projects

The Story Navigator is a computational tool that supports researcher when analyzing texts. The tool gives the researcher information to easily identify 5 main elements: who is important, what is he/she/it doing, how do the actions happen, with what purpose and where and when does the story take place. With the help of BDSi data scientists, a number of requirements are implemented to contribute to the quality of the analysis: splitting into main and subordinate clauses, identifying words that refer to the same person or object across the story and taking negations into account.

During social crises like hostage situations, terrorist attacks, and mass shootings, platforms like X are often used by the public and law enforcement to share updates and communicate. However, because this information is publicly accessible, it can also be misused by individuals such as hostage-takers. In this project, social psychologists and BDSI data analysists will collect microblogging messages shared during various social crises. The next step will involve exploring whether content analysis to identify risky messages can be automated. This research aims to enhance police preparedness for managing social crises in the digital world.

Narratives or stories in serious games can benefit learning because they facilitate motivation and knowledge construction. Narratives also present an essential element in serious games. Here narratives can vary, from providing an interesting background to playing a crucial interactive role in game completion. Moreover, different types of narratives can be distinguished. The presence and type of a narrative may be an influential factor in game-based learning. The envisioned meta-review seeks to offer a comprehensive overview of the relationship between narratives and digital game-based learning, exploring conditions for effectiveness and presenting a research agenda to address unresolved issues in this field.

This grant proposal seeks to address key challenges in the integration of reinforcement learning (RL) into app-based mental health interventions, specifically for the creation of just-in-time adaptive interventions (JITAIs). The main obstacles include developing RL algorithms that don’t require large datasets, are resource-efficient on smartphones, and can balance between trying new intervention strategies and using proven ones. The proposal outlines a collaboration with the Behavioral Data Science Incubator (BDSI) and the BMSLab to overcome these challenges by leveraging the BDSI expertise to implement cutting-edge, innovative RL algorithms suitable for JITAIs within the constraints of existing app platforms.

This project is looking to improve the way we can study which mental health treatments work best for each person. Usually, researchers use group studies to test whether treatments work, but these don't always tell us what works for each individual. In this project, a new approach is developed for testing treatments more personally and more often using mobile technology. This will help researchers and clinicians understand what treatment is best for each person and may lead to improved mental health treatments.

This project, led by a principal researcher serving as an Embassy Science Fellow in the UK, addresses the urgent need for digital open strategic autonomy (DOSA) and sustainable transformation policies. Focused on comparing Dutch and UK strategies, the study dissects six critical layers toward economic resilience in the digital economy. Employing a data-driven approach, the research involves gathering and analyzing official documents from Dutch and UK government websites, aiming to reveal differences, similarities, and dependencies behind policies. The key objectives include data scraping, descriptive statistics, visualizing layer dependencies, and comparing findings between the Netherlands and the UK.

This research uses advanced social network analysis tools to explore how students converse in collaborative games about climate change. By analyzing their communication patterns, we aim to understand how to learn and collaborate effectively within a gaming context.

Out of hospital cardiac arrests (OHCA) are a leading cause of death. In the Netherlands, an innovative app alerts registered volunteers when an OHCA occurs nearby, and dispatches them either to the victim for CPR, or to an automated external defibrillator (AED) and then to the victim. Strategic location of AEDs with respect to both OHCA victims and volunteers is vital to decrease response time. The aim of this project is to predict the response rate of volunteers given the available volunteer and alert data. Modelling this acceptance behaviour will enable more realistic assumptions on availability and number of volunteers in the AED deployment problem.

The race is on to develop smart and sustainable solutions to today’s most pressing challenges. Congestion, air pollution, rising energy costs, and public safety are just a few of the challenges developers of connected and automated vehicles (CAVs) claim to address. CAVs have captured the imagination of a whole host of stakeholders and sparked a new “gold rush” in the automotive industry.

Creating a recommender system for suggesting research topics and relevant thesis supervisors based on the professional ambition and educational path of the student.

