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
  2. The FAIR principles of data access

FAIR data assessment tools

There are a number of freely available tools that you can use to help you consider whether the data you are looking to publish meets the expectations of the FAIR principles

PreviousThe FAIR principles of data accessNextData quality and metadata

Last updated 2 years ago

  • is a web service to programmatically assess FAIRness of research datasets based on metrics developed by the project.

  • is a tool created by Data Archiving and Networked Services which will show you how FAIR (Findable, Accessible, Interoperable, Reusable) your dataset is and will provide you with tips to score (even) higher on FAIRness.

  • has been developed by the Australian Research Data Commons to help you assess how FAIR your research dataset is and give you practical tips on how to improve its FAIRness.

  • is a prototype tool designed by the UK Data Service to help you score the FAIRness of a particular dataset.

For guidance specifically on accessibility, check out our

F-UJI
FAIRsFAIR
SATIFYD
The FAIR Data Self Assessment Tool
The FAIR data assessment tool (FAIRdat)
‘Accessibility’ resources list