Quick start

This walks one crate through every step: create it, check it, view it, grade it and publish it.

Install

pip install fairscape-models fairscape-conversion
pip install git+https://github.com/fairscape/fairscape_artifacts
pip install git+https://github.com/fairscape/AIreadiness-grader
pip install git+https://github.com/fairscape/fairscape_publish

1. Create

Convert a Datasheet for Datasets into a crate with fairscape-conversion. Other formats (Snakemake, Cromwell, Galaxy, MLflow, REDCap, …) work the same way.

mkdir my-crate
python -m fairscape_conversion.core.cli convert d4d import datasheet.yaml my-crate/ro-crate-metadata.json

2. Validate

Check it against the data model in fairscape-models:

import json
from fairscape_models import ROCrateV1_2

ROCrateV1_2.model_validate(json.load(open("my-crate/ro-crate-metadata.json")))

3. View

Build the datasheet, evidence graph and preview with fairscape-artifacts:

fairscape-artifacts all my-crate
# my-crate/ro-crate-datasheet.html, ro-crate-evidence-graph.html, ro-crate-preview.html

4. Assess

Grade it against the AI-Ready rubric with the AI-Readiness grader:

fairscape-evidence my-crate -o review

5. Publish

Check that it’s ready, then create a draft deposit with fairscape-publish:

fairscape-publish check my-crate
fairscape-publish zenodo my-crate --token $ZENODO_TOKEN --sandbox

To host crates on your own server instead, use fairscape-lite.