In the nineteenth century, the Netherlands quickly adopted the time ball -- a British innovation for maritime chronometer calibration -- in its main naval ports (Nieuwediep/Den Helder, Vlissingen, Hellevoetsluis) and commercial centres (Amsterdam, Rotterdam). A large sphere dropped from a mast at a fixed time, the device enabled ships to verify their chronometers against a standard, essential for accurate longitude determination and safe navigation. Its ready acceptance was eased by indigenous Dutch traditions. Rural communities had long used visual time signals like the sjouw on Terschelling island, a wicker ball raised on a mast to mark the lunch hour and milking time for farmers, and the lawei, a basket or sack used in the peat bogs of Friesland to regulate labourers' hours. The Dutch time-signal system was distinguished by its strong institutional backing from the country's Royal Navy, its Hydrographic Service and by professional astronomers. Among the latter, Frederik Kaiser was a pivotal figure, vehemently defending the system's accuracy and pioneering technical improvements. He successfully advocated for replacing the traditional falling ball with a system of rotating flaps,
We develop a foundation model using 1.2m high resolution satellite images of the Netherlands. By combining a Convolutional Neural Network and a Vision Transformer, the model captures both low- and high-frequency landscape features, such as fine textures, edges, and small objects as well as large terrain structures, elevation patterns, and land-cover distributions. Leveraging temporal data as input, the model learns from broader contextual information across time, allowing the model to exploit the temporal dependencies, such as topographic features, land-cover changes, and seasonal dynamics. These additional constraints reduce feature ambiguity, improve representation learning, and enable better generalization with fewer labeled samples. The foundation model is evaluated on multiple downstream tasks, ranging from use cases within the Netherlands to global benchmarking datasets. On the vegetation monitoring dataset of the Netherlands, the model shows clear performance improvements by incorporating temporal information instead of relying on a single time point. Despite using a smaller model and less pretraining data limited to the Netherlands, it achieves competitive results on global
Population-level dynamics of social cohesion and its underlying mechanisms remain difficult to study. In this paper, we propose a network approach to measure the evolution of social cohesion at the population scale and identify mechanisms driving the change. We use twelve annual snapshots (2010-2021) of a population-scale social network from the Netherlands linking all residents through family, household, work, school, and neighbor relations. Results show that over this period, social cohesion, quantified as average closure in the network, declines by more than 15%. We demonstrate that the decline is not due to changes in demographic composition, but to rewiring in individual ego networks. Statistical models confirm a decreasing overlap of social contexts and greater geographical mobility as drivers. Residential relocation, however, temporarily increases closure, suggesting that local cohesion-seeking behavior can yield global network fragmentation, with implications for policies related to housing, urban planning, and social integration.
Scientific workflows have become essential for orchestrating complex computational processes across distributed resources, managing large datasets, and ensuring reproducibility in modern research. The Workflows Community Summit 2025, held in Amsterdam on June 6th, 2025, convened international experts to examine emerging challenges and opportunities in this domain. Participants identified key barriers to workflow adoption, including tensions between system generality and domain-specific utility, concerns over long-term sustainability of workflow systems and services, insufficient recognition for those who develop and maintain reproducible workflows, and gaps in standardization, funding, training, and cross-disciplinary collaboration. To address these challenges, the summit proposed action lines spanning technology, policy, and community dimensions: shifting evaluation metrics from raw computational performance toward measuring genuine scientific impact; formalizing workflow patterns and community-driven benchmarks to improve transparency, reproducibility, and usability; cultivating a cohesive international workflows community that engages funding bodies and research stakeholders; an
In recent years, cities have increasingly reduced speed limits from 50 km/h to 30 km/h to enhance road safety, reduce noise pollution, and promote sustainable modes of transportation. However, achieving compliance with these new limits remains a key challenge for urban planners. This study investigates drivers' compliance with the 30 km/h speed limit in Milan and examines how street characteristics influence driving behavior. Our findings suggest that the mere introduction of lower speed limits is not sufficient to reduce driving speeds effectively, highlighting the need to understand how street design can improve speed limit adherence. To comprehend this relationship, we apply computer vision-based semantic segmentation models to Google Street View images. A large-scale analysis reveals that narrower streets and densely built environments are associated with lower speeds, whereas roads with greater visibility and larger sky views encourage faster driving. To evaluate the influence of the local context on speeding behaviour, we apply the developed methodological framework to two additional cities: Amsterdam, which, similar to Milan, is a historic European city not originally develo
