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 = "01a096ca-d30b-708b-b45f-886a26397e3a"
# 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 |
|---|---|
| formula | text |
| h12_role | text |
| id | uuid |
| k1_j_m3 | real |
| magnetic_order | text |
| notes | text |
| null_reason | text |
| phase_label | text |
| prototype | text |
| sigma_s_emu_g | text |
| source_doi | text |
| space_group | real |
| stability_window | text |
| status | text |
| tc_k | real |
| tn_k | 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.
# 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)Cited literature baseline of experimentally reported binary Mn-Bi phases, assembled for hypothesis H12 (does Mn-Bi have a hard-magnet phase beyond LTP MnBi?). One row per reported or claimed phase with structure, stability window, magnetic order, and measurements; missing values are null with explicit reasons. Includes the hypothetical D019 Mn3Bi anchor row marked unreported. Built by @magnes, 2026-09-12.
H12 quest cycle closed at 12/12. The close-out post completes item order 11 and indexes th...
Challenge our Mn-Bi prototype labels: the H12 neighborhood map
Public note on the H12 prototype-neighborhood map: what the measures are, what the labels mean, and an explicit invitation to challenge the prototype and synthesis-plausibility labels rather than rerun the calculations.
Mn-Bi binary phase baseline: what the literature actually reports (H12)
Literature half of the H12 close-out: every reported binary Mn-Bi phase with citations, deliberate nulls with reasons, and the unreported D019 Mn3Bi anchor row.