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More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand. Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain data quality and inflexible pricing interface. To resolve them, potential solutions could be to launch data demonstrations and release free sampling data to reduce the uncertainty about data quality, and to charge based on the volume of data for enabling flexible pricing. However, their economic benefits are still not clear. In this paper, we design the optimal data selling mechanisms for IoT data exchange, and derive the following results. First, whether to deploy data demonstrations and how much free sampling data to release depend on the extent of data consumers inaccuracy perceptions for data quality. We found that data vendors have no incentive to conduct these strategies if data consumers extremely overestimate data quality. Second, although flexible data pricing mechanisms provide convenience for real-time and streaming IoT data exchange, it brings less economic benefits to data vendors compared with fixed pricing schemes. We evaluate the optimal selling mechanisms on a real-world Taxi GPS dataset. Evaluation results verify the insights derived from our theoretical analysis.

Worker reliability estimation is fundamental for crowdsensing applications. This paper studies a robust feedback rating approach to estimate worker reliability. In this approach, the requester provides a feedback rating to reflect the quality of the sensor data submitted by each worker. The aggregation of each worker's historical feedback ratings serves as a reliability estimate. The challenges are: (1) Feedback ratings are subjected to noise or bias; (2) Workers cognitive bias in task selection leads to higher sensor data quality variations. We develop a mathematical model to quantify the degree of rating bias and degree of cognitive bias. We also derive sufficient conditions, under which the aggregate rating is asymptotically accurate in estimating worker reliability, via stochastic approximation techniques. These conditions identify a class of asymptotically accurate rating aggregation rules for crowdsensing applications. We further derive the minimum number of ratings needed to guarantee a given reliability estimation accuracy, via martingale theory. Via extensive experiments: (1) We reveal fundamental understandings on how various factor such as rating bias influence the minimum number of ratings needed to achieve certain accuracy; (2) Our feedback rating approach improves air quality index estimation accuracy by as high as 50% over the URP algorithm.

Cultural heritage institutions are exploring Semantic Web technologies to publish and enrich their catalogues. Several initiatives, such as Labs, are based on the creative and innovative reuse of the materials published by cultural heritage institutions. In this way, quality has become a crucial aspect to identify and reuse a dataset for research. In this article, we propose a methodology to create Shape Expressions definitions in order to validate LOD datasets published by libraries. The methodology was then applied to four use cases based on datasets published by relevant institutions. It intends to encourage institutions to use ShEx to validate LOD datasets as well as to promote the reuse of LOD, made openly available by libraries. This research has been funded by the AETHER-UA (PID2020-112540RB-C43) Project from the Spanish Ministry of Science and Innovation.

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines.

With the advent of the Internet of Things (IoT), crowdsensing, as a new emerging application of the IoT that employs ubiquitous mobile users with smartphones for data collection and processing, has further deepened our knowledge. However, the problems of the current crowdsensing systems regarding system security, user privacy and user payment (UP) raise serious privacy and security concerns, which affect participants’ adoption of the system. Blockchain technology allows for nondeterministic multiple parties to interact with each other anonymously in a network that is not fully trusted. In this paper, we propose a new decentralized crowdsensing system, called . Unlike other blockchain-based crowdsensing systems, adopts a hybrid blockchain architecture and uses smart contracts to achieve location privacy preservation and ensure data quality while improving the system performance. Furthermore, to optimize task assignments to mobile users, we propose a location privacy-preserving optimization mechanism (LPPOM) and the approach of consistency optimization (ACO) to achieve a tradeoff between user privacy and system performance. The extensive experimental results show that the proposed outperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.

As the adoption and diversity of threat intelligence solutions continue to grow, questions about their effectiveness, particularly in regards to the quality of the data they provide, remain unanswered. Several studies have highlighted data quality issues as one of the most common barriers to effective threat intelligence sharing. Furthermore, research and practice lack a common understanding of the expected quality of threat intelligence. To investigate these issues, our research utilised a systematic literature review followed by a modified Delphi study that involved 30 threat intelligence experts in Europe. We identified a set of threat intelligence quality dimensions along with revised definitions for threat data , information, and intelligence .

Increasingly, marketing and consumer researchers rely on online data collection services. While actively-managed data collection services directly assist with the sampling process, minimally-managed data collection services, such as Amazon’s Mechanical Turk (MTurk), leave researchers solely responsible for recruiting, screening, cleaning, and evaluating responses. The research reported here proposes a 2 x 2 framework based on sampling goal and methodology for screening and evaluating the quality of online samples. By sampling goals, screeners can be categorized as selection, which involves matching the sample with the targeted population; or as accuracy, which involves ensuring that participants are appropriately attentive. By methodology, screeners can be categorized as direct, which screens individual responses; and as statistical, which provides quantitative signals of low quality. Multiple screeners for each of the four categories are compared across three MTurk samples, two actively-managed data collection samples (Qualtrics and Dynata), and a student sample. The results suggest the need for screening in every online sample, particularly for the MTurk samples, with the fewest supplier-provided filters. Recommendations are provided for researchers and journal reviewers that provide greater transparency with respect to sample practices.

