Leveraging synthetic data to improve open science practices in criminology – releasing a synthetic homicide dataset and dashboard

In criminology, the call for transparency and openness in research practices is resounding louder than ever. FAIR ( f indable, a ccessible, i nteroperable and r eusable) and open data are recognized as a necessity to enable replication of criminological research, enhance future empirical studies, foster collaboration and inform evidence-based policymaking. Yet, criminologists are faced with an intricate web of privacy regulations governing the dissemination of sensitive data about crime victims, perpetrators, witnesses or other stakeholders. In this article, we examine criminology-specific challenges posed by regulations like General Data Protection Regulation, which mandate safeguarding of personally identifiable information, often rendering original data unsuitable for open sharing. To reconcile this dilemma, we discuss the use of synthetic data generation as a tool to overcome (parts of) this challenge. Synthetic data entails creating artificial datasets that mimic the statistical characteristics of real data while obfuscating identifying information. By utilizing synthetic data, criminologists can both adhere to stringent regulations while disseminating data and facilitating open science practices. This article discusses benefits, as well as ethical considerations and limitations of synthetic data. As a proof-of-concept, we showcase the synthesis and publicly release a synthetic version of the Dutch Homicide Monitor, using an existing open-source tool for data synthetization.