Search

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]

In democratic societies, the political process ultimately is about making value judgements. Comparative cultural policy research indicates different value orientations may be behind cultural policies of different nations. It has also indicated developments that are common, such as democratisation of culture, neoliberalisation of politics and globalisation. As of yet, such value changes have not been studied at the level of cultural policy documents analysed as text. This article proposes a methodology to study value changes behind cultural policies over time and across nations. Based in the "pragmatic sociology" of Boltanski, Thévenot, and Chiapello, the article presents a big data methodology to uncover value changes of policy documents over time, between different policy agents, and across nations using material from the UK and the Netherlands. The paper explains the methodology, discusses the implications of the preliminary outcomes and provides suggestions for further comparative policy analyses. [ABSTRACT FROM AUTHOR]

This paper describes an advanced form of pseudonymisation in a large cohort study on Parkinson's disease, called Personalized Parkinson Project (PPP). The study collects various forms of biomedical data of study participants, including data from wearable devices with multiple sensors. The participants are all from the Netherlands, but the data will be usable by research groups worldwide on the basis of a suitable data use agreement. The data are pseudonymised, as required by Europe's General Data Protection Regulation (GDPR). The form of pseudonymisation that is used in this Parkinson project is based on cryptographic techniques and is 'polymorphic': it gives each participating research group its own 'local' pseudonyms. Still, the system is globally consistent, in the sense that if one research group adds data to PPP under its own local pseudonyms, the data become available for other groups under their pseudonyms. The paper gives an overview how this works, without going into the cryptographic details. [ABSTRACT FROM AUTHOR]

Re‐use and recycling in the construction sector is essential to keep resource use in check. Data availability about the material contents of buildings is significant challenge for planning future re‐use potentials. Compiling material intensity (MI) data is time and resource intensive. Often studies end up with only a handful of datapoints. In order to adequately cover the diversity of buildings and materials found in cities, and accurately assess material stocks at detailed spatial scopes, many more MI datapoints are needed. In this work, we present a database on the material intensity of the Dutch building stock, containing 61 large‐scale demolition projects with a total of 781 datapoints, representing more than 306,000 square meters of built floor space. This dataset is representative of the types of buildings being demolished in the Netherlands. Our data were empirically sourced in collaboration with a demolition company that explicitly focuses on re‐using and recycling materials and components. The dataset includes both the structural building materials and component materials, and covers a wide range of building types, sizes, and construction years. Compared to the existing literature, this paper adds significantly more datapoints, and more detail to the different types of materials found in demolition streams. This increase in data volume is a necessary step toward enabling big data methods, such as data mining and machine learning. These methods could be used to uncover previously unrecognized patters in material stocks, or more accurately estimate material stocks in locations that have only sparse data available. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science—data science methods—we are now able to analyze data sets that can be huge, multidimensional, and unstructured and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012–2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population—outside the domain of entrepreneurs—is a rewarding subject for future research. [ABSTRACT FROM AUTHOR]

Global consumer lending has seen a compound annual growth rate (CAGR) of 4.8 per cent forecasted to 2020. The financial system is once again at risk; it is a decade since the credit crunch, yet the causes have not been solved; however, globally, the outstanding amount of credit doubled compared to the lending volume of 2008. Also, increasingly more credit decisions are being taken today. Furthermore, millennials' service expectations drive transformation from traditional lending into digital lending. The CAGR for digital lending is 53 per cent until 2025. Therefore, in this growing information age, new methods for credit risk scoring could form the central pillar for the continuity of a financial institution and the stability of the global financial system. This paper contains research from across the UK and the Netherlands: two advanced lending markets, selected because of their advancements in digital lending, to examine to what extent lenders can advance their credit decisions with individual risk assessments with artificial intelligence (AI). The research has applied supervised learning and has been performed on 133,152 mortgage and credit card customers in prime, near prime and sub-prime lending segments of three European lenders across the UK and the Netherlands during the period January 2016 to July 2017. As candidate models, we chose neural nets and random forests, as they are the most popular supervised learning methods in credit risk for their benefit of applying both structured and unstructured data. The research describes three experiments that develop the AI probability of default models and compares the model quality with the quality of the traditional applied logistic probability of default (PD) models. In all experiments, AI models performed better than the traditional models. Scalable automated credit risk solutions can therefore build on AI in their risk scoring. [ABSTRACT FROM AUTHOR]

