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STAMPED principles for reproducible research objects

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Scientific claims increasingly rely upon the interplay of code, data, and computational environments. Yet the record of how they are used together is often incomplete, scattered, or lost. This undermines rigor, reproducibility, reusability, and efficiency. Previous approaches have improved the governance of digital objects but do not specify how research objects ought to be structured and managed so they can be re-executed and extended. The community is missing a shared vocabulary for this operational layer. Building on recurrent practices and convergent patterns across disciplines, we formalize seven principles: Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and Distributability (STAMPED). Together they give researchers guidelines for building and assessing research objects that others can trust, rerun, and build upon. We frame each principle as a spectrum so that adoption is incremental and starts from existing practice. We support each principle with normative requirements, an interactive checklist, and a map of enabling tools. As conventions mature, tooling improves, and AI agents become increasingly involved in research workflows, the goals of rigor, reproducibility, reusability, and efficiency are becoming more attainable. STAMPED gives researchers, collaborators, reviewers, and agents a common language, concrete goals, and an aligned direction for making computational research more durable.https://github.com/stamped-principles/stamped-paper/

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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
STAMPED principles for reproducible research objects
Date Crossref
26/05/2026
Éditeur
Center for Open Science
Type
posted-content

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Sujets associés

Scientific Computing and Data ManagementResearch Data Management PracticesMachine Learning in Materials Science

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