CARE Principles for Indigenous Data Governance

Module 3 of 5 — Open Science Training

Learning objectives¶

By the end of this notebook you will be able to:

Explain the four CARE principles and why they were developed.
Distinguish between FAIR and CARE and explain how they complement each other.
Identify situations where CARE principles should inform your data practice.
Describe the concept of Indigenous Data Sovereignty.

Estimated time: 45–60 minutes
Level: Introductory
Prerequisites: Module 02 recommended

1. Why CARE? The limits of FAIR alone¶

The FAIR principles (Findable, Accessible, Interoperable, Reusable) do not pertain to data governance and ownership. In particular, FAIR princinples do not address data matters relating to Indigenous peoples and marginalised populations, whose data has historically been:

Collected without consent
Used in ways that harmed or misrepresented communities
Used in ways that communities did not consent to and would not see as acceptable
Owned by researchers or institutions rather than the communities they describe
Made “open” in ways that enabled commercial exploitation without benefit to source communities

The CARE Principles for Indigenous Data Governance were developed by the Global Indigenous Data Alliance (GIDA) and published in 2020 to address these gaps.

2. The CARE Principles¶

Letter	Principle	Core idea
C	Collective Benefit	Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data
A	Authority to Control	Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered
R	Responsibility	Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit
E	Ethics	Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle

CARE is not about restricting openness — it is about justice¶

CARE principles do not say data should be hidden. They say that Indigenous communities should be in the driver’s seat when decisions are made about data that relates to them.

3. FAIR and CARE together¶

The two frameworks are complementary, not competing. A useful way to think about them:

import pandas as pd

comparison = pd.DataFrame({
    "Dimension": [
        "Primary focus",
        "Designed for",
        "Key question",
        "Relationship to openness",
        "Who decides?",
    ],
    "FAIR": [
        "Technical infrastructure for data sharing",
        "Scientific/research data broadly",
        "Can machines and humans find and use this data?",
        "Encourages open access",
        "Researchers, institutions, repositories",
    ],
    "CARE": [
        "Rights, governance, and benefit",
        "Data about or from Indigenous communities",
        "Does data use benefit the source community?",
        "May require controlled access",
        "Indigenous communities and data sovereignty bodies",
    ],
})

pd.set_option('display.max_colwidth', None)
comparison.set_index('Dimension', inplace=True)
comparison

4. Indigenous Data Sovereignty¶

Indigenous Data Sovereignty (IDSov) is the right of Indigenous peoples to govern the collection, ownership, and application of data about their communities, territories, languages, and cultural practices.

Summary¶

CARE was developed to address the limitations of FAIR for data involving Indigenous peoples.
CARE is about rights, governance, and benefit — not just technical interoperability.
The two frameworks work together: FAIR addresses the how of data sharing; CARE addresses the who and why.
Indigenous Data Sovereignty is a right, not a request.