🛡️ Protecting Data Value: FAIR + CARE in Practice

FAIR
CARE
data sharing
data ethics
data management
Author

Dr. Adrian Correndo

Published

January 16, 2026

1 🎓 Why talk about data protection in a coding class?

When we learn coding, we often focus on how to analyze data. But in real projects, the hardest (and most important) questions are often:

  • Should we collect/share this data at all?
  • Who could be harmed if it’s misused?
  • How do we make data useful without losing trust or violating rights?

Good data management protects three things:

  1. Privacy 🔒 — preventing inappropriate exposure of personal or sensitive information
  2. Integrity ✅ — ensuring data aren’t altered, corrupted, or misrepresented
  3. Value 💡 — making data understandable and reusable so it can support learning, decisions, and research

2 ✨ FAIR principles

FAIR is about making data (and metadata) easy to discover and reuse—by both people and computers.

  • Findable: others can locate the dataset (titles, keywords, metadata, persistent links)
  • Accessible: it can be retrieved in a controlled way (open when possible, restricted when needed)
  • Interoperable: it uses standard formats and vocabularies so tools can combine it with other data
  • Reusable: it includes enough context (methods, units, codes, license) to be reused correctly
✅ Key idea

FAIR does not mean “open.”
Data can be restricted and still be FAIR if it is well-described and the access conditions are clear.

3 🌿 CARE principles

CARE focuses on the people and communities behind data, especially when data relate to Indigenous Peoples and Indigenous Knowledge.

  • Collective benefit: data use should support community wellbeing (not just outside interests)
  • Authority to control: communities have rights and interests in how data are collected, accessed, and used
  • Responsibility: users of data must be accountable and support respectful relationships
  • Ethics: data use should minimize harm and align with community values and consent
🧭 Key idea

CARE emphasizes rights, power, relationships, and purpose—not just technical usability.

4 🧩 FAIR + CARE together

FAIR and CARE answer different questions:

  • FAIR asks: Can this dataset be found and reused properly?
  • CARE asks: Should it be used this way, and who benefits or could be harmed?

They complement each other:

  • You can make data technically reusable (FAIR) while still respecting rights, consent, and community governance (CARE).
  • A dataset can be well-organized but still inappropriate to share if it ignores authority, consent, or potential harm.
⭐ Takeaway

FAIR improves reusability. CARE protects people and communities.
Strong data practice needs both.

5 ✅ Practical checklist for student projects

Use this before sharing data, code, or a repository.

5.1 🔒 1) Privacy & sensitivity

  • Does the dataset include personal, confidential, location-based, or culturally sensitive information?
  • Have you removed identifiers (when appropriate) and documented what was removed?
  • Are access rules clear (open vs restricted)?
⚠️ Caution

Even if you remove names, small datasets + detailed locations + rare characteristics can still identify people or communities.

5.2 ✅ 2) Integrity & trust

  • Do you track versions of files (e.g., Git commits, file naming, or release tags)?
  • Can someone reproduce your results from your code + data description?
  • Did you document cleaning steps and assumptions?

5.3 ✨ 3) FAIR basics

  • Is there a clear README describing what the dataset is and how to use it?
  • Are variables explained (a data dictionary)?
  • Are formats standard (e.g., .csv, consistent units, consistent column names)?
  • Is there a license or clear re-use statement?

5.4 🌿 4) CARE basics

  • Who does the data describe, and who should have a say in how it is used?
  • Does the project involve Indigenous data or knowledge?
  • Do you have permission/consent and the right governance process for use and sharing?
  • Can you explain who benefits from this work and how potential harms are reduced?

6 🏁 In a nutshell

  • FAIR helps others use data well.
  • CARE helps ensure data use is right and respectful.
  • Together, they help protect privacy, integrity, and the value of data. 🛡️

7 🗣️To think about…

Pick one scenario:

  1. A dataset contains farm trial results with GPS coordinates
  2. A dataset includes survey responses from a small community
  3. A dataset includes information related to Indigenous lands, communities, or knowledge

Consider the following questions: - What could go wrong if the data are shared publicly? - What would you do to make the project more FAIR? - What CARE questions should be asked before publishing?

🧠 Optional prompt

Write a 3–4 sentence “sharing decision” for your scenario:
- What will be shared?
- What will be restricted (and why)?
- How will you document access conditions and responsibilities?

8 📚 Suggested reading

8.1 ✨ FAIR

8.2 🌿 CARE + Indigenous Data Governance

8.3 🧩 FAIR + CARE together