Data

The data section links to the most relevant datasets, for the benefit of users who want to access data but may not know where to find the tables they need. 

Provide links to up to five datasets that users are most likely to be interested in. 

When deciding which five to pick, consider:

  • what tasks users who are interested in this topic might want to achieve, and which data might help them
  • which datasets are the most integral to your analysis
  • which datasets get the most downloads

For users wanting to access all the datasets used in the bulletin, there is a prominent link at the top right of the page.


You can also include a sentence at the end of the Data section to help users access the datasets. Use the following standard text to link to the related data page:

“View all data used in this statistical bulletin on the Related data page.”

How to format links

Each link should include:

  • the title of the dataset
  • the type of content (dataset or time series) and release date
  • up to 30 words describing the dataset – use the dataset’s summary if appropriate

Migration data
Provisional Long-Term International Migration estimates
Dataset | Released 29 November 2018
Migration flows to and from the UK, quarterly tables and charts

If linking to a dataset containing many tables, you can mention the most useful tables alongside the type of content (for example, “Dataset A02 | Released 29 November 2018”).

This section should not provide additional commentary or caveats about the data. Extra content is likely to slow down users’ journeys to the data they need.

Glossary

Provide short, understandable definitions for users who may not be familiar with the terms or concepts described on the page.

Briefly explain technical terms in your analysis using plain English. Use the Glossary to give a little more detail about terms and concepts without interrupting your analysis. You do not need to hyperlink each term used in the analysis to the Glossary; the Glossary is in the table of contents and is used consistently across publications.

Include at least three and up to six terms, with a description of up to 50 words for each. List the terms in alphabetical order. 

Choose the most relevant terms that are commonly used in the analysis. Include a clear definition of any complex terms to prevent misunderstanding.

If you have a more detailed list of definitions on another page, link to it at the bottom of this section.

Employment
Employment measures the number of people in paid work and differs from the number of jobs because some people have more than one job. The employment rate is the proportion of people aged from 16 to 64 years who are in paid work.

The Guide to labour market statistics contains a glossary of other terms used in this bulletin.

The content design team can help you choose which terms to include and help you to write short, clear definitions – email content.design@ons.gov.uk.

Measuring the data

Finding out more about how the data are gathered and measured is an important task for a smaller group of our audience, namely technical users. Providing short and clear explanations of our methodology helps us establish trust and credibility in our statistics. 

In fewer than 200 words, provide explanations of the data used in your bulletin, covering the following:

  • where we get the data from, for example, the survey or source
  • how we measure the data, for example, sample size and collection method
  • time periods covered, for example, the time periods and geography covered by the data

See the October 2019 Labour market overview bulletin for an example.

If necessary, summarise upcoming changes to the bulletin or methodology. You can also include information on why data revisions may occur; revised data and figures should be included in their own analysis section. If it is not possible to provide enough detail on these topics in this section, link to relevant articles.

Use a clear subheading for each topic to direct users to this information. For example, “Data source”, “Collection method”, “Coverage” and “Upcoming changes”.

For users who need more detail, include links to the Quality and Methodology Information (QMI) report and the user guide under a subheading called “Quality”. 

When linking to the QMI, using the following standard text: 

“More quality and methodology information on strengths, limitations, appropriate uses, and how the data were created is available in the (name of release) QMI.”

This section should include only text; it should provide summary information and so should not need any charts or tables.

Avoid including formulas or lengthy technical explanations in this section. This can be overwhelming for users and the information is available in the QMI and can be easily linked to.

Strengths and limitations

Users need to know about the quality of our statistics. Use this section to explain how the data should or should not be used and to ensure users do not misunderstand the data.

This can include:

  • guidance on the accuracy and reliability of the data, for example, sampling error, time lag between collection and publication
  • information about uncertainty or comparability with other sources or countries’ data
  • whether this bulletin contains National Statistics or Experimental Statistics
  • further information or detail on the warnings used in the bulletin

See the October 2019 Labour market overview bulletin for an example.

If the bulletin has National Statistics status, use the standard wording and format to detail when it was last assessed and subsequent improvements to the statistics.

This section should only include brief information critical to the way people use the data and so should not include any tables or charts. If necessary, link to relevant articles so that users can find out more about the quality of the data.

Break up this section with subheadings to make it easier to read and signpost users to the most relevant content.

If your QMI has a section that clearly explains the strengths and limitations of your data, link directly to it.

If the strengths and limitations from your QMI are crucial to interpreting the data and can be summarised clearly in a few short bullet points, include them under subheadings of “Strengths” and “Limitations”.

Next section: Related links