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Objective: Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric 'Proof of Principle' study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. Materials and methods: Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. Results: ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. Discussion and conclusion: PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures. [ABSTRACT FROM AUTHOR]

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government's responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present the DEDA framework as an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices. [ABSTRACT FROM AUTHOR]

Integrated care means doing justice to life in all its facets and supporting it where that is necessary for the client. This advocates the coordination of different forms of care and support and the integrated use of data that is available on these different facets of life. It can be challenging to provide the necessary care, in particular when different people are involved and decisions need to be based on care plans, reports, and other types of information originating from different individuals. However, there is a common understanding that technology is an important facilitator in tackling this challenge. Technology already supports care processes and complements the role of people in many ways. For instance, it takes over tasks and helps to save time, obtain new insights and simplify complexity. Most of the time, using technology comes down to creating, collecting, analyzing and applying big data, or combinations of these. In fact, all our actions - in healthcare, as in society in general - are directed by subjective and objective data, which is often hidden in the minds of people, but also increasingly collected digitally. Until now, the use of different technologies and data is too often not integrated in the organization and put away in separate silos as the use of a technical instrument in care processes is often initiated for specific reasons that do not affect the entire organization or client population. Meanwhile though, more and more opportunities are coming up to connect the different data flows that exist in healthcare, and to utilize the data both organization-transcending as for the individual client. Developments in intelligent software systems AI, amongst others, offer the possibility to rapidly collect, integrate, exchange, edit and analyze large amounts of data. This allows people to respond more proactively to incidents, gain new insights about health, provide customized care and support and improve the cooperation between individuals and disciplines. However, many longterm care organizations in The Netherlands experience these opportunities as something elusive and find it hard to take advantage of them. Though many organizations in Dutch longterm care are interested in the exploration of data-driven healthcare, a common challenge is that enthusiastic innovators within care organizations often have limited time to invest in data-driven care, and do so alone or with the support of only few colleagues. Therefore, innovators from different organizations regularly come together within networks where they share experiences and experiment. At the same time, despite the promises of data-driven healthcare, there are many dilemmas around privacy, security, access to data, risk of stigmatization and medicalization and the increasing dependence on technology, for instance. When experimenting, the dilemmas should be taken seriously and in co-creation between multiple organizations, codes of conduct on these issues can be developed step-by-step. This paper will share some experiences and lessons learned, both by individual organizations and in workshops in which they collaborated. The approach is that by creating room for experimenting, starting small and sharing experiences with other parties, 'big data' can be made small. [ABSTRACT FROM AUTHOR]

The surprising and rapid flowering of Dutch art and the Dutch art market from the late 16th century to the mid-17th century have propelled scholars to quantify the volume of production and to determine the source of its growth. However, existing studies have not explored the use of known paintings to specify and visualize the fluctuations of painting production in the Dutch Republic. Employing data mining techniques to leverage the most comprehensive datasets of Netherlandish paintings (RKD), this paper visualizes and analyzes the trend of painting production in the Northern Netherlands throughout the 17th-century. The visualizations verify the existing observations on the market saturation and industry stagnation in 1630–1640. In spite of this market condition, the growth of painting production was sustained until the 1660s. This study argues that the irrational risk-taking behavior of painters and the over-enthusiasm for painting in the public created a "social bubble" and the subsequent contraction of the production was a market correction back to a stable state. However, these risk-taking attitudes during the bubble time spurred exuberant artistic innovations that highlight the Dutch contribution to the development of art. [ABSTRACT FROM AUTHOR]

Recent crises have shown that the knowledge of the structure of input–output networks, at the firm level, is crucial when studying economic resilience from the microscopic point of view of firms that try to rewire their connections under supply and demand constraints. Unfortunately, empirical inter-firm network data are protected by confidentiality, hence rarely accessible. The available methods for network reconstruction from partial information treat all pairs of nodes as potentially interacting, thereby overestimating the rewiring capabilities of the system and the implied resilience. Here, we use two big data sets of transactions in the Netherlands to represent a large portion of the Dutch inter-firm network and document its properties. We, then, introduce a generalized maximum-entropy reconstruction method that preserves the production function of each firm in the data, i.e. the input and output flows of each node for each product type. We confirm that the new method becomes increasingly more reliable in reconstructing the empirical network as a finer product resolution is considered and can, therefore, be used as a realistic generative model of inter-firm networks with fine production constraints. Moreover, the likelihood of the model directly enumerates the number of alternative network configurations that leave each firm in its current production state, thereby estimating the reduction in the rewiring capability of the system implied by the observed input–output constraints. [ABSTRACT FROM AUTHOR]

