Quickstart

This example checks a dataset description using F-UJI 3.5.1.

Install

Requires Python 3.12+ and access to the repository. Add the library to your uv project:

uv add git+https://github.com/Dans-labs/local-offline-assessor-for-fair-loaf.git

Assess metadata

Save this as assess.py:

from fair_offline_assessor import Assessor

result = Assessor("FUJI", version="3.5.1").assess(
    metadata={
        "@context": "https://schema.org",
        "@type": "Dataset",
        "@id": "https://example.org/datasets/1",
        "name": "Example dataset",
        "license": "https://creativecommons.org/licenses/by/4.0/",
    }
)

print(result.model_dump_json(indent=2))

Run it from your project:

uv run python assess.py

This description uses JSON-LD, a JSON format that gives field names agreed meanings. Here, @context says to use Schema.org's meanings for Dataset, name and license. The library includes that context, so it needs no network request.

Read the result

• tests: what each check found and the points awarded.
• diagnostics: problems with the supplied metadata.

This small example does not meet every requirement. Missing information can cause a check to fail or leave it indeterminate, depending on the check. indeterminate means the library cannot decide; it receives no score.

Omit version to use the selected assessor's default. See F-UJI for its requirements and other formats, and Results for the response.

Use FAIR Champion

Choose Assessor("FAIR_CHAMPION", version="0.5.12") and pass the identifier being assessed as target_identifier alongside the JSON-LD metadata. It returns check outcomes without numerical scores. The version refers to FAIR Core Tests. See the Champion example and its input requirements.