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The present study introduces the concept of validating data quality indicators through re-classification. We use the term re-classification to mean the evaluation of how well an indicator detects the quality of different versions of a survey question for which the quality is known a priori. We illustrate its application with two examples. In both, we make use of 12 questions from prior experiments that manipulated text features of questions to create 'low' and 'high' quality versions of each question. In the first example, we coded each question version in SQP 2.1 to obtain indicators of validity, reliability, and quality. We compared these indicators between the two versions of each question to assess whether the SQP outcomes were sensitive to text features. In the second example, we used a pretest survey to obtain three indicators of survey quality: response latencies, item nonresponse, and consistency over time. Again, we compared these indicators between question versions to assess whether the indicators were sensitive to text features. We give recommendations for applying re-classification and an outlook for future research opportunities. [ABSTRACT FROM AUTHOR]

Self-administered web surveys provide respondents only limited opportunities for feedback. Therefore, many web surveys include so-called "final comment questions" (FCQs) that allow respondents to elaborate on the survey in general and the survey questions in particular. Typing in open answers in text boxes is burdensome – especially via smartphones with virtual on-screen keypads – so that respondents frequently provide short or no answers at all. In this study, we make use of new technological advancements in web survey methodology and investigate FCQs with requests for written and oral answers. For this purpose, we conducted an experiment in a smartphone survey (N = 1,001) in a German online panel. The results reveal that FCQs with a request for written and oral answers do not differ with respect to item-nonresponse. However, oral answers are substantially longer than written answers pointing to more in-depth information. The oral answer condition also results in more positive comments than the written answer condition. This study is a methodological showcase for innovative web survey design that contributes to the improvement of data quality. [ABSTRACT FROM AUTHOR]

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]

Aiming at the problem of imprecise reputation evaluation caused by too much subjective and qualitative evaluation of the traditional reputation evaluation model in data transactions, An Evaluation Entity-Based Reputation Evaluation model (EEBRE) is proposed. In order to realize multi-dimensional reputation evaluation, the structure of the evaluation entity is proposed to dynamically obtain the reputation data of the trading parties; a reputation evaluation assembly method combining objective indicators and feedback rating indicators is proposed, which replaces the qualitative scoring of data set quality indicators with quantitative scoring, introduces the static individual information indicators as well as transaction context attribute indicators, such as transaction amount, time, etc., and retains the qualitative evaluation of the feedback ratings to provide an interactive experience feedback pathway for the data requesting party. The experimental results show that the EEBRE model proposed in this paper can safely and effectively evaluate the seller's reputation. [ABSTRACT FROM AUTHOR]

In low voltage distribution systems, series arc fault current is small and hidden, and traditional circuit protection devices cannot effectively identify it. To address this problem, a series arc fault detection method based on multi-domain depth feature association is proposed in this paper. By building an experimental platform, the current signal data on normal and arc fault states under different loads are obtained. The time-domain features, frequency-domain features and wavelet packet energy features of the current signal are extracted. To enhance the quality of the data, the importance of each domain characteristic is ranked using four distinct tree techniques, and the most useful features are chosen. A one-dimensional stacked neural network (1D-SNN) fault detection model is constructed to further extract the depth features of each domain. To achieve series arc defect detection, the depth features are combined and fed into a fully connected neural network. The Radam algorithm is used to optimize the detection model. It is then compared with Adam, SGD, and RMSprop optimization algorithms, which verifies that Radam has a better effect on the optimization of the arc detection model. Experimental results show that the average detection accuracy is 99.63%. [ABSTRACT FROM AUTHOR]

The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC) mapping. This approach taps into the collective power of the public to share spatial information, providing a relevant data source for producing LULC maps. Through the analysis of 262 papers published from 2012 to 2023, this work provides a comprehensive overview of the field, including prominent researchers, key areas of study, major CGI data sources, mapping methods, and the scope of LULC research. Additionally, it evaluates the pros and cons of various data sources and mapping methods. The findings reveal that while applying CGI with LULC labels is a common way by using spatial analysis, it is limited by incomplete CGI coverage and other data quality issues. In contrast, extracting semantic features from CGI for LULC interpretation often requires integrating multiple CGI datasets and remote sensing imagery, alongside advanced methods such as ensemble and deep learning. The paper also delves into the challenges posed by the quality of CGI data in LULC mapping and explores the promising potential of introducing large language models to overcome these hurdles. [ABSTRACT FROM AUTHOR]

Objectives. To investigate characteristics of data reported in US medical cannabis registries across states. Methods. Data included 2021 medical cannabis registry reports from 34 states, Puerto Rico, and the District of Columbia (hereafter, states) with active medical cannabis programs. The data from the reports were manually coded into domains and subcategories, including information related to patients (e.g., number, demographics), authorizing clinicians, sales (e.g., content, revenue), license tracking, and health and safety outcomes. Results. Among 36 states, 97% reported total patient number and 75% reported number of authorizing clinicians. Least reported subcategories included patient race/ethnicity (8%), adverse events (11%), therapeutic benefits (6%), and product recalls (6%). States that recently legalized medical cannabis (2013–2018) reported a higher number of subcategories overall, with a median of 11 versus 8 for early adopting states (1996–2012). More medical-use states reported data on authorizing clinicians compared with nonmedical adult-use states but were otherwise similar. Conclusions. Medical cannabis state registries generally reported data on consumers, clinicians, and sales rather than health and safety outcomes. More comprehensive and uniform medical cannabis public health surveillance is needed. (Am J Public Health. 2024;114(S8):S685–S693. https://doi.org/10.2105/AJPH.2024.307728) [ABSTRACT FROM AUTHOR]

