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How to display questions that are part of a battery in self-administered surveys is an important decision. Battery items may be displayed in a grid in a mail survey or computer web survey, but are often displayed as individual items on mobile devices. Although past research has compared grids to item-by-item displays in computer and mobile web surveys, almost no work has compared these displays in mail surveys. Additionally, many web survey templates use wide rectangular buttons to select response options in individual items using a mobile-optimized design, different from the standard round answer space format typically used in mail surveys. In this study, we experimentally test grid versus item-by-item displays and round radio buttons versus wide rectangular buttons for battery items in a probability-based general population mixed-mode mail + web survey of adults in Nebraska. Consistent with past research, we find that item-by-item displays reduce straightlining rates compared to grid designs. We also find that respondents are less likely to select the last two response categories in the item-by-item displays than in the grid displays. Smartphone and computer web respondents have higher item nonresponse rates than mail respondents, and web respondents have lower straightlining rates than mail respondents, accounting for respondent characteristics. Reassuringly, there is no difference in data quality outcomes across radio button versus wide button formats. These findings replicate past research that item-by-item displays reduce straightlining but may shift answer categories. These findings suggest that questionnaire designers can combine round radio button answer spaces on mail surveys with wide buttons on web surveys on battery items with little difference in data quality. [ABSTRACT FROM AUTHOR]

In this research note, we address the potentials of using interviewer-observed paradata, typically collected during face-to-face-only interviews, in mixed-mode and innovative data collection methods that involve an interviewer at some stage (e.g., during the initial contact or during the interview). To this end, we first provide a systematic overview of the types and purposes of the interviewer-observed paradata most commonly collected in face-to-face interviews—contact form data, interviewer observations, and interviewer evaluations—using the methodology of evidence mapping. Based on selected studies, we illustrate the main purposes of interviewer-observed paradata we identified—including fieldwork management, propensity modeling, nonresponse bias analysis, substantive analysis, and survey data quality assessment. Based on this, we discuss the possible use of interviewer-observed paradata in mixed-mode and innovative data collection methods. We conclude with thoughts on new types of interviewer-observed paradata and the potential of combining paradata from different survey modes. [ABSTRACT FROM AUTHOR]

This paper proposes an interpretation methodology for the effect of transient process data on quality of injection molded parts. The transient process data measured in the actual processing space have been regarded as the most relevant information to manufacturing processes and product quality. However, its interpretation to pinpoint which feature in the data would affect part quality has traditionally relied on knowledge and understanding of the manufacturing process. The main objective of this method is to reduce the dependency of the transient process data analysis on process knowledge and understanding by using explainable artificial intelligence (XAI). The contribution of the 'section-wise' features in the transient process data to the quality prediction of machine learning (ML) models was investigated for the first time. The interpretation results of the effect of cavity pressure and mold surface temperature on four different quality factors represented reasonable explanations of the characteristics of the polymer materials, product geometry, and molding process. Due to the intermediate relationship of the transient process data with the user-specified process parameters and the resulting quality variables, the interpretation results can be further utilized to optimize the process and provide the optimal transient process data profile for best part quality. [ABSTRACT FROM AUTHOR]

In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.

This report describes how current methods of estimating truck traffic volumes from existing fixed roadway sensors could be improved by using tracking data collected from commercial truck fleets and other connected technology sources (e.g., onboard GPS-enabled navigation systems and smartphones supplied by third-party vendors). Using Caltrans District 1 in Northern California as an example, the study first reviews existing fixed-location data collection capabilities and highlights gaps in the ability to monitor truck movements. It then reviews emerging data sources and analyzes the analytical capabilities of StreetLight 2021, a commercial software package. The study then looks at the Sample Trip Count and uncalibrated Index values obtained from three weigh-in-motion (WIM) and twelve Traffic Census stations operated by Caltrans in District 1. The study suggests improvements to StreetLight’s “single-factor” calibration process which limits its ability to convert raw truck count data into accurate traffic volume estimates across an area, and suggests how improved truck-related calibration data can be extracted from the truck classification counts obtained from Caltrans’ WIM and Traffic Census stations. The report compares uncalibrated StreetLight Index values to observed truck counts to assess data quality and evaluates the impacts of considering alternate calibration data sets and analysis periods. Two test cases are presented to highlight issues with the single-factor calibration process. The report concludes that probe data analytical platforms such as StreetLight can be used to obtain rough estimates of truck volumes on roadway segments or to analyze routing patterns. The results further indicate that the accuracy of volume estimates depends heavily on the availability of sufficiently large samples of tracking data and stable and representative month-by-month calibration data across multiple reference locations.

BackgroundResearchers have implemented multiple approaches to increase data quality from existing web-based panels such as Amazon's Mechanical Turk (MTurk).ObjectiveThis study extends prior work by examining improvements in data quality and effects on mean estimates of health status by excluding respondents who endorse 1 or both of 2 fake health conditions ("Syndomitis" and "Chekalism").MethodsSurvey data were collected in 2021 at baseline and 3 months later from MTurk study participants, aged 18 years or older, with an internet protocol address in the United States, and who had completed a minimum of 500 previous MTurk "human intelligence tasks." We included questions about demographic characteristics, health conditions (including the 2 fake conditions), and the Patient Reported Outcomes Measurement Information System (PROMIS)-29+2 (version 2.1) preference-based score survey. The 3-month follow-up survey was only administered to those who reported having back pain and did not endorse a fake condition at baseline.ResultsIn total, 15% (996/6832) of the sample endorsed at least 1 of the 2 fake conditions at baseline. Those who endorsed a fake condition at baseline were more likely to identify as male, non-White, younger, report more health conditions, and take longer to complete the survey than those who did not endorse a fake condition. They also had substantially lower internal consistency reliability on the PROMIS-29+2 scales than those who did not endorse a fake condition: physical function (0.69 vs 0.89), pain interference (0.80 vs 0.94), fatigue (0.80 vs 0.92), depression (0.78 vs 0.92), anxiety (0.78 vs 0.90), sleep disturbance (-0.27 vs 0.84), ability to participate in social roles and activities (0.77 vs 0.92), and cognitive function (0.65 vs 0.77). The lack of reliability of the sleep disturbance scale for those endorsing a fake condition was because it includes both positively and negatively worded items. Those who reported a fake condition reported significantly worse self-reported health scores (except for sleep disturbance) than those who did not endorse a fake condition. Excluding those who endorsed a fake condition improved the overall mean PROMIS-29+2 (version 2.1) T-scores by 1-2 points and the PROMIS preference-based score by 0.04. Although they did not endorse a fake condition at baseline, 6% (n=59) of them endorsed at least 1 of them on the 3-month survey and they had lower PROMIS-29+2 score internal consistency reliability and worse mean scores on the 3-month survey than those who did not report having a fake condition. Based on these results, we estimate that 25% (1708/6832) of the MTurk respondents provided careless or dishonest responses.ConclusionsThis study provides evidence that asking about fake health conditions can help to screen out respondents who may be dishonest or careless. We recommend this approach be used routinely in samples of members of MTurk.

Abstract Citizen science (CS) is the most effective tool for overcoming the limitations of government and/or professional data collection. To compensate for quantitative limitations of the 'Winter Waterbird Census of Korea', we conducted a total of four bird monitoring via CS from 2021 to 2022. To use CS data alongside national data, we studied CS data quality and improvement utilizing (1) digit-based analysis using Benford’s law and (2) comparative analysis with national data. In addition, we performed bird community analysis using CS-specific data, demonstrating the necessity of CS. Neither CS nor the national data adhered to Benford's law. Alpha diversity (number of species and Shannon index) was lower, and total beta diversity was higher for the CS data than national data. Regarding the observed bird community, the number of species per family was similar; however, the number of individuals per family/species differed. We also identified the necessity of CS by confirming the possibility of predicting bird communities using CS-specific data. CS was influenced by various factors, including the perceptions of the survey participants and their level of experience. Therefore, conducting CS after systematic training can facilitate the collection of higher-quality data

Abstract Real-world data (RWD) can provide intel (real-world evidence, RWE) for research and development, as well as policy and regulatory decision-making along the full spectrum of health care. Despite calls from global regulators for international collaborations to integrate RWE into regulatory decision-making and to bridge knowledge gaps, some challenges remain. In this work, we performed an evaluation of Austrian RWD sources using a multilateral query approach, crosschecked against previously published RWD criteria and conducted direct interviews with representative RWD source samples. This article provides an overview of 73 out of 104 RWD sources in a national legislative setting where major attempts are made to enable secondary use of RWD (e.g. law on the organisation of research, "Forschungsorganisationsgesetz"). We were able to detect omnipresent challenges associated with data silos, variable standardisation efforts and governance issues. Our findings suggest a strong need for a national health data strategy and data governance framework, which should inform researchers, as well as policy- and decision-makers, to improve RWD-based research in the healthcare sector to ultimately support actual regulatory decision-making and provide strategic information for governmental health data policies.

ABSTRACTOpenAerialMap (OAM) is a crowdsourcing platform for uploading, hosting, sharing, displaying, searching, and downloading openly licensed Earth image data from around the world. OAM was launched with the primary goal to facilitate rapid disaster response mapping after natural events, such as floods or hurricanes. Images contributed to OAM can be used as a background layer in OpenStreetMap (OSM) editors to edit map features, such as buildings or facilities, which may have been affected by such events. This study analyzes how the provision of OAM images is associated with changes in underlying editing patterns of OSM features by comparing the number of edited OSM objects between 2 weeks before and 2 weeks after an image has been shared through OAM. The comparison also involves other aspects of OSM editing patterns, including geometry types and OSM primary feature types of edited objects, type of feature editing operations, edits per user, and contribution distribution across continents. Results show that the number of point features added to OSM more than quadrupled within the first 2 weeks after OAM image upload compared to that of 2 weeks before, and that the number of ways added almost doubled. This suggests that the OSM community utilizes provided OAM images for OSM map updates in the respective geographic areas. The study provides a showcase which demonstrates how information from one crowdsourcing platform (OAM) can be used to enhance the data quality of another (OSM) with regards to completeness and timeliness.

Abstract Background and purpose The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. Methods A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values. Results Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance. Conclusion This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

Abstract Background Research has highlighted a need to improve the quality of clinical documentation and data within aged care and disability services in Australia to support improved regulatory reporting and ensure quality and safety of services. However, the specific causes of data quality issues within aged care and disability services and solutions for optimisation are not well understood. Objectives This study explored aged care and disability workforce (referred to as ‘data-users’) experiences and perceived root causes of clinical data quality issues at a large aged care and disability services provider in Western Australia, to inform optimisation solutions. Methods A purposive sample of n = 135 aged care and disability staff (including community-based and residential-based) in clinical, care, administrative and/or management roles participated in semi-structured interviews and web-based surveys. Data were analysed using an inductive thematic analysis method, where themes and subthemes were derived. Results Eight overarching causes of data and documentation quality issues were identified: (1) staff-related challenges, (2) education and training, (3) external barriers, (4) operational guidelines and procedures, (5) organisational practices and culture, (6) technological infrastructure, (7) systems design limitations, and (8) systems configuration-related challenges. Conclusion The quality of clinical data and documentation within aged care and disability services is influenced by a complex interplay of internal and external factors. Coordinated and collaborative effort is required between service providers and the wider sector to identify behavioural and technical optimisation solutions to support safe and high-quality care and improved regulatory reporting.

There is a growing demand for time series data analysis in industry areas. Apache IoTDB is a time series database designed for the Internet of Things (IoT) with enhanced storage and I/O performance. With User-Defined Functions (UDF) provided, computation for time series can be executed on Apache IoTDB directly. To satisfy most of the common requirements in industrial time series analysis, we create a UDF library, IoTDQ, on Apache IoTDB. This library integrates stream computation functions on data quality analysis, data profiling, anomaly detection, data repairing, etc. IoTDQ enables users to conduct a wide range of analyses, such as monitoring, error diagnosis, equipment reliability analysis. It provides a framework for users to examine IoT time series with data quality problems. Experiments show that IoTDQ keeps the same level of performance compared to mainstream alternatives, and shortens I/O consumption for Apache IoTDB users.

With the great opportunities created by the new advances in Industry 4.0, many manufacturers are testing and investing in new equipment and infrastructure to deploy these technologies. However, there are a huge number of small and medium-sized manufacturers (SMMs) that are lagging behind due to the lack of in-house R&D capabilities and workforce shortage and/or financial constraints to afford such investment. Additionally, application of theoretical production research in SMMs often confront challenges such as low data availability and data quality, etc. In this paper, we describe a case study at a local medium-sized manufacturer of electromechanical devices for industrial, consumer, and medical applications, who was struggling to meet ever-growing market demand, and apply a novel approach of production system modelling to overcome the challenge of unavailability of the operation up-and downtime data. Specifically, the parametric model of the production system is identified using several system performance metrics derived based on the parts flow data of the in-process buffer. With the mathematical model constructed, the system bottleneck is analysed anda number of improvement scenarios are explored that can potentially enhance the system throughput. Finally, model sensitivity is analysed by calculating the deviation of the model-predicted performance metrics to those produced by a reference nominal model. This analysis demonstrates that the model constructed using our proposed approach is robust even when the system parameters vary from the baseline ones. [ABSTRACT FROM AUTHOR]

In dental research, it is particularly common for studies to collect data that are fundamentally correlated. Some common dental situations in which correlation arises include patients being observed across multiple teeth and/or across multiple time points, such as before and after treatment, or groups of patients being clustered (ie, familial units). For a number of traditional statistical tests and modeling techniques, the assumption of independence between observations is imperative in order to receive valid results and make accurate conclusions. This article describes how ignoring inherent correlations in data can lead to erroneous results when using traditional methods as well as the types of modeling techniques that are available to handle correlated data. Furthermore, two simulation studies are performed to further illustrate and prove the advantages of adequately handling correlated data in statistical analyses. [ABSTRACT FROM AUTHOR]

In e-commerce recommender systems, the sparsity of user-item rating data limits the quality of semantic embedding representation of users and items, which affects the accuracy of rating prediction. Previous studies have focused on learning attribute features or interaction features of nodes for recommendation and ignore the preference interest of rating tags in terms of attributes. In this paper, we propose a novel fusion representation method of multi-type node feature embedding for recommendation (MultiFEFR). The framework introduces rating preferences in terms of attributes and interactions and develops three kinds of feature presentations including rating-aware interaction feature embedding, rating-aware attribute feature embedding and heterogeneous attribute matched embedding. We design a multi-task joint learning strategy to optimize the accuracy of rating prediction. Experimental results show that the MultiFEFR outperforms the state-of-the-art models in MAE and RMSE metrics. Our findings are that the fusion of multi-type feature embedding can improve the accuracy of recommendation and the preference of attribute and interaction features are very explanatory for score grades. [ABSTRACT FROM AUTHOR]

The publication of research activities and results as well as the various participants pose major challenges for data exchange, exploration and visualization. The German Centre for Higher Education Research and Science Studies (DZHW) has developed an open data portal for the National Academics Panel Study (Nacaps), funded by the Federal Ministry of Education and Research (BMBF). With interactive graphics on topics such as doctoral conditions, career intentions, mobility and financing, the portal offers an overview of the life and work reality of doctoral students in Germany. Survey data is prepared in the data portal so that it can be used at a low threshold. Since the data is made available free of charge in open and reusable formats without restrictions, it can be used by researchers and the public alike. At the same time, the portal offers cooperating partner universities university-specific reporting on the data of doctoral students at their institution in a protected space. The possible further development and effective use of the Nacaps data portal for the management of open survey data will be discussed. In particular, the quality of open survey data as well as the potential and special features of the Nacaps data portal when visualizing survey data are discussed. [ABSTRACT FROM AUTHOR]

Objective: The goal of this study was to provide complete, robust data on annual systemic lupus erythematosus (SLE) incidence rates over nearly two decades from the Southeast Norway area (2.9 million inhabitants) and assess accuracy of SLE‐specific International Classification of Diseases (ICD) codes for SLE diagnosis. Methods: From administrative databases, we identified all cases International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD‐10) coded as SLE during 1999 through 2017 in Southeast Norway. We manually reviewed the chart of every case ICD‐10 coded as SLE to either confirm or reject SLE diagnosis. Using SLE classification criteria, we classified all cases with confirmed SLE. We estimated annual incidence rates of classified SLE, and subsets, defined by age at diagnosis, sex, and parental country of birth. The chi‐square test was applied for linear time‐trend analyses of incidence. Results: Among the 3,488 cases ICD‐10 coded as SLE, chart reviews confirmed SLE diagnosis in 1,558 (45%), of which 797 had new‐onset disease during 1999 through 2017. Annual SLE incidence rates fell during 1999 to 2017. The fall was most pronounced in female persons 50 to 59 years old at diagnosis, in whom incidence fell from 3.4 to 1.1 per 100,000 persons (P trend < 0.001). Concurrent ecological data from the study area showed a 74% reduction in prescriptions of menopausal hormone treatment. Accuracy of ICD‐10 codes for incident SLE diagnosis was acceptable in juveniles and young adults (up to 20 years) but otherwise low. Conclusion: In a presumably complete population‐based cohort, we identified decreasing incidence of SLE, especially among female persons 50 to 59 years old. Although reasons for declining incidence are not clear, ecological data indicate a possible role of environmental factors, for example, menopausal hormone treatments. [ABSTRACT FROM AUTHOR]

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467–474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty. [ABSTRACT FROM AUTHOR]

Background: The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders' skills acquired during ICD training, which varies greatly across countries. Objective: To characterise the current landscape of international ICD clinical coding training. Method: An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics. Results: Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders. Conclusion: Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data. Implications: These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training. [ABSTRACT FROM AUTHOR]

The topic of riverine microplastics is of great interest to the general public, yet the univocal scientific knowledge on this topic is limited. This review investigated the occurrence of microplastics in 6 major European rivers and their tributaries based on the results from 29 studies. We examined the reviewed studies in regard to data quality and reproducibility and assessed the abundance of microplastics in different sections of the water column. Furthermore, we investigated the chemical composition and potential origin of the reported riverine microplastics. We found that polystyrene, polypropylene, and polyethylene were the most abundant polymer types. The majority of primary microplastics arose from the industry sector as well as from personal care and cleaning products, whereas secondary microplastics constituted fibers from synthetic textiles and fragments of diverse origins. We highlighted the diversity of experimental and analytical approaches that could lead to high uncertainties in the measurements of microplastics abundance. Furthermore, the presence of microplastics in rivers was found to vary spatially likely due to point and nonpoint pollution sources of anthropogenic activities. Heterogenous environmental processes impacted the fate of microplastics characterized by various forms, sizes, and densities, in different ways. This impeded the identification of representative quantitative measurements of microplastics across different time frames. We advocate for the development of standardized protocols by the research community to ensure higher reproducibility of sampling, processing, and analysis of microplastics in aquatic environments. We recommend long‐term and site‐specific monitoring on microplastics with high data comparability to better inform policy making. This article is categorized under:Science of Water > Water QualityWater and Life > Conservation, Management, and Awareness [ABSTRACT FROM AUTHOR]

Image data augmentation plays a crucial role in data augmentation (DA) by increasing the quantity and diversity of labeled training data. However, existing methods have limitations. Notably, techniques like image manipulation, erasing, and mixing can distort images, compromising data quality. Accurate representation of objects without confusion is a challenge in methods like auto augment and feature augmentation. Preserving fine details and spatial relationships also proves difficult in certain techniques, as seen in deep generative models. To address these limitations, we propose OFIDA, an object-focused image data augmentation algorithm. OFIDA implements one-to-many enhancements that not only preserve essential target regions but also elevate the authenticity of simulating real-world settings and data distributions. Specifically, OFIDA utilizes a graph-based structure and object detection to streamline augmentation. Specifically, by leveraging graph properties like connectivity and hierarchy, it captures object essence and context for improved comprehension in real-world scenarios. Then, we introduce DynamicFocusNet, a novel object detection algorithm built on the graph framework. DynamicFocusNet merges dynamic graph convolutions and attention mechanisms to flexibly adjust receptive fields. Finally, the detected target images are extracted to facilitate one-to-many data augmentation. Experimental results validate the superiority of our OFIDA method over state-of-the-art methods across six benchmark datasets. [ABSTRACT FROM AUTHOR]

Sub‐surface characterization in fractured aquifers is challenging due to the co‐existence of contrasting materials namely matrix and fractures. Transient hydraulic tomography (THT) is proved to be an efficient and robust technique to estimate hydraulic (Km, Kf) and storage (Sm, Sf) properties in such complex hydrogeologic settings. However, performance of THT is governed by data quality and optimization technique used in inversion. We assessed the performance of gradient and gradient‐free optimizers with THT inversion. Laboratory experiments were performed on a two‐dimensional, granite rock (80 cm × 45 cm × 5 cm) with known fracture pattern. Cross‐hole pumping experiments were conducted at 10 ports (located on fractures), and time‐drawdown responses were monitored at 25 ports (located on matrix and fractures). Pumping ports were ranked based on weighted signal‐to‐noise ratio (SNR) computed at each observation port. Noise‐free, good quality (SNR > 100) datasets were inverted using Levenberg–Marquardt: LM (gradient) and Nelder–Mead: NM (gradient‐free) methods. All simulations were performed using a coupled simulation‐optimization model. Performance of the two optimizers is evaluated by comparing model predictions with observations made at two validation ports that were not used in simulation. Both LM and NM algorithms have broadly captured the preferential flow paths (fracture network) via K and S tomograms, however LM has outperformed NM during validation (RLM2=0.76,RMSELM=1.75cm,RNM2=0.73,RMSENM=1.77cm). Our results conclude that, while method of optimization has a trivial effect on model predictions, exclusion of low quality (SNR ≤ 100) datasets can significantly improve the model performance. Article impact statement: We assessed the role of two optimizers as well as signal‐to‐noise ratio in estimating hydraulic and storage properties of fractured aquifers using hydraulic tomography. [ABSTRACT FROM AUTHOR]

