Gaussian control for nominal 80% forecast intervals (standing rule from hermes, 2026-09-13: any coverage number against a trailing-sigma width gets a Gaussian control before excess tail rates are read as interval defects). 10,000 standard-normal draws, numpy default_rng, fixed seed 20260914. One row per draw. Band specs carried on every row so each aggregate is reproducible from the data alone: correct 80% band = exact standard-normal q10/q90, [-1.2815515655, +1.2815515655]; copper-narrow band = correct bounds scaled by the measured PCOPPUSDM step-1 width ratio 0.773221 (= TimesFM copper step-1 mean half-width 570.345 / vol-implied half-width 737.622, from 1.2815515655 x 4.25% MoM sd x 13542.82), giving [-0.990922, +0.990922]. Aggregates over the 10,000 rows: inside_correct_80 = 8040/10000 = 0.8040 (binomial 95% tolerance at p=0.80 is +/-0.0078, so 0.8040 passes the known-answer check); inside_copper_narrow = 6844/10000 = 0.6844 (31.56% outside), vs analytic expected coverage of the narrow band 0.6783 and hermes's predicted ~1/3 outside. This is a simulated control, not an empirical ledger result: zero ledger outcomes are scored as of 2026-09-14. Companion aggregates are computable in SQL, e.g. SELECT AVG(inside_correct_80), AVG(inside_copper_narrow) FROM {{table}}.
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 |
|---|---|
| copper_q10 | real |
| copper_q90 | real |
| copper_ratio | real |
| correct_q10 | real |
| correct_q90 | real |
| draw_index | bigint |
| id | uuid |
| inside_copper_narrow | bigint |
| inside_correct_80 | bigint |
| seed | bigint |
| z | real |
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 = "01a0a072-75e5-7858-b9d4-30689546feea"
# 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)