dv_schema_models

Pydantic models for Dataverse metadata — parse the schema, load dataset exports, and validate field values against the schema.

[!CAUTION] This library is under active development and the API is not yet stable. Breaking changes may occur between releases. Please pin to a specific version in your pyproject.toml or requirements.txt if you want to avoid surprises.

Pre-requisites

1. Python 3.13+

Installation

1. With uv (recommended):

uv add dv_schema_models

2. With pip:

pip install dv_schema_models

Concepts

Usage

1. Load and query the schema

import json
from dv_schema_models.dataverse_schema import load_schema

schema = load_schema(json.load(open("dv_schema.json")))

schema.block_names()                        # ['citation', 'geospatial', ...]
block = schema.get_block("citation")
block.fields.keys()                         # top-level field names
block.required_fields()                     # leaf fields where isRequired=True
block.all_leaf_fields()                     # flattened, including nested compound fields

field = block.get_field("keyword")
field.is_compound()                         # True — has childFields
field.iter_leaf_fields()                    # [keywordValue, keywordVocabulary, ...]

2. Load a dataset and read values

import json
from dv_schema_models.dataset_instance import load_dataset

dataset = load_dataset(json.load(open("ds_metadata.json")))

# Shortcut from the top level
dataset.get_value("citation", "title")      # plain string

# Or drill down
block = dataset.data.latestVersion.metadataBlocks.get("citation")
block.get_value("keyword")                  # unwrapped Python value (str / list / dict)
block.get_field("author").simple_value()    # same, from the DatasetFieldValue directly

3. Validate instance values against the schema

import json
from dv_schema_models.dataverse_schema import load_schema
from dv_schema_models.dataset_instance import load_dataset
from dv_schema_models.schema_driven_records import build_record_model, flatten_instance


schema = load_schema(json.load(open("dv_schema.json")))
dataset = load_dataset(json.load(open("ds_metadata.json")))

citation_schema = schema.get_block("citation")
CitationRecord = build_record_model(citation_schema)   # dynamic Pydantic model

block = dataset.data.latestVersion.metadataBlocks.get("citation")
raw = flatten_instance(block)              # {typeName: value, ...}
record = CitationRecord.model_validate(raw)

The generated model enforces field names, required/optional status, list wrapping for multiple=True fields, and int/float types where declared by the schema.

4. Discover available fields

# Fields actually present in this dataset instance
block = dataset.data.latestVersion.metadataBlocks.get("citation")
block.field_names()                            # e.g. ['title', 'author', 'keyword', ...]

# All fields the schema defines (including absent/optional ones)
schema.get_block("citation").all_leaf_fields().keys()

# After validation, access as typed attributes
record = CitationRecord.model_validate(flatten_instance(block))
record.title          # str
record.author         # list[...] for multiple=True compound fields
record.keyword        # None if not present in this dataset (optional fields default to None)
# Note: field names with dots become underscores — e.g. 'resolution.Spatial' → record.resolution_Spatial

Input file shapes

Schema — output of Dataverse /api/metadatablocks:

{"status": "OK", "data": [{"id": 10, "name": "citation", "fields": {...}}]}

Dataset — output of Dataverse GET /api/datasets/:id:

{"status": "OK", "data": {"latestVersion": {"metadataBlocks": {"citation": {"fields": [...]}}}}}

Citation

If you use this library in your work, please cite according to CITATION

License

MIT