FAIRSCAPE

FAIRSCAPE packages research data, software and computations as RO-Crates with full provenance: what was used, what produced it, and who ran it. The tools are small Python packages. Each one does one step:

  CREATE                        VIEW / ASSESS                        PUBLISH
  ──────                        ─────────────                        ───────
  fairscape_models      ─┐                ┌─ fairscape_artifacts ─┐    ┌──▶ fairscape_publish   (Dataverse, Zenodo, Figshare, DataCite)
                         ├──▶ RO-Crate ───┤                       ├────┤
  fairscape_conversion  ─┘                └─ AIreadiness-grader ──┘    └──▶ fairscape_lite      (your own server)

Step	Package	What it does
Create	fairscape_models	The data model. Pydantic classes for every entity in a crate (Dataset, Software, Computation, MLModel, …). You use it to build and validate ro-crate-metadata.json.
Create	fairscape_conversion	Turns metadata you already have into a crate: Datasheets for Datasets, Snakemake, Cromwell/WDL, Galaxy, MLflow, REDCap, Frictionless, C2M2, Workflow Run RO-Crate. Exports to Croissant, D4D and others.
View	fairscape_artifacts	Takes a finished crate and builds the pages people read: an HTML datasheet, an interactive evidence graph, a preview of every entity and an AI-Ready review.
Assess	AIreadiness-grader	Grades a crate (or a Croissant or schema.org JSON-LD file) against the AI-Ready rubric: 28 criteria in seven areas. It writes a review page and suggests fixes. fairscape_artifacts uses it for the review section of the datasheet.
Publish	fairscape_publish	Pushes the crate and its files to Dataverse, Zenodo or Figshare, or mints a DataCite DOI.
Publish	fairscape_lite	A small server you run yourself. It indexes a folder of crates and gives you search, ARK resolution and evidence graphs in a web UI.

Quick start

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

1. Create: convert a Datasheet for Datasets into a crate:

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

2. Check: validate it against the model:

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 and evidence graph:

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:

fairscape-evidence my-crate -o review
# review/ai-ready-review.html, review/ai-ready-evidence.json

5. Publish: check the crate is ready, then deposit a draft:

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

Or host it yourself with fairscape_lite.

The profile

Crates built with these tools conform to the FAIRSCAPE Release RO-Crate Profile v0.1 (https://w3id.org/fairscape/profile/0.1). Its JSON Schemas, TypeScript types and EVI vocabulary are generated from fairscape_models.