STAMPED Principles: Pragmatic practices for reproducible science

Neuroscience increasingly depends on the interplay of code, data, and computational
environments, yet the record of how they were used together is often incomplete, scattered
across repositories, wikis, and notebooks, or lost entirely. This fragmentation undermines rigor,
reproducibility, reusability, and efficiency in BRAIN Initiative pipelines that routinely span
multiple institutions, archives, and compute platforms. Existing frameworks such as FAIR and
FAIR4RS govern discovery and interoperability of digital objects, but do not specify how
research objects should be structured and managed so they can be re-executed, extended,
and audited. The community lacks a shared vocabulary for this operational layer.

Building on the YODA and VAMP traditions from neuroimaging, and on patterns that have
independently converged across geophysics, genomics, statistics, and neuroscience over
three decades, we formalize seven principles a research object should satisfy:
Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and
Distributability, collectively STAMPED. Each spans a spectrum from practical minimum to
aspirational ideal, so adoption is non-prescriptive and incremental. Formal LinkML schemas, an
interactive compliance checklist, and the curated collection of examples are provided as
enabling tools to this end.

We demonstrate STAMPED through two major neuroscience pipelines. OpenNeuroDerivatives
reorganized derivative neuroimaging datasets so they exist as independent Ephemeral units
that reference raw inputs as subdatasets rather than nesting under them, removing an upward
dependency that previously violated Self-containment, Modularity, and Portability. DANDI
Compute, utilizing the Allen Institute for Neural Dynamics electrophysiology pipeline, packages
spike-sorting outputs into nested BIDS-derivative units in which each leaf contains the exact
code, runtime logs, outputs, and provenance metadata needed to re-execute the analysis,
satisfying STAMPED end-to-end.

These adoptions show that STAMPED provides a tool-agnostic, incrementally adoptable
vocabulary that lets researchers, reviewers, collaborators, and emerging AI agents evaluate and
improve the operational maturity of computational neuroscience. By making research objects
re-executable and inspectable by construction, STAMPED converts reproducibility from an
aspiration into a measurable property of everyday neuroscience practice.