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.

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 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.

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 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.