Dataverse Python API Docs | dltHub

Dataverse is a research data repository software that exposes its functionality through a native REST API. The REST API base URL is https://<your-dataverse-host>/api/ and The API requires an X-Dataverse-key header containing an API token..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Dataverse data in under 10 minutes.

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What data can I load from Dataverse?

Here are some of the endpoints you can load from Dataverse:

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How do I authenticate with the Dataverse API?

Authentication is performed by sending an API token in the X-Dataverse-key HTTP header. The token is an alphanumeric string unique to the server instance.

1. Get your credentials

To obtain your Dataverse API token, log in to your Dataverse installation, click on your account name in the navigation bar, and select API Token from the dropdown menu. In the resulting tab, click Create Token to generate your unique credential. If your token is compromised or expires, you can use the same menu to Recreate Token. Treat this token with the same security precautions as a password.

2. Add them to .dlt/secrets.toml

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[sources.dataverse_source]
api_key = "your_api_token_here"
server_url = "https://your-dataverse-instance.org"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Dataverse API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python dataverse_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline dataverse_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dataverse_data
The duckdb destination used duckdb:/dataverse.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads /api/dataverses/{alias}/contents and /api/users/

from the Dataverse API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def dataverse_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-dataverse-host>/api/",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "accounts", "endpoint": {"path": "api/data/v9.2/accounts", "data_selector": "value"}},
            {"name": "contacts", "endpoint": {"path": "api/data/v9.2/contacts", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dataverse_pipeline",
        destination="duckdb",
        dataset_name="dataverse_data",
    )
    load_info = pipeline.run(dataverse_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("dataverse_pipeline").dataset()
sessions_df = data.accounts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dataverse_data.accounts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dataverse_pipeline").dataset()
data.accounts.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Dataverse data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack