Single-button reproducible research projects.
Instead of a loosely related collection of files split across multiple systems or apps, "integrated" via manual steps, your project becomes a version-controlled, self-contained "calculation kit," so you, your collaborators, and your readers can go from raw data to research article with a single command.
curl -LsSf install.calkit.org | sh
Linux, macOS, or Windows. See the installation docs for other options.
questions
answers
And yet most projects fail to reproduce.
The tools to create single-button reproducible research projects already exist, e.g., version control with Git, environment management with uv, Make for a build system or pipeline, and LaTeX for document compilation. If you and your team can work effectively with a system like that, there's no need for any additional complexity. And yet these practices are still not common. What happens in reality is that the costs are deemed too high and the benefit too low, so workflows remain manual and fragmented, and many team members are not able to contribute to their full potential.
Success comes from iteration, and iteration is made possible through integration.
One lifecycle, one project
Calkit allows all stages of a research project to live in the same repository, making feedback loops both inside and across stages fast and painless, providing full context to both humans and AI agents.
Coupled components close together
A change in a dataset requires reanalyzing, which creates a change in a figure, which creates a change in a research article. Software teams learned this long ago, integrating development, testing, deployment, and infrastructure into the same repo. Similar productivity and quality gains can be had in research.
Lock in without lock-in
Everything is connected right out of the box, with a CLI, web app, and more to reduce friction for every task and team member involved. The underlying tools can be used directly by more tolerant team members, and others can contribute at a higher level while maintaining single-button reproducibility.
Defined provenance
The project information in calkit.yaml gives a full
picture: its research questions, the artifacts generated as evidence
to answer them, and a way to verify everything back to its origin,
including whether a figure or table is stale with respect to its
input data. Critical when using generative AI.
Works with the tools you already use
- Git
- Docker
- DVC
- Python
- R
- MATLAB
- LaTeX
- Overleaf
- Julia
- Jupyter
Start unifying and simplifying
Install Calkit, run through a tutorial, and move faster today.