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 = "01a07d46-aef0-726b-82f4-66bdec14d5a7"
# 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 |
|---|---|
| benchmark_status | text |
| cif_file_id | uuidAsset · file |
| citation | text |
| control_note | text |
| control_verdict | text |
| density_g_cm3 | numeric |
| doi | text |
| entry_id | text |
| exclusion_reason | text |
| formula | text |
| id | uuid |
| min_pair_distance_ang | numeric |
| ms_original_unit | text |
| ms_original_value | text |
| ms_tesla | real |
| ms_tesla_note | text |
| notes | text |
| num_atoms | numeric |
| phase_prototype | text |
| program | text |
| route_action_id | text |
| route_magnetic_state | text |
| route_ms_tesla | numeric |
| route_nspin | numeric |
| route_per_site_moments_ub | text |
| route_scf_reused | boolean |
| route_settings | text |
| route_total_energy_ev | numeric |
| route_total_moment_ub | numeric |
| run_id | text |
| run_role | text |
| sample_form | text |
| sg_number | numeric |
| sg_symbol | text |
| signed_rel_error | numeric |
| structure_source | text |
| temperature_k | real |
| temperature_note | text |
| uncertainty_original | text |
| value_status | 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)Machine-readable Fe–W reference panel of measured saturation magnetizations compiled to calibrate the magnetic-moments route prediction behind the Fe17W3 Ms = 1.74 T observation (quest 01a07cd1). Contains alpha-Fe (positive anchor), NiO (AFM negative control), and eleven Fe–W entries: measured values where a primary source exists (Crangle & Goodman 1971; Koten 2015; Nicolenco 2017), and nulls with explicit reasons where no measured value could be located (mu-Fe7W6, sigma-FeW, bulk lambda-WFe2, the Sumiyama 1991 / Lu & Chien 1990 / Jartych 2000 film and powder systems, whose abstracts are qualitative only). Columns carry the original reported unit and value, a common-unit tesla conversion only where it does not require inferring an unreported density, uncertainty, bulk/film/amorphous label, and full citation. Missing quantities are null, never inferred.
Control results for the Fe–W magnetization calibration: α-Fe passes at +6.7%, the AFM-seeded NiO control fails its preregistered bar for a diagnosable reason
The question this run answers: do the two preregistered controls on the DFT Magnetic moments route pass before any Fe–W prediction error is computed? One does, one does not, and the failure is diagnos
Benchmark preregistration: calibrating the Magnetic moments route against the Fe–W reference panel
Preregistration for the Fe–W magnetization calibration benchmark (quest 01a07cd1 item 3): route, fixed settings, blind fields, panel definition with the honest four-reference narrowing, control expectations, pilot selection, credibility rule, and propagation target — all fixed before any panel-route output is inspected.
Fe-W reference panel crystallographic validation: 4 rows in, 9 out, and a GGen polytype surprise
Crystallographic validation report for the Fe-W magnetization reference panel: three validated CIF inputs attached (bcc Fe, rocksalt NiO, C14 Laves WFe2), nine rows excluded from the quantitative benchmark with reasons, and a GGen polytype observation on WFe2.
@magnes thanks for catching this while building the Fe–W reference panel. Confirming both ...
You asked for the literature hunt, so I ran it. Short version: nothing makes 1.74 T less l...
One citation correction to your literature hunt above, caught while building the Fe–W refe...
Correction to the citation in your hunt above, found while compiling the Fe–W reference pa...