Background: Advances in computing power have enabled the collection, linkage and processing of big data. Big data in conjunction with robust causal inference methods can be used to answer research questions regarding the mechanisms underlying an exposure–outcome relationship. The g‐formula is a flexible approach to perform causal mediation analysis that is suited for the big data context. Although this approach has many advantages, it is underused in perinatal epidemiology and didactic explanation for its implementation is still limited. Objective: The aim of this was to provide a didactic application of the mediational g‐formula by means of perinatal health inequalities research. Methods: The analytical procedure of the mediational g‐formula is illustrated by investigating whether the relationship between neighbourhood socioeconomic status (SES) and small for gestational age (SGA) is mediated by neighbourhood social environment. Data on singleton births that occurred in the Netherlands between 2010 and 2017 (n = 1,217,626) were obtained from the Netherlands Perinatal Registry and linked to sociodemographic national registry data and neighbourhood‐level data. The g‐formula settings corresponded to a hypothetical improvement in neighbourhood SES from disadvantaged to non‐disadvantaged. Results: At the population level, a hypothetical improvement in neighbourhood SES resulted in a 6.3% (95% confidence interval [CI] 5.2, 7.5) relative reduction in the proportion of SGA, that is the total effect. The total effect was decomposed into the natural direct effect (5.6%, 95% CI 5.1, 6.1) and the natural indirect effect (0.7%, 95% CI 0.6, 0.9). In terms of the magnitude of mediation, it was observed the natural indirect effect accounted for 11.4% (95% CI 9.2, 13.6) of the total effect of neighbourhood SES on SGA. Conclusions: The mediational g‐formula is a flexible approach to perform causal mediation analysis that is suited for big data contexts in perinatal health research. Its application can contribute to providing valuable insights for the development of policy and public health interventions. [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]

At the start of each research enterprise, historical sociolinguists have to deal with the key issue of the required historical data. In the present world of big data optimism, the idea may arise that the time-consuming compilation of specialised corpora is no longer needed. Can taking a shortcut still lead us to convincing results? In this article I discuss the crucial relationship between specific research questions and appropriate historical data. This methodological issue will be illustrated by concentrating on three historical-sociolinguistic research programmes, conducted at Leiden University: Letters as loot: Towards a non-standard view on the history of Dutch (2008-2013), Going Dutch: The construction of Dutch in policy, practice and discourse (2013-2018) and Pardon my French? Dutch-French language contact in the Netherlands, 1500-1900 (2018-2023). What do we learn from these large-scale projects which address different research questions and focus on different periods in the history of Dutch? The use of specific sources, handwritten material such as ego-documents, a multi-genre approach and details of corpus compilation will be discussed. The various approaches and results are considered against the background of methodological developments and current debates in historical sociolinguistics. I argue and conclude that the careful compilation of specialised corpora remains essential as a solid foundation for historical sociolinguistic research. [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]

In this article, survey, sensor, and administrative data are combined to correct for survey point estimate bias due to underreporting. The response to the Dutch Road Freight Transport Survey is linked to records from a road sensor network consisting of automated weighing stations installed on highways in the Netherlands. Capture–recapture (CRC) methods are used to estimate underreporting in the survey. Heterogeneity of the vehicles with respect to capture and recapture probabilities is modeled through logistic regression and log-linear models. Six different estimators are discussed and compared. Results show a downward bias in the survey estimate due to underreporting, whereas the CRC estimators yield larger estimates. This research is a new example of multisource statistics, a promising approach to improve the benefits of sensor data in the field of official statistics. [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]

