Skip to content
Go to file
Cannot retrieve contributors at this time
151 lines (102 sloc) 6.65 KB

Open Data Engagement Checklist

A series of best practices and tips when it comes to achieving engagement and impact in Open Data programmes.

This checklist is primarily concerned with improving the user experience of open data or open data initiatives. If you find these are broad strokes, detailed guidelines and examples can be found all over the web, such as this one published by ODILeeds.


Credits and further reading

Best Practices

Make it Findable

When you publish your data, help people to find it.

  • Have a clear Open Data landing page on your website
  • If your data is elsewhere, link to it from your landing page
  • Make sure that your website search and navigation point to it
  • If you list it on external sites, such as, make sure that you update inbound links
  • Consider using a platform such as CKAN or DKAN to index it
  • See also the Access and usability tips below.

Clear licencing

Tells us what we can do with the data.

  • Explain or link to an explanation of Open Data
  • Accompany datasets with a summary and a link to the full version of the licence
  • Make sure the licence sets out the conditions of attribution, reuse, redistribution and commercialisation

Access and usability

Help finding and accessing the right data.

  • Explain conditions of access to data e.g. any signup requirements, API keys, rate limits etc.
  • Document the method of retrieving data e.g. API documentation
  • Allow users to search datasets based on metadata
  • Allow users to download extracts from datasets
  • Give datasets a unique identifier
  • Provide previews of datasets
  • Make the catalogue of datasets with metadata available as a dataset
  • State when more recent versions of a dataset are available and provide a link to the latest vesrion

Be demand driven

Build your data and data publishing programme around the needs of others.

  • Take account of all the communities that use or rely on your data
  • Find out the needs and resources of your data users
  • Be open to suggestions and requests to publish new data
  • Structure your publication choices, support, and tools around the your communities' needs and demands

Provide quality metadata

Metadata also helps usability. Simple bits of information like creation dates can help prospective users understand whether data is suitable.

  • Include at a title and a description for each dataset
  • Provide meta-data including information about frequency of updates, data formats, and standards adopted
  • Indicate chosen date formats
  • Provide a creation date for each dataset
  • Provide a last-updated date for each dataset
  • Report when a dataset has links to other sources
  • Provide metadata in a structured format
  • Indicate the file format and version or syntax (doctype)
  • Indicate the size of downloadable files

Provide context

Beyond metadata provide the qualitative information about what your data is, where it came from, and how it can be used.

  • Provide descriptions of your datasets, e.g. what's contained, how it was gathered, and for what purpose
  • Provide information on datasets' origins, especially where they have been derived from other sources
  • Explain or link to explanations of domain specific terms
  • Explain or link to explanations of identifiers
  • Link to published vocabularies and taxonomies
  • State where datasets contain errors, uncertainties, or are incomplete
  • Where multiple versions of a dataset are available, provide a changelog
  • Make previous versions of datasets available
  • Link to work produced using the data e.g. reports, analyses, visualisations, etc.

Support conversation

Build and join up networks of people around your data.

  • Accompany datasets with at least one means of contact to the publisher/maintainer
  • Provide a way for users to keep informed of updates and new releases e.g. social media account or mailing list
  • Keep users informed about new applications of data, both your own and from third parties
  • Provide or join online spaces where users comment on or discuss datasets
  • Provide or join offline opportunities to have conversations around your data
  • Enable users to rate and/or evaluate datasets

Build capacity & skills

Build data literacy and show users to use your data.

  • Provide examples of reuse cases / applications (fictitious or real) of your data
  • Provide or link to guides for working with your data
  • Provide or suggest tools for working with your data
  • Run workshops on using your data
  • Sponsor or support community capacity building

Collaborate on data as a common resource

Open Data belongs to all, involve and work with others to make it better.

  • Write contributor guidelines
  • State requests for contribution
  • Join others in creating / improving data standards
  • Provide feedback loops so users can provide suggestions and/or corrections
  • Collaborate with the community to create new data resources (e.g. derived datasets)
  • Submit your data to projects that aggregate or crowdsource data e.g. wikipedia, wikidata, openstreetmap, etc.
  • Submit your data to relevant data portals or sign posting sites
  • Actively acknowledge others where you have used their data/services in your own project
  • Support those building tools or services that work with your data e.g. open source projects


Adapted & expanded from:

Further reading

Open Leadership Training Series - Mozilla

Open Data Maturity Model - The Open Data Institute

Data on the Web Best Practices - W3C recommendations for data publishers

Open data priorities and engagement (workshop report) - Share-PSI Network

You can’t perform that action at this time.