Data Equity Principles

In recent years, data equity—also referred to as data ethics, data justice, inclusive data, or responsible data—has gained increasing attention as a complementary perspective on the research data lifecycle. This approach not only emphasises good data practices, such as open data and adherence to the FAIR principles, but also highlights the importance of the people involved as stakeholders and the broader purpose of research data.

By focusing on the intersection of data, people, and purpose, this perspective raises important questions, such as:

• How can participants and their wider communities access and meaningfully benefit from their own data?
• How should the balance be struck between making data widely available and protecting culturally, historically, or ethnically sensitive information?
• Who should be involved in the preparation and collection of research data in order to minimise bias, ensure accountability for potential harm, and respect cultural and social norms?

The growing field of data equity brings together insights from areas such as ethics, citizen science, Indigenous data governance, and humanitarian data. Although these domains originate from different perspectives, they converge around similar principles, ideas, and recommendations.

This publication explores that convergence through a comparative analysis of more than twenty sources on data equity, aligning them within a unified framework. These sources are mapped onto the principles and key issues that have been identified by the Data Equity Framework, which is chosen as the starting point for its recent and broadly applicable synthesis of various data equity concepts. The resulting overview highlights how a wide range of different data equity approaches correspond with the framework’s key principles, but also differ in how they choose to answer these questions. 

The resulting table is particularly relevant for universities and research institutes seeking to deepen their understanding of data equity and who wish to define what data equity means in their local practice of doing research. It offers guidance on how to develop and formulate data equity ideas across key dimensions, such as accessibility, accountability, originality, expertise, relationships, responsibility, sensitivity, trust, and value.

This dataset contains three files:

• 20260518 Data Equity Principles sources.csv serves as a list of the source material that was reviewed as part of this analysis. Included are the title, category, source, year and URL of each source. 
• 20260518 Data Equity Principles.csv contains the actual analysis, in which various data equity topics of these sources have been mapped on the principles and key issues of the Data Equity Framework. 
• 20260518 Data Equity Principles.xlsx combines these two .csv files into a sortable Excel file with two tabs.