Now and in the coming years a new European Rail Traffic Management System (ERTMS) is being deployed on a larger scale. In the system level chosen in the Netherlands (Level 2) lineside signals will disappear and the train driver will receive all information needed for safe and efficient train operation through an interface inside the train cab. Furthermore, procedures, driving skills and communication will alter. Many train drivers will thus need to be trained in driving with ERTMS. Train operating companies (TOC) have to decide on efficient and effective strategies to organise this extensive training volume and to reassure that drivers become and remain proficient after training when roll-out of ERTMS may not have been fully completed. In this paper the central questions to be answered are: How many hours/duties does a driver have to do during a certain period of time on an ERTMS track section to become proficient with ERTMS (learning period), and to stay proficient (retaining period)? The approach taken is unique for rail industry (to the authors' knowledge) and is based on big data analyses. In this study, we were able to study the collective performance of train drivers, by big data analysis while at the same time maintaining privacy through pseudonymisation of the train driver database and their individual performances, characteristics and other potential privacy sensitive data. Databases containing detailed information on all planned and accomplished train service times/positions of the last three years (2015-2017) of a large TOC were combined with personal data from HR databases (age, experience, ERTMS training history, job assessment results, etcetera). From the data, delays caused by rolling stock and train operating related issues were identified. The amount and duration of these delays were correlated with the personal data to test research hypotheses. Again, at the same time we maintained privacy of the individuals' personal data. Although the research has some limitations due to the small amount of existing ERTMS lines, and associated rolling stock, the results show some clear correlations. Thus, quantifiable directions to the minimum amount of ERTMS driving per 3 months during the first year and in the years after are presented. The results are now being translated into models for training and rostering related to the phase of ERTMS roll-out. [ABSTRACT FROM AUTHOR]

Crowdsourced online genealogies have an unprecedented potential to shed light on long-run population dynamics, if analyzed properly. We investigate whether the historical mortality dynamics of males in familinx, a popular genealogical dataset, are representative of the general population, or whether they are closer to those of an elite subpopulation in two territories. The first territory is the German Empire, with a low level of genealogical coverage relative to the total population size, while the second territory is The Netherlands, with a higher level of genealogical coverage relative to the population. We find that, for the period around the turn of the 20th century (for which benchmark national life tables are available), mortality is consistently lower and more homogeneous in familinx than in the general population. For that time period, the mortality levels in familinx resemble those of elites in the German Empire, while they are closer to those in national life tables in The Netherlands. For the period before the 19th century, the mortality levels in familinx mirror those of the elites in both territories. We identify the low coverage of the total population and the oversampling of elites in online genealogies as potential explanations for these findings. Emerging digital data may revolutionize our knowledge of historical demographic dynamics, but only if we understand their potential uses and limitations. [ABSTRACT FROM AUTHOR]

Background: Despite the increasing availability of clinical data due to the digitalisation of healthcare systems, data often remain inaccessible due to the diversity of data collection systems. In the Netherlands, Cardiology Centers of the Netherlands (CCN) introduced "one-stop shop" diagnostic clinics for patients suspected of cardiac disease by their general practitioner. All CCN clinics use the same data collection system and standardised protocol, creating a large regular care database. This database can be used to describe referral practices, evaluate risk factors for cardiovascular disease (CVD) in important patient subgroups, and develop prediction models for use in daily care.Construction and Content: The current database contains data on all patients who underwent a cardiac workup in one of the 13 CCN clinics between 2007 and February 2018 (n = 109,151, 51.9% women). Data were pseudonymised and contain information on anthropometrics, cardiac symptoms, risk factors, comorbidities, cardiovascular and family history, standard blood laboratory measurements, transthoracic echocardiography, electrocardiography in rest and during exercise, and medication use. Clinical follow-up is based on medical need and consisted of either a repeat visit at CCN (43.8%) or referral for an external procedure in a hospital (16.5%). Passive follow-up via linkage to national mortality registers is available for 95% of the database.Utility and Discussion: The CCN database provides a strong base for research into historically underrepresented patient groups due to the large number of patients and the lack of in- and exclusion criteria. It also enables the development of artificial intelligence-based decision support tools. Its contemporary nature allows for comparison of daily care with the current guidelines and protocols. Missing data is an inherent limitation, as the cardiologist could deviate from standardised protocols when clinically indicated.Conclusion: The CCN database offers the opportunity to conduct research in a unique population referred from the general practitioner to the cardiologist for diagnostic workup. This, in combination with its large size, the representation of historically underrepresented patient groups and contemporary nature makes it a valuable tool for expanding our knowledge of cardiovascular diseases.Trial Registration: Not applicable. [ABSTRACT FROM AUTHOR]

