Safeguarding qualitative data with QualiAnon
Year: 2025
Type: article
Abstract: Join us for practical tips on qualitative data de-identification and strategies to tackle the unique challenges researchers face when working with human subject data. This session will feature QualiAnon, an open-source tool designed to help researchers securely anonymize and pseudonymize sensitive qualitative data with precision and flexibility. Unlike automated tools that rely on blind substitutions, QualiAnon gives you full control over how and when data is anonymized, allowing you to define replacement rules for specific datasets or variables manually. This approach ensures a higher level of accuracy and privacy by stripping identifying information without compromising the richness of the data. In this session, we’ll demonstrate how QualiAnon can streamline your data protection efforts, improve your workflow, and ensure compliance with privacy standards. Don’t miss the chance to discover how this tool can simplify the de-identification process while safeguarding sensitive information in your research! This demonstration was part of the UC Love Data Week 2025 program (https://uc-love-data-week.github.io)
Author: Curty, Renata Gonçalves
Institution: University of California, Santa Barbara
Language: English
Topic: Data Analysis and Archiving
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893150350
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