Family income questions in general purpose surveys are usually collected with either a single-question summary design or a multiple-question disaggregation design. It is unclear how estimates from the two approaches agree with each other. The current paper takes advantage of a large-scale survey that has collected family income with both methods. With data from 14,222 urban and rural families in the 2018 wave of the nationally representative China Family Panel Studies, we compare the two estimates, and further evaluate factors that might contribute to the discrepancy. We find that the two estimates are loosely matched in only a third of all families, and most of the matched families have a simple income structure. Although the mean of the multiple-question estimate is larger than that of the single-question estimate, the pattern is not monotonic. At lower percentiles up till the median, the single-question estimate is larger, whereas the multiple-question estimate is larger at higher percentiles. Larger family sizes and more income sources contribute to higher likelihood of inconsistent estimates from the two designs. Families with wage income as the main income source have the highest likelihood of giving consistent estimates compared with all other families. In contrast, families with agricultural income or property income as the main source tend to have very high probability of larger single-question estimates. Omission of certain income components and rounding can explain over half of the inconsistencies with higher multiple-question estimates and a quarter of the inconsistencies with higher single-question estimates. [ABSTRACT FROM AUTHOR]

Across waves of a panel survey, panel members are repeatedly exposed to the same or very similar survey questions, which might lead to learning effects. We used data from 24 waves of online interviews in a probability-based panel survey to investigate the positive and negative effects of becoming more familiar with the survey questions. We found that respondents became increasingly faster when answering the same questions across waves. Our analyses revealed that this effect could be attributed only partly to satisficing response behaviors associated with negative learning effects (i.e. speeding). In fact, positive learning effects (i.e. answering faster because of greater familiarity with the response tasks) appear to be the primary driver of change in response times. In summary, our research suggests that learning across waves of a panel survey has a positive effect on response behavior, which researchers should consider in survey planning and operations. [ABSTRACT FROM AUTHOR]

This article investigates digital and non-digital traces reused beyond the context of creation. A central idea of this article is that no (reused) dataset is perfect. Therefore, data quality assessment becomes essential to determine if a given dataset is "good enough" to be used to fulfill the users' goals. Biases, a possible source of discrimination, have become a relevant data challenge. Consequently, it is appropriate to analyze whether quality assessment indicators provide information on potential biases in the dataset. We use examples representing two opposing sides regarding data access to reflect on the relationship between quality and bias. First, the European Union open data portal fosters the democratization of data and expects users to manipulate the databases directly to perform their analyses. Second, online behavioral advertising systems offer individualized promotional services but do not share the datasets supporting their design. Quality assessment is socially constructed, as there is not a universal definition but a set of quality dimensions, which might change for each professional context. From the users' perspective, trust/credibility stands out as a relevant quality dimension in the two analyzed cases. Results show that quality indicators (whatever they are) provide limited information on potential biases. We suggest that data literacy is most needed among both open data users and clients of behavioral advertising systems. Notably, users must (be able to) understand the limitations of datasets for an optimal and bias-free interpretation of results and decision-making. [ABSTRACT FROM AUTHOR]

This paper aims to show how changes in survey design, supervision, and data collector affect travel survey data. The results can be used by policymakers and travel behaviour researchers when evaluating or choosing survey design. A document study of public documentation of national travel survey (NTS) methodology, and research using NTS data material from six European countries were conducted to compare the survey design of their respective NTSs. The results show that all countries included in this analysis have some sort of quality challenges. However, the countries have handled them differently, at different stages, and the transparency of the data collection process varies. Transparency in the data collection process and processing of data is essential for improving the survey design, evaluating quality, and ensuring that the time series is intact. [ABSTRACT FROM AUTHOR]