Software engineering educators strive to continuously improve their courses and programs. Understanding the current state of practice of software engineering higher education can empower educators to critically assess their courses, fine-tune them by benchmarking against observed practices, and ultimately enhance their curricula. In this study, we aim to provide an encompassing analysis of higher education on software engineering by considering the higher educational offering of an entire European country, namely the Netherlands. We leverage a crowd-sourced analysis process by considering 10 Dutch universities and 207 university courses. The courses are analysed via knowledge areas adopted from the SWEBOK. The mapping process is refined via homogenisation and internal consistency improvement phases, and is followed by a data analysis phase. Given its fundamental nature, Construction and Programming is the most covered knowledge area at Bachelor level. Other knowledge areas are equally covered at Bachelor and Master level (e.g., software engineering models), while more advanced ones are almost exclusively covered at Master level. We identify three clusters of tightly coupled knowled
The COVID-19 pandemic disrupted schooling worldwide, raising concerns about widening educational inequalities. Using population-level administrative data from the Netherlands (N = 1,471,217), this study examines how socio-economic disparities in secondary school performance evolved before, during, and after pandemic-related school closures. We analyze final central examination scores for cohorts graduating between 2017 and 2023 across four educational tracks, estimating generalized linear models with interactions between pandemic exposure and key stratification variables: parental education, household income, migration background, and urbanicity. Results show that while average performance partially recovered by 2023, inequalities by parental education and migration background persisted or intensified, particularly in vocational tracks. First-generation students with a non-Western background experienced the largest sustained losses, whereas students in rural areas (previously disadvantaged) narrowed or reversed pre-pandemic performance gaps. Findings suggest that systemic shocks can both exacerbate and recalibrate inequality patterns, depending on the socio-demographic dimension an
FPGAs have transformed digital design by enabling versatile and customizable solutions that balance performance and power efficiency, yielding them essential for today's diverse computing challenges. Research in the Netherlands, both in academia and industry, plays a major role in developing new innovative FPGA solutions. This survey presents the current landscape of FPGA innovation research in the Netherlands by delving into ongoing projects, advancements, and breakthroughs in the field. Focusing on recent research outcome (within the past 5 years), we have identified five key research areas: a) FPGA architecture, b) FPGA robustness, c) data center infrastructure and high-performance computing, d) programming models and tools, and e) applications. This survey provides in-depth insights beyond a mere snapshot of the current innovation research landscape by highlighting future research directions within each key area; these insights can serve as a foundational resource to inform potential national-level investments in FPGA technology.
Since the Google Spain judgment of the Court of Justice of the European Union, Europeans have, under certain conditions, the right to have search results for their name delisted. This paper examines how the Google Spain judgment has been applied in the Netherlands. Since the Google Spain judgment, Dutch courts have decided on two cases regarding delisting requests. In both cases, the Dutch courts considered freedom of expression aspects of delisting more thoroughly than the Court of Justice. However, the effect of the Google Spain judgment on freedom of expression is difficult to assess, as search engine operators decide about most delisting requests without disclosing much about their decisions.
This paper employs agent-based modelling to explore the factors driving the high rate of tertiary education completion in the Netherlands. We examine the interplay of economic motivations, such as expected wages and financial constraints, alongside sociological and psychological influences, including peer effects, student disposition, personality, and geographic accessibility. Through simulations, we analyse the sustainability of these trends and evaluate the impact of educational policies, such as student grants and loans, on enrollment and borrowing behaviour among students from different socioeconomic backgrounds, further considering implications for the Dutch labour market.
Software has the potential to improve lives. Yet, unethical and uninformed software practices are at the root of an increasing number of ethical concerns. Despite its pervasiveness, few research has analyzed end-users perspectives on the ethical issues of the software they use. We address this gap, and investigate end-user's ethical concerns in software through 19 semi-structured interviews with residents of the Netherlands. We ask a diverse group of users about their ethical concerns when using everyday software applications. We investigate the underlying reasons for their concerns and what solutions they propose to eliminate them. We find that our participants actively worry about privacy, transparency, manipulation, safety and inappropriate content; with privacy and manipulation often being at the center of their worries. Our participants demand software solutions to improve information clarity in applications and provide more control over the user experience. They further expect larger systematic changes within software practices and government regulation.
In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.
This paper focuses on the complex dynamics of trust and distrust in digital government technologies by approaching the cancellation of machine voting in the Netherlands (2006-07). This case describes how a previously trusted system can collapse, how paradoxical the relationship between trust and distrust is, and how it interacts with adopting and managing electoral technologies. The analysis stresses how, although being a central component, technology's trustworthiness dialogues with the socio-technical context in which it is inserted, for example, underscoring the relevance of public administration in securing technological environments. Beyond these insights, the research offers broader reflections on trust and distrust in data-driven technologies, advocating for differentiated strategies for building trust versus managing distrust. Overall, this paper contributes to understanding trust dynamics in digital government technologies, with implications for policymaking and technology adoption strategies.
This study explores the effectiveness of multi-temporal satellite imagery for better functional field boundary delineation using deep learning semantic segmentation architecture on two distinct geographical and multi-scale farming systems of Netherlands and Pakistan. Multidate images of April, August and October 2022 were acquired for PlanetScope and Sentinel-2 in sub regions of Netherlands and November 2022, February and March 2023 for selected area of Dunyapur in Pakistan. For Netherlands, Basic registration crop parcels (BRP) vector layer was used as labeled training data. while self-crafted field boundary vector data were utilized for Pakistan. Four deep learning models with UNET architecture were evaluated using different combinations of multi-date images and NDVI stacks in the Netherlands subregions. A comparative analysis of IoU scores assessed the effectiveness of the proposed multi-date NDVI stack approach. These findings were then applied for transfer learning, using pre-trained models from the Netherlands on the selected area in Pakistan. Additionally, separate models were trained using self-crafted field boundary data for Pakistan, and combined models were developed usi
Background: Men and women with a migration background comprise an increasing proportion of incident HIV cases across Western Europe. Several studies indicate a substantial proportion acquire HIV post-migration. Methods: We used partial HIV consensus sequences with linked demographic and clinical data from the opt-out ATHENA cohort of people with HIV in the Netherlands to quantify population-level sources of transmission to Dutch-born and foreign-born Amsterdam men who have sex with men (MSM) between 2010-2021. We identified phylogenetically and epidemiologically possible transmission pairs in local transmission chains and interpreted these in the context of estimated infection dates, quantifying transmission dynamics between sub-populations by world region of birth. Results: We estimate the majority of Amsterdam MSM who acquired their infection locally had a Dutch-born Amsterdam MSM source (56% [53-58%]). Dutch-born MSM were the predominant source population of infections among almost all foreign-born Amsterdam MSM sub-populations. Stratifying by two-year intervals indicated shifts in transmission dynamics, with a majority of infections originating from foreign-born MSM since 2018,
