Data

Contribution studio

Draft a dataset YAML file with the same schema, vocabulary, and guide-copy rules used in CI. Download the file and open a GitHub pull request. This page does not execute Python or create a pull request.

Python syntax, live URL, and provider checks still run in repository CI. This studio never executes submitted code.

Open contribution guide

Guide preview

Microproduct data guidebeginner

Example Dataset

Continuously updated example records for building a small bounded monitoring tool for a local workflow.

Start the guide Open official source
Report a problem

At a glance

Difficulty
Beginner — comfortable for a first prototype
Size
Tiny · ≤0.001 GB
Formats
CSV
Access
Download
API key
Not required
Provider
Example Agency
Updates
Continuous
Last verified
Aug 19, 2026
Source type
Government source
Data terms
Example data terms
  • Python syntax checked
  • Runnable notebook

From source to product signal

Test a product idea in four steps

Start with one small extract from the official source. Missing values and provisional status can change a product decision.

1

Check the setup

  • Python 3.10 or newer
  • A notebook environment such as Jupyter or Google Colab
2

Access the data

  1. 1.Open the official source and review access limits.
  2. 2.Download a bounded sample from the authoritative file or API.
Open official source
3

Run the Python example

Install the packages, then run the notebook cell.

python -m pip install pandas

import pandas as pd

data = pd.read_csv("https://example.com/sample.csv")
print(data.head())
4

Test a useful signal

Summarize one local extract

Can a beginner confirm the extract is usable for a monitoring prototype?

  1. 01Inspect the first rows and keep a retrieval timestamp.
  2. 02Summarize the columns needed for the product signal.
  3. 03Explain one pattern and one limitation that would change a product decision.

Dataset details

Example Agency is a government source. Last verified 2026-08-19. Temporal coverage: not applicable.

Data types

Event Data

Formats

CSV

Provider

Example Agency

Data terms

Example data terms

Send feedback

id: example-dataset
name: Example Dataset
description: Continuously updated example records for building a small bounded
  monitoring tool for a local workflow.
theme: Environment & Hazards
url: https://example.com/dataset
access_type:
  - download
api_key_required: false
free_to_access: true
size_gb_min: 0
size_gb_max: 0.001
formats:
  - CSV
license: Example data terms
license_url: https://example.com/data-terms
url_checks:
  source_marker: Example Dataset Downloads
  license_marker: Example Dataset Terms of Use
domains:
  - Natural Hazards
data_types:
  - Event Data
tasks:
  - Monitoring
difficulty: beginner
geography:
  - Not applicable
temporal_coverage: null
update_frequency: continuous
provider: Example Agency
source_type: government
last_verified: 2026-08-19
catalog_status: active
getting_started:
  overview: Start with one small extract from the official source. Missing values and
    provisional status can change a product decision.
  prerequisites:
    - Python 3.10 or newer
    - A notebook environment such as Jupyter or Google Colab
  access_steps:
    - Open the official source and review access limits.
    - Download a bounded sample from the authoritative file or API.
  python:
    packages:
      - pandas
    code: |-
      import pandas as pd

      data = pd.read_csv("https://example.com/sample.csv")
      print(data.head())
  first_project:
    title: Summarize one local extract
    goal: Can a beginner confirm the extract is usable for a monitoring prototype?
    steps:
      - Inspect the first rows and keep a retrieval timestamp.
      - Summarize the columns needed for the product signal.
      - Explain one pattern and one limitation that would change a product decision.