Dataset Structured Data: Complete Implementation Guide

Why Use Dataset Schema?

• Make your datasets discoverable through Google Dataset Search, reaching researchers and analysts who might not find your site through standard search
• Provide clear metadata about your datasets including descriptions, licensing, and coverage, building trust with potential data consumers
• Increase citations and usage of your published data by making it easier to find and evaluate
• Support open data initiatives by ensuring your datasets are properly indexed and accessible to the broader research community
• Improve your organisation's visibility as a data publisher and authority in your domain

Required Properties

These properties are required by Google for Dataset structured data to be eligible for rich results.

Recommended Properties

Adding these properties improves your chances of appearing in rich results and provides more context to search engines.

JSON-LD Example

Here is a complete Dataset structured data example you can use as a starting point. Or use our Dataset generator to create one automatically.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "London Borough Crime Statistics 2019-2025",
  "description": "Monthly crime statistics for all 32 London boroughs and the City of London, compiled from Metropolitan Police Service records. The dataset includes crime counts broken down by category (theft, burglary, violence, fraud, etc.), borough, and month. Data is provided in CSV format with approximately 250,000 rows covering the period from January 2019 to December 2025.",
  "url": "https://data.example.org/datasets/london-crime-2019-2025",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "creator": {
    "@type": "Organization",
    "name": "London Data Observatory",
    "url": "https://data.example.org"
  },
  "distribution": [
    {
      "@type": "DataDownload",
      "encodingFormat": "text/csv",
      "contentUrl": "https://data.example.org/downloads/london-crime-2019-2025.csv",
      "contentSize": "48MB"
    },
    {
      "@type": "DataDownload",
      "encodingFormat": "application/json",
      "contentUrl": "https://data.example.org/downloads/london-crime-2019-2025.json",
      "contentSize": "62MB"
    }
  ],
  "temporalCoverage": "2019-01-01/2025-12-31",
  "spatialCoverage": {
    "@type": "Place",
    "name": "Greater London, United Kingdom",
    "geo": {
      "@type": "GeoCoordinates",
      "latitude": 51.5074,
      "longitude": -0.1278
    }
  },
  "keywords": [
    "crime statistics",
    "London",
    "metropolitan police",
    "borough data",
    "public safety"
  ],
  "datePublished": "2025-03-15",
  "dateModified": "2026-01-10",
  "variableMeasured": [
    "Crime count by category",
    "Crime count by borough",
    "Monthly crime totals"
  ],
  "measurementTechnique": "Compiled from official Metropolitan Police Service recorded crime data, aggregated by borough and calendar month."
}
</script>

How to Implement Dataset Schema

1. 1Ensure each dataset has its own dedicated landing page with a clear description, download links, and metadata. Google indexes the page, not the data file itself.
2. 2Add a JSON-LD script block to the dataset landing page with @type set to 'Dataset' and include the required name and description properties.
3. 3Specify the licence using the license property with a URL to the full licence text. This is critical for researchers evaluating whether they can use your data.
4. 4Add distribution entries for each available download format using DataDownload objects with encodingFormat and contentUrl properties.
5. 5Include temporal and spatial coverage properties to help users find datasets relevant to their time period and geographic area of interest.
6. 6Submit your dataset pages to Google Search Console and check indexing status. You can also provide a sitemap specifically for dataset pages to aid discovery.

Common Mistakes to Avoid

• Writing vague dataset descriptions that do not explain what the data contains, how it was collected, or what time period it covers. Be specific and detailed.
• Omitting the license property. Without a clear licence, researchers cannot determine whether they are legally permitted to use your data, significantly reducing its value.
• Adding Dataset markup to pages that do not directly describe or provide access to a dataset, such as blog posts that merely mention data.
• Not providing download links through the distribution property. The primary purpose of Dataset Search is to help people find and access data, so direct download links are essential.
• Using inconsistent or incorrect date formats for temporalCoverage. Always use ISO 8601 format (YYYY-MM-DD/YYYY-MM-DD) for date intervals.

Testing & Validation

Before deploying your Dataset structured data, validate it using these tools:

1. 1Google Rich Results Test — Paste your page URL or code snippet to check if your structured data is eligible for rich results.
2. 2Schema Markup Validator — Validates your JSON-LD syntax against the schema.org specification.
3. 3Google Search Console — After deployment, monitor the Enhancements report for any errors or warnings.

Frequently Asked Questions