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Generated Artifacts

Download the generated model serializations. These are produced automatically from the LinkML schema and reflect the current state of the wss-test data model.

Model Serializations

Format Description Download
JSON Schema Validate JSON/YAML data against the schema wss_test.schema.json
Excel Spreadsheet Workbook with a sheet per class — use as a data entry template wss_test.xlsx
SQL DDL Relational database schema for loading into SQL databases wss_test.sql
Python Dataclasses Python classes generated from the schema wss_test.py
Pydantic Model Pydantic v2 model for validation and serialization wss_test_pydantic.py

About the Excel Workbook

The Excel workbook contains one sheet for each class in the schema:

  • Dataset — top-level dataset metadata (id, name, description)
  • Sample — sample provenance (id, site_code, medium, replicate)
  • Variable — variable definitions (id, label, expression_basis, default_unit, missing_value_code)
  • Measurement — individual measurements (attribute, numeric_value, unit, method_id, flag, datetime_measured, statistic, temporal_aggregation, reported_precision, notes)
  • Attribute — base attribute definitions
  • QuantityValue — base quantity values
  • AttributeValue — abstract base (for reference)
  • TextValue — text-typed values

Use this workbook as a template for data submission. Fill in the Variable sheet first to define your measured variables, then populate Sample and Measurement sheets with your data.

About the JSON Schema

The JSON Schema can be used to validate data files programmatically:

# Validate a YAML dataset
pip install check-jsonschema
check-jsonschema --schemafile docs/artifacts/wss_test.schema.json tests/data/valid/Dataset-001.yaml

About the SQL DDL

The SQL DDL creates tables matching the schema classes. This is useful for loading validated data into a relational database for querying:

-- Tables created: Dataset, Sample, Variable, Measurement,
--                 Attribute, QuantityValue, AttributeValue, TextValue