Reusable

The Reusable principle aims to ensure that data and metadata are well-described so they can be used and combined in future research. This requires:

Describing data with a plurality of accurate and relevant attributes.
Releasing data with a clear and accessible data usage license.
Associating data with detailed provenance information.
Meeting domain-relevant community standards for data and metadata.

STAMPED practices directly support reusability through comprehensive provenance tracking, machine-readable metadata, explicit licensing, and modular data organization. When every processing step is recorded and the full history of a dataset is available, other researchers can confidently reuse and build upon the work.

Walkthrough: stellar distances from Gaia parallax

12 March 2026·14 mins

Reproducibility Rigor Transparency Reusable Accessible Workflow S — Self-contained T — Tracked A — Actionable M — Modular P — Portable E — Ephemeral D — Distributable gaia parallax walkthrough python datalad make

Incrementally builds a research object from a bare script to a tracked, portable, reproducible pipeline — motivated by real problems, not by acronym order.

Container venv Overlay for Python Development

2 March 2026·10 mins

Reproducibility Efficiency Reusable Accessible Pattern S — Self-contained A — Actionable P — Portable E — Ephemeral container docker singularity venv uv python development

Demonstrates how to mount local code into stock or third-party containers and create lightweight venv overlays, bridging the container’s fixed environment with project-specific dependencies.

From Script to STAMPED Research Object

20 February 2026·9 mins

Reproducibility Rigor Transparency Reusable Accessible Pattern S — Self-contained T — Tracked A — Actionable M — Modular P — Portable E — Ephemeral D — Distributable STAMPED-intro shell POSIX awk make git singularity containers

Four scenarios showing how to incrementally add STAMPED properties to a shell-based analysis using git, make, and singularity.

Tracking AI Agent Contributions with Entire

20 February 2026·10 mins

Reproducibility Transparency Reusable Accessible Tool T — Tracked A — Actionable D — Distributable ai-agents provenance git claude-code attribution

When AI agents write code, the conversation behind their decisions lives only in a local transcript on the developer’s machine and never travels with the repository. Entire captures that conversation in a git orphan branch, making AI contributions as trackable and distributable as the code itself.

Ephemeral Shell Scripts for Reproducing Issues

19 February 2026·6 mins

Reproducibility Rigor Transparency Reusable Accessible Pattern S — Self-contained A — Actionable E — Ephemeral P — Portable shell POSIX reproducer bug-report testing

Distills a common practice among open-source developers: writing throwaway shell scripts that set up a fresh environment, reproduce a problem, and can be shared as actionable bug reports or starting points for test cases.

Git Submodules for Modular Dataset Composition

19 February 2026·6 mins

Reproducibility Rigor Interoperable Reusable Tool M — Modular T — Tracked git submodules dataset-organization

Demonstrates how git submodules enable independent versioning and composition of dataset components.

Re-executing Computations with 'datalad rerun'

19 February 2026·7 mins

Reproducibility Rigor Reusable Tool A — Actionable E — Ephemeral datalad reproducibility re-execution

Demonstrates how datalad rerun enables re-execution of previously recorded ‘datalad run’ commands, turning provenance records into actionable recipes.

Recording Computational Provenance with 'datalad run'

19 February 2026·6 mins

Reproducibility Transparency Reusable Tool T — Tracked A — Actionable datalad provenance reproducibility

Shows how ‘datalad run’ wraps arbitrary commands to record inputs, outputs, and the exact command in machine-reexecutable form.