How to anonymise qualitative and quantitative data

Organised by

UK Data Service

Presenter

Anca Vlad and Maureen Haaker

Date

18/10/2021

Venue

The Cathie Marsh Institute
School of Social Sciences
Humanities Bridgeford St Building
University of Manchester
Manchester

Map

View in Google Maps  (M13 9PL)

Contact

Sorcha O'Callaghan

sorcha.ocallaghan@manchester.ac.uk

Description

The past two years has seen a huge change in expectations for researchers in how they manage and share participants’ information. There are new legal obligations, such as the GDPR, as well as a greater emphasis in sharing data after the completion of a research project. The process of anonymisation is an essential part to protect the identities of research participants while complying with these ethical and legal standards. Before sharing, archiving, or publishing data, you should ensure that all identifying and disclosive information is managed appropriately and redacted when necessary.

Join us as we critically discuss what is meant by “disclosive data” and strategies for anonymising quantitative and qualitative data. We will look specifically at key differences between anonymisation and pseudonymisation and discuss how to responsibly use and share data using a three-prong strategy for protecting participants’ identities. In this interactive workshop, you will have an opportunity to develop an anonymisation plan, test your knowledge on legal and ethical obligations, and discuss how to strike a balance between anonymisation (or pseudonymisation), access regulation, and consent.

This free workshop will consist of a 90-minute session, including presentations, exercises and questions.

Audience: This workshop is intended for anyone who wants to learn about data anonymisation. No preparation is necessary, however basic knowledge of research methods is assumed.

Level

Entry (no or almost no prior knowledge)

Cost

Free

Website and registration

Region

North West

Keywords

Data Quality and Data Management

Related publications and presentations

Data Quality and Data Management

Back to archive...