User Centric Open Data Publishing
CtrlK
  • Welcome to the User-centric Open Data Publishing Toolkit
    • How This Toolkit Is Organised
    • Tools
    • Case Studies and Research
  • Thinking about users
    • What Do We Mean By User-Centric?
      • Internal or External Users?
    • What Are Users Doing With Your Data?
      • What Are the Data Discovery Activities I Need to Know About?
      • What Features of Datasets Make Reuse More Likely?
      • How Will Generative AI Affect Open Data?
  • Selection
    • Why Are You Opening Data?
      • Managing Risks
      • Creating a Vision
      • Ecosystems
    • Creating an Aligned Open Data Culture
    • Engagement
    • Data Literacy
    • Responsible Data
      • Data Ethics
      • Data Justice
    • Trustworthy Data Practices
  • PREPARATION
    • Preparing Your Data
    • Metadata, Standards and Accessibility
    • Data Quality
    • Data Quality 2
    • Data Quality Issues for Machine Learning
    • Laws and Regulations
    • Licenses
  • Sustainability
  • PUBLICATION
    • Optimising Data Discovery
    • Key Publishing Decisions
    • Where to Publish?
    • Best Practices for User-Centric Data Portals
    • The FAIR principles
      • FAIR Data Assessment Tools
    • Improving Findability
    • Content Co-location
      • How Do Users Evaluate Your Data For Use?
    • Dataset Summarisation
      • Data Summarisation Template
  • EVALUATION
    • Maintaining User-Centric Publishing
    • Monitoring Data Publishing
    • Monitoring Data Use
    • Monitoring Data Impact
    • Facilitating Impact
    • Measuring Impact
  • CONTACT
    • Next Steps
    • Questions?
    • Who Wrote This?
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Using AI in data preparation