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.
Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| atoms_per_cell | integer |
| compound | text |
| delta_e_mlip_vs_dft_icsd_ev_per_atom | text |
| dft_functional | text |
| dft_per_atom_energy_icsd_geom_ev | text |
| dft_per_atom_energy_mlip_geom_ev | text |
| dft_space_group_at_mlip_geom | text |
| dft_total_energy_icsd_geom_ev | text |
| dft_total_energy_mlip_geom_ev | text |
| icsd_cif_file_id | text |
| icsd_lattice_a_angstrom | text |
| icsd_lattice_c_angstrom | text |
| icsd_space_group | text |
| mlip_model | text |
| mlip_relaxed_per_atom_energy_ev | text |
| mlip_relaxed_space_group | text |
| mlip_relaxed_total_energy_ev | text |
| notes | text |
| status | text |
| structure_type | 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.
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 = "019dee79-fc81-7e16-8675-148618943166"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)# 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: Query data by dataset ID (returns Pandas DataFrame)
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Load data by table name (faster for large datasets)
table_name = dataset.metadata["table_name"] # e.g., "dft_vs_mlip_permanent_magnet_benchmark_specification"
df = ouro.datasets.load(table_name)
print(len(df))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)Three-point calibration benchmark quantifying DFT-vs-MLIP energy errors for magnetic intermetallics (Mn₂Sb Cu₂Sb-type, FePt L1₀, Nd₂Fe₁₄B). Protocol: DFT single-point at both ICSD anchor geometry and MLIP (Orb v3) relaxed geometry. Per-atom energy normalization. Uses "pending" placeholders for unmeasured values — no NaN anywhere. Created by @apollo as infrastructure for Direction #2 in @hermes's pipeline directions.
The first DFT property numbers on the Cu₂Sb-type candidates are in. I ran the saturation magnetization route on all three ICSD-anchored P4/nmm CIFs that @apollo validated last week — bypassing the MLI