scientific solutions (tools and routines), each in its own micromamba environment, and shares them through catalogues kept in Git.

    A good fit when you want to share a scientific tool as a solution that others install and run through Album.

    Checked 11 October 2026

    DocumentationWebsite

  • JupyterLab Desktop

    A desktop application for JupyterLab on Windows, macOS and Linux. The Python environment can be set globally or per project.

    A good fit when you want JupyterLab on your own computer as a general-purpose application, with a choice of Python environment.

    Checked 11 October 2026

    jupyterlab-desktop

  • DL4MicEverywhere

    Runs deep-learning microscopy notebooks inside Docker containers, started from a graphical launcher. The notebooks can run locally, in the cloud or on high-performance computing systems.

    A good fit when you want to run microscopy deep-learning notebooks in containers on your own, remote or HPC machines.

    Hidalgo-Cenalmor et al. (2024), Nature Methods 21(6), 925-927, doi:10.1038/s41592-024-02295-6

    Shares authors with LabConstrictor.

    Checked 11 October 2026

    DL4MicEverywhere

  • repo2docker

    Builds a container image from the configuration files in a Git repository, so that its environment can be reproducibly built and run. BinderHub uses it to build images on demand.

    A good fit when you want a reproducible container image of a repository's environment.

    Checked 11 October 2026

    repo2dockerDocumentation

  • Binder

    Opens a Git repository as a live Jupyter session in a web browser. BinderHub builds the image on demand, and a free public deployment of BinderHub is open to anyone.

    A good fit when collaborators should be able to open a repository's notebooks in a browser without installing anything.

    Project Jupyter et al. (2018), Proceedings of the Python in Science Conference, 113-120, doi:10.25080/Majora-4af1f417-011

    Checked 11 October 2026

    DocumentationWebsite

  • Voilà

    Turns Jupyter notebooks into standalone web applications and dashboards. Code cells are hidden by default, and widgets stay connected to a live Jupyter kernel.

    A good fit when you want to present a notebook as a web application, run from a local or hosted process.

    Checked 11 October 2026

    voilaDocumentation

  • BiaPy

    An open-source library and application for common deep-learning workflows in bioimage analysis, with a graphical interface for Windows, Linux and macOS.

    A good fit when you want ready-made deep-learning workflows for bioimage tasks such as segmentation, detection or denoising.

    Franco-Barranco et al. (2025), Nature Methods 22(6), 1124-1126, doi:10.1038/s41592-025-02699-y

    Shares authors with LabConstrictor.

    Checked 11 October 2026

    BiaPyDocumentation

  • ZeroCostDL4Mic

    A collection of Jupyter notebooks for Google Colab, with simple forms, for deep learning in microscopy.

    A good fit when you want to run deep-learning microscopy notebooks in Google Colab.

    von Chamier et al. (2021), Nature Communications 12, 2276, doi:10.1038/s41467-021-22518-0

    Shares authors with LabConstrictor.

    Checked 11 October 2026

    ZeroCostDL4Mic

These approaches are not mutually exclusive. A scientific Python package can support a notebook distributed with LabConstrictor and also offer workflows through containers or cloud notebooks, and the same notebooks can be opened in Google Colab. In our preprint we describe the trade-offs as we see them: cloud notebooks lower the first barrier, containers encapsulate the runtime more fully, and the pinned conda environments of an installed app depend on upstream packages staying available. These are our views, not those of the other projects.

The descriptions are ours, written from each project's own pages. If something is wrong or a project is missing, edit this list on GitHub or open an issue. Cite LabConstrictor · Edit this list on GitHub · open an issue

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