="http://www.w3.org/2000/svg" viewBox="0 0 512 512">

Case Study 10.5: We Count—Open Data and Equity

Researchers sharing datasets and visualizations should also take accessibility into account in the materials they share. While this chapter doesn’t delve into specifics, some resources regarding R and spreadsheet accessibility are included in the resources section.


Generally speaking, data that is well-formed and well-described should be more accessible to everybody, including people with disabilities. Standardizing data, providing accessible file formats, and following best practices for particular environments are all key. See the Data Curation Network for some of these best practices.


We Count suggests other questions for researchers to consider when collecting data. They point out four ways in which people with disabilities are often excluded when it comes to the data ecosystem. These include:


  • Exclusion from data sets: People with disabilities are not included in studies, and study results are, therefore, skewed toward ableist perspectives.


  • Bias in data systems: AI and machine-learning tools and platforms normalize non-disabled experience.


  • Inaccessible data science and visualization tools: People with disabilities are unable to access datasets and, therefore, have difficulty using data and participating in data-driven fields.


  • Data abuse and misuse: Data use that subjects the disability community to increased discrimination.


We Count is inviting anyone to join in understanding how data can be collected and curated more ethically. You can participate by submitting an inclusion challenge and raising these conversations with faculty and student researchers.


Creative Commons License
Case Study 10.5: We Count—Open Data and Equity by Talea Anderson is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

Share This Book