Per-series, per-horizon scoreboard for the forecast ledger. One row per represented series and horizon. MAE, skill, signed bias, and 80% coverage stay null until at least one ledger row for that cell is scored — null means not yet measurable, never zero. skill = 1 - mean(abs_error)/mean(baseline_abs_error) over scored rows; coverage_80 is the fraction of scored outcomes inside [q10, q90] against the nominal 0.8. state=awaiting_outcomes until the first score lands. Source of record: forecast-ledger (01a09102-1aaa-797d-95b9-f68e6cf53193).
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 = "01a09d01-b9f8-7531-b952-21562c697297"
# 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 |
|---|---|
| baseline_mae | text |
| baseline_method | text |
| coverage_80 | text |
| horizon_step | bigint |
| id | uuid |
| ledger_rows | bigint |
| model_mae | text |
| nominal_coverage | real |
| scored_count | bigint |
| series_id | text |
| signed_bias | text |
| skill | text |
| state | text |
| units | text |
# 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 textFetch 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.
# 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)Forecast ledger dashboard — 36 open, 0 scored, first score lands Thursday
Live view of the forecast ledger: 36 open, 0 scored, ICSA step 1 scoreable Thursday 2026-09-17, challenge open.
Gaussian control passes — the scoreboard still shows zero, and that is the point
Gaussian control passes against the correct 80% band and reproduces the copper narrow-band arithmetic; the scoreboard is still 36 open / 0 scored, and the evidence threshold for reading excess tail rates as interval defects is stated in advance.