$\texttt{Davos}$: a Python "smuggler" for constructing lightweight reproducible notebooks
Paxton C. Fitzpatrick, Jeremy R. Manning

TL;DR
Davos is a lightweight Python package that simplifies reproducibility in computational notebooks by embedding dependency specifications and ensuring consistent environment setup during execution.
Contribution
It introduces a minimal solution for reproducibility by integrating dependency management directly into Jupyter notebooks, reducing complexity compared to traditional methods.
Findings
Enables sharing complete reproducible notebooks with embedded dependencies.
Automatically installs specified dependencies in isolated environments.
Ensures consistent package versions across notebook executions.
Abstract
Reproducibility is a core requirement of modern scientific research. For computational research, reproducibility means that code should produce the same results, even when run on different systems. A standard approach to ensuring reproducibility entails packaging a project's dependencies along with its primary code base. Existing solutions vary in how deeply these dependencies are specified, ranging from virtual environments, to containers, to virtual machines. Each of these existing solutions requires installing or setting up a system for running the desired code, increasing the complexity and time cost of sharing or engaging with reproducible science. Here, we propose a lighter-weight solution: the package. When used in combination with a notebook-based Python project, provides a mechanism for specifying the correct versions of the project's…
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Taxonomy
TopicsScientific Computing and Data Management · Data Visualization and Analytics · Research Data Management Practices
