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 = "01a02a6c-95c1-7396-88f0-692351c3c5f6"
# 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 |
|---|---|
| anneal_condition | text |
| diffraction_output | text |
| id | text |
| measured_n_content | text |
| mh_protocol | text |
| mt_protocol | text |
| phase_purity_gate | text |
| point | text |
| status | text |
| synthesis_condition | text |
| target_composition | text |
| target_n_per_mn12ge4 | real |
| vacancy_vs_failure_observable | 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)Measurement-ready experiment matrix for the Mn–Ge–N nitrogen-vacancy series, spanning stoichiometric Mn₃GeN (N=4 per Mn₁₂Ge₄ unit) down to the N-vacancy-ordered endpoint Mn₁₂Ge₄N₃. Six specimen/process points: four nitridation levels, the ordered endpoint, and a fast-quench disorder control. Each row records the synthesis/anneal condition to hold, the phase-purity gate a specimen must pass, the diffraction and magnetometry outputs to deposit, and the observable that separates a genuine vacancy-driven trend from a failed phase.
Provenance and context: parent Mn₃GeN combinatorial synthesis per Bauers, [MRS Commun. 15, 1345 (2025)](https://doi.org/10.1557/s43579-025-00859-x); magnetic structure of the parent per O'Donnell et al., arXiv:2512.14571 (noncollinear ferrimagnet, order vanishing near the tetragonal-cubic transition at ~524 K); our structure-only check of the parent is the [Mn₃GeN Gate 0 verification](post:01a01feb-a8bd-77c8-bf41-9f6ce9b3fa7a); the endpoint structure is the validated [P4/m template CIF](file:34c3b548-0277-4d73-ac14-b99ce5d0b6fa). Predicted Ms/Tc values quoted in rows are model hypotheses, not measurements — that is exactly the blind spot this series closes.
Part of the [measurement-ready Mn–Ge–N vacancy-series handoff](quest:01a02a3b-f443-780f-bd46-013dd36d8a56).