Data Readiness Knowledge Base

01

Where it fits and where it does not

Use these four checks before committing implementation time.

Use it when: Any data activity involving Indigenous peoples, lands, resources, knowledge, or community interests where openness and reuse must be governed through rights, authority, benefit, and accountability.
Limits: CARE is not a checklist that an external organization can self-certify; obligations are community and context specific, and implementation requires continuing participation by legitimate Indigenous authorities.
Best for: Cross-cutting teams working across Plan → Acquire → Harmonize → 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.

InputWhat starts
Cross-cutting source data, metadata, and local mappings
CAREWhat changes
Use CARE as a pinned governance framework across Plan → Acquire → Harmonize → Exchange → Learn + reuse
OutputWhat becomes possible
A handoff the next system or team can validate against the same release

Readiness gateCARE is not a checklist that an external organization can self-certify; obligations are community and context specific, and implementation requires continuing participation by legitimate Indigenous authorities.

03

A concrete example

Co-design governance with the relevant Indigenous rights holders, document authority and decision rights, define collective benefits and responsibilities, control access and reuse, and review outcomes throughout the data lifecycle.

Why it matters: Prevents technically reusable data from being treated as automatically appropriate for AI, while machine-readable controls remain secondary to community authority and accountable review.

04

What it fits with

Complements FAIR by adding people, power, purpose, and collective rights; DPV, ODRL, provenance, and access controls may record decisions but cannot substitute for legitimate Indigenous authority.

05

Implementation starter

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

Define one handoff, its accountable owner, and the decision CARE must support.
Pin the exact version and companion artifacts: Unversioned principles · drafted 2018; published 2020.
Map one representative input to the required governance framework artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.

06

Test the main limitation

Risk

CARE is not a checklist that an external organization can self-certify; obligations are community and context specific, and implementation requires continuing participation by legitimate Indigenous authorities.

Test

Run one representative end-to-end pilot and record exactly where CARE 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. Prevents technically reusable data from being treated as automatically appropriate for AI, while machine-readable controls remain secondary to community authority and accountable review.

07

Official resources

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

Primary sourceUnversioned principles · drafted 2018; published 2020
GIDA CARE Principles
Official publisher or steward guidance for this governance framework profile.
Publisher
Global Indigenous Data Alliance
Open official source

CARE Principles for Indigenous Data Governance