To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging, genomic, and environmental data to understand the biological mechanisms of psychiatric illness before this knowledge is applied into clinical setting. Machine learning (ML) is an automated process that can detect patterns from large multidimensional datasets and can supersede conventional statistical methods as it can detect both linear and non-linear relationships. Due to this advantage, ML has potential to enhance our understanding, improve diagnosis, prognosis and treatment of psychiatric disorders. The current review provides an in-depth examination of, and offers practical guidance for, the challenges encountered in the application of ML models in psychiatric research and clinical practice. These challenges include the curse of dimensionality, data quality, the 'black box' problem, hyperparameter tuning, external validation, class imbalance, and data representativeness. These challenges are particularly critical in the context of psychiatry as it is expected that researchers will encounter them during the stages of ML model development and deployment. We detail practical solutions and best practices to effectively mitigate the outlined challenges. These recommendations have the potential to improve reliability and interpretability of ML models in psychiatry. [ABSTRACT FROM AUTHOR]

Mixed-mode surveys are popular as they can save costs and maintain (or improve) response rates relative to single-mode surveys. Nevertheless, it is not yet clear how design decisions like survey mode or questionnaire length impact measurement quality. In this study, we compare measurement quality in an experiment of three distinct survey designs implemented in the German sample of the European Values Study: a single-mode face-to-face design, a mixed-mode mail/web design, and a shorter (matrix) questionnaire in the mixed-mode design. We compare measurement quality in different ways, including differences in distributions across several data quality indicators as well as equivalence testing over 140 items in 25 attitudinal scales. We find similar data quality across the survey designs, although the mixed-mode survey shows more item nonresponse compared to the single-mode survey. Using equivalence testing we find that most scales achieve metric equivalence and, to a lesser extent, scalar equivalence across the designs. [ABSTRACT FROM AUTHOR]

The neurological basis for perceptual awareness remains unclear, and theories disagree as to whether sensory cortices per se generate awareness. Critically, neural activity in the sensory cortices is only a neural correlate of consciousness (NCC) if it closely matches the contents of perceptual awareness. Research in vision and touch suggest that contralateral activity in sensory cortices is an NCC. Similarly, research in hearing with two sound sources (left and right) presented over headphones also suggests that a candidate NCC called the auditory awareness negativity (AAN) matches perceived location of sound. The current study used 13 different sound sources presented over loudspeakers for natural localization cues and measured event‐related potentials to a threshold stimulus in a sound localization task. Preregistered Bayesian mixed models provided moderate evidence against an overall AAN and very strong evidence against its lateralization. Because of issues regarding data quantity and quality, exploratory analyses with aggregated data from multiple loudspeakers were conducted. Results provided moderate evidence for an overall AAN and strong evidence against its lateralization. Nonetheless, the interpretations of these results remain inconclusive. Therefore, future research should reduce the number of conditions and/or test over several sessions to procure a sufficient amount of data. Taken at face value, the results may suggest issues with AAN as an NCC of auditory awareness, as it does not laterally map onto experiences in a free‐field auditory environment, in contrast to the NCCs of vision and touch. In vision and touch, contralateral activity in sensory cortices is a neural correlate of consciousness (NCC) as the neural activity matches lateralized percepts. In hearing, this has not been thoroughly investigated. Here, we measured event‐related potentials from 13 free‐field sound sources. The neural correlate of auditory consciousness (AAN) was found; however, results do not resolve whether AAN is lateralized in response to lateral stimuli. Taken at face value, results may suggest that AAN is not an NCC of lateralized auditory experience to free‐field sound sources. [ABSTRACT FROM AUTHOR]

Intermodal transport not only changes the traditional mode of cargo transport, hut also builds an efficient and intelligent logistics system. At present, there is a problem of low efficiency of intermodal transport in China, and the development of intermodal transport lacks a clear direction. In order to further strengthen the objective understanding and quantitative evaluation of the development level of intermodal transport in China, and to comprehensively consider factors such as data availability and data quality, in order to improve the efficiency of intermodal transport, the connotation requirements of intermodal transport were analyzed in depth firstly. Then, an intermodal transport development index system composed of 11 specific indicators in four aspects was established in this paper, including service capacity, service quality, organizational efficiency and comprehensive benefits. After data normalization, the intermodal transport development index was constructed by combining Analytic Hierarchy Process and Entropy Weight Method. Then, the first batch of national intermodal transport demonstration projects were taken as an example, and the intermodal transport key enterprise filling system was used to collect and process data. At last, the intermodal transport development index of each project was calculated and suggestions were made accordingly. The intermodal transport development index shows that the national intermodal transport demonstration projects in the southeast coastal areas of China have good results, and the construction of the national intermodal transport demonstration projects in inland areas still needs to be further strengthened. In summary, the development level of the national intermodal transport demonstration projects could be truly and effectively reflected by the intermodal transport development index, which has a good guiding effect on promoting the development of intermodal transport in China. [ABSTRACT FROM AUTHOR]