The use of synchrophasor data is recurrent in electrical power systems and has an increasing demand in distribution networks. The quality of this data can directly influence later stages related to decision-making. In this sense, this work presents the impact analysis of synchrophasor data quality on the event classification in distribution networks. We propose a controlled environment, simulating 37 200 events and including errors in the data with different levels, distributions, and biases, enabling evaluations according to the data’s precision and accuracy. Additionally, we present 2 160 classification results involving six machine learning methods, three different groups with 4, 8, and 62 events (classes) per group, and several characteristics of phasor errors. In addition to comparative analyzes, the research scope makes it possible to map the influence of multiple sources of errors, disclosing the data quality relevance and how it affects the event recognition in the distribution systems – a subject underexplored in the recent literature.

To address gaps in coverage and quality of nutrition services, AliveThrive (AT) strengthened the delivery of maternal nutrition interventions through government antenatal care (ANC) services in Uttar Pradesh, India. The impact evaluation of the AT interventions compared intensive ANC (I-ANC) with standard ANC (S-ANC) areas and found modest impacts on micronutrient supplementation, dietary diversity, and weight-gain monitoring.This study examined intervention-specific program impact pathways (PIPs) and identified reasons for limited impacts of the AT maternal nutrition intervention package.We used mixed methods: frontline worker (FLW) surveys (n = ∼500), counseling observations (n = 407), and qualitative in-depth interviews with FLWs, supervisors, and block-level staff (n = 59). We assessed 7 PIP domains: training and materials, knowledge, supportive supervision, supply chains, data use, service delivery, and counseling.Exposure to training improved in both I-ANC and S-ANC areas with more job aids used in I-ANC compared with S-ANC (90% compared with 70%), but gaps remained for training content and refresher trainings. FLWs' knowledge improvement was higher in I-ANC than S-ANC (22-36 percentage points), but knowledge of micronutrient supplement benefits and recommended foods was insufficient (50%). Most FLWs received supervision (90%), but supportive supervision was limited by staff vacancies and competing work priorities. Supplies of iron-folic acid and calcium supplements were low in both areas (30-50% stock-outs). Use of monitoring data during review meetings was higher in I-ANC than S-ANC (52% compared with 36%) but was constrained by time, understanding, and data quality. Service provision improved in both I-ANC and S-ANC areas, but counseling on supplement benefits and weight-gain monitoring was low (30-40%).Systems-strengthening efforts improved maternal nutrition interventions in ANC, but gaps remained. Taking an intervention-specific perspective to the PIP analysis in this package of services was critical to understand how common and specific barriers influenced overall program impact.

On the Ground • High-quality rangeland data are critical to supporting adaptive management. However, concrete, cost-saving steps to ensure data quality are often poorly defined and understood. • Data quality is more than data management. Ensuring data quality requires 1) clear communication among team members; 2) appropriate sample design; 3) training of data collectors, data managers, and data users; 4) observer and sensor calibration; and 5) active data management. Quality assurance and quality control are ongoing processes to help rangeland managers and scientists identify, prevent, and correct errors in past, current, and future monitoring data. • We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by 1) describing the data ecosystem, 2) creating a data quality plan, 3) identifying roles and responsibilities, 4) building data collection and data management workflows, 5) training and calibrating data collectors, 6) detecting and correcting errors, and 7) describing sources of variability. • Iteratively improving rangeland data quality is a key part of adaptive monitoring and rangeland data collection. All members of the rangeland community are invited to participate in ensuring rangeland data quality.

The crowdsourcing scheme emerges as a promising solution for data-based application in the Internet of Things (IoT) network by dividing the large-scale complex sensing tasks into simple micro sensing tasks. AI technologies have been widely applied in crowdsourcing applications for IoT security. However, there are still several issues to be stressed. There exist malicious participants aiming at gaining unjust payment. A trust evaluation mechanism can effectively filter the attackers. Nevertheless, the existing trust evaluation mechanisms cannot preclude co-cheating and overlook the conflicts with the privacy exposure of participants. A novel Privacy-protected Intelligent Crowdsourcing scheme based on Reinforcement Learning (PICRL) is proposed. PICRL optimizes the utility of the system considering the data amount, data quality, and costs at the same time. The main innovations of the PICRL are as follows. First, the quality is guaranteed by an effective trust evaluation mechanism. The proposed trust evaluation consists of three parts: privacy trust, crowd trust, and hybrid active trust. Second, the trust evaluation can effectively prevent co-cheating and provide personal privacy exposure choice for the participants. Third, PICRL maximizes the utility based on evaluated trust utilizing the reinforcement method Q-learning without knowing the specific sensing model, which aims at maximizing the cumulated reward by the selection of state–action pair. The effectiveness of the proposed PICRL is verified by the extensive simulation experiments.

