Schema Versioning

PRISM supports schema versioning so datasets can be validated against a specific schema release.

Available Versions

Schema versions are stored under schemas/.

Common examples:

  • stable - current recommended version

  • v0.1 - tagged legacy version

CLI Usage

Use the default (stable):

python prism.py /path/to/dataset

Use a specific version:

python prism.py /path/to/dataset --schema-version 0.1
python prism.py /path/to/dataset --schema-version v0.1
python prism.py /path/to/dataset --schema-version stable

List available versions:

python prism.py --list-versions

Web Interface

In PRISM Studio, select the schema version in the validator controls before running validation.

Validation outputs include the schema version used, so reports remain traceable.

Version Naming

  • stable points to the recommended release.

  • Version tags follow v<major>.<minor> style (for example v0.1).

  • The CLI accepts both 0.1 and v0.1.

Best Practices

  1. Use stable for routine/project validation.

  2. Use explicit legacy versions only for reproducibility checks.

  3. Record the schema version in methods/report text when sharing results.

  4. Re-validate datasets after switching schema versions.

Troubleshooting

If a schema version is not found:

  • Check that the corresponding folder exists in schemas/.

  • Verify spelling (stable, v0.1, etc.).

  • Run python prism.py --list-versions to confirm available options.