A — Actionable · STAMPED Principles Examples

Actionability elevates a research object from a static collection of metadata to an operational unit. A research object is actionable when its procedures can be carried out by executing its contents – not merely read about.

Actionability is cross-cutting: it applies to every other STAMPED dimension. Tracking is more actionable when recorded commands can be re-executed (e.g., datalad rerun), not just inspected. Modularity is more actionable when components can be composed via tooling (e.g., git submodule), not just organized into directories. Portability is more actionable when environments can be instantiated from a specification (e.g., singularity run), not just documented in a README.

See the STAMPED paper for the full treatment.

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.

Datasette and MVC Pattern for Actionable Data Exploration

19 February 2026·8 mins

Transparency Efficiency Accessible Interoperable Pattern A — Actionable S — Self-contained datasette MVC data-exploration sqlite

Shows how datasette turns static datasets into interactive, queryable web applications following an MVC-like pattern.

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.

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.