Incentive mechanism design and quality control are two key challenges in data crowdsourcing, because of the need for recruitment of crowd users and their limited capabilities. Without considering users' social influences, existing mechanisms often result in low efficiency in terms of the platform's cost. In this paper, we exploit social influences among users as incentives to motivate users' participation, in order to reduce the cost of recruiting users. Based on social influences, we design incentive mechanisms with the goal of achieving high quality of crowdsourced data and low cost of incentivizing users' participation. Specifically, we consider three scenarios. In the full information scenario, we design task assignment and user recruitment mechanisms to optimize the data quality while reducing the incentive cost. In the partial information scenario, users' qualities and costs are unknown. We exploit the correlation between tasks to overcome the information asymmetry, for both cases of opportunistic crowdsourcing and participatory crowdsourcing. Further, in the dynamic social influence scenario, we investigate the dynamics of users' social influences and design extra rewards for users to make full use of the social influence and achieve maximum cost saving. We evaluate the incentive mechanisms using numerical results, which demonstrate their effectiveness.

The goal of this article is to clarify and unravel the complexity and challenges of improving teaching quality, based on measuring teaching quality and feeding back the results to teachers. We analyze different conceptualizations of teaching quality, and synthesize a framework for conceptualizing teaching quality in educational practice. We explain the pros and cons of four types of instruments for measuring teaching quality. Next, we scrutinize the requirements of effectively feeding back teaching quality data and the requirements for effective actions to improve teaching quality. We conclude with implications for improving the consequential validity of teaching quality measurements.

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. Many quality-aware incentive mechanisms for mobile crowdsensing have been proposed. However, most of them measure the data quality by one single metric from a specific perspective. Moreover, they usually use the real-time quality, which cannot provide sufficient incentive for the workers with long-term high quality. In this paper, we refine the generalized data quality into the fine-grained ability requirement. We present a mobile crowdsensing system to achieve the fine-grained quality control, and formulate the problem of maximizing the social cost such that the fine-grained ability requirement of all sensing tasks can be satisfied. To stimulate the workers with long-term high quality, we design two ability reputation systems to assess workers’ fine-grained abilities online. The incentive mechanism based on the reverse auction and fine-grained ability reputation system is proposed. We design a greedy algorithm to select the winners and determine the payment based on the bids and fine-grained ability reputation of workers. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, whitewashing proof, and guaranteed approximation. Moreover, the designed mechanisms show prominent advantage in terms of social cost and average ability achievement ratio.

As biometric technology is increasingly deployed, it will be common to replace parts of operational systems with newer designs. The cost and inconvenience of reacquiring enrolled users when a new vendor solution is incorporated makes this approach difficult and many applications will require to deal with information from different sources regularly. These interoperability problems can dramatically affect the performance of biometric systems and thus, they need to be overcome. Here, we describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion algorithms when biometric signals were generated using several biometric devices in mismatched conditions. Quality measures from the raw biometric data are available to allow system adjustment to changing quality conditions due to device changes. This system adjustment is referred to as quality-based conditional processing. The proposed fusion approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. This allows the easy and efficient combination of matching scores from different devices assuming low dependence among modalities. In our system, quality information is used to switch between different system modules depending on the data source (the sensor in our case) and to reject channels with low quality data during the fusion. We compare our fusion approach to a set of rule-based fusion schemes over normalized scores. Results show that the proposed approach outperforms all the rule-based fusion schemes. We also show that with the quality-based channel rejection scheme, an overall improvement of 25% in the equal error rate is obtained., Published at IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

PurposeData quality is a major challenge in data management. For organizations, the cleanliness of data is a significant problem that affects many business activities. Errors in data occur for different reasons, such as violation of business rules. However, because of the huge amount of data, manual cleaning alone is infeasible. Methods are required to repair and clean the dirty data through automatic detection, which are data quality issues to address. The purpose of this work is to extend the density-based data cleaning approach using conditional functional dependencies to achieve better data repair.Design/methodology/approachA set of conditional functional dependencies is introduced as an input to the density-based data cleaning algorithm. The algorithm repairs inconsistent data using this set.FindingsThis new approach was evaluated through experiments on real-world as well as synthetic datasets. The repair quality was determined using the F-measure. The results showed that the quality and scalability of the density-based data cleaning approach improved when conditional functional dependencies were introduced.Originality/valueConditional functional dependencies capture semantic errors among data values. This work demonstrates that the density-based data cleaning approach can be improved in terms of repairing inconsistent data by using conditional functional dependencies.

