Schema Endpoints

Schemas define the expected structure and constraints for tabular or structured datasets registered in FAIRSCAPE. Associating a schema with a dataset enables automated validation.

All URLs are relative to https://fairscape.net/api.

Endpoint Summary

Method Path Auth Required Description
POST /schema ✓ Create a schema record
GET /schema/ark:{NAAN}/{postfix} – Get schema metadata
DELETE /schema/ark:{NAAN}/{postfix} ✓ Delete a schema

POST /schema

Register a schema. The request body is a JSON object describing the schema structure and its properties.

Key metadata fields:

{
  "@id": "ark:59853/apms-embeddings-schema-2024",
  "@type": "Schema",
  "name": "AP-MS Embeddings Schema",
  "description": "Schema for the AP-MS protein embedding CSV",
  "author": "Krogan Lab",
  "version": "1.0",
  "datePublished": "2024-01-15",
  "keywords": ["proteomics"],
  "properties": [
    {
      "name": "protein_id",
      "type": "string",
      "description": "UniProt protein identifier",
      "required": true
    },
    {
      "name": "embedding_dim_1",
      "type": "float",
      "description": "First embedding dimension"
    }
  ]
}
schema = {
    "@id": "ark:59853/apms-embeddings-schema-2024",
    "@type": "Schema",
    "name": "AP-MS Embeddings Schema",
    "description": "Schema for the AP-MS protein embedding CSV",
    "author": "Krogan Lab",
    "version": "1.0",
    "datePublished": "2024-01-15",
    "keywords": ["proteomics"],
    "properties": [
        {"name": "protein_id", "type": "string", "description": "UniProt ID", "required": True},
        {"name": "embedding_dim_1", "type": "float", "description": "Embedding dim 1"}
    ]
}

response = requests.post(
    f"{BASE_URL}/schema",
    json=schema,
    headers={"Authorization": f"Bearer {token}"}
)
print(response.json())
curl -X POST "https://fairscape.net/api/schema" \
     -H "Authorization: Bearer <token>" \
     -H "Content-Type: application/json" \
     -d @schema.json

GET /schema/ark:{NAAN}/{postfix}

Retrieve schema metadata. Public endpoint.

response = requests.get(
    f"{BASE_URL}/schema/ark:59853/apms-embeddings-schema-2024"
)
print(response.json())
curl "https://fairscape.net/api/schema/ark:59853/apms-embeddings-schema-2024"

DELETE /schema/ark:{NAAN}/{postfix}

Delete a schema record.

curl -X DELETE "https://fairscape.net/api/schema/ark:59853/apms-embeddings-schema-2024" \
     -H "Authorization: Bearer <token>"

Note

Use fairscape-cli to infer schemas directly from CSV or HDF5 files with fairscape-cli schema infer.