## Wild-sample calibration of structure sanity card v4.3
88 inorganic CIFs sampled from the Crystallography Open Database (8 stratified years 1991-2024, uniform random within year, organic entries excluded via C&H formula filter), each run through structure sanity card v4.3 with no declared prototype. This is the card's **wild base rate**: how often each gate fires on real published files rather than synthetic corruption.
Headline: 1 clean pass, 63 CHECK, 21 FAIL, 3 parser refusals. The one clean pass is SrTiO3 (COD 1512124), a 5-atom cubic perovskite — exactly the prototype class the card was tuned on.
Per-gate manual verification (2026-08-08):
- All 10 min_pair FAILs are split-site or mixed-site disorder representations (occupancy sum <= 1 on the offending pair) except COD 4500668 (partially occupied W 0.33 A from a fully occupied O), which remains genuinely questionable.
- All 15 reference_match FAILs trace to non-standard space-group settings (Pcab/Pbnm/P 21/n), triclinic P-1 cells, or large disordered cells — the matcher's setting-sensitivity, not corrupted coordinates.
- The 82-entry metadata CHECK storm is mostly declaration-convention noise (formula-unit vs cell contents, unlocated H) plus a card bug: the v4.2 dominant-species surrogate deletes minority species before the formula comparison, and SG labels are string-compared (Pbnm vs Pnma false-fires; both are #62).
- 3 parser refusals are real file problems: malformed or occupancy-inconsistent atom_site loops, including a 2024 NMC-111 cathode CIF (COD 3000558) whose O rows are missing a column field.
Columns: per-entry gate levels (PASS/NOTE < CHECK < FAIL), min-pair distance, declared vs detected space group, and a manual fail_class classification. Companion post: "The card meets the wild" in #materials-science. Card script: file 700b11cd-ee77-4a03-88e0-736b8089ff88. Synthetic-corruption counterpart: dataset 019fdeba-fcb5-7fc9-9c9e-97c27f87d6a9.
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 = "019fdf2a-4ed9-73fe-ba1e-c74c7f492884"
# 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 |
|---|---|
| card_version | text |
| cod_id | integer |
| declared_sg | text |
| doi | text |
| error | text |
| fail_class | text |
| formula_declared | text |
| gate_bond_dispersion | text |
| gate_metadata | text |
| gate_min_pair | text |
| gate_occupancy | text |
| gate_reference_match | text |
| gate_stoichiometry | text |
| gate_symmetry | text |
| id | uuid |
| journal | text |
| min_pair_distance_a | real |
| n_sites | real |
| outcome | text |
| parsed_formula | text |
| tight_sg | text |
| worst_verdict | text |
| year | integer |
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)