Overcoming data siloes in cultural heritage crime research: a consolidated OSINT-derived dataset on art, antiquities, and the trade in cultural goods

The current landscape for provenance researchers, cultural heritage crime analysts, and law enforcement working in the culture sector is characterised by the siloing of data across dozens of databases, resulting in fragmented and incomplete resources that must be manually correlated and validated to provide insights into cultural heritage crime. The European Union-funded Research, Intelligence, and Technology for Heritage and Market Security (RITHMS) project is developing a platform to assist law enforcement agencies across Europe in tackling the illicit trafficking of cultural goods by aggregating open, specialised, and police data from a range of sources. This article outlines one of the initial phases of data collection, which has developed 30 tailored web scrapers for the collection of data from existing databases of stolen, missing, protected, and unprovenanced cultural goods. This has resulted in the largest known non-police dataset of these and associated data objects addressing the real-world challenge of data siloes in heritage crime and provenance research. This article details the multi-step process of data collection and pre-processing that has produced the novel consolidated dataset. The mechanism for knowledge discovery developed during this project has immediate applications and has resulted in actionable intelligence for the investigation of cultural goods crimes, highlighting the value of consolidated data as a resource. This research also offers a cursory analysis of the resulting dataset, demonstrating how mining of this resource can enable new scientific insights and offer promising opportunities for intelligence-led policing of cultural heritage crimes.