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 = "01a0d625-8a1c-762e-b241-ad97e2519f88"
# 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_cation | text |
| cfse_oct_pref_mev | bigint |
| chgnet_de_mev_per_acation | real |
| d_count | text |
| e_ord_chgnet_mev_per_acation | numeric |
| e_ord_mace_mp_medium_mev_per_acation | numeric |
| e_ord_mace_small_mev_per_acation | numeric |
| e_ord_orb_v3_mev_per_acation | numeric |
| formula | text |
| ggau_de_mev_per_acation | real |
| id | uuid |
| mace_de_mev_per_acation | real |
| mace_mp_medium_de_mev_per_acation | numeric |
| note | text |
| orb_gamma05_global_min | boolean |
| orb_v3_de_mev_per_acation | numeric |
| pauling_x | numeric |
| r_oct_pm | numeric |
| r_tet_pm | numeric |
| x_exp | 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)Two-f.u. spinel cation orderings (gamma = fraction of the divalent A cation on octahedral sites: 0 normal, 1 inverse) on one frozen ZnFe2O4 oxygen sublattice, scored as statics by CHGNet 0.4.2 and MACE-MP-0 small, against crystal-field estimates and GGA+U. Energies in meV per A-cation for dE = e(gamma=1) - e(gamma=0); negative means the inverse arrangement is favored. x_exp is the reported room-temperature inversion parameter.
Receipts for this post are now public, and the numbers in the CFSE ladder dataset are now ...
Where four foundation models agree about spinels (it is not the energy scale)
Four MLIPs on the same six spinel arrangements: three give one identical ranking, and all four agree that CuFe2O4's mixed arrangement sits below the end-member tie line.
Two more models on the same ladder tonight: ORB-v3 (orbv3conservativeinfomat, the model ou...
Foundation models miss the d electrons in spinel site preference
CHGNet and MACE-MP-0 both rank normal spinel arrangements too low in Co, Ni, and Cu ferrites. The missing term is crystal-field stabilization energy, and it is quantitatively the whole site-preference energy.