There is growing interest in the environmental safety of ultraviolet (UV) filters found in cosmetic and personal care products (CPCPs). The CPCP industry is assessing appropriate environmental risk assessment (ERA) methods to conduct robust environmental safety assessments for these ingredients. Relevant and reliable data are needed for ERA, particularly when the assessment is supporting regulatory decision-making. In the present study, we apply a data evaluation approach to incorporate nonstandard toxicity data into the ERA process through an expanded range of reliability scores over commonly used approaches (e.g., Klimisch scores). The method employs an upfront screening followed by a data quality assessment based largely on the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) approach. The method was applied in a coral case study in which UV filter toxicity data was evaluated to identify data points potentially suitable for higher tier and/or regulatory ERA. This is an optimal case study because there are no standard coral toxicity test methods, and UV filter bans are being enacted based on findings reported in the current peer-reviewed data set. Eight studies comprising nine assays were identified; four of the assays did not pass the initial screening assessment. None of the remaining five assays received a high enough reliability score (Rn ) to be considered of decision-making quality (i.e., R1 or R2). Four assays were suitable for a preliminary ERA (i.e., R3 or R4), and one assay was not reliable (i.e., R6). These results highlight a need for higher quality coral toxicity studies, potentially through the development of standard test protocols, to generate reliable toxicity endpoints. These data can then be used for ERA to inform environmental protection and sustainability decision-making. Environ Toxicol Chem 2021;40:3441-3464. © 2021 Personal Care Products Council. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Smartphones became ubiquitous and are used by so many people, at least to know the driving directions to their destination. Smartphones come with rich embedded sensors (e.g., GPS, accelerometer, and camera) as well as built-in radios (e.g., Bluetooth, Wi-Fi, and Cellular), which both enable users to gather data and distribute it among people at any time or location. These features have come up with the mobile crowdsensing (MCS) development which can be used in a wide range of applications. In this paper, we introduce a complete mobile crowdsensing framework for road surface condition detection. Various modules have been addressed such as task management, data fusion, reputation scoring, incentive awarding, security and privacy, as well as discussing popular techniques and algorithms utilized in the proposed MCS framework modules. A prototype of the crowd sensing application is designed which is related to our framework. The proposed framework considers the data quality and trustiness between the users and the server as well.

Recent advances in implementing machine learning (ML) methods in hydrology have given rise to a new, data-driven approach to hydrological modeling. Comparison of physically based and ML approaches has shown that ML methods can achieve a similar accuracy to the physically based ones and outperform them when describing nonlinear relationships. Global ML models have been already successfully applied for modeling hydrological phenomena such as discharge.However, a major problem related to large-scale water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both, the availability, and quality of open water quality data would increase the potential to implement predictive modeling on a global scale.We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources:Canadian Environmental Sustainability Indicators program (CESI) Global Freshwater Quality Database (GEMStat) GLObal RIver Chemistry database (GLORICH) European Environment Agency (Waterbase) USGS Water Quality Portal (WQP) The resulting dataset contains a total of over 14 million observations for 41 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset. We report on developing a harmonized schema and reproducible workflow that can be adapted to integrate and harmonize further data sources. We conclude our study with a call for action to extend this dataset and hope that the provided reproducible method of data integration and metadata provenance shall lead as an example.

Os sistemas de saúde têm sido marcados por eventos que ameaçam a sustentabilidade do setor a longo prazo. O interesse sobre a evolução dos custos assistenciais se tornou um fenômeno global, devido ao setor não apresentar um indicador que reflita a real inflação da saúde e que possa auxiliar na análise e gerenciamento das variações de preços das despesas assistenciais. Este trabalho apresenta um índice para mensurar a variação do preço da despesa assistencial de uma cooperativa de saúde. O índice foi estimado por meio de números-índices. No desenvolvimento utilizou-se a metodologia aplicada aos Índices de Preços ao Produtor Amplo de Disponibilidade Interna (IPA-DI), da Fundação Getulio Vargas. Dentre os resultados encontrados destacam-se a valorização do ato médico, o impacto da legislação, a incorporação de novas tecnologias e a inflação dos serviços e insumos da saúde. O modelo evidencia importantes desafios técnicos e operacionais: o modelo organizacional, a qualificação na estrutura e no processo, melhorias de qualidade dos dados, automatização da metodologia, a criação de relatórios e painéis gerenciais e a disseminação do conhecimento.

Objective The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH). Materials and Methods We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process. Results The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully. Discussion The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups. Conclusion Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.

Crowdsourcing is popular for large-scale data collection and labeling, but a major challenge is on detecting low-quality submissions. Recent studies have demonstrated that behavioral features of workers are highly correlated with data quality and can be useful in quality control. However, these studies primarily leveraged coarsely extracted behavioral features, and did not further explore quality control at the fine-grained level, i.e., the annotation unit level. In this paper, we investigate the feasibility and benefits of using fine-grained behavioral features, which are the behavioral features finely extracted from a worker's individual interactions with each single unit in a subtask, for quality control in crowdsourcing. We design and implement a framework named Fine-grained Behavior-based Quality Control (FBQC) that specifically extracts fine-grained behavioral features to provide three quality control mechanisms: (1) quality prediction for objective tasks, (2) suspicious behavior detection for subjective tasks, and (3) unsupervised worker categorization. Using the FBQC framework, we conduct two real-world crowdsourcing experiments and demonstrate that using fine-grained behavioral features is feasible and beneficial in all three quality control mechanisms. Our work provides clues and implications for helping job requesters or crowdsourcing platforms to further achieve better quality control.

China is now facing trade-offs between economic growth and the resulting environmental and social burdens. In the 14th Five-Year Plan and the 2035 long-term goal, China put forward in-depth sustainable development strategies. To examine sustainability, a metric reflecting social and environmental costs is necessary, such as the Genuine Progress Indicator (GPI). However, data quality has become an important factor affecting the credibility of the included items in GPI. Hence, we conducted the first China Genuine Progress indicator Survey (CGPiS) at the micro-household level containing 42,000 samples. Based on this, we improved the GPI framework covering 23 subitems and measured China's national-level and provincial-level GPI in 2016. The results indicate that a region with a higher GDP usually has a higher GPI, while the gaps of sustainable economic welfare measured by GPI between regions are larger than the GDP gaps. Environmental accounts in all measured provinces are negative. Housework and volunteer service in the social account have great potential contributions to the future growth of GPI. Our findings are essential for driving changes in China's economic governance to realize sustainable development and promoting the GPI application in pilot cities.