Effective quality management is essential for ensuring construction quality, adhering to schedules, and controlling costs. The application of Building Information Modeling (BIM) for quality control during the construction phase presents challenges, particularly with the real-time monitoring and tracing of onsite quality data. In response, we propose a framework that integrates Internet of Things (IoT)-based quality data with four-dimensional (4D) BIM for enhanced quality monitoring and acceptance. This framework includes (1) automatic segmentation of 4D BIM, (2) collection and mapping of quality data, and (3) real-time monitoring and assessment of quality. We validated our framework in a large-scale building project in Wuhan, China, focusing on concrete engineering quality acceptance. The results demonstrate that our integrated framework effectively captures and integrates live quality data in real-time into 4D BIM models for ongoing monitoring and assessment. The contribution of our research lays the foundation for the development of an integrated BIM-IoT framework for real-time quality monitoring, improving its efficiency and accuracy during quality acceptance in construction. Practical Applications: This study presents a practical solution for real-time quality monitoring and acceptance at construction sites through the integration of 4D Building Information Modeling with IoT technologies. By facilitating real-time quality monitoring and data capture, the framework significantly enhances decision-making processes. Construction managers can use this system to promptly detect and resolve quality issues, thereby reducing rework costs and likelihood. Additionally, this integrated framework ensures compliance with stringent quality standards, providing a reliable method to meet all construction project specifications. The capability to continuously update the 4D BIM model with live data enables dynamic resource management and optimizing workerpower. Consequently, this approach leads to improved project outcomes, higher-quality buildings, and greater client satisfaction. In a case study involving a large-scale high-rise building project, the framework demonstrated its effectiveness by integrating live quality data into 4D BIM for ongoing monitoring and acceptance. The results underscored the framework's potential to provide stakeholders with timely information and implement corrective actions, significantly enhancing the efficiency and reliability of construction quality acceptance. [ABSTRACT FROM AUTHOR]

This case study discusses the concept of big data analytics, which involves managing and analyzing large, complex data to discover patterns and insights. Implementing big data analytics has many benefits, such as improving decision-making processes, enhancing accounting processes and compliance, boosting marketing campaigns, and providing better credit risk assessment procedures. This case study aims for the student to understand the challenges of big data analytics implementation in a Malaysian commercial bank. Students take on the role of a Senior IT Manager who needs to consider solutions to the implementation problems. Although the team proposed using cloud-based services as a solution to these issues, the CEO expressed concerns about security risks and requested other alternative solutions. The team has also been requested to come up with solutions to the data quality issues experienced by the case study bank. [ABSTRACT FROM AUTHOR]

Conventional statistical methods for missing data imputation have been challenging to adapt to the large-scale new features of high dimensionality. Moreover, the missing data imputation methods based on Generative Adversarial Networks (GAN) are plagued with gradient vanishing and mode collapse. To address these problems, we have proposed a new imputation method based on GAN to enhance the accuracy of missing data imputation in this study. We refer to our missing data method using Generative Adversarial Imputation Networks (MGAIN). Specifically, the least squares loss is first introduced to solve the gradient vanishing problem and ensure the high quality of the output data in MGAIN. To mitigate mode collapse, dual discriminator is used in the model, which improved the diversity of output data to avoid the degradation of computational performance caused by single data. As a result, MGAIN generates rich and accurate imputation values. The MGAIN enhances imputation accuracy and reduces the root mean square error metric by 21.66% compared to the baseline model. We evaluated our method on baseline datasets and found that MGAIN outperformed state-of-the-art and popular imputation methods, demonstrating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

This article explores the significance of high-quality healthcare data and the challenges involved in maintaining data quality. Healthcare data possess unique characteristics such as heterogeneity, incompleteness, timeliness, longevity, privacy, and ownership. Inaccurate data can result in financial losses, increased expenses, and can impact the performance of artificial intelligence models. The article emphasizes the necessity for tailored quality assessment indicators and methods for healthcare data, as well as the implementation of quality management measures. Additional research is required to redefine quality assessment criteria and develop quantification methods specifically for healthcare data. The authors stress the importance of interdisciplinary collaboration among experts from diverse fields to ensure effective data quality assessment. [Extracted from the article]

Understanding the underlying causes of maternal death across all regions of the world is essential to inform policies and resource allocation to reduce the mortality burden. However, in many countries there exists very little data on the causes of maternal death, and data that do exist do not capture the entire population at risk. In this article, we present a Bayesian hierarchical multinomial model to estimate maternal cause of death distributions globally, regionally, and for all countries worldwide. The framework combines data from various sources to inform estimates, including data from civil registration and vital systems, smaller‐scale surveys and studies, and high‐quality data from confidential enquiries and surveillance systems. The framework accounts for varying data quality and coverage, and allows for situations where one or more causes of death are missing. We illustrate the results of the model on three case‐study countries that have different data availability situations. [ABSTRACT FROM AUTHOR]

