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?
Powered by GitBook
Page cover
On this page
  1. Welcome to the User-centric Open Data Publishing Toolkit

Case Studies and Research

We refer to case studies and research throughout this toolkit, but for ease of reference they are all indexed here.

Data governance processes and tools with Natural England (public sector publisher)

Improving impact with Open Active (Sport England initiative to standardise data about opportunities for physical exercise and promote innovation)

Leading industry wide data infrastructure with the Stream project (utility providers).

Impact case studies with various publishers

Talking Datasets: Understanding dataset sensemaking behaviour

Dataset Reuse: Towards translating principles to practice

The trials and tribulations of working with structured data

A survey of data quality requirements that matter for machine learning datasets

When Citizens Meet Data: An Investigation of citizen engagement in data-driven innovation programmes

Developing Microeconomic Indicators Through Open Data Reuse The Future of Open Data Portals

Data Prompting: Assessing the Potential of Conversational Generative AI for Dataset Discovery

Croissant: A Metadata Format for ML Ready Datasets

The MediaFutures Toolkit

PreviousToolsNextWhat Do We Mean By User-Centric?

Last updated 8 months ago