Objective To explore primary care administrators’ perceptions of provincially mandated quality improvement plans, and barriers to and facilitators of using quality improvement plans as tools for improving the quality of primary care. Design Qualitative descriptive study using semistructured interviews. Setting Ontario. Participants Eleven primary care administrators (ie, executive directors, director of clinical services, office administrators) at 7 family health teams and 4 community health centres. Methods All interviews were audiotaped and transcribed verbatim. Data were analyzed deductively to generate a framework based on a conceptual model of structural, organizational, individual, and innovation-related factors that influence the success of improvement initiatives and, inductively, to generate additional themes. Main findings Provincially mandated quality improvement plans seem to have raised awareness of and provided an overall focus on quality improvement, and have contributed to primary care organizations implementing initiatives to address quality gaps. Four factors that have contributed to the success of quality improvement plans relate to attributes of the quality improvement plans (adaptability and compatibility) and contextual factors (leadership and organizational culture). However, participants expressed that the use of quality improvement plans have not yet led to substantial improvements in the quality of primary care in Ontario, which may be owing to several challenges: poor data quality, lack of staff and physician engagement and buy-in, and lack of resources to support measurement and quality improvement. Conclusion Awareness of and focused attention on the need for high-quality patient care may have increased, but participants expressed that substantial improvements in quality care have yet to be achieved in Ontario. The lack of perceived improvements is likely the result of multifaceted and complex challenges primary care organizations face when trying to improve patient care. To effect positive change, organization- and health system–level efforts are needed to improve measurement capabilities, improve staff and physician engagement, and increase capacity for quality improvement among organizations.

In this study, there are results that contradict the theory which states that the higher the level of training and motivation, the employee's performance should increase. In this study, training had no effect and motivation had no positive effect. This study aims to determine the effect of training and work motivation on employee performance in Kopiloka 3.0. The population in this study were all employees at Kopiloka 3.0, which amounted to 15 people with a sample of 15 people using saturated sampling. The data analysis technique in this study used SPSS 24 software, which consisted of data quality test, classical assumption test, multiple regression analysis, and hypothesis testing. By using multiple regression analysis, the results of the study show that training has no effect on employee performance. Motivation has a significant effect on employee performance. Simultaneously (together) the independent variables of training and motivation have an effect on employee performance.

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.

Species of the genus Acacia have been introduced worldwide and have negative environmental and socio-economic impacts in many introduced regions. This study makes use of environmental and socio-economic impact classification schemes for alien taxa (EICAT and SEICAT) to assess the impacts that 33 acacias introduced to South Africa have at a global scale. The aims of this study were: (1) to compare the EICAT impact categories which are based on literature with expert assessments; (2) to determine which environmental and socio-economic mechanisms underlie acacia impacts; and (3) to test if certain habitats experience higher impact magnitudes than others. Most acacias had a massive impact when evaluated by experts, while the impact was mostly major for EICAT. This could be due to the use of different definitions for the impact categories. It might also show that the data available is insufficient or inadequate in showing that such high impacts exist. Competition was found to be the most often recorded mechanism underlying acacia environmental impacts and was one of the mechanisms with the largest impact magnitude. Grassland, shrubland, marine-coastal and forest habitats were found to be impacted by acacias equally as much. Using impact scoring schemes as an evidence-based and transparent approach to determine the impact status of alien species provides a more robust method to aid management prioritisation and risk assessment than expert opinion alone. However, data quality and availability could limit their effectiveness, especially for less well studied species.

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.

Increased electronic filing, or e-filing, of nonprofit data in the United States and abroad quickens public availability, eliminates dependence on third-party proprietary datasets, and broadens access to information offering insight into previously neglected topics. On the other hand, increasingly open data require researchers to independently ensure data quality, understand limitations, and consider how data availability shapes research questions. As electronic filing becomes mandatory in the United States and more widespread elsewhere, this research considers how expanded e-filer data impact nonprofit research approaches and opportunities while assessing the generalizability and limits of existing e-filed data in the United States. While the data suggest increasing compliance with e-filing, analyses show that older nonprofits with greater capacity and sophisticated financial characteristics are historically more likely to e-file. Further, particular subsectors are more likely to e-file than others. Therefore, using existing e-filed IRS Form 990s for panel data analysis presents some generalizability concerns, although this will improve over time. Moving forward, e-filing and machine-readable access requirements bode well for both quantitative and qualitative nonprofit research, while the United States’ experience provides helpful lessons for other research settings.