This study aims to provide a more sensitive understanding of the dynamics and tipping points of issue attention in news media by combining the strengths of quantitative and qualitative research. The topic of this 25-year longitudinal study is the volume and the content of newspaper articles about the emerging risk of gas drilling in The Netherlands. We applied supervised machine learning (SML) because this allowed us to study changes in the quantitative use of subtopics at the detailed sentence level in a large number of articles. The study shows that the actual risk of drilling-induced seismicity gradually increased and that the volume of newspaper attention for the issue also gradually increased for two decades. The sub-topics extracted from media articles during the low media attention period, covering factual information, can be interpreted as a part of episodic frame patterns about the drilling and its consequences. However, a sudden major shift in newspaper attention can be observed in 2013. This sudden disjointed expansion in the volume of media attention on this large-scale technology occurred after a governmental authority classified the drilling-induced earthquakes as a safety issue. After the disjointed issue expansion, safety and decision making were the main subtopics linked to the thematic frames, responsibility, conflict, human interest, and morality. We conclude that SML is a promising tool for future analysis of the growing number of publicly available digitalized textual big datasets, particularly for longitudinal studies and analysis of tipping points and reframing. [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]

Exponential increases in digital data and calls for participation in human research raise questions about when and why individuals voluntarily provide personal data. We conducted 36 in-depth interviews with ex-participants, participants, and nonparticipants in a biobank to identify key factors influencing trust in centralized large-scale data repository for human research. Our findings indicated that trust depends strongly on whether such data repository benefits the public, the interests of data collectors, the characteristics of the collected data, and application of informed consent for retaining control over personal data. Concerns about the aims and range of data repository appeared to influence withdrawal of participation. Our findings underscore ethical and practical issues relating to data collection and consent procedures in human research. [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]

An exceptionally well‐exposed part of the Flin Flon Greenstone Belt (Manitoba/Saskatchewan) is used to characterize the mineral assemblage evolution associated with prehnite–pumpellyite through amphibolite facies metamorphism of basalts. Data from these rocks are combined with a large literature data set to assess the ability of current thermodynamic models to reproduce natural patterns, evaluate the use of metabasic rocks at these grades to estimate pressure–temperature (P–T) conditions of metamorphism, and to comment on the metamorphic devolatilization that occurs. At Flin Flon, five major isograds (actinolite‐in, prehnite‐ and pumpellyite‐out, hornblende‐in, oligoclase‐in, and actinolite‐out) collectively represent passage from prehnite–pumpellyite to lower amphibolite facies conditions. The evolution in mineral assemblages occurs in two narrow (~1,000 m) zones: the prehnite–pumpellyite to greenschist facies (PP‐GS) transition and greenschist to amphibolite facies (GS‐AM) transition. Across the GS‐AM transition, significant increases in the hornblende and oligoclase proportions occur at the expense of actinolite, albite, chlorite, and titanite, whereas there is little change in the proportions of epidote. The majority of this mineral transformation occurs above the oligoclase‐in isograd within the hornblende–actinolite–oligoclase zone. Comparison with thermodynamic modelling results suggests data set 5 (DS5) of Holland and Powell (1998, Journal of Metamorphic Geology, 16(3):309–343) and associated activity–composition (a–x) models is generally successful in reproducing natural observations, whereas data set 6 (DS6) (Holland & Powell, 2011, Journal of Metamorphic Geology, 29(3):333–383) and associated a–x models fail to reproduce the observed mineral isograds and compositions. When the data from Flin Flon are combined with data from the literature, two main pressure‐sensitive facies series for metabasites are revealed, based on prograde passage below or above a hornblende–albite bathograd at ~3.3 kbar: a low‐pressure 'actinolite–oligoclase type' facies series, characterized by the appearance of oligoclase before hornblende, and a moderate‐ to high‐pressure 'hornblende–albite type' facies series, characterized by the appearance of hornblende before oligoclase. Concerning the PP‐GS transition, the mineral assemblage evolution in Flin Flon suggests it occurs over a small zone (<1,000 m), in which assemblages containing true transitional assemblages (prehnite and/or pumpellyite coexisting with actinolite) are rare. This contrasts with thermodynamic modelling, using either DS5 or DS6, which predicts a wide PP‐GS transition involving the progressive appearance of epidote and actinolite and disappearance of pumpellyite and prehnite. Patterns of mineral assemblages and thermodynamic modelling suggest a useful bathograd ('CHEPPAQ bathograd'), separating prehnite–pumpellyite‐bearing assemblages at low pressures and pumpellyite–actinolite‐bearing assemblages at higher pressures, occurs at ~2.3 to 2.6 kbar. Observations from the Flin Flon sequence suggests devolatilization across the GS‐AM transition (average: ~1.8 wt% H2O) occurs over a very narrow interval within the actinolite–hornblende–oligoclase zone, associated with the loss of >75% of the total chlorite. By contrast, modelling of the GS‐AM transition zone predicts more progressive dehydration of ~2 wt% H2O over a >50°C interval. Observations from the field suggest devolatilization across the PP‐GS transition occurs over a very narrow interval given the rarity of transitional assemblages. Modelling suggests fluid release of 1.0–1.4 wt% resulting from prehnite breakdown over a ~10°C interval. This fluid may not be entirely lost from the rock package due to involvement in the hydration of igneous mineralogy across the PP‐GS transition as observed in the Flin Flon sequence. [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]

An introduction is presented in which the editor discusses articles in the issue on topics including big data application, density-based spatial clustering of applications with noise (DBSCAN) technique in retail center areas, and data sets of urban dynamics in Amsterdam, Netherlands.

This article focuses on the use of big data for urban geography research. We collect data from the location-based service Foursquare in The Netherlands and employ it to obtain a rich catalogue of restaurant locations and other urban amenities, as well as a measure of their popularity among users. Because the Foursquare data can be combined with traditional sources of socio-economic data obtained from Statistics Netherlands, we can quantify, document and characterise some of the biases inherent in these new sources of data in the context of urban applications. A detailed analysis is given as to when this type of big data is useful and when it is misleading. Although the users of Foursquare are not representative of the whole population, we argue that this inherent bias can be exploited for research about the attractiveness of urban landscapes and consumer amenities in addition to the more traditional data on urban amenities. [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]

Background: External quality assessment (EQA) programs for general chemistry tests have evolved from between laboratory comparison programs to trueness verification surveys. In the Netherlands, the implementation of such programs has reduced inter-laboratory variation for electrolytes, substrates and enzymes. This allows for national and metrological traceable reference intervals, but these are still lacking. We have initiated a national endeavor named NUMBER (Nederlandse UniforMe Beslisgrenzen En Referentie-intervallen) to set up a sustainable system for the determination of standardized reference intervals in the Netherlands. Methods: We used an evidence-based 'big-data' approach to deduce reference intervals using millions of test results from patients visiting general practitioners from clinical laboratory databases. We selected 21 medical tests which are either traceable to SI or have Joint Committee for Traceability in Laboratory Medicine (JCTLM)-listed reference materials and/or reference methods. Per laboratory, per test, outliers were excluded, data were transformed to a normal distribution (if necessary), and means and standard deviations (SDs) were calculated. Then, average means and SDs per test were calculated to generate pooled (mean±2 SD) reference intervals. Results were discussed in expert meetings. Results: Sixteen carefully selected clinical laboratories across the country provided anonymous test results (n=7,574,327). During three expert meetings, participants found consensus about calculated reference intervals for 18 tests and necessary partitioning in subcategories, based on sex, age, matrix and/or method. For two tests further evaluation of the reference interval and the study population were considered necessary. For glucose, the working group advised to adopt the clinical decision limit. Conclusions: Using a 'big-data' approach we were able to determine traceable reference intervals for 18 general chemistry tests. Nationwide implementation of these established reference intervals has the potential to improve unequivocal interpretation of test results, thereby reducing patient harm. [ABSTRACT FROM AUTHOR]

