Dataset

Datasets are easier to find when you provide supporting information such as their name, description, creator and distribution formats as structured data. Google's approach to dataset discovery makes use of schema.org and other metadata standards that can be added to pages that describe datasets. The purpose of this markup is to improve discovery of datasets from fields such as life sciences, social sciences, machine learning, civic and government data, and more.

Here are some examples of what can qualify as a dataset:

  • A table or a CSV file with some data
  • An organized collection of tables
  • A file in a proprietary format that contains data
  • A collection of files that together constitute some meaningful dataset
  • A structured object with data in some other format that you might want to load into a special tool for processing
  • Images capturing data
  • Files relating to machine learning, such as trained parameters or neural network structure definitions
  • Anything that looks like a dataset to you

Our approach to dataset discovery

We can understand structured data in Web pages about datasets, using either schema.org Dataset markup, or equivalent structures represented in W3C's Data Catalog Vocabulary (DCAT) format. We also exploring experimental support for structured data based on W3C CSVW, and expect to evolve and adapt our approach as best practices for dataset description emerge. For more information about our approach to dataset discovery, see Facilitating the discovery of public datasets.

Examples

Here's an example for datasets using JSON-LD syntax (preferred) in the Structured Data Testing Tool. The same vocabulary can also be used in RDFa 1.1, Microdata, or W3C DCAT vocabulary. The following example is based on a real-world dataset description.

JSON-LD

Here's an example of a dataset in JSON-LD:

RDFa

Here's an example of a dataset in RDFa:

Guidelines

Sites should follow the structured data guidelines. In addition to the structured data guidelines, we recommend the following sitemap and source and provenance best practices listed below.

Sitemap best practices

Use a sitemap file to help Google find your URLs. Using sitemap files and sameAs markup helps document how dataset descriptions are published throughout your site.

If you have a dataset repository, you likely have at least two types of pages: the canonical ("landing") pages for each dataset and pages that list multiple datasets (for example, search results, or some subset of datasets). We recommend that you add structured data about a dataset to the canonical pages. Use the sameAs property to link to the canonical page if you add structured data to multiple copies of the dataset, such as listings in search results pages.

Source and provenance best practices

It is common for open datasets to be republished, aggregated, and to be based on other datasets. This is an initial outline of our approach to representing situations in which a dataset is a copy of, or otherwise based upon, another dataset.

  • Use the sameAs property to indicate the most canonical URLs for the original in cases when the dataset or description is a simple republication of materials published elsewhere.
  • Use the isBasedOn property in cases where the republished dataset (including its metadata) has been changed significantly.
  • When a dataset derives from or aggregates several originals, use the isBasedOn property.
  • Use the identifier property to attach any relevant Digital Object identifiers (DOIs) or Compact Identifiers. If the dataset has more than one identifier, repeat the identifier property. If using JSON-LD, this is represented using JSON list syntax.

We hope to improve our recommendations based on feedback, in particular around the description of provenance, versioning, and the dates associated with time series publication. Please join in community discussions.

Textual property recommendations

We recommend limiting all textual fields to 5000 characters or less. Google Dataset Search only uses the first 5000 characters of any textual field. Names and titles are typically a few words or a short sentence.

Known Errors and Warnings

You may experience errors or warnings in Google's Structured Data Testing Tool and other validation systems. Specifically, warnings about fileFormat (renamed recently to encodingFormat) can be safely ignored. Validation systems may also suggest that organizations should have contact information including a contactType; useful values include customer service, emergency, journalist, newsroom, and public engagement. You can also ignore errors for csvw:Table being an unexpected value for the mainEntity property.

Structured data type definitions

You must include the required properties for your content to be eligible for display as a rich result. You can also include the recommended properties to add more information about your content, which could provide a better user experience.

You can use the Structured Data Testing Tool to validate your markup.

The focus is on describing information about a dataset (its metadata) and representing its contents. For example, dataset metadata states what the dataset is about, which variables it measures, who created it, and so on. It does not, for example, contain specific values for the variables.

Dataset

The full definition of Dataset is available at schema.org/Dataset.

You can describe additional information about the publication of the dataset, such as the license, when it was published, its DOI, or a sameAs pointing to a canonical version of the dataset in a different repository. Add identifier, license, and sameAs for datasets that provide provenance and license information.

Required properties
description Text

A short summary describing a dataset.
name Text

A descriptive name of a dataset. For example, "Snow depth in Northern Hemisphere".
Recommended properties
citation Text or CreativeWork

A citation for a publication that describes the dataset. For example, "J.Smith 'How I created an awesome dataset', Journal of Data Science, 1966".
identifier URL, Text, or PropertyValue

An identifier, such as a DOI or a Compact Identifier. If the dataset has more than one identifier, repeat the identifier property. If using JSON-LD, this is represented using JSON list syntax.
keywords Text

Keywords summarizing the dataset.
license URL, Text

A license under which the dataset is distributed.
sameAs URL

A link to a page that provides more information about the same dataset, usually in a different repository.
spatialCoverage Text, Place

You can provide a single point that describes the spatial aspect of the dataset. Only include this property if the dataset has a spatial dimension. For example, a single point where all the measurements were collected, or the coordinates of a bounding box for an area.

Points

"spatialCoverage:" {
  "@type": "Place",
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": 39.3280,
    "longitude": 120.1633
  }
}

Coordinates

Use GeoShape to describe areas of different shapes. For example, to specify a bounding box.

"spatialCoverage:" {
  "@type": "Place",
  "geo": {
    "@type": "GeoShape",
    "box": "39.3280 120.1633 40.445 123.7878"
  }
}

Named locations

"spatialCoverage:" "Tahoe City, CA"
temporalCoverage Text

The data in the dataset covers a specific time interval. Only include this property if the dataset has a temporal dimension. Schema.org uses the ISO 8601 standard to describe time intervals and time points. You can describe dates differently depending upon the dataset interval. Indicate open-ended intervals with two decimal points (..).

Single date

"temporalCoverage" : "2008"

Time period

"temporalCoverage" : "1950-01-01/2013-12-18"

Open-ended time period

"temporalCoverage" : "2013-12-19/.."
variableMeasured Text, PropertyValue

The variable that this dataset measures. For example, temperature or pressure.
version Text, Number

The version number for the dataset.
url URL

Location of a page describing the dataset.

DataCatalog

The full definition of DataCatalog is available at schema.org/DataCatalog.

Datasets are often published in repositories that contain many other datasets. The same dataset can be included in more than one such repository. You can refer to a data catalog that this dataset belongs to by referencing it directly.

Recommended properties
includedInDataCatalog DataCatalog

The catalog to which the dataset belongs.

DataDownload

The full definition of DataDownload is available at schema.org/DataDownload. In addition to Dataset properties, add the following properties for datasets that provide download options.

The distribution property describes how to get the dataset itself because the URL often points to the landing page describing the dataset. The distribution property describes where to get the data and in what format. This property can have several values: for instance, a CSV version has one URL and an Excel version is available at another.

Required properties
distribution.contentUrl URL

The link for the download.
Properties
distribution DataDownload

The description of the location for download of the dataset and the file format for download.
distribution.fileFormat Text

The file format of the distribution.

Tabular datasets

A tabular dataset is one organized primarily in terms of a grid of rows and columns. For pages that embed tabular datasets, you can also create more explicit markup, building on the basic approach described above. At this time we understand a variation of CSVW ("CSV on the Web", see W3C), provided in parallel to user-oriented tabular content on the HTML page.

Here is an example showing a small table encoded in CSVW JSON-LD format. There are some known errors in the Structured Data Testing Tool.

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