By means of a statistical model we study the adoption of Protestantism during the Reformation for 262 territories of the Holy Roman Empire. Our unit of analysis is a territory and the dependent variable indicates whether and when the territorial ruler adopted Protestantism. The independent variables are based on seven theoretical factors that historiographical research has identified to be important for the adoption of Protestantism, and on neighbourhood relations. We use an Event History Model to track changes in the variables over time and compute the importance of each driving factor. Our results reveal that geographic neighbourhood relations explain the adoption of Protestantism best. The more neighbourshad become protestant in the recent past, the more likely is a territory to become protestant itself. This effect is strongest for weak territories, which may point towards a strategic hesitation to adopt Protestantism in politically uncertain times.

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, nonequilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

The Digital Humanities face the problem of multiple hypothesis testing: Evermore hypotheses are tested until a desired pattern has been found. This practice is prone to mistaking random patterns for real ones. Instead, we should reduce the number of hypothesis tests to only test meaningful ones. We address this problem by using theory to generate hypotheses for statistical models. We illustrate our approach with the example of the European Reformation, where we test a theory on the role of opinion leaders for the adoption of Protestantism with a logistic regression model. Given our specific setting, including choice of data and operationalisation of variables, we do not find enough evidence to claim that opinion leaders contributed via personal visits and letters to the adoption of Protestantism. To falsify or to support a theory, it has to be tested in different settings. Our presented approach helps the Digital Humanities bridge the gap between the qualitative and quantitative camp, advance understanding of structures resulting from human activity, and increase scientific credibility.

The transmission of ideas plays a vital role in shaping society, fostering critical thinking, driving innovation, and facilitating cultural development. Previous studies have predominantly employed aggregated networks to investigate how ideas propagate through interactions and relationships among individuals. However, these approaches overlook the temporal ordering of interactions, distorting topological network measures and potentially leading to erroneous conclusions about idea transmission. To address this limitation, this study explores the transmission of ideas using time-respecting paths. A time-respecting path is defined as a sequence of nodes connected by time-consecutive edges, where the inter-edge time is constrained within specific bounds representing the minimum reaction time and maximum memory period before ideas fade away. By constructing time-respecting paths from a network of letter correspondences among 16th-century protestant reformers, this research unveils key reformers and communication patterns that significantly influenced the transmission of ideas. The findings are interpreted in the light of case studies, such as the Osiandrian controversy, which provides valuable insights into historical contexts.

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.