Load OSF data to DuckDB

The Open Science Framework (OSF) API provides programmatic access to manage and interact with research projects, files, and user data hosted on the OSF platform. Everything needed to build a working OSF → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your OSF to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from OSF to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the OSF API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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OSF API at a glance

These values come from the OSF API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the OSF API?

The OSF API uses token-based authentication via the Authorization header. Users must include the header 'Authorization: Bearer <your_token_here>' in requests that require authentication, where the token can be either a Personal Access Token or an OAuth2 access token.

1. Get your credentials

1. Sign in to your account at https://osf.io/. 2. Click your avatar in the top right corner and select 'Settings'. 3. Navigate to the 'Personal Access Tokens' page in the sidebar menu. 4. Click 'Create token', provide a descriptive name, and select the required scopes (e.g., 'osf.full_read', 'osf.full_write'). 5. Copy the generated token immediately, as it will not be displayed again.

2. Add them to .dlt/secrets.toml

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[sources.osf_source]
api_token = "your_personal_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What OSF data can I load into DuckDB?

These are the OSF endpoints dlt can load into DuckDB:

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How do I load only new OSF records?

OSF exposes date_modified on v2/nodes/, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "nodes", "endpoint": {
    "path": "v2/nodes/",
    "data_selector": "data",
    "incremental": {"cursor_path": "date_modified", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated OSF pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /nodes and /users from the OSF API into DuckDB:

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

@dlt.source
def osf_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.osf.io/v2",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "nodes", "endpoint": {"path": "v2/nodes/", "data_selector": "data"}},
            {"name": "users", "endpoint": {"path": "v2/users/", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_osf_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="osf_pipeline",
        destination="duckdb",
        dataset_name="osf_data",
    )
    load_info = pipeline.run(osf_source())
    print(load_info)


if __name__ == "__main__":
    load_osf_to_duckdb()

Run it with python osf_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query OSF data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("osf_pipeline").dataset()
df = data.nodes.df()
print(df.head())

SQL:

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SELECT * FROM osf_data.nodes LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the OSF to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw OSF loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load OSF data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack