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.
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 = "01a0d550-9b28-7605-a306-bbb5e3e65e9a"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| anchor | text |
| hart_data_tc_values | text |
| id | uuid |
| measured_source | text |
| measured_tc_k | bigint |
| rf_loo_pred_k | real |
| rf_residual_k | real |
| rows_in_hart_data | bigint |
| tb2j_mc_tc_k | real |
| tb2j_source | text |
# 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 textFetch 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.
# 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)Measured Curie temperatures for seven magnet anchors compared against two cheap prediction methods. rf_loo_pred_k is the BYU group's released random-forest model (github.com/msg-byu/ML-for-CurieTemp-Predictions, commit 80841ce4) with every row of the anchor's composition removed before training, so no value is scored from a model that saw the compound's own measured Tc. rows_in_hart_data / hart_data_tc_values report what the released DS1+DS2 data holds for that composition (their data encodes non-ferromagnetic elements as Tc = 0). tb2j_mc_tc_k is our own TB2J plus Monte Carlo prediction from the RE-Free Permanent Magnet Leaderboard work. Method controls (2026-09-24): their 67/33 recipe reproduces at MAE 74.5 K (R2 0.849); train-DS1 test-DS2 gives 90.5 K; leave-one-out on 100 random compositions of their own data gives MAE 61.7 K with bias -23.8 K.