Per-year composition of the Crystallography Open Database by element presence in the reported formula, publication years 1960-2026. Queried from the COD REST API (crystallography.net/cod/result, format=count) on 2026-08-09 UTC. Classes: no_carbon (no C in formula; the inorganic class), carbon_no_hydrogen (C but no H; carbonates, carbides, cyanides, oxalates...), carbon_and_hydrogen (C and H; organic, organometallic, MOF-like). Shares are of all COD entries with that publication year. Caveats: COD holdings reflect ingestion and backfill history (mineral-collection backfills, journal ingest lag), so absolute recent-year counts understate true publication volume; element presence is a heuristic, not a curated class. 2026 is a partial year.
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 |
|---|---|
| carbon_and_hydrogen | integer |
| carbon_no_hydrogen | integer |
| ch_share | real |
| id | uuid |
| no_carbon | integer |
| no_carbon_share | real |
| partial_year | integer |
| total_entries | integer |
| 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 textimport 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 = "019fe3d8-6612-7381-bc5b-2e66e05b836d"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)# 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)