Data visualisation refers to the techniques used to communicate data through visual objects (points, lines or bars) in graphics. Currently, data visualisation is not systematically taught in skills lines in the various BMS programs. The goal of this project is to bring together and further develop teaching materials to enable and stimulate BMS students to find and use novel data, and to visualise these data in engaging ways using R and related programming languages.

In this project, we professionalise existing BMS educational material and extend it to include online web apps. We also aim to empower BMS teachers to make their own web apps.

Last year, BDSi created YET (is your eye tracker), a capable, DIY eye tracking device, that many students have used for their own projects, since. This year, we are back with: YLab (is your Lab)

The COVID-19 pandemic has affected the public health, business activity, and life of individuals on an unprecedented scale. In March 2020, the Dutch government introduced a number of measures in order to contain and control the virus spread. Their success relied to a great extent on citizens’ collective mindset and willingness to adhere to the new rules and standards. As the government currently explores new public health surveillance technology, such as through so-called “corona apps”, it is important to understand the limits of citizens’ willingness to accept governmental interference in their daily life for the reward of the common good. Solutions that require sharing sensitive health data touch upon not only the issues of political trust and trade-offs between individual freedom and governmental authority, but also upon people’s perceptions of technology (e.g., trust in technology, transparency of data usage, privacy considerations, effectiveness, etc.). This study maps people’s attitudes in all these key areas. This way it gives an insight into people’s willingness to share potentially sensitive data with the government and private companies for the sake of the health and wellbeing of vulnerable groups in society. In particular, it helps to understand which personal and background characteristics, which type of health data, and which conditions affect this willingness. Extending the previous survey, we suggest to identify factors that determine whether people are willing to share personal information with the government for the benefit of collective public health using a Big Data approach. With an eye on policy makers considering ideas for new health surveillance technology, we aim to explore people’s perceptions of data sharing, in relation to their perceptions (e.g., trust) of relevant factors, such as the government, politicians, experts, technology, and the media.

Eye Tracking is a powerful method for understanding human attention, visual processing, problem solving and preferences. It is useful in a variety of applied research domains, such as Human Factors, Communication Science, Marketing and Education. Eye tracking is expected to gain even more significance with the emerge of VR/AR systems in research and training.

The proposed study is a large-scale quantitative sentiment analysis of the public discourse on the Covid-19 vaccination in the Netherlands based on big data, scraped from both social and traditional media. The sentiment analysis will provide insights in the legitimacy of a vaccination in terms of expectations (positive or negative), emotions behind these expectations (anger, fear, sadness, disgust, surplice, anticipation, trust, joy), and the prominence and contents of the different pillars of legitimacy (cognitive, normative, pragmatic, regulative). As Covid-19 has a great influence on society and vaccines seem to be the only solution for ending the pandemic, it is important to study the prominence and strength of negative or positive feelings regarding a potential vaccine at an early stage, whether they are included in the mainstream debate, and how the public discourse is developing in this regard. This study provides crucial information to policymakers about how to inform the general public about vaccinations for Covid-19. By insights in how the public discourse is shaped along the pillars of legitimacy, the most prominent sentiment and underlying emotions, policy makers can proactively shape their communication and information campaigns for gaining public support for a vaccine.

Covid-19 antibody tests used to identify whether an individual has been infected with SARS-CoV-2, can be employed to estimate the prevalence of this disease on a more aggregate or population level. If, for instance, such a test is taken among a number of Health Care Workers (HCW) in a well-specified region, then a sufficiently large sample suffices for a reasonably accurate estimation of the infection rate (proportion) among all HCWs in that region. The central ideas are that investigating a sample is quicker, cheaper and uses fewer scarce resources (e.g., the tests themselves) than investigating the entire population of interest. Moreover, the evolution in the rate can be monitored by taking samples distributed over time. The knowledge in the form of qualitatively sound estimates obtained may be relevant for policy decisions, for instance, in the case that a second wave is approaching, we may want to have an informed idea about the speed of the spreading of the virus as a crucial input for a second lockdown decision. Later on, we might want to have an idea about whether thresholds regarding the decline of the speed of spreading, or even the famed herd immunity, have been reached in order to end a lockdown.