Surface microseismic monitoring is widely used in hydraulic fracturing. Real‐time monitoring data collected during fracturing can be used to perform surface‐microseismic localization, which aids in assessing the effects of fracturing and provides guidance for the process. The accuracy of localization critically depends on the quality of monitoring data. However, the signal‐to‐noise ratio of the data is often low due to strong coherent and random noise, making denoising essential for processing surface monitoring data. To suppress noise more effectively, this paper introduces a novel denoising method that integrates the Ramanujan subspace with dynamic time warping and adaptive singular value decomposition. The new method consists of two steps: First, a Ramanujan subspace is constructed to suppress periodic noise. Then, dynamic time warping and adaptive singular value decomposition are applied to eliminate remaining coherent and random noise. The method has been evaluated using both synthetic and field data, and its performance is compared with traditional microseismic denoising techniques, including bandpass filtering and empirical mode decomposition. [ABSTRACT FROM AUTHOR]

The issue of data quality has emerged as a critical concern, as low-quality data can impede data sharing, diminish intrinsic value, and result in economic losses. Current research on data quality assessment primarily focuses on four dimensions: intrinsic, contextual, presentational, and accessibility quality, with intrinsic and presentational quality mainly centered on data content, and contextual quality reflecting data usage scenarios. However, existing approaches lack consideration for the behavior of data within specific application scenarios, which encompasses the degree of participation and support of data within a given scenario, offering valuable insights for optimizing resource deployment and business processes. In response, this paper proposes a data contribution assessment method based on maximal sequential patterns of behavior paradigms (DecentralDC). DecentralDC is composed of three steps: (1) mining the maximal sequential patterns of sharing and exchange behavior paradigms; (2) determining the weights of these paradigms; (3) calculating the contribution of sharing and exchange databases combined with data volume. To validate our approach, two sharing and exchange scenarios of different scales are established. The experimental results in two scenarios validate the effectiveness of our method and demonstrate a significant reduction in cumulative regret and regret rate in data pricing due to the introduction of data contribution. Specifically, compared to the most competitive baseline, the improvements of mean average precision in two scenarios are 6% and 8%. The code and simulation scenarios have been open-sourced and are available at https://github.com/seukgcode/DecentralDC. [ABSTRACT FROM AUTHOR]

Generating real‐world evidence about the effect of medication discontinuation or dose reduction on outcomes, such as reduction of adverse drug effects (ADE; intended benefit) and occurrence of adverse drug withdrawal events (ADWE; unintended harm), is crucial to informing deprescribing decisions. Determining the causal effects of deprescribing is difficult for many reasons, including lack of randomization in real‐world study designs and other design and measurement issues that pose threats to internal validity. The inherent challenge is how to identify the effects, both intended benefits and unintended harms, of a new medication stoppage or reduction when implemented in patients with many potential clinical and social risks that may influence the likelihood of deprescribing as well as outcomes. We discuss methodological issues of estimating the effect of medication discontinuation or reduction on risk of ADEs and ADWEs considering: (1) sampling study populations of sufficient size with the potential to demonstrate clinically meaningful and quantifiable outcomes, (2) accurate and appropriately timed measurement of covariates, outcomes, and discontinuation, and (3) statistical approaches to managing confounding and other biases inherent in long‐term medication use by individuals with multiple morbidities. Designing rigorous deprescribing studies that address internal validity threats will support evidence generation by improving the ability to assess benefits and harms when the exposure of interest is the absence of a medication. Iterative learnings about data quality, variable definition, variable measurement, and exposure‐outcome associations will inform strategies to improve the causal inferences possible in real‐world deprescribing studies. [ABSTRACT FROM AUTHOR]

ABSTRACT Accuracy of catch landing data reported by captains of commercial vessels is crucial in the assessment of marine species stocks and in fisheries policy and management. However, this data can be subject to estimation biases, such as a tendency to inflate some catches (to the detriment of others) and refusal to fill in logbooks. We assessed the accuracy of catch reports from the Spanish tropical tuna purse seine fleet (which accounts for 26% of catches in the Indian Ocean) using Benford's law, a mathematical principle effective for detecting irregularities across multiple datasets. During 2013–2020, including periods before and after the implementation of total allowable catch (TAC) limits in 2017, reported catches differed from Benford's expected distribution, especially for bigeye tuna, indicating potential inaccuracies in reported catches. Changes in data reporting after TAC limits were imposed in 2017 suggested modifications in fishing operations and reporting practices. Use of Benford's law highlighted its potential as an auditing tool in fisheries management and provided insights into data integrity that are crucial for sustainable fisheries governance. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions (1) Assess the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues. (2) Identify issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance.Queries were distributed to 3 Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at 4 conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs.We found that the median number of codes associated with a given laboratory/medication name (and vice versa) generally met expectations, though there were DP‐specific issues that resulted in outliers. In addition, there were drastic differences in the percentage of patients with a given concept depending on provenance.The harmonization queries surfaced several mapping errors, as well as issues with overly specific codes and records with “null” codes. The completeness queries demonstrated having access to multiple types of data provenance provides more robust results compared with any single provenance type. Harmonization errors between source data and reference terminologies may not be widespread but do exist within CDMs, affecting tens of thousands or even millions of records. Provenance information can help identify potential completeness issues with EHR data, but only if it is represented in the CDM and then populated by DPs. [ABSTRACT FROM AUTHOR]

Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F 1-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future. [ABSTRACT FROM AUTHOR]

The editorial discusses efforts to enhance data quality and harmonization among European population-based cancer registries. Population-based cancer registries play a crucial role in cancer surveillance, aiding in quantifying cancer burden, describing trends, and informing policy decisions. The editorial highlights challenges in data comparability, tools for improving data quality, and the impact of data harmonization on cancer indicators. It also emphasizes the importance of data quality in improving cancer epidemiology insights and addressing inequalities across European countries and regions. [Extracted from the article]

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening. A comprehensive benchmarking of existing synthetic lethality (SL) prediction methods is lacking. Here, the authors compare 12 recently developed machine learning methods for SL prediction, assess their performance, and provide guidance on the selection of the most suitable method. [ABSTRACT FROM AUTHOR]

The goal of information security is to prevent unauthorized access to data. There are several conventional ways to confirm user identity, such as using a password, user name, and keys. These conventional methods are rather limited; they can be stolen, lost, copied, or cracked. Because multimodal biometric identification systems are more secure and have higher recognition efficiency than unimodal biometric systems, they get attention. Single-modal biometric recognition systems perform poorly in real-world public security operations because of poor biometric data quality. Some of the drawbacks of current multimodal fusion methods include low generalization and single-level fusion. This study presents a novel multimodal biometric fusion model that significantly enhances accuracy and generalization through the power of artificial intelligence. Various fusion methods, encompassing pixel-level, feature-level, and score-level fusion, are seamlessly integrated through deep neural networks. At the pixel level, we employ spatial, channel, and intensity fusion strategies to optimize the fusion process. On the feature level, modality-specific branches and jointly optimized representation layers establish robust dependencies between modalities through backpropagation. Finally, intelligent fusion techniques, such as Rank-1 and modality evaluation, are harnessed to blend matching scores on the score level. To validate the model's effectiveness, we construct a virtual homogeneous multimodal dataset using simulated operational data. Experimental results showcase significant improvements compared to single-modal algorithms, with a remarkable 2.2 percentage point increase in accuracy achieved through multimodal feature fusion. The score fusion method surpasses single-modal algorithms by 3.5 percentage points, reaching an impressive retrieval accuracy of 99.6%. [ABSTRACT FROM AUTHOR]

Pioneering the use of the Geostationary Environment Monitoring Spectrometer's (GEMS) observation data in air quality modeling, we adjusted Asia's NOx emissions inventory by leveraging the instrument's unprecedented sampling frequency. GEMS tropospheric NO2 columns served as top-down constraints, guiding our Bayesian inversion to constrain NOx emissions in Asia during spring 2022. This enabled the model to better capture the diurnal variation in NOx emissions, such as its morning rush hour peak, particularly when more retrievals were available each day, improving the simulation accuracy to a certain extent. The GEMS-informed adjustment reduced the extent of model underestimation of surface NO2 concentrations from 17.38 to 5.58% in Korea and from 13.05 to 4.54% in China, showing about 9.40% and 5.77% greater improvements, respectively, compared to the adjustment based on the sun-synchronous low earth orbit observation proxy. Our findings highlight the potential of geostationary observation data in refining the diurnal cycle of inventoried NOx emissions, thereby more effectively improving the accuracy of air quality simulations. [ABSTRACT FROM AUTHOR]

In Lebanon, the lack of quality data on road traffic injuries (RTIs) led to the implementation of a hospital-based RTI surveillance system by the Ministry of Health in in private and public-run hospitals in the Bekaa governorate. This paper aims to describe the characteristics and severity of RTIs recorded over two years during the pilot phase. It also assesses the strengths and challenges of the surveillance system, highlighting areas for enhancement. The data collected from the Emergency department (ED) was used to conduct a retrospective analysis of population-based injuries hospitalized for road traffic crashes (RTC). Designated focal persons reported injuries weekly using a standardized form, which included demographic and crash-related variables, body lesions, and vital signs. Data were coded per the International Classification of Diseases (ICD-10), entered into Epidata, and analyzed using SPSS. The RTI surveillance system was assessed using Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Over two years, a total of 1576 cases of RTIs were reported. The male-to-female ratio was 2.16 and the majority of RTIs (44.4%) were recorded among those aged between 15 and 29 years old. From 2013 to 2015, a decrease of 0.7% in the revised trauma score (RTS < 4) was recorded. On the contrary, an increase of 3.9% in injury severity score (ISS) that ranged between 15 and 75 was reported. The probability of survival of an injured individual at one month was improved. The hospital-based surveillance system demonstrated strengths in structured data collection and ethical considerations but faced challenges like underreporting, limited coverage, and resource constraints. Recommendations for improvement include enhancing data quality and timely reporting, ultimately supporting evidence-based road safety interventions. [ABSTRACT FROM AUTHOR]

