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 = "01a0bf5a-a02b-7011-86c8-69ee6f7d50bd"
# 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 |
|---|---|
| close_match | text |
| close_match_doi | text |
| family | text |
| has_cif | text |
| ic_bulk_s_per_cm | real |
| ic_total_s_per_cm | real |
| icsd_id | real |
| id | text |
| ionic_conductivity_numeric_s_per_cm | real |
| ionic_conductivity_s_per_cm | text |
| laskowski_id | real |
| lattice_a | real |
| lattice_alpha | real |
| lattice_b | real |
| lattice_beta | real |
| lattice_c | real |
| lattice_gamma | real |
| liion_id | real |
| reduced_composition | text |
| source_doi | text |
| space_group | text |
| space_group_number | bigint |
| split | text |
| true_composition | text |
| z | bigint |
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)Full OBELiX dataset hosted for open benchmarking: 599 synthesized lithium solid-electrolyte materials with experimentally measured room-temperature ionic conductivity, curated by the NRC-Mila team (Therrien et al., arXiv:2502.14234; Digital Discovery 2026, 5, 910). Includes the authors' official leakage-resistant train/test split (478 train / 121 test, in the `split` column) and 321 entries with CIF structures (the `has_cif` column; structures are the authors' noise-randomized CIFs, available as the companion file asset). Notes: `ionic_conductivity_s_per_cm` keeps the authors' original notation, including 37 upper-limit entries written as "<1E-10" or "<1E-8"; `ionic_conductivity_numeric_s_per_cm` carries the parsed value and is empty for those 37. The `close_match` column flags the ~291 entries whose structure is a close database match rather than an exact experimental structure (see the paper for the distinction). Benchmarking context: on the paper's own leakage-resistant split, a random forest beat the geometric GNNs tested, which is the open question this dataset lets the community settle. Credit and license: data and structures by Therrien, Abou Haibeh, Sharma, Hendley, Hernández-GarcÃa, Sun, Tchagang, Su, Huberman, Bengio, Guo, and Shin (NRC / Mila), CC-BY-4.0, from github.com/NRC-Mila/OBELiX. Hosted on Ouro with their permission path (public repo, CC-BY); cite the original paper.