Complete Data Research Articles

Projective Incomplete Multi-View Clustering.

Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering, MVC improves clustering performance by exploiting complementary and consistent information among different views. Such methods are all based on the assumption of complete views, which means that all the views of all the samples exist. It limits the application of MVC, because there are always missing views in practical situations. In recent years, many methods have been proposed to solve the incomplete MVC (IMVC) problem and a kind of popular method is based on matrix factorization (MF). However, such methods generally cannot deal with new samples and do not take into account the imbalance of information between different views. To address these two issues, we propose a new IMVC method, in which a novel and simple graph regularized projective consensus representation learning model is formulated for incomplete multi-view data clustering task. Compared with the existing methods, our method not only can obtain a set of projections to handle new samples but also can explore information of multiple views in a balanced way by learning the consensus representation in a unified low-dimensional subspace. In addition, a graph constraint is imposed on the consensus representation to mine the structural information inside the data. Experimental results on four datasets show that our method successfully accomplishes the IMVC task and obtain the best clustering performance most of the time. Our implementation is available at https://github.com/Dshijie/PIMVC.

Development and application of multi-parameter real-time acquisition system for intelligent secondary equipment

Abstract A multi-parameter real-time collection and analysis system, including intelligent secondary equipment, data collection gateway, and health management backend, has been constructed to solve the problem of current incomplete monitoring data and inability to providential warning of hidden faults of intelligent secondary equipment in substations. The key feature parameters of the device’s functional modules were extracted, and a data collection gateway was developed to achieve IoT access to feature data. A fault identification and warning model for the secondary equipment functional module considering temperature is provided, which realizes the assessment of equipment health status and ensures the stable and reliable operation of the power grid.

A challenging decision for emergency physicians: Routine repeat computed brain tomography of the brain in head trauma in infants and neonates.

Head trauma is a leading cause of death and disability. While standard treatment protocols exist for severe head trauma, no clear follow-up standards are available for mild head trauma with positive imaging findings in infants and newborns. Although routine follow-up brain computed tomography (CT) imaging is not recommended for children with moderate and mild head trauma, the necessity for follow-up imaging in infants and newborns remains uncertain. Our study is a retrospective, observational, and descriptive study. Infants under 1 year old presenting to the emergency department with isolated head trauma were reviewed with the approval of the Ethics Committee of Ankara Etlik City Hospital. Inclusion criteria included presentation to the emergency department, undergoing more than one brain CT scan, and sustaining mild head trauma (Glasgow Coma Scale [GCS] >13). Patients with incomplete follow-up data or multiple traumas were excluded. Age, gender, mechanism of trauma, initial and follow-up brain CT findings, hospital admission, and surgical procedures were recorded and analyzed using the SPSS statistical package. Out of 238 screened patients, 154 were included in the study. Of these, 66.9% were male and the average age was 5.99 months. The most common presenting symptom was swelling at the trauma site, observed in 79.2% of cases. The most common mechanism of injury was falling from a height of less than 90 cm, accounting for 85.1% of cases. Pathological progression on follow-up CT was observed in 5.2% of the patients, and only 1.9% required surgical treatment. A total of 34.4% of the patients required hospitalization. Patients with parenchymal brain pathology had a higher rate of pathological progression on follow-up CT and a longer hospital stay. Follow-up CT scans in infants with mild head trauma do not alter patient outcomes except in cases with brain parenchymal pathology. Study data indicated that repeat imaging is not beneficial for isolated skull fractures. Imaging artifacts often necessitated repeated scans, contributing to increased radiation exposure. Unnecessary repeat imaging escalates radiation exposure and healthcare costs. Only a small percentage of patients exhibited progression of intracranial pathology, justifying follow-up imaging solely in the presence of brain parenchymal injury. Larger prospective studies are necessary to confirm these findings.

Characteristics and career outcomes of dermatology-focused medical student research grant recipients

Although several dermatology-focused research grants for medical students exist, studies have yet to evaluate the outcomes of grant recipients, such as entry into dermatology residency and academic careers. We have described the characteristics of recipients of dermatology-focused medical student research grants and outcomes, including entry into dermatology residency and academic careers, and we have focused on seven dermatology-focused national and regional research grants eligible for U.S. medical students. Data were obtained from publicly available online sources for grants from 2004 to 2023. Of the 235 medical student recipients of dermatology research grants between 2004 and 2023, 45.5% attended one of the top 20 medical schools funded by National Institutes of Health research. Of those who completed medical school, 68.3% advanced to a dermatology residency (n=123/180). Among board-certified dermatologists, 44.7% held an academic position (n=34/76); among those who attended a top 20 medical school, 50% held an academic position (n=23/46) compared to 36.7% who did not (n=11/30). Limitations of this study include selection bias and incomplete data availability. Medical student research grants allow students to thoughtfully engage in dermatology research early in medical education. They may facilitate entry into dermatology residency and academic careers and lead to continued research endeavors.

