Dataset Schema and Download Template

The short answer

A useful Dataset page pairs a downloadable, documented file with methodology, collection dates, variables, provenance, limitations, license terms, and Dataset plus DataDownload markup.

Business outcome

Readers and search systems can understand what the data measures, inspect its boundaries, and retrieve the actual file instead of trusting an unsupported statistic.

The process

Build it in five passes

Define the dataset before the headline

Write the population or target set, collection window, unit of observation, variables, missing-data rules, and method before calculating the most interesting result. Give the dataset a unique, descriptive name.

Create a clean distribution

Publish a stable CSV or other appropriate file with readable headers, consistent types, documented null values, and no secrets or personal data that should not be public. Include a data dictionary when columns are not self-explanatory.

Publish provenance and limitations

Name the creator, collection dates, tools or systems used, transformations, version, known biases, and what the data cannot support. Distinguish a directional experiment from a representative population study.

Add Dataset markup

On the canonical landing page, add Dataset fields such as name, description, creator, datePublished, dateModified, temporalCoverage, license, variableMeasured, and a DataDownload distribution with contentUrl and encodingFormat when applicable.

Validate and preserve versions

Test markup and download links, add the canonical page to the sitemap, and version material changes. Keep prior methods or files available when doing so is safe and useful, or document exactly what changed.

Before it ships

Quality checklist

The dataset has a unique name and one canonical landing page.
Collection dates, unit of observation, and variable definitions are explicit.
A downloadable file opens and matches the described format.
Method, transformations, limitations, and missing-data rules are published.
License or reuse terms are clear.
Dataset markup describes the page and file without exaggerating representativeness.

Copyable artifact

Dataset with CSV distribution JSON-LD

Replace placeholders with your study facts and keep the same information visible on the canonical dataset page.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "@id": "https://www.example.com/research/example-study/#dataset",
  "name": "Example Study Dataset, August 2026",
  "description": "A 50 to 5000 character description of what the dataset measures.",
  "url": "https://www.example.com/research/example-study/",
  "creator": { "@id": "https://www.example.com/#organization" },
  "datePublished": "2026-08-13",
  "dateModified": "2026-08-13",
  "temporalCoverage": "2026-08-01/2026-08-07",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "variableMeasured": ["prompt", "engine", "brand mentioned", "cited URL"],
  "distribution": {
    "@type": "DataDownload",
    "encodingFormat": "text/csv",
    "contentUrl": "https://www.example.com/data/example-study.csv"
  }
}
</script>

Validation

How you know it is ready

01The CSV, landing page, and JSON-LD agree on dates, name, variables, and scope.
02The download works without authentication and contains no unintended sensitive data.
03A reviewer can reproduce the headline calculation from the published method and file.

Do not overclaim

Valid Dataset markup can improve dataset discovery but does not guarantee a search feature or citation. Publishing weak, undocumented, or sensitive data creates risk rather than authority.

Questions

What teams usually ask

Does a table count as a dataset?

Google's guidance lists tables and CSV files among content that can qualify. The key is a clear canonical description, useful metadata, and an actual organized collection of data.

Do I need to publish raw model responses?

Not always. Rights, privacy, product terms, and readability matter. Publish enough structured observations and method detail to support the stated claims, and label any summary file accurately.

Should an updated study replace the old file?

Prefer explicit versions or a clear change log for material updates. Readers should be able to tell which data produced which claim.

Sources reviewed

Dataset schema and download template