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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.

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.

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]

Pumps are important components in aviation fuel hydraulic systems, and thanks to the development of sensor technology and industrial intelligence technology, it is possible to achieve efficient state monitoring of pumps. However, when data quality is poor or the amount of data is small, a single data-driven model may not be able to meet diagnostic accuracy. A condition monitoring method for hydraulic gear pumps based on mechanism-data fusion is proposed. The method combines a mechanism model based on the volumetric efficiency formula with a data-driven model based on vibration signals. First, the parameters of volumetric efficiency are solved by fitting the pressure–flow relationship. Subsequently, a multichannel fusion and multikernel function-weighted ensemble support vector classification (MCMK-SVC) is developed, to establish a data-driven model. Finally, through data-level fusion, feature-level fusion, and decision-level fusion, a condition monitoring model based on mechanism-data fusion is built. Experimental verification shows that the accuracy of the three levels of fusion models exceeds 96.9%. Compared to the single data-driven model or other traditional data-driven models, the accuracy of the proposed method has improved by 3% to 33%, demonstrating the effectiveness of the mechanism-data fusion model. [ABSTRACT FROM AUTHOR]

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions. [ABSTRACT FROM AUTHOR]

The present article develops the generalized multiple dependent state sampling plan for testing the quality of the ocean data. The plan parameters for the inspection of the ocean data are selected through the optimization process. The operational procedure for the inspection of ocean data is given under the proposed sampling scheme. The efficiency of the proposed plan is compared with the existing plan used for the inspection of the quality of ocean data in terms of sample size. From the comparative study, it is concluded that the proposed plan is efficient than the existing plan in an inspection of the ocean data. [ABSTRACT FROM AUTHOR]

Lightning protection is important for weather radars to prevent critical damage or outages but can have negative effects on data quality. The existing lightning protection of the DWD polarimetric C-Band weather radar network consists of four vertical poles with a maximum diameter of 10 cm. During radar operation, these rods cause local scattering in the near field of the antenna, resulting in negative impacts on radar products. One effect is the removal of significant transmit power off the main beam axis and its addition to other areas or the side lobes. This results in wrongly localised precipitation fields in radial direction. The second effect is the loss of transmitted and received power, appearing as a decrease in system gain, and subsequently a underestimation of all power based radar moments in the vicinity of the rods. The underestimation in radar reflectivity Z then leads to a negative bias in the actual rain rate of approximately 20 % if a Z/R relationship is applied. These detrimental effects on data quality led to the requirement of developing a new lightning protection concept. The new concept must minimise the effect on data quality, but also provide sufficient protection from lightning strikes according to the existing regulations and requirements. Three possible lighting protection concepts are described in this paper: two using vertical rods of different diameters (16 and 40 mm, respectively) and one with horizontally placed rods outside the antenna aperture. Their possible influence on data quality is quantified through a dedicated measurement campaign by analysing resulting antenna patterns and precipitation sum products. Antenna patterns are analysed with respect to the side lobe levels compared to antenna patterns without lightning protection and the original lightning protection. With the newly tested lightning rods, the apparent side lobe levels are slightly increased compared to an antenna pattern taken without lightning protection, but are within the accepted antenna specifications. Compared to the original lightning protection, a decrease of up to -15 dB in apparent side lobe levels is found for all tested lightning protection options. Beam blockage is substantially reduced compared to the existing lightning protection as shown by the evaluation of QPE sums. These results and some structural considerations are a solid basis to recommend the installation of four rods with maximum 40 mm diameter for all 17 radar systems of the DWD weather radar network. [ABSTRACT FROM AUTHOR]

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation. [ABSTRACT FROM AUTHOR]

AbstractDiscussions surrounding digital libraries and their metadata have become less robust than before, given that creating and hosting digital collections have become the norm for most cultural heritage institutions over the last 20 years. Consequently, there are fewer discussions of how the characteristics of digital collections and their metadata have changed over time. This article showcases the evolution of digital collections and their metadata at the University of Illinois Urbana-Champaign (UIUC) Library in the last 20 years. It discusses the growth of its collections and their characteristics, examines historical changes in the use of metadata elements, and explores responses to the changing nature of digitized and born-digital materials. Based on a large-scale data analysis of the digital collections and their metadata housed in UIUC Digital Library, the paper also examines the challenges and opportunities of the curation and management of digital collections and digital libraries in the future. [ABSTRACT FROM AUTHOR]

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16 seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model. Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R² is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model. [ABSTRACT FROM AUTHOR]

As the importance of utilizing real‐world data (RWD)/real‐world evidence (RWE) for supporting regulatory scientific decision‐making continues to grow, experiences and inputs from experts become crucial for developing a systematic and practice‐oriented plan for the use of fit‐for‐purpose RWD/RWE. This study aimed to survey relevant experts from government agencies, industries, and academia to identify prerequisites for the drug life cycle in Korea. The questionnaire comprised the following: (A) the definition and categories of RWD/RWE, (B) the suitability and feasibility of using RWD/RWE at each authorization stage by the types of RWD, and (C) the challenges and solutions for the use of RWD/RWE. A total of 46 respondents completed the online survey, with 89.1% of them having prior experience with RWD/RWE usage. A majority of respondents agreed that RWD can be obtained from various sources. Among these sources, the registry was the most suitable source. It is suitable to compensate for the limitations of randomized control trials and ensure quality in data collection. Though there was consensus among the respondents for the use of RWD/RWE in post‐marketing surveillance, the use of such data in new drug application (NDA) was disagreeable. Respondents considered it necessary to write a protocol in advance for RWD collection and RWE generation, for all RWD types. In conclusion, this study examined the perceptions of experts for RWD/RWE use at each approval stage of drugs. The results suggest that guidelines for the fit‐for‐purpose use of RWD/RWE should be developed via careful deliberation among experts in the future. [ABSTRACT FROM AUTHOR]

Extended hydrometric (water level and flow) records are presented for eight Irish catchments subject to arterial drainage. The procedures employed to collect and process historical data, extend flow records and compile key metadata and information about each gauging station are described. Procedures are developed to handle data quality issues related to hydrometric practices and equipment malfunction and to quality assure rescued data using quality codes that complement modern hydrometric practices. The workflow developed will assist other hydrometric data rescue efforts and minimize subjectivity during the rescue process. The newly extended records represent the longest continuous river flow series available in Ireland, extending to the commencement of formal hydrometric monitoring in the country in 1940. The resultant data sets add 150 years of daily data across eight stations and will provide a key new resource for hydrological studies into the impacts of arterial drainage and flow nonstationarity. [ABSTRACT FROM AUTHOR]