Reproducibility

Reproducibility is the cornerstone aspiration of scientific data management. A result is reproducible when re-running the same analysis on the same data yields identical outcomes. This requires:

Fixed inputs – The exact data used in an analysis must be identified and retrievable.
Captured computation – The code, software environment, parameters, and execution order must be recorded.
Deterministic execution – Given the same inputs and computation, the outputs must be the same.

STAMPED principles support reproducibility through version control (pinning exact states of data and code), provenance tracking (recording what was run and how), containerization (freezing computational environments), and modular dataset organization (making it possible to re-assemble all components of an analysis). When every element of an analysis is tracked and versioned, reproduction becomes a matter of checking out the right state and re-executing.

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.