New Practical Guide on Research Data Anonymisation and Pseudonymisation Published by DCC Data Stewards

Researchers frequently encounter challenges in minimising personally identifiable information, also known as personal data, in their research. What is personal data in research? How to collect only the minimum amount of personal data necessary for your research? How to minimize the risk of identification, including selecting suitable anonymisation and pseudonymisation techniques? What practical considerations are important? These are the questions covered in the new Research Data Anonymisation and Pseudonymisation Practical Guide, recently published by a team of UT Data Stewards. The guide introduces key principles underlying anonymisation and pseudonymisation and provides actionable steps for implementing them across a wide range of data types, including textual, numerical, visual, audio, and geospatial data. It also provides practical examples to illustrate how various techniques can impact data usability, enabling researchers to make informed decisions when applying the techniques.

In addition, the guide provides ready-to-use code for anonymisation and pseudonymisation, offering examples in Microsoft Excel, R, and Python, ensuring that researchers with diverse technical backgrounds can apply these techniques efficiently. This guide will continue to expand, with other data types, additional techniques, and examples to be added in the future. 

For more information visit the guide Research Data Pseudonymisation and Anonymisation: A Practical Guide.