Per-year counts (1990-2026, 2026 partial as of 2026-08-09) of Crystallography Open Database entries whose chemical formula contains no carbon (COD REST API, nel1=C exclusion), grouped by publisher family of the source journal. share_of_year is the family's fraction of that year's total no-carbon intake. Journal-to-family mapping (normalized COD journal strings): ACS = Inorganic Chemistry, JACS, Chemistry of Materials, Crystal Growth & Design, J. Phys. Chem. C, ACS Appl. Mater. Interfaces; Elsevier = J. Solid State Chemistry, J. Alloys Compd., Solid State Sciences, Materials Research Bulletin, J. Magn. Magn. Mater., Solid State Ionics, J. Less-Common Metals, Physica C, Microporous and Mesoporous Materials, Int. J. Inorganic Materials, J. Phys. Chem. Solids, Solid State Commun., Polyhedron, Inorg. Chem. Commun., Eur. J. Solid State Inorg. Chem., Intermetallics; Wiley = Z. Anorg. Allg. Chem., Angew. Chem. Int. Ed., Chem. Eur. J., Eur. J. Inorg. Chem.; RSC = Dalton Trans., CrystEngComm, J. Mater. Chem., Chem. Commun., New J. Chem., Chem. Sci., RSC Adv.; IUCr = Acta Cryst. B/C/E, J. Appl. Cryst., IUCrJ; Mineralogy = Am. Mineral., Can. Mineral., Phys. Chem. Miner., Eur. J. Mineral., Mineral. Mag., J. Mineral. Petrol. Sci., Neues Jahrbuch Mineral., Minerals; De Gruyter = Z. Kristallogr. + New Crystal Structures; Russian journals = Kristallografiya, Crystallogr. Rep., Zh. Neorg. Khim., Zh. Strukt. Khim., Doklady, Izv. Akad. Nauk Neorg. Mater.; Physics = Phys. Rev. B, J. Phys. Condens. Matter, J. Phys. Soc. Jpn., J. Appl. Phys.; MDPI = Crystals, Materials, Inorganics, Magnetochemistry; 'All other' = ~600 small journals. Companion analysis to 'The open crystallographic record flipped in 1995' (dataset 019fe3d8). Raw per-entry CSV cache: projects/analyses/cod_composition/noc_csv_cache.jsonl.
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 |
|---|---|
| family | text |
| id | uuid |
| n_entries | integer |
| share_of_year | 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)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 = "019fe93a-b6b6-7709-878c-965348078904"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)