Understanding metadata
What metadata is and how to use it to check if a dataset is right for you.
Metadata is information about a dataset. It includes:
the title
what the dataset covers
who published it
when it was last updated
You can find metadata on a dataset page alongside the data files.
Why metadata matters
Metadata can tell you if a dataset is:
relevant – does it cover what you need
up to date – when it was last updated
trustworthy – who published it and under what licence
usable – the format, time period and geographic area
Checking metadata
To help you decide if a dataset is right for you, check the following metadata fields:
title and description – what the dataset contains in plain English
organisation – the public body that published it
published – when it was first published
last updated – when the data was most recently refreshed
next update – when the next release is expected
update frequency – how often new data is added
reference period – the time period the data covers
geographic coverage – the area the data covers
licence – what you can do with the data
contact – the email address of the data publisher
Example
If you wanted to find data on recent hospital admissions for NHS Lothian, you could check the metadata of a dataset to confirm:
who published the data
it covers the NHS Lothian health board area
it was updated this quarter
it's published under an open licence
Metadata formats
You can download metadata in machine-readable formats. This is useful for developers and analysts building integrations or analysing the data catalogue.
We publish metadata using three vocabularies in a few common serialisations. A ‘vocabulary’ defines what each field means. A ‘serialisation’ is the file format the vocabulary uses.
DCAT-AP 2 (data catalogue vocabulary application profile)
This is a vocabulary for describing public-sector open data. It's used by open data sites across Europe. We publish DCAT-AP 2 metadata in RDF/XML, JSON-LD and Turtle formats.
Find out more about the DCAT-AP 2 vocabulary on the GitHub website.
Schema.org
This is a vocabulary for describing structured data on the web. Search engines use it to read and display dataset information. We publish Schema.org metadata in JSON-LD format.
Find out more about the Schema.org vocabulary on schema.org.
Croissant
Croissant is a vocabulary for machine-learning datasets, built using Schema.org. It adds extra details that machine-learning projects need. This includes information about data structure and how datasets are split for training and evaluation.
We publish Croissant metadata in JSON-LD format.
Find out more about the Croissant vocabulary on the ML Commons website.