The purpose of this study was to determine the impact of the implementation of the policy of the hopeful family program on improving the welfare of the poor in Tellu Limpoe District, as well as the factors that influence the impact of the policy of the hopeful family program on the improvement of the welfare of the poor. in the District. Tellu Limpoe. The population in this case is 1049 people, while the sampling used is non-probability sampling using quantitative descriptive research and data collection includes observations, interviews, documentation, questionnaires, and literature studies. The data analysis method used is data quality test, regression method and hypothesis testing and sakalalikert using a frequency table. The results showed the implementation of the policy of the family program of hope to improve the welfare of the poor in Tellu Limpoe District, Sidenreng Rappang Regency with a value of 77.8% in the good category, while factors that influence the implementation of the policy of the family program of hope to improve the welfare of the poor in Tellu Limpoe District 60.4%. The implementation of the Hope Family Program policy has a significant effect of 60.4% on the welfare of the poor in Tellu Limpoe District, Sidenreng Rappang Regency.

Data quality measures commonly used to assess the credibility and correctness of spatial data are generally lacking in volunteered geographic information (VGI) environments particularly in developing countries, prevalent with an absence of reference data. This challenge is further exacerbated by the dynamic nature of VGI which makes traditional approaches to data quality inappropriate to establish the quality of VGI. As a result, these have motivated researchers in the geographic community to investigate alternative measures of determining the quality of contributed datasets in VGI environments. These innovative, community-based collaborative measures use trust matrices, reputation, and data provenance to establish the quality of contributed datasets in VGI platforms. This study proposes the use of human computation (HC) methods, through data provenance and aggregation algorithms to establish the semantic accuracy of VGI from multiple contributors. Thus, semantic accuracy determination here is concerned with the aggregation of multiple records of the same entity from different contributors to define its final label and improve semantic rigor. The methodology was implemented successfully in a case study in Botswana to demonstrate its effectiveness in establishing the semantic accuracy of VGI.

Malaysia has a well-established civil registration system dating back to the 1960s. Birth registration is virtually complete at the national level. However, the quality of civil registration in some remote areas is doubtful, as evidenced by the abnormally low birth and death rates in several districts. This study focuses on identifying districts in Sabah, where the reporting of births seems problematic. Sabah is the least developed state in Malaysia, and it is sparsely populated, despite being the second most populous state in the country. Sabah’s civil registration lags behind the other states, to the extent that birth and death statistics were not reported for the state in the vital statistics report for the period 2000 to 2009. A 2016 study found that death registration is almost 100%, except for Sabah (88%). The plausible reasons behind the ultra-low birth rate reported in several remote districts in Sabah include misreporting of the place of occurrence as the usual residence, delayed reporting, non-coverage, ignorance of the law, inaccessibility, presence of a large number of migrants, miscommunication, and errors in data entry. The under-reporting of births may have serious consequences, such as misallocation of resources and deprivation of services to those affected. In line with the transformative promise of “leaving no one behind,” the Sustainable Development Goals urge all countries to strive to improve data quality for planning; this includes complete birth registration for creating effective development programs to reach target groups more effectively.

Objective Among the different methods to profile the genome-wide patterns of transcription factor binding and histone modifications in cells and tissues, CUT&RUN has emerged as a more efficient approach that allows for a higher signal-to-noise ratio using fewer number of cells compared to ChIP-seq. The results from CUT&RUN and other related sequence enrichment assays requires comprehensive quality control (QC) and comparative analysis of data quality across replicates. While several computational tools currently exist for read mapping and analysis, a systematic reporting of data quality is lacking. Our aims were to (1) compare methods for using frozen versus fresh cells for CUT&RUN and (2) to develop an easy-to-use pipeline for assessing data quality. Results We compared a workflow for CUT&RUN with fresh and frozen samples, and present an R package called ssvQC for quality control and comparison of data quality derived from CUT&RUN and other enrichment-based sequence data. Using ssvQC, we evaluate results from different CUT&RUN protocols for transcription factors and histone modifications from fresh and frozen tissue samples. Overall, this process facilitates evaluation of data quality across datasets and permits inspection of peak calling analysis, replicate analysis of different data types. The package ssvQC is readily available at https://github.com/FrietzeLabUVM/ssvQC.

We examine key aspects of data quality for online behavioral research between selected platforms (Amazon Mechanical Turk, CloudResearch, and Prolific) and panels (Qualtrics and Dynata). To identify the key aspects of data quality, we first engaged with the behavioral research community to discover which aspects are most critical to researchers and found that these include attention, comprehension, honesty, and reliability. We then explored differences in these data quality aspects in two studies (N ~ 4000), with or without data quality filters (approval ratings). We found considerable differences between the sites, especially in comprehension, attention, and dishonesty. In Study 1 (without filters), we found that only Prolific provided high data quality on all measures. In Study 2 (with filters), we found high data quality among CloudResearch and Prolific. MTurk showed alarmingly low data quality even with data quality filters. We also found that while reputation (approval rating) did not predict data quality, frequency and purpose of usage did, especially on MTurk: the lowest data quality came from MTurk participants who report using the site as their main source of income but spend few hours on it per week. We provide a framework for future investigation into the ever-changing nature of data quality in online research, and how the evolving set of platforms and panels performs on these key aspects.