Effective governance is crucial for the success of conservation projects aimed at protecting wildlife populations and supporting human well‐being. However, few large‐scale, comprehensive syntheses have been conducted on the effects of different environmental governance types on conservation outcomes (i.e., biological and ecological effectiveness or effects of conservation on human well‐being), and clarity on the quantity and quality of evidence remains dispersed and ambiguous. We attempted a systematic map of the evidence on the effectiveness of different governance types to meet desired conservation outcomes in Africa, Asia, and Latin America. However, early in this effort, we observed a general lack of empirical research on the links between governance and conservation outcomes. To fill observed data gaps in the evidence base, we tried triangulating governance data from alternative sources (Protected Planet database) and pooling evidence from research conducted within the same conservation areas. Limited data were contained in the Protected Planet database, and governance types in conservation areas and landscapes were complex, making it difficult to use these approaches to assign governance types to conservation areas. To illustrate our observations from the failed systematic map attempt, we prepared a rapid evidence map that outlines a subset of the evidence base of articles linking governance types and governance principles with conservation outcomes. Only 3.2% (34 of 1067) of the articles we screened directly related conservation outcomes to governance type, and even fewer related governance principles to conservation outcomes. Based on our findings, we recommend improving the evidence base by supporting empirical research and increasing the availability and quality of governance data in freely accessible databases. These recommendations are critical for enhancing understanding of the role of governance in conservation projects and improving conservation outcomes. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) enables decentralized data utilization while maintaining edge user privacy, but it faces challenges due to statistical heterogeneity. Existing approaches address client drift and data heterogeneity issues. However, real-world settings often involve low-quality data with noisy features, such as covariate drift or adversarial samples, which are usually ignored. Noisy samples significantly impact the global model's accuracy and convergence rate. Assessing data quality and selectively aggregating updates from high-quality clients is crucial, but dynamically perceiving data quality without additional computations or data exchanges is challenging. In this paper, we introduce the FedDQA (Federated learning via Data Quality-Aware) (FedDQA) framework. We discover increased data noise leads to slower loss reduction during local model training. We propose a loss sharpness-based Data-Quality-Awareness (DQA) metric to differentiate between high-quality and low-quality data. Based on the DQA, we design a client selection algorithm that strategically selects participant clients to reduce the negative impact of noisy clients. Experiment results indicate that FedDQA significantly outperforms the baselines. Notably, it achieves up to a 4% increase in global model accuracy and demonstrates faster convergence rates. [ABSTRACT FROM AUTHOR]

This study presents the water column temperature data collected during several cruises on board the Italica, Araon and Laura Bassi research vessels, in the framework of the Climatic Long-term Interaction for the Mass balance in Antarctica (CLIMA), Southern Ocean Chokepoints Italian Contribution (SOChIC), and Marine Observatory of the Ross Sea (MORSea) projects, funded by the Italian National Antarctic Research Program (PNRA). Data were collected between New Zealand and the Ross Sea during the austral summers from 1994/1995 to 2023/2024. Across this chokepoint of the Antarctic Circumpolar Current, XBT Sippican T7 probes were launched with a regular 20 km sampling, providing temperature profiles with a vertical resolution of 65 cm and a maximum nominal depth of 760 m. All temperature profiles underwent a rigorous quality control, including a general malfunctioning verification, the removal of spikes, the consistency check of adjacent profiles, the comparison to regional oceanographic features and satellite altimetry observations, and a final visual check by operator. Data quality checks led us to discard about 12 % of acquired XBT measurements. This dataset contributes to the improvement of our understanding of Southern Ocean features, being highly valuable for studies focusing on climate variability, especially across the Antarctic Circumpolar Current and its fronts. Furthermore, we expect that the collected XBT data will serve as a useful tool for the calibration and validation of recent satellite observations and for the improvement of Southern Ocean oceanographic simulations. [ABSTRACT FROM AUTHOR]

How much information does a dataset contain about an outcome of interest? To answer this question, estimates are generated for a given dataset, representing the minimum possible absolute prediction error for an outcome variable that any model could achieve. The estimate is produced using a constrained omniscient model that mandates only that identical observations receive identical predictions, and that observations which are very similar to each other receive predictions that are alike. It is demonstrated that the resulting prediction accuracy bounds function effectively on both simulated data and real-world datasets. This method generates bounds on predictive performance typically within 10% of the performance of the true model, and performs well across a range of simulated and real datasets. Three applications of the methodology are discussed: measuring data quality, model evaluation, and quantifying the amount of irreducible error in a prediction problem. [ABSTRACT FROM AUTHOR]

Skin conductance is a commonly used physiological measure during psychology experiments, such as during fear conditioning. Methods for scoring skin conductance responses (SCRs) are highly heterogeneous, though most researchers agree that manually inspected scores provide the highest quality data when compared to most available fully automated scoring methods. However, manual scoring is extremely time‐consuming. We developed a semi‐automated scoring program that reduces the time required to process SCR data at a level of quality akin to manual scoring. In contrast to all previous scoring programs, our program enables scoring of first interval response (FIR), second interval response (SIR), and third interval response (TIR) SCRs. Using interclass correlation coefficients (ICCs), Bland–Altman plots and Pareto analysis, we show here that our method is highly reliable and produces data that are almost identical to data that are manually scored and scored using LEDALAB. This software is very easy to use and is freely available to download and modify. We expect that this software will be helpful in reducing the time required to produce high quality FIR, SIR, and TIR skin conductance data for psychology researchers around the world. [ABSTRACT FROM AUTHOR]