Sandy coasts are constantly changing environments governed by complex, interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and to support analysis of geomorphological deformation processes. This novel technique delivers 3-D representations of the coast at hourly temporal and centimetre spatial resolution and allows us to observe small-scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatiotemporal data set is needed. To enable automated data mining, we extract time series of surface elevation and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well-known clustering algorithms (k -means clustering, agglomerative clustering and density-based spatial clustering of applications with noise; DBSCAN), apply them on the set of time series and identify areas that undergo similar evolution during 1 month. We test if these algorithms fulfil our criteria for suitable clustering on our exemplary data set. The three clustering methods are applied to time series over 30 d extracted from a data set of daily scans covering about 2 km of coast in Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer, is used to evaluate the performance of the algorithms against a ground truth. The k -means algorithm and agglomerative clustering deliver similar clusters, and both allow us to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The level of detail found with these algorithms depends on the choice of the number of clusters k. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused, for example, by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatiotemporal data set for predominant deformation patterns with the associated regions where they occur. [ABSTRACT FROM AUTHOR]

Environmental exposures are increasingly investigated as possible drivers of health behaviours and disease outcomes. So-called exposome studies that aim to identify and better understand the effects of exposures on behaviours and disease risk across the life course require high-quality environmental exposure data. The Netherlands has a great variety of environmental data available, including high spatial and often temporal resolution information on urban infrastructure, physico-chemical exposures, presence and availability of community services, and others. Until recently, these environmental data were scattered and measured at varying spatial scales, impeding linkage to individual-level (cohort) data as they were not operationalised as personal exposures, that is, the exposure to a certain environmental characteristic specific for a person. Within the Geoscience and hEalth Cohort COnsortium (GECCO) and with support of the Global Geo Health Data Center (GGHDC), a platform has been set up in The Netherlands where environmental variables are centralised, operationalised as personal exposures, and used to enrich 23 cohort studies and provided to researchers upon request. We here present and detail a series of personal exposure data sets that are available within GECCO to date, covering personal exposures of all residents of The Netherlands (currently about 17 M) over the full land surface of the country, and discuss challenges and opportunities for its use now and in the near future. [ABSTRACT FROM AUTHOR]

We present the inter-comparison of delta slant column densities (SCDs) and vertical profiles of nitrous acid (HONO) derived from measurements of different multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments and using different inversion algorithms during the Second Cabauw Inter-comparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) in September 2016 at Cabauw, the Netherlands (51.97 ∘ N, 4.93 ∘ E). The HONO vertical profiles, vertical column densities (VCDs), and near-surface volume mixing ratios are compared between different MAX-DOAS instruments and profile inversion algorithms for the first time. Systematic and random discrepancies of the HONO results are derived from the comparisons of all data sets against their median values. Systematic discrepancies of HONO delta SCDs are observed in the range of ±0.3×1015 molec. cm -2 , which is half of the typical random discrepancy of 0.6×1015 molec. cm -2. For a typical high HONO delta SCD of 2×1015 molec. cm -2 , the relative systematic and random discrepancies are about 15 % and 30 %, respectively. The inter-comparison of HONO profiles shows that both systematic and random discrepancies of HONO VCDs and near-surface volume mixing ratios (VMRs) are mostly in the range of ∼±0.5×1014 molec. cm -2 and ∼±0.1 ppb (typically ∼20 %). Further we find that the discrepancies of the retrieved HONO profiles are dominated by discrepancies of the HONO delta SCDs. The profile retrievals only contribute to the discrepancies of the HONO profiles by ∼5 %. However, some data sets with substantially larger discrepancies than the typical values indicate that inappropriate implementations of profile inversion algorithms and configurations of radiative transfer models in the profile retrievals can also be an important uncertainty source. In addition, estimations of measurement uncertainties of HONO dSCDs, which can significantly impact profile retrievals using the optimal estimation method, need to consider not only DOAS fit errors, but also atmospheric variability, especially for an instrument with a DOAS fit error lower than ∼3×1014 molec. cm -2. The MAX-DOAS results during the CINDI-2 campaign indicate that the peak HONO levels (e.g. near-surface VMRs of ∼0.4 ppb) often appeared in the early morning and below 0.2 km. The near-surface VMRs retrieved from the MAX-DOAS observations are compared with those measured using a co-located long-path DOAS instrument. The systematic differences are smaller than 0.15 and 0.07 ppb during early morning and around noon, respectively. Since true HONO values at high altitudes are not known in the absence of real measurements, in order to evaluate the abilities of profile inversion algorithms to respond to different HONO profile shapes, we performed sensitivity studies using synthetic HONO delta SCDs simulated by a radiative transfer model with assumed HONO profiles. The tests indicate that the profile inversion algorithms based on the optimal estimation method with proper configurations can reproduce the different HONO profile shapes well. Therefore we conclude that the features of HONO accumulated near the surface derived from MAX-DOAS measurements are expected to represent the ambient HONO profiles well. [ABSTRACT FROM AUTHOR]