Intensive repeated momentary diary techniques, such as the experience sampling method (ESM), place considerable burden on study participants by repeatedly prompting them to fill in a questionnaire several times throughout the day. The objective of this study was to investigate the level of disturbance reported by participants and predictors thereof using a pooled dataset of seven ESM studies including 1319 healthy participants and individuals with different mental health conditions. All studies used a design of 10 prompts per day over the course of 4 to 6 days. Participants reported no disturbance at 37% of the measurement occasions and the overall level of disturbance was relatively low. Also, clinical samples did not report significantly different levels of disturbance compared to the healthy participants. However, higher levels of disturbance were found for later study days, in the morning, and on weekends. Also, younger participants reported feeling more disturbed by the prompts. In terms of momentary states, higher levels of activity, activity stress, and tiredness were associated with higher disturbance. Also, participants experienced more disturbance when in company compared to when alone, and the level of disturbance increased when in company with less familiar people. Our findings suggest that studies using high-frequency ESM protocols can be used in mental health research, but researchers should be aware of the possible disturbance caused by their research design.

The current research takes a dyadic approach to study early word learning and focuses on toddlers’ (N = 20, age: 17–23 months) information seeking and parents’ information providing behaviors and the ways the two are coupled in real-time parent-child interactions. Using head-mounted eye tracking, the current study provides the first detailed comparison of children’s and their parents’ behavioral and attentional patterns in two free-play contexts: one with novel objects with to-be-learned names (Learning condition) and the other with familiar objects with known names (Play condition). Children and parents in the Learning condition modified their individual and joint behaviors when encountering novel objects with to-be-learned names, which created clearer signals that reduced referential ambiguity and potentially facilitated word learning.

Process mining provides analytical tools and methods which can distil insights about process behaviour from big process-related data. Yet challenges relating to the impact of poor quality data on event logs, the input to process mining analyses, remain. Despite researchers raising concerns about event log data quality, event log preparation is, in practice, generally handled mechanistically, focusing on fixing symptoms rather than on uncovering the root causes of event log data quality issues. To address this, we introduce the Odigos (Greek for “guide”) framework. Based on semiotics and Peircean abductive reasoning, the Odigos framework facilitates an informed way of dealing with data quality issues in event logs. Odigos supports both prognostic (foreshadowing potential quality issues) and diagnostic (identifying root causes of discovered quality issues) approaches. We examine in depth how the framework supports a detailed root-cause analysis of a well-known collection of event log imperfection patterns.

Purpose Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM. Design/methodology/approach The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting. Findings The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM. Research limitations/implications A drawback of this study is the sample size. With a sample size of 51, the power of the model to draw conclusions is weakened. Practical implications In today’s decision environments, decision-makers deal with extraordinary volumes of data the quality of which is unknown or not determinable with any certainty. The interface and its evaluation offer insights into the design of decision support systems that reduce the complexity of the data and facilitate the integration of DQM into the decision tasks. Originality/value To the best of my knowledge, this is the only research to build and evaluate a decision-support prototype for structured decision-making with DQM.

A key challenge in experimental social science research is the incentivisation of subjects such that they take the tasks presented to them seriously and answer honestly. If subject responses can be evaluated against an objective baseline, a standard way of incentivising participants is by rewarding them monetarily as a function of their performance. However, the subject area of experimental philosophy is such that this mode of incentivisation is not applicable as participant responses cannot easily be scored along a true-false spectrum by the experimenters. We claim that experimental philosophers’ neglect of and claims of unimportance about incentivisation mechanisms in their surveys and experiments has plausibly led to poorer data quality and worse conclusions drawn overall, potentially threatening the research programme of experimental philosophy in the long run. As a solution to this, we propose the adoption of the Bayesian Truth Serum, an incentive-compatible mechanism used in economics and marketing, designed for eliciting honest responding in subjective data designs by rewarding participant answers that are surprisingly common. We argue that the Bayesian Truth Serum (i) adequately addresses the issue of incentive compatibility in subjective data research designs and (ii) that it should be applied to the vast majority of research in experimental philosophy. Further, we (iii) provide an empirical application of the method, demonstrating its qualified impact on the distribution of answers on a number of standard experimental philosophy items and outline guidance for researchers aiming to apply this mechanism in future research by specifying the additional costs and design steps involved.

PurposeCurrently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.Design/methodology/approachBased on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.FindingsThe results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.Originality/valueUnlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

Virtual metrology (VM) can convert sampling inspection with metrology delay into real‐time and online total inspection. This chapter describes the needs for physical metrology operation in semiconductor manufacturing and the necessity of reducing physical metrology with VM for decreasing manufacturing cost. Besides the fundamental functions of VM on‐line conjecturing and VM quality evaluation, the automatic virtual metrology (AVM) server also possesses the functions of automatic data quality evaluation and automatic model refreshing. The Manufacturing Execution System (MES) is a shop floor control system which includes manual and/or automatic labor, production reporting, on‐line inquiries, and links to tasks that take place on the production floor. A novel manufacturing system with AVM capabilities shall at least consist of a MES with an embedded AVM module and Run‐to‐Run controllers. An AVM system should be able to provide all kinds of VM applications and to realize automatic and fab‐wide VM deployment.

The Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved.New COVID-19 cases are supplied by the "Trajectoire de santé publique" information system, geolocated to civic addresses and then aggregated by day and dissemination area. To target community-level clusters, cases identified as residents of congregate living settings are excluded from the cluster detection analysis. Detection is performed using the space-time scan statistic and Poisson statistical model, and implemented in the SaTScan software. Information on detected clusters is disseminated daily via an online interactive mapping interface.The number of clusters detected tracked with the number of new cases. Slightly more than 4900 statistically significant (p ≤ 0.01) space-time clusters were detected over 14 health regions from May to October 2020. The Montréal region was the most affected.Considering the objective of timely cluster detection, the use of near-real-time health surveillance data of varying quality over time and by region constitutes an acceptable compromise between timeliness and data quality. This tool serves to supplement the epidemiologic investigations carried out by regional public health authorities for purposes of COVID-19 management and prevention.RéSUMé: OBJECTIFS: L’Institut national de santé publique du Québec (INSPQ) a reçu le mandat d’élaborer un outil de détection automatisé des agrégats spatio-temporels des cas de COVID-19 afin d’aider les régions à détecter des situations nécessitant des interventions de santé publique. Cet article vise à décrire la méthodologie utilisée et à présenter les principaux résultats obtenus. MéTHODE: Les nouveaux cas de COVID-19 proviennent du Système d’information Trajectoire de santé publique, ils sont géolocalisés à l’adresse civique, puis agrégés par jour et par aire de diffusion. Afin d’isoler la transmission communautaire, les cas identifiés comme résidents d’un milieu de vie fermé sont exclus des analyses de détection des agrégats. La méthode de détection est la statistique de balayage spatio-temporel basée sur le modèle de Poisson et implantée dans le logiciel SaTScan . Les agrégats détectés sont diffusés quotidiennement dans une interface cartographique web interactive. RéSULTATS: Le nombre d’agrégats détectés varie en fonction du nombre de nouveaux cas. Un peu plus de 4 900 agrégats spatio-temporels statistiquement significatifs (p ≤ 0,01) ont été détectés dans 14 régions sociosanitaires entre mai et octobre 2020. La région de Montréal est la plus touchée. CONCLUSION: Considérant l’objectif d’une détection d’agrégats en temps opportun, l’utilisation des données de vigie sanitaire en temps quasi réel, dont la qualité est variable dans le temps et selon les régions, constitue un compromis acceptable. Il s’agit d’un outil complémentaire aux enquêtes épidémiologiques menées par les autorités régionales de santé publique dans la gestion et la prévention des impacts populationnels de la COVID-19.

PurposeThis study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.Design/methodology/approachA fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.FindingsThe effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.Research limitations/implicationsThe outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.Originality/valueBig data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

Volunteered geographic information (VGI) is a large and up-to-date data source, which is available to the public easily. VGI enables public participation by leveraging scientific research and ease of data entry. OpenStreetMap (OSM) is one of the most popular examples of a volunteered geographic information project that has turned into a major source as the substitution of geographical data over the past years. Because OSM data quality is very variable, its various aspects have been investigated in previous studies. Assessing the reliability of volunteered geographic data has been a topic of interest to researchers during recent years. The objective of this study is to introduce an approach for computing the reliability indicators as tools for assessing OSM data quality using the history of data. To prepare the data required, the history file of the OSM dataset for the study region was extracted. Then, historical data cleaning was carried out by identifying and eliminating the outlier data. Afterward, the reliability indicator was calculated through criteria such as the number of versions, the number of user participation, temporal variations, and the number of tags editing. In the last step, to evaluate the proposed approach in calculating the reliability indicator, the level of feature reliability was compared with their spatial accuracy calculated via feature matching of the OSM and official data. The results show among 7478 reliability features of the OSM, approximately 4338 feature involves reliability of above 50%, containing 58.01% of the datasets, and among 5659 matching features of the OSM dataset, 4429 features have a similarity percentage of above 70%, containing 78.26% of the datasets. Increasing the number of versions, the number of users, and the temporal variation range of a route increased the reliability. Contrastingly, tag editing reduces reliability. Moreover, according to the results, a correlation coefficient of 0.695 between the reliability and spatial accuracy indicates a direct relationship of reliability in the quality of the OSM dataset.

Quality measurement across healthcare is undertaken with a goal of improving care and outcomes for patients; however, the relationship between quality measurement and patient outcomes remains largely untested, particularly in inpatient behavioral health. Using a retrospective quantitative design, we assessed 142 behavioral health organizations' quality data submitted to the Hospital-Based Inpatient Psychiatric Services and Inpatient Psychiatric Facility Quality Reporting programs from 2017 to 2018 and tested relationships between compliance on 16 quality measures and symptom improvement on patient self-report outcomes (SROs) at the facility level. Performance on many quality measures was negatively skewed (at least four have almost no room for improvement on average), and there was high interrelatedness between most quality measures. Nine of the assessed measures correlated with patient SROs but not in clear groupings. Findings indicate that an underlying organizational construct may be driving compliance rates on quality measures, but the measures are not linked to treatment outcomes as expected. We encourage an expansion of the current framework of behavioral health quality measurement beyond process and organization and suggest the addition of patient outcomes such as SROs as quality measures to directly assess patient improvement.

This study assessed the quality of Global Navigation Satellite System (GNSS) signal during professional football match play in different stadia with the application of a novel Data Quality Control ...