FAIR Principles

Module 2 of 5 — Open Science Training

Learning objectives¶

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

Explain each of the four FAIR principles (Findable, Accessible, Interoperable, Reusable).
Distinguish between FAIR and “open” — data can be FAIR without being fully open.
Apply a basic FAIR self-assessment to a dataset.
Identify what metadata is needed to make data FAIR.

Estimated time: 60–75 minutes
Level: Introductory
Prerequisites: Module 01 or basic familiarity with research data

1. What are the FAIR principles?¶

The FAIR principles were published by Wilkinson et al. (2016) in Scientific Data and have since become the international standard for describing what good research data looks like.

FAIR stands for:

Letter	Principle	Core idea
F	Findable	Data has a persistent identifier and rich metadata so humans and machines can find it
A	Accessible	Data (or at least its metadata) can be retrieved using a standard, open protocol
I	Interoperable	Data uses formal, shared vocabularies and formats that can be combined with other data
R	Reusable	Data has a clear licence and enough context (provenance, methods) for others to reuse it

Important: FAIR doesn’t equal Open¶

FAIR data can still be access-controlled or restricted. For example:

Clinical trial data may be FAIR (well-described, with a PID, with clear metadata) but only accessible to qualified researchers via an application process — this is still FAIR.
Indigenous knowledge data may follow the CARE principles to restrict access while still being findable via metadata.

The guiding phrase is: “as open as possible, as closed as necessary.”

2. The principles in detail¶

Findable¶

F1. Data is assigned a globally unique and persistent identifier (PID) — e.g. a DOI.
F2. Data is described with rich metadata.
F3. Metadata clearly includes the identifier of the data it describes.
F4. Data and metadata are registered or indexed in a searchable resource.

Accessible¶

A1. Data is retrievable by its identifier using an open, free, standardised protocol (e.g. HTTPS).
A1.1. The protocol allows for authentication and authorisation where necessary.
A2. Metadata remains accessible even if the data itself is no longer available.

Interoperable¶

I1. Data uses a formal, accessible, shared, and broadly applicable language for knowledge representation (e.g. RDF, JSON-LD).
I2. Data uses vocabularies that follow FAIR principles (e.g. controlled vocabularies, ontologies).
I3. Data includes qualified references to other data.

Reusable¶

R1. Data has a plurality of accurate and relevant attributes.
R1.1. Data is released with a clear and accessible data usage licence.
R1.2. Data is associated with detailed provenance.
R1.3. Data meets domain-relevant community standards.

3. What metadata does a FAIR dataset need?¶

Metadata is data about data. For a dataset to be FAIR, it needs at minimum:

import json

# A DataCite-style metadata record (simplified)
example_metadata = {
    "identifier": {"identifier": "10.5281/zenodo.0000000", "identifierType": "DOI"},
    "creators": [
        {"name": "Pawlik, Aleksandra",
         "nameIdentifier": {"nameIdentifier": "0000-0000-0000-0000", "nameIdentifierScheme": "ORCID"}}
    ],
    "titles": [{"title": "Example research dataset on soil moisture"}],
    "publisher": "Zenodo",
    "publicationYear": 2025,
    "resourceType": {"resourceTypeGeneral": "Dataset"},
    "subjects": [
        {"subject": "soil moisture", "subjectScheme": "AGROVOC"},
        {"subject": "remote sensing"}
    ],
    "rightsList": [{"rights": "Creative Commons Attribution 4.0 International",
                    "rightsURI": "https://creativecommons.org/licenses/by/4.0/"}],
    "descriptions": [{"description": "Soil moisture measurements collected via ...",
                       "descriptionType": "Abstract"}],
    "relatedIdentifiers": [
        {"relatedIdentifier": "10.1000/example-paper-doi",
         "relatedIdentifierType": "DOI",
         "relationType": "IsSupplementTo"}
    ]
}

print(json.dumps(example_metadata, indent=2))
print("\nKey FAIR elements present:")
print("  F1: DOI as persistent identifier")
print("  F2: Rich metadata (title, creators, subjects, description)")
print("  A1: Retrievable via HTTPS (Zenodo)")
print("  I2: AGROVOC subject vocabulary used")
print("  I3: Related identifier links to the publication")
print("  R1.1: CC BY 4.0 licence")
print("  R1.2: Creator ORCID for provenance")

Summary¶

FAIR means Findable, Accessible, Interoperable, Reusable — for both humans and machines.
FAIR ≠ open. Data can be FAIR and still access-controlled.
Persistent identifiers (DOIs, ORCIDs), licences, and rich metadata are the core practical steps.