Welcome to the Data Anonymisation Course

This website is work in progress. Please come back later in 2026 :)

Explain purpose of tutorial and conditions (quantiative data in social sciences)

Tutorial Overview

This self-paced tutorial is intended to take X hours to complete.

The tutorial is split into the following sections:

  1. FOUNDATIONS OF DATA PROTECTION talks about data protection basics in ethics and law, mechanisms of data protection in research, and foundational terms.
  2. DATA ANONYMIZATION PROCESS walks you through the process of anonymizing your research data based on example data.
  3. BALANCING DATA PROTECTION AND OPENNESS presents methods for aligning your data protection and open science interests.
  4. ANONYMIZATION WORKFLOW closes this tutorial by summarizing the learned workflow.

What You’ll Learn

By the end of this tutorial, you will be able to:

  • Understand key concepts in the world of privacy (e.g., anonymization, differential privacy)

  • Classify data in relevant categories for data protection (e.g., personal data, sensitive data)

  • Apply anonymization techniques using R in a coherent workflow

  • Make informed decisions when balancing the risks and utility of the anonymized data

What You Will NOT Learn

  • anonymizing neuroimaging metadata (recommendations here)
  • insert other areas that we will not tackle and link to resoruces here

Prerequisites

  • link other tutorials?

  • RStudio, installed software

Tutorial Navigation

Callout boxes

  • further reading

  • attention/warnings

  • exercise solutions

TipRecommended Software

This tutorial assumes you have the following software installed:

  • SOME SOFTWARE + LINK HERE
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