The Mount Brown South ice core (MBS 69.111° S 86.312° E) is a new, high resolution ice core drilled in coastal East Antarctica. With mean annual accumulation estimated to be 20–30 cm ice equivalent accumulation throughout the length of the core (∼295 m), MBS represents a high resolution archive of ice core data spanning 1137 years (873–2009 CE), from an area previously underrepresented by high resolution ice core data. Here, we present a high-resolution dataset of chemistry and impurities obtained via continuous flow analysis (CFA). The dataset consists of meltwater electrolytic conductivity, sodium (Na+), ammonium (NH4+), hydrogen peroxide (H2O2), and insoluble microparticle measurements. The data are presented in three datasets: as a 1 mm depth resolution record, 3 cm averaged record, and decadal average record. The 1 mm record represents an oversampling of the true resolution, as due to smoothing effects the actual resolution is closer to 3 cm for some species. Therefore, the 3 cm resolution dataset is considered to be the minimum true resolution given the system setup. We also describe the current Copenhagen CFA system, and provide a detailed assessment of data quality, precision, and functional resolution. The 1 mm averaged, 3 cm averaged, and MBS2023 decadal averaged datasets are available at the Australian Antarctic Data Center: http://dx.doi.org/doi:10.26179/9tke-0s16 (Harlan et al., 2024). [ABSTRACT FROM AUTHOR]

ABSTRACT The positioning uncertainty of mobile phone location (MPL) data greatly influences location services and crowd behavior analysis. Although many achievements have been made in controlling its main sources (signal drift and ping‐pong effects), several problems, such as single‐oscillation patterns, insufficient position optimization, and a lack of effective evaluation, remain. In this study, a set of MPL data quality optimization methods are proposed. First, the characteristics of drift records and the oscillation patterns of ping‐pong records are discussed. The quality of the MPL data is subsequently controlled with the proposed feature‐based drift‐record detection method, complex oscillation pattern‐based ping‐pong‐record detection method, and cumulative duration weighting‐based ping‐pong‐record optimization method. These methods are applied to the MPL dataset of a major operator in Nanjing city, and the optimization effect is evaluated with GPS data collected synchronously. The results show that the proposed detection and optimization methods can effectively improve the accuracy of MPL data. [ABSTRACT FROM AUTHOR]

During the development activities of SuperCam Calibration Target, target intended for one of the two first Raman instruments to be deployed on another planetary body, our group developed a laboratory instrument that could simulate to some extent the Raman capabilities of one of such instruments and could provide data with similar quality. The use of this kind of laboratory instruments has demonstrated its utility in the evaluation of potential calibration targets or anticipating the science outcome that an instrument could provide. The present work describes our laboratory setup to support SuperCam, evaluating similarities between both instruments, despite of differences in the hardware. Evaluation of data gathered by SuperCam on Mars and the availability of one replica of SuperCam's Calibration Target allowed the comparison on the same set of targets, demonstrating how similar Signal-to-Noise Ratio (SNR) could be achieved from both instruments. The higher energy per pulse on SimulCam is compensated by a greater analytical footprint and the use of smaller collection optics. The results show how spectra obtained at representative distances of SuperCam are comparable. Operational principles are also comparable in terms of time resolution, and close in terms of spectral resolution. This similarity has allowed different science support works using SimulCam data, as well as the support to Mars detections using our setup. We provide examples of this support that will be shared with the community in different papers, as well as examples of possible operations activities that could benefit from experiments performed with SimulCam. We show how this setup can complement the two laboratory replicas in Los Alamos and Toulouse in providing support data to different experiments. [ABSTRACT FROM AUTHOR]

Software defect prediction datasets often suffer from issues such as class imbalance, noise, and class overlap, making it difficult for classifiers to identify instances of defects. In response, researchers have proposed various techniques to mitigate the impact of these issues on classifier performance. Oversampling is a widely used method to address class imbalance. However, in addition to inherent noise and class overlap in the datasets themselves, oversampling methods can introduce new noise and class overlap while addressing class imbalance. To tackle these challenges, we propose a software defect prediction method called AS-KDENN, which simultaneously improves the effects of class imbalance, noise, and class overlap on classification models. AS-KDENN first performs oversampling using the Adaptive Synthetic Sampling Method (ADASYN), followed by our proposed KDENN method to address noise and class overlap. Unlike traditional methods, KDENN takes into account both the distance and local density information of overlapping samples, allowing for a more reasonable elimination of noise and instances of overlapping. To demonstrate the effectiveness of the AS-KDENN method, we conducted extensive experiments on 19 publicly available software defect prediction datasets. Compared to four commonly used oversampling techniques that also address class overlap or noise, the AS-KDENN method effectively alleviates issues of class imbalance, noise, and class overlap, subsequently improving the performance of the classifier models. [ABSTRACT FROM AUTHOR]

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation. [ABSTRACT FROM AUTHOR]

