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 = "01a02703-7926-7ac1-ac81-08cf9b2848e1"
# 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 |
|---|---|
| alpha_a_ppm_k | real |
| alpha_b_ppm_k | real |
| alpha_c_ppm_k | real |
| alpha_se_ppm_k | real |
| alpha_v_ppm_k | real |
| cod_files | text |
| formula | text |
| has_h | text |
| id | uuid |
| journal | text |
| n_temps | integer |
| sg_number | real |
| sg_symbol | text |
| significant_alpha | text |
| t_max_k | real |
| t_min_k | real |
| 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)Experimental thermal-expansion database harvested from the Crystallography Open Database: every same-phase, same-spacegroup entry cluster (1990-2026 depositions) with >=3 distinct measured temperatures, fitted volumetric expansion coefficient alpha_V with standard error, and anisotropic axis alphas for orthogonal cells. 3,538 series total. Built by grouping COD REST metadata on (formula, spacegroup, volume cluster) and linear-fitting V(T).
COLUMN SEMANTICS (clarified 2026-08-28; column renamed from `confident_nte` to `significant_alpha`): `significant_alpha = yes` means the linear fit is significant at 2 sigma AND |alpha_V| >= 1 ppm/K. It is a SIGN-AGNOSTIC fit-quality flag, NOT an NTE verdict: 1,723 of 3,538 rows are yes, and 1,531 of those expand POSITIVELY. An earlier version of this description incorrectly stated that the flag included alpha_V < -1 and a >=4-temperature clause; it does not (878 yes rows have fewer than 4 temperatures). The confident-NTE subset discussed in the companion posts is: significant_alpha = yes AND alpha_V < -1 ppm/K AND n_temps >= 4, which gives 53 series (44 organic molecular crystals, 9 H-free frameworks). CSVs downloaded before 2026-08-28 carry the old `confident_nte` header with the same sign-agnostic meaning. Note the sibling dataset "Genuine single-structure COD multi-temperature series (corrected)" uses `confident_nte` with a different, genuinely NTE-specific rule (alpha_V < 0 at 2 sigma). Code and raw cache in projects/research/nte_structures/cod_celltemp_series/.
| Column | Range | Mean | Filled |
|---|---|---|---|
| formula | 2,827 distinct | — | 100% |
| sg_number | 1–230 | 22.75 | 100% |
| sg_symbol | 149 distinct | — | 100% |
| n_temps | 3–52 | 4.394 | 100% |
| t_min_k | 1.5–1,123 | 120.4 | 100% |
| t_max_k | 90–2,773 | 294.2 | 100% |
| alpha_v_ppm_k | -766.7–777.3 | 73.39 | 100% |
| alpha_se_ppm_k | 0–745.4 | 67.04 | 100% |
| alpha_a_ppm_k | -17,560–13,290 | 175 | 19% |
| alpha_b_ppm_k | -11,390–15,689 | 93.96 | 19% |
| alpha_c_ppm_k | -14,150–11,730 | -180.3 | 19% |
| has_h | 2 distinct | — | 100% |
| journal | 139 distinct | — | 100% |
| year | 1,993–2,026 | 2,013 | 100% |
| cod_files | 3,538 distinct | — | 100% |