Framework · 2.0 · published 2020 · current

ARRIVE Guidelines 2.0

Maintained by NC3Rs · international ARRIVE working group

What it helps you do

ARRIVE 2.0 supports the Essential 10 and Recommended Set for study design, sample size, inclusion and exclusion, randomization, blinding, outcome measures, statistics, animal characteristics, procedures, results, and interpretation.

  • Preclinical
  • Discovery
PlanAcquireHarmonizeExchangeLearn + reuse

01

Where it fits and where it does not

Use these four checks before committing implementation time.

Use it when
Planning, recording, reporting, and reviewing in vivo experiments so readers can assess methodological rigor and reproduce the work.
Limits
ARRIVE is a human-facing reporting guideline rather than a machine-readable study schema. Checklist completion does not establish ethical approval, statistical validity, or reproducibility.
Best for
Preclinical and Discovery teams working across Plan → Acquire → Exchange → Learn + reuse.
Maturity
EstablishedSuitable for production assessment. Pin the exact release and any implementation profile.

02

See it in the workflow

This view shows the input, the change the standard introduces, and the resulting output.

  1. InputWhat starts

    Preclinical and Discovery source data, metadata, and local mappings

  2. ARRIVE 2.0What changes

    Use ARRIVE 2.0 as a pinned framework across Plan → Acquire → Exchange → Learn + reuse

  3. OutputWhat becomes possible

    A handoff the next system or team can validate against the same release

Readiness gateARRIVE is a human-facing reporting guideline rather than a machine-readable study schema. Checklist completion does not establish ethical approval, statistical validity, or reproducibility.

03

A concrete example

An animal study uses the Essential 10 during protocol design, captures allocation and exclusion decisions as the work proceeds, and completes the Recommended Set before data and manuscript release.

Why it matters: Makes bias-sensitive design and reporting decisions visible for downstream reuse, but narrative compliance must be translated into structured variables and verified against source records.

04

What it fits with

Complements CDISC SEND, which structures nonclinical submission datasets, by preserving design and reporting context that tabulation conformance does not guarantee.

05

Implementation starter

Start with one bounded handoff. Pin, test, and review it before scaling.

  1. Define one handoff, its accountable owner, and the decision ARRIVE 2.0 must support.

  2. Pin the exact version and companion artifacts: 2.0 · published 2020 · current.

  3. Map one representative input to the required framework artifacts.

  4. Test the result against the canonical source and record every exception.

  5. Preserve the source data, mappings, and review evidence before scaling.

06

Test the main limitation

Risk

ARRIVE is a human-facing reporting guideline rather than a machine-readable study schema. Checklist completion does not establish ethical approval, statistical validity, or reproducibility.

Test

Run one representative end-to-end pilot and record exactly where ARRIVE 2.0 loses context, needs an extension, or depends on another standard.

Risk

Machine-readable output may still be unfit for analysis or ML.

Test

Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Makes bias-sensitive design and reporting decisions visible for downstream reuse, but narrative compliance must be translated into structured variables and verified against source records.

07

Official resources

Specifications, diagrams, examples, and guides from the organizations that maintain them.

  • Primary source2.0 · published 2020 · current

    ARRIVE Guidelines 2.0

    Official publisher or steward guidance for this framework profile.

    Publisher
    NC3Rs · international ARRIVE working group
    Open official source

Next action

Put this profile in context

Compare its role with adjacent standards or place it inside an end-to-end data pathway before choosing an implementation.