Background: Error-corrected next-generation sequencing (ecNGS) technologies have enabled the direct evaluation of genome-wide mutations after exposure to mutagens. Previously, we reported an ecNGS methodology, Hawk-Seq™, and demonstrated its utility in evaluating mutagenicity. The evaluation of technical transferability is essential to further evaluate the reliability of ecNGS-based assays. However, cutting-edge sequencing platforms are continually evolving, which can affect the sensitivity of ecNGS. Therefore, the effect of differences in sequencing instruments on mutation data quality should be evaluated. Results: We assessed the performance of four sequencing platforms (HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400) with the Hawk-Seq™ protocol for mutagenicity evaluation using DNA samples from mouse bone marrow exposed to benzo[a]pyrene (BP). The overall mutation (OM) frequencies per 106 bp in vehicle-treated samples were 0.22, 0.36, 0.46, and 0.26 for HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400, respectively. The OM frequency of NextSeq2000 was significantly higher than that of HiSeq2500, suggesting the difference to be based on the platform. The relatively higher value in NextSeq2000 was a consequence of the G:C to C:G mutations in NextSeq2000 data (0.67 per 106 G:C bp), which was higher than the mean of the four platforms by a ca. of 0.25 per 106 G:C bp. A clear dose-dependent increase in G:C to T:A mutation frequencies was observed in all four sequencing platforms after BP exposure. The cosine similarity values of the 96-dimensional trinucleotide mutation patterns between HiSeq and the three other platforms were 0.93, 0.95, and 0.92 for NovaSeq, NextSeq, and DNBSeq, respectively. These results suggest that all platforms can provide equivalent data that reflect the characteristics of the mutagens. Conclusions: All platforms sensitively detected mutagen-induced mutations using the Hawk-Seq™ analysis. The substitution types and frequencies of the background errors differed depending on the platform. The effects of sequencing platforms on mutagenicity evaluation should be assessed before experimentation. [ABSTRACT FROM AUTHOR]

Introduction: The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. Methods: The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis. Results: The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project. Discussion: Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios. [ABSTRACT FROM AUTHOR]

An efficient data packet delivery in a vehicular ad hoc network (VANET) is still a challenging task because of the rapid changes in the network topology and the instability of link quality, especially for nonsafety applications. Several routing schemes are proposed to improve the reliability of data delivery between vehicles. These protocols still suffer from the growth of the data packet delivery delay, the lost data packets, and the control packets. For this reason, we propose a novel cross‐layer routing protocol, which is the first that uses virtual multibackbones and combines them with the quality of the link to choose the next forwarder vehicle. The next candidate forwarders, among neighbors, are those that keep a minimum of link quality during the data packet delivery delay. Then, among these candidates, we select that is closest to the destination vehicle to receive and forward the data. In addition, our proposal gives priority to those that travel toward the destination vehicle avoiding the forwarding of data several times by the same vehicle. The substantial simulations show that our protocol outperforms certain of the well‐known existing schemes in the VANET area by varying the vehicle density on the highway. It exhibits an improvement of 6.72% in the average end‐to‐end delay of data delivery, 14.44% in the packet delivery ratio, 11.2% in the throughput, and 6.4% in the control overhead as compared to other schemes. [ABSTRACT FROM AUTHOR]

Background: Preprints are scientific articles that have not undergone the peer-review process. They allow the latest evidence to be rapidly shared, however it is unclear whether they can be confidently used for decision-making during a public health emergency. This study aimed to compare the data and quality of preprints released during the first four months of the 2022 mpox outbreak to their published versions. Methods: Eligible preprints (n = 76) posted between May to August 2022 were identified through an established mpox literature database and followed to July 2024 for changes in publication status. Quality of preprints and published studies was assessed by two independent reviewers to evaluate changes in quality, using validated tools that were available for the study design (n = 33). Tools included the Newcastle-Ottawa Scale; Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2); and JBI Critical Appraisal Checklists. The questions in each tool led to an overall quality assessment of high quality (no concerns with study design, conduct, and/or analysis), moderate quality (minor concerns) or low quality (several concerns). Changes in data (e.g. methods, outcomes, results) for preprint-published pairs (n = 60) were assessed by one reviewer and verified by a second. Results: Preprints and published versions that could be evaluated for quality (n = 25 pairs) were mostly assessed as low quality. Minimal to no change in quality from preprint to published was identified: all observational studies (10/10), most case series (6/7) and all surveillance data analyses (3/3) had no change in overall quality, while some diagnostic test accuracy studies (3/5) improved or worsened their quality assessment scores. Among all pairs (n = 60), outcomes were often added in the published version (58%) and less commonly removed (18%). Numerical results changed from preprint to published in 53% of studies, however most of these studies (22/32) had changes that were minor and did not impact main conclusions of the study. Conclusions: This study suggests the minimal changes in quality, results and main conclusions from preprint to published versions supports the use of preprints, and the use of the same critical evaluation tools on preprints as applied to published studies, in decision-making during a public health emergency. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri‐ictal periods in performing a feature‐based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring.This study assesses the signal quality of wearable data during peri‐ictal phases of generalized tonic–clonic and focal to bilateral tonic–clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri‐ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS.We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre‐ and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity.Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist‐worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts. [ABSTRACT FROM AUTHOR]