The SFB will research the prosopographical networks around Emperor Maximilian I. (1459-1519) from political, cultural, literary, and artistic perspectives. It will create a prosopographical database and study the relationships of the people documented in this database. The DH sub-project builds on the current state of digital prosopography as applied to the later middle ages. The project will build the necessary data models, tools and infrastructure for the capture and dissemination of digital data created by the other sub-projects. It will use the collected data for innovative DH research in modelling of implicit knowledge and in the application of advanced network science techniques (temporal graphs, network motifs) to the study of historical networks. The “Factoid” approach to prosopography has proved useful for modelling prosopographical information represented in source material, and has been adapted to the requirements of Linked Open Data standards. These CIDOC -CRM-compliant models will be adapted to the requirements of individual sub-projects. The sub-project will research the modelling of implicit information and inferences drawn by researchers. The project will assess the viability of existing ontologies (CRMinf, PROV, FPO, and “fuzzy” ontologies). Developing robust approaches to these problems will comprise fundamental research for the sub-project. The sub-project provides overall digital support for the project partners. Data creation and analysis will be supported by a virtual research environment, built around the APIS system. Based on the research questions of the project partners, a suite of tools will be built to enable data analysis and visualisation (as timelines, network graph visualisations). The sub-project will also conduct its own innovative research on the collected data using methods derived from network science. How to adapt these methods to complex historical networks, which are marked by degrees of uncertainty and incomplete information, temporality and heterogeneous connections, are currently under-studied in social network research.