We study the elasticity of taxable labour income in the Netherlands. We use a large and rich data set, including both financial and demographic variables, for the period 1999–2005. The 2001 tax reform generates large exogenous variation in marginal tax rates at different segments of the income distribution. For all workers, we find an elasticity of 0.10 in the short run, 1 year after the reform, rising to 0.24 in the medium to longer run, 5 years after the reform. Furthermore, we find that the elasticity is higher for higher incomes and for women. Also, we find that the elasticity of taxable labour income is higher than the elasticity of (contractual) annual hours worked. [ABSTRACT FROM AUTHOR]

We study heterogeneity in provider altruism among self-employed mental health care providers in the Netherlands. Using a large administrative data set, we exploit the discontinuities in the compensation scheme to separate altruistically motivated providers from financially motivated providers. We find strong heterogeneity in provider altruism. Moreover, we find that more altruistically motivated providers treat mental health patients for shorter durations and report better treatment outcomes, as measured by the improvement in Global Assessment of Functioning. These results suggest that provider altruism is positively related to treatment outcomes. [ABSTRACT FROM AUTHOR]

The current energy prices do not include the environmental, social, and economic short and long-term external effects. There is a gap in the literature on the decision-making model for the energy transition. True Cost Accounting (TCA) is an accounting management model supporting the decision-making process. This study investigates the challenges and explores how big data, AI, or blockchain could ease the TCA calculation and indirectly contribute to the transition towards more sustainable energy production. The research question addressed is: How can IT help TCA applications in the energy sector in Europe? The study uses qualitative interpretive methodology and is performed in the Netherlands, Germany, and Poland. The findings indicate the technical feasibilities of a big data infrastructure to cope with TCA challenges. The study contributes to the literature by identifying the challenges in TCA application for energy production, showing the readiness potential for big data, AI, and blockchain to tackle them, revealing the need for cooperation between accounting and technical disciplines to enable the energy transition. [ABSTRACT FROM AUTHOR]

Background: An increasing number of retirees continue to work beyond retirement despite being eligible to retire. As the prevalence of chronic disease increases with age, working beyond retirement may go along with having a chronic disease. Working beyond retirement may be different for retirees with and without chronic disease. We aim to investigate whether demographic, socioeconomic and work characteristics, health and social factors predict working beyond retirement, in workers with and without a chronic disease.Methods: Employees aged 56-64 years were selected from the Study on Transitions in Employment, Ability and Motivation (N = 1125). Questionnaire data on demographic and work characteristics, health, social factors, and working beyond retirement were linked to registry data from Statistics Netherlands on socioeconomic characteristics. Separate prediction models were built for retirees with and without chronic disease using multivariate logistic regression analyses.Results: Workers without chronic disease were more likely to work beyond retirement compared to workers with chronic disease (27% vs 23%). In retirees with chronic disease, work and health factors predicted working beyond retirement, while in retirees without a chronic disease, work, health and social factors predicted working beyond retirement. In the final model for workers with chronic disease, healthcare work, better physical health, higher body height, lower physical load and no permanent contract were positively predictive of working beyond retirement. In the final model for workers without chronic disease, feeling full of life and being intensively physically active for > = 2 days per week were positively predictive of working beyond retirement; while manual labor, better recovery, and a partner who did not support working until the statutory retirement age, were negatively predictive of working beyond retirement.Conclusions: Work and health factors independently predicted working beyond retirement in workers with and without chronic disease, whereas social factors only did so among workers without chronic disease. Demographic and socioeconomic characteristics did not independently contribute to prediction of working beyond retirement in any group. As prediction of working beyond retirement was more difficult among workers with a chronic disease, future research is needed in this group. [ABSTRACT FROM AUTHOR]