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L-11 · Data standard

A reproducibility package for AI-answer research

Primary intent
Package methods, observations, transformations and limitations so another practitioner can audit the study.
Evidence state
Source-grounded reference
Review owner
Matthias Ramahi · independent review not claimed
Last reviewed
2026-08-22
Direct answer

Reproducibility package

A reproducibility package connects a frozen protocol, control set, observation records, data dictionary, transformation code, environment and version notes, denominators, missing-data report, rights statement, results and correction log. When raw outputs cannot be shared, it must state exactly what cannot be reproduced.

Use this checklist before publishing a dataset, benchmark, tracker update or research claim.
01#

Package the chain, not just the chart

A result is reproducible only to the extent that its inputs, transformations and environment can be understood. Publish the protocol version, control-set version, observation schema, records or permitted derivatives, calculation code and result artifact as linked entities.

W3C provenance concepts are useful here: distinguish the entities, activities and responsible agents involved in creating a release. The implementation can remain simple while still preserving who did what with which input and version.

02#

Minimum release contents

Include a plain-language readme, machine-readable metadata, data dictionary, inclusion flow, missing-data table, calculation definitions, execution instructions, dependency lock, known comparability breaks, license or rights statement and contact for corrections.

The package should name the tested environment and collection dates. “Latest” is not a version. A reader should be able to identify the exact release used by a report.

  • Stable release identifier and publication date.
  • Protocol, schema and control-set versions.
  • Checksums for released files.
  • Source and transformation provenance.
  • Rights, exclusions and redistribution limits.
  • Correction and supersession record.
03#

Be precise about partial reproducibility

Public AI surfaces may change, and some outputs may not be redistributable. A later researcher may reproduce the procedure without receiving the same answer. Name whether the package supports computational reproduction, methodological review, rerunning the collection, or only inspection of aggregate calculations.

If a critical input cannot be inspected, do not use “fully reproducible.” Describe the remaining audit path and the resulting limit on the claim.

04#

Treat corrections as part of the package

Never overwrite a released result without a trace. Publish the reason, affected files or observations, corrected version, reviewer and effect on conclusions. Preserve the prior release when lawful and practical.

A correction log is not a sign of weak research. It is the mechanism that lets a changing evidence base remain trustworthy.

S

Source notes

These sources support the definitions, standards or project boundaries named in this reference. They do not prove that a public observation dataset exists.

  1. nist-ai-rmf-genai
    NIST AI RMF Generative AI Profile

    Supports explicit measurement, documentation, monitoring and limitations for generative-AI evaluations.

    Open
  2. w3c-prov-o
    PROV-O: The PROV Ontology

    Provides provenance concepts for entities, activities, agents, derivations, sources and versions.

    Open
  3. rfc-3339
    RFC 3339: Date and Time on the Internet

    Supports an interoperable timestamp representation tied to UTC.

    Open
  4. fair-principles
    The FAIR Data Principles

    Supports reusable research data with metadata, provenance and clear usage licenses.

    Open
Next
  1. L-10Observation schemaData standard
  2. L-08Comparability breaksMethod
  3. L-12Audit AI-answer sourcesField guide