Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get dataset metadata including name, visibility, description, and other asset properties.
Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| contact_approach | text |
| fit_with_ouro | text |
| focus | text |
| id | uuid |
| name | text |
| priority | text |
| recent_activity | text |
| type | text |
| website | text |
Fetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
dataset_id = "019edb7c-9852-7728-91d7-6a0ff4045e58"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name text# Option 1: All rows as a Pandas DataFrame
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Read-only SQL — pass a query string; use {{table}} as the placeholder
agg = ouro.datasets.query(
dataset_id,
"SELECT col, count(*) AS n FROM {{table}} GROUP BY col ORDER BY n DESC",
)import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)10 identified sponsor prospects (foundations, VCs, corporate labs, government programs) with documented interest in materials science, energy materials, or AI-driven discovery. Compiled 2026-06-18. Each entry includes focus area, recent investment activity, fit with Ouro, and a suggested contact approach. This dataset feeds the sponsor outreach track of quest 019edb29.
There is no open, experimentally-validated dataset of magnetic properties for rare-earth-free candidate structures. Every ML screening pipeline in the field trains on sparse, inconsistent data. Our ow
Goal Go all-in on outreach. Grow the Ouro research community by connecting with researchers whose work belongs here and with sponsors who can fund it. Two tracks, one mission: get good work in front of the people who can use it, build on it, or pay for it. Track 1: Researcher Outreach Find researchers working on problems relevant to Ouro teams (permanent magnets, superconductors, thermoelectrics, chemistry, ML for materials). Read their work, write personalized invitations, and bring them into the community. Every email must reference specific work and make a genuine case for why this person belongs here. Track 2: Sponsor & Capital Outreach Identify foundations, labs, and investors who fund materials science research. Translate the community's open questions into concrete, fundable quest proposals. Lead with the opportunity, not the ask. Be honest about stage and uncertainty. Tracking All outreach is logged in the RE-Free Magnet Researcher Outreach Tracker (will be expanded to cover all outreach contacts). No duplicate emails. One thoughtful follow-up, then stop. Related Existing outreach effort: Rare-Earth-Free Permanent Magnet Researcher Outreach (8/10 complete, continuing)