Big data have become a driver of innovation in multiple sectors, including the management of infrastructures employed for the provision of essential goods and services, such as drinking water. As technology enables new possibilities of action for infrastructure managers, it could be questioned whether the regulations in place still deal adequately with such possibilities or if certain adjustments are necessary, especially considering that infrastructure managers usually operate in highly regulated environments. This study explores the regulatory challenges of introducing smart water meters (SWM) in the Netherlands. In particular, it discusses whether the introduction of SWM will require adjusting the regulations of the sector, to deal with the new possibilities of action enabled by this technology. [ABSTRACT FROM AUTHOR]

Background: The costs of performing research are an important input in value of information (VOI) analyses but are difficult to assess.Objective: The aim of this study was to investigate the costs of research, serving two purposes: (1) estimating research costs for use in VOI analyses; and (2) developing a costing tool to support reviewers of grant proposals in assessing whether the proposed budget is realistic.Methods: For granted study proposals from the Netherlands Organization for Health Research and Development (ZonMw), type of study, potential cost drivers, proposed budget, and general characteristics were extracted. Regression analysis was conducted in an attempt to generate a 'predicted budget' for certain combinations of cost drivers, for implementation in the costing tool.Results: Of 133 drug-related research grant proposals, 74 were included for complete data extraction. Because an association between cost drivers and budgets was not confirmed, we could not generate a predicted budget based on regression analysis, but only historic reference budgets given certain study characteristics. The costing tool was designed accordingly, i.e. with given selection criteria the tool returns the range of budgets in comparable studies. This range can be used in VOI analysis to estimate whether the expected net benefit of sampling will be positive to decide upon the net value of future research.Conclusion: The absence of association between study characteristics and budgets may indicate inconsistencies in the budgeting or granting process. Nonetheless, the tool generates useful information on historical budgets, and the option to formally relate VOI to budgets. To our knowledge, this is the first attempt at creating such a tool, which can be complemented with new studies being granted, enlarging the underlying database and keeping estimates up to date. [ABSTRACT FROM AUTHOR]

Background: Previous studies comparing restraint data from different countries had to rely on randomly published data and showed wide variance in the prevalence of restraint between countries.Aim: To systematically compare datasets from four similar European countries with regard to restraint prevalence.Methods: We analysed whole country or area datasets on restraint from Wales, Ireland, Germany and the Netherlands systematically, thus excluding selection, patient and setting bias. Learning disability (LD) and forensic settings were analysed separately. Differences in proportions between countries were tested by means of Chi square, with number of admissions, admission days and catchment area as denominator and counts of restraint as numerators.Results: Full datasets were obtained allowing calculations of total admissions, total restraint numbers, numbers of patients involved and total occupied bed days. Data for Ireland is from 2012 and from 2013 for the other three countries. The percentage of patients exposed to restraint varies between 4.5 and 9.4 %. The average number of restraints per patient is stable at around 3 in all countries. Patient numbers affected by restraint per 100 occupied bed days per month vary between 0.095 and 0.200. The Netherlands have the highest use of seclusion (79 %), the longest restraint times and low use of enforced medication. Wales the lowest use of seclusion (2 %), followed by Ireland (29 %) and Germany (49 %). Events per 100 admissions per month vary between 17 and 21. Patients affected by restraint per 100 admissions per month vary between 5.4 and 7.5. LD services account for a disproportionately high number of restraint events.Conclusion: Patient related restraint data are remarkably similar between countries. Type and length of restraint still vary significantly. [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]