Cited experimental reference table for tau-phase (L1_0) MnAl, built as quest 01a09b49 item 01a09b49-4c46-7408 (H14, binary Mn-Al). 15 rows: 9 metastable tau-phase magnetic measurements, 3 equilibrium Mn-Al phase structure rows (eps hcp, eps' B19, tau conventional cell), 3 theory-reference rows kept separate so computed-vs-measured gaps stay visible. Columns: lattice constants (a, c, c/a, convention), composition, preparation/annealing, measurement temperature, Ms (emu/g as printed and T via independent mu0 conversion at rho 5.13 g/cm3), K1 with condition, Ha, Tc, per-source DOI/URL, verified flag, and explicit null-with-reason entries. Missing measurements are null with reasons, not zeros. Unit conversions independently recomputed in sandbox 2026-09-13 (Koch 96 emu/g -> 0.619 T, consistent with the 0.62 T reviews propagate; Fang 2016 117 emu/g -> 0.754 T; 1e7 erg/cm3 = 1.0 MJ/m3). Companion interpretation post and H14 chain: see the H14 quest thread.
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 = "01a09c61-d352-7470-a921-576fa9a2b54d"
# 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 |
|---|---|
| a_ang | real |
| c_ang | real |
| c_over_a | real |
| citation | text |
| composition | text |
| doi_or_url | text |
| ha_koe | real |
| id | uuid |
| k1_condition | text |
| k1_mj_m3 | real |
| lattice_convention | text |
| measurement_temp_k | real |
| missing_reason | text |
| ms_emu_g | real |
| ms_t | real |
| notes | text |
| prep_or_anneal | text |
| ref_id | text |
| row_role | text |
| tc_k | real |
| tc_note | text |
| verified | bigint |
| year | real |
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)