User-centric data publishing (Alpha)
  • User-centric data publishing
    • Introduction
    • Who is this toolkit for?
    • How to use this toolkit
    • Dictionary of data terms
  • Contents
  • Section 1. Building the foundation for open data
    • A basic introduction to open data
    • Understanding our rights to access data
    • Open data maturity
      • Resources: Open data maturity
    • Ethics and transparency
  • Section 2. Planning for impactful open data initiatives
    • An introduction to the Data Landscape Playbook
    • Play one: Explore the problem and how data can address it
    • Play two: Map the data ecosystem
    • Play three: Assess the policy, regulatory and ethical context
    • Play four: Assess the existing data infrastructure
    • Play five: Plan for impact when designing your data initiative
  • Section 3. A user-centric approach to publishing
    • Understanding the user journey
      • The use case
      • Understanding different user needs
      • Targeting intended audiences
    • Engaging effectively with data users
      • Two-way communication and feedback
      • From data to story
    • Building communities around open data use
      • Characteristics of an open data user community
        • Purpose
        • Community enabler(s)
        • Collaborative method
        • Other observations
      • The current landscape of open data user communities
      • Engagement with data communities
    • Resources: User-centric publishing
  • Section 4: Publishing guidance for new data publishers
    • Open data licensing
    • The FAIR principles of data access
      • FAIR data assessment tools
    • Data quality and metadata
      • Tools and frameworks to help you assess open data quality
    • Publishing data on the web
  • Thank you
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  1. Section 4: Publishing guidance for new data publishers

The FAIR principles of data access

An open data licence is not enough to guarantee that open data will be useful. To meet our users' expectations of the data we publish, the data must be fit for purpose

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Last updated 2 years ago

The are a set of four guiding principles that help organisations make sure that the data they publish openly meets the expected quality standards. Under the FAIR principles, data must be Findable, Accessible, Interoperable and Reusable.

There are several sources that describe what each of the FAIR principles means, but one of the more accessible examples that we’ve found comes from the UK’s Geospatial Commission, in their (figure 4):

  • Findable: Findability is simply the capability of something to be found. In recent years this means data discovery through a web search engine. Data should be easily and repeatedly discovered by experts and non-experts. Having found data, it should be easy to determine whether the data is appropriate for their intended use. To support this any data should always have good discovery metadata and be published somewhere that it can be easily searched.

  • Accessible: Accessibility refers to reducing the hurdles between discovering and obtaining the data. To be accessible, the terms for using the data should be clear and simple, with registration and authentication to data services minimised. Data should be designed with accessibility in mind. Wherever possible data should be turned into products that are tailored to common usage and delivery methods, using open and widely-used standards and formats.

  • Interoperable: Interoperability is the ability of different geospatial systems to accurately and unambiguously exchange data. Data needs to be designed to support interoperability, which means some consideration must be given to potential systems that may want to consume the data that is published. To enable interoperability, geospatial data should be available in multiple standardised open formats. These formats should contain information to support the transformation of the data between common datums, projections and systems. Data should also contain, where appropriate, persistent, globally unique identifiers (for example Unique Property Reference Number (UPRN)) at the record or feature level.

  • Reusable: Reusable is the ability for value to be derived from using the data. In practical terms this means supplying information that supports the Findable, Accessible and Interoperability aspects of the data. Usability information is typically documented as part of the dataset metadata. Metadata should be written such that both a specialist and non-specialist user can understand whether the data is suitable for their purpose. This includes clear advice on whether there are any constraints on the data’s suitability for common applications outside its primary intended use and what appropriate mitigations may be applied.

FAIR data principles
assessment of the UK’s national geospatial data assets