Interoperable

The Interoperable principle ensures that data can be integrated with other data and can work with applications and workflows for analysis, storage, and processing. This means:

Using a formal, accessible, shared, and broadly applicable language for knowledge representation.
Using vocabularies that follow FAIR principles themselves.
Including qualified references to other datasets and metadata.

In STAMPED terms, interoperability is promoted by adopting standard file formats, using community-accepted metadata schemas, structuring data so that it can be combined across studies, and maintaining explicit links between datasets and their dependencies. Version control and provenance tracking make these cross-references robust and verifiable.

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.

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.