Direct comparison
CARE vs FAIR Principles Explained
CARE and FAIR principles compared: what each covers, who developed them, and why Indigenous data governance requires applying both together.
Side-by-side comparison
| Dimension | CARE Principles | FAIR Principles |
|---|---|---|
| Purpose / origin | Developed to embed Indigenous Peoples' rights and interests in data governance, addressing a gap FAIR does not cover | Developed to make research data findable, accessible, and machine-actionable for reuse, regardless of subject matter |
| What it stands for | Collective Benefit, Authority to Control, Responsibility, Ethics | Findable, Accessible, Interoperable, Reusable |
| Who developed it | Global Indigenous Data Alliance (GIDA), first published 2019 | FORCE11 (Wilkinson et al., 2016); stewarded internationally via GO FAIR |
| Scope | Data by, about, or affecting Indigenous Peoples, their lands, and territories specifically | All research data, regardless of subject matter or who is affected by it |
| Core concern | Who controls the data, who benefits from its use, and what ethical obligations govern stewardship | Whether data and metadata are technically discoverable, accessible, and reusable |
| Relationship to data sovereignty | Directly operationalizes Indigenous data sovereignty — the right of Indigenous Peoples to govern data about themselves | Silent on sovereignty; a dataset can be fully FAIR while ignoring who has authority over it |
| Openness requirement | Not an openness standard; can require restricted or community-controlled access | Not an openness standard either — data can be FAIR while access-restricted, provided conditions are machine-readable |
| How it is applied | As a governance and ethics layer for data involving Indigenous Peoples, alongside FAIR-compliant infrastructure | As the general technical data-management baseline for research data |
| Typical use case | Genomic, health, cultural, environmental, or land data collected from or about Indigenous communities | General open-science and funder-mandated research data sharing |
| Replaces the other? | No — governs authority and ethics, not technical discoverability | No — governs technical usability, not authority or ethics |
Common questions
FAQ
Are CARE and FAIR principles in conflict?+
No. They address different concerns: FAIR is about technical data management (making data findable, accessible, interoperable, and reusable), while CARE is about governance and ethics (who controls the data and for whose benefit). GIDA and the broader research data management community frame CARE as a complement to FAIR, not a replacement.
Does FAIR-compliant data automatically meet CARE?+
No. A dataset can be fully FAIR-compliant — technically findable, accessible, interoperable, and reusable — while still failing to respect Indigenous Peoples' authority to control data about themselves or their territories. FAIR compliance and CARE compliance are assessed separately, and satisfying one does not satisfy the other.
Who developed the CARE Principles and when?+
The Global Indigenous Data Alliance (GIDA) developed the CARE Principles for Indigenous Data Governance, first published in 2019.
Do the CARE Principles apply to all research data?+
No. CARE was developed specifically for data by, about, or affecting Indigenous Peoples, their lands, and their territories. FAIR applies to research data generally, with no such subject-matter restriction.