In many countries financial service providers have to elicit their customers risk preferences, when offering products and services. For instance, in the Netherlands pension funds will be legally obliged to factor in their clients risk preferences when devising their investment strategies. Therefore, assessing and measuring the risk preferences of individuals is critical for the analysis of individuals' behavior and policy prescriptions. In the psychology and economics, a number of methods to elicit risk preferences have been developed using hypothetical scenarios and economic experiments. These methods of eliciting individual risk preferences are usually applied to small samples because they are expensive and the implementation can be complex and not suitable when large cohorts need to be measured. A large number of supervised learning models ranging from linear regression to support vector machines are used to predict risk preference measures using socio-economic register data such as age, gender, migration background and other demographic variables in combination with data on income, wealth, pension fund contributions, and other financial data. The employed machine learning model
This work addresses the challenge of short-term precipitation forecasting by applying Convolutional Long Short-Term Memory (ConvLSTM) neural networks to weather radar data from the Royal Netherlands Meteorological Institute (KNMI). The research exploits the combination of Convolutional Neural Networks (CNNs) layers for spatial pattern recognition and LSTM network layers for modelling temporal sequences, integrating these strengths into a ConvLSTM architecture. The model was trained and validated on weather radar data from the Netherlands. The model is an autoencoder consisting of nine layers, uniquely combining convolutional operations with LSTMs temporal processing, enabling it to capture the movement and intensity of precipitation systems. The training set comprised of sequences of radar images, with the model being tasked to predict precipitation patterns 1.5 hours ahead using the preceding data. Results indicate high accuracy in predicting the direction and intensity of precipitation movements. The findings of this study underscore the significant potential of ConvLSTM networks in meteorological forecasting, particularly in regions with complex weather patterns. It contributes
The efficiency of urban logistics is vital for economic prosperity and quality of life in cities. However, rapid urbanization poses significant challenges, such as congestion, emissions, and strained infrastructure. This paper addresses these challenges by proposing an optimal urban logistic network that integrates urban waterways and last-mile delivery in Amsterdam. The study highlights the untapped potential of inland waterways in addressing logistical challenges in the city center. The problem is formulated as a two-echelon location routing problem with time windows, and a hybrid solution approach is developed to solve it effectively. The proposed algorithm consistently outperforms existing approaches, demonstrating its effectiveness in solving existing benchmarks and newly developed instances. Through a comprehensive case study, the advantages of implementing a waterway-based distribution chain are assessed, revealing substantial cost savings (approximately 28%) and reductions in vehicle weight (about 43%) and travel distances (roughly 80%) within the city center. The incorporation of electric vehicles further contributes to environmental sustainability. Sensitivity analysis un
A field experiment was conducted in Zuidbroek, the Netherlands to compare the performance of a DAS and horizontal-geophone system for shear-wave (SV) reflection surveying. The data were subjected to processing for reflection imaging, including conversion of the geophone data to strain-rate data, to enable such a comparison on migrated-section level. Our findings indicate that DAS straight-fibre data shows a lower-frequency information content, but achieves better reflector continuity than the geophone data due to the more continuous and denser sampling with the DAS system.
In this work, we present an efficient approach for capturing sign language in 3D, introduce the 3D-LEX v1.0 dataset, and detail a method for semi-automatic annotation of phonetic properties. Our procedure integrates three motion capture techniques encompassing high-resolution 3D poses, 3D handshapes, and depth-aware facial features, and attains an average sampling rate of one sign every 10 seconds. This includes the time for presenting a sign example, performing and recording the sign, and archiving the capture. The 3D-LEX dataset includes 1,000 signs from American Sign Language and an additional 1,000 signs from the Sign Language of the Netherlands. We showcase the dataset utility by presenting a simple method for generating handshape annotations directly from 3D-LEX. We produce handshape labels for 1,000 signs from American Sign Language and evaluate the labels in a sign recognition task. The labels enhance gloss recognition accuracy by 5% over using no handshape annotations, and by 1% over expert annotations. Our motion capture data supports in-depth analysis of sign features and facilitates the generation of 2D projections from any viewpoint. The 3D-LEX collection has been alig