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing views or some uncertain labels. Recently, many approaches have been proposed for incomplete data, but few of them can handle the case of both missing views and labels. Moreover, these few existing works commonly ignore potentially valuable information about unknown labels or do not sufficiently explore latent label information. Therefore, in this paper, we propose a label semantic-guided contrastive learning method named LSGC for the dual incomplete multi-view multi-label classification problem. Concretely, LSGC employs deep neural networks to extract high-level features of samples. Inspired by the observation of exploiting label correlations to improve the feature discriminability, we introduce a graph convolutional network to effectively capture label semantics. Furthermore, we introduce a new sample-label contrastive loss to explore the label semantic information and enhance the feature representation learning. For missing labels, we adopt a pseudo-label filling strategy and develop a weighting mechanism to explore the confidently recovered label information. We validate the framework on five standard datasets and the experimental results show that our method achieves superior performance in comparison with the state-of-the-art methods.

Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.

Missed Follow-up is associated with worse survival in stage I lung cancer: results from a large multi-site academic hospital system

The purpose of this study is to examine the effect of early incomplete follow-up on overall survival among stage I lung cancer patients. Patients with clinical stage I lung cancer at our institution between 2007 and 2016 were identified (N = 1111). Exclusions included < 18 years of age (N = 2), missing stage or demographics (N = 56), incomplete appointment data or had only one scheduled appointment (N = 351), or did not survive for at least 1 year after diagnosis (N = 120). Missed appointments were defined as unattended follow-up appointments within the first year of diagnosis without an attended appointment in the subsequent 60 days. The primary outcome was the hazard ratio (HR) for death associated per 10% increase in missed oncology follow-up appointments. Univariable and descriptive statistics were performed, and a multivariable landmark Cox regression model was created to examine the effect of missed oncology follow-up on survival. A total of 582 patients were analyzed with median follow-up of 3.2 years and median age of 69 years. On multivariable analysis controlling for age, sex, race, insurance status, and definitive treatment type the HR for death was 1.44 (95% CI 1.05–1.97) for every 10% increase in missed appointments. Incomplete oncologic follow-up may negatively impact overall survival among survivors of early-stage lung cancer.

Data Completion-Guided Unified Graph Learning for Incomplete Multi-View Clustering

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data for performance improvement. Unfortunately, some instances may be with partially available information because of some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVC aims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneity of multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods like BSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without taking advantage of the correlation between samples and information redundancy. To overcome the above issue, we propose one novel IMVC method named data completion-guided unified graph learning (DCUGL), which could complete the data of missing views and fuse multiple learned view-specific similarity matrices into one unified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specific similarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensor with the low-rank constraint, such that sample correlation within and between views can be well explored. Finally, by dividing the original data into observed data and unobserved data, DCUGL can infer and complete the missing data according to the view-specific similarity matrices, and obtain a unified graph, which can be directly used for clustering. To solve the proposed model, we design an iterative algorithm, which is based on the alternating direction method of multipliers framework. The proposed model proves to be superior by benchmarking on six challenging datasets compared with state-of-the-art IMVC methods.

Harnessing machine learning for predictive maintenance in energy infrastructure: A review of challenges and solutions

Predictive maintenance (PdM) has emerged as a vital strategy for optimizing the reliability and efficiency of energy infrastructure. In this paper, we present a comprehensive review of the challenges and solutions associated with harnessing machine learning (ML) techniques for predictive maintenance in the energy sector. The adoption of ML algorithms in predictive maintenance holds immense promise for mitigating equipment failures, reducing downtime, and optimizing maintenance schedules. However, several challenges impede the effective implementation of ML-based PdM strategies. These challenges include the need for large and high-quality data sets, the complexity of integrating heterogeneous data sources, and the interpretability of ML models in real-world settings. To address these challenges, we discuss various solutions and best practices. These include data preprocessing techniques to handle noisy and incomplete data, feature engineering methods for extracting meaningful insights, and model interpretability approaches for enhancing trust and understanding of ML predictions. Additionally, we explore the integration of domain knowledge and human expertise into ML algorithms to improve predictive accuracy and relevance. Furthermore, we examine the role of edge computing and distributed ML techniques in enabling real-time predictive maintenance, particularly in remote or resource-constrained environments. We also discuss the importance of regulatory compliance, privacy protection, and ethical considerations in the deployment of ML-based PdM solutions.

Industrial Battery State-of-Health Estimation with Incomplete Limited Data Towards Second-Life Applications

Battery State of Health (SOH) estimation is vital across applications ranging from portable electronics to electric vehicles, particularly in second-life applications where accurate prediction becomes complex due to varying degradation levels. This paper introduces a novel SOH estimation model to address the lack of labeled data, employing Domain Adversarial Neural Networks (DANN) combined with 1-dimensional Convolutional Neural Networks (CNN). The proposed method allows for effective transfer of knowledge between diverse battery conditions, enhancing adaptability and efficiency by utilizing both source and target datasets. Experimental results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 1.68% and a Root Mean Squared Error (RMSE) of 2.50%, with minimal data. Specifically, the model requires only one cell of unlabeled data from the second-life target domain, utilizing only the dQ/dV curve for estimation. Proposed model sets a new standard in second-life battery health monitoring and management by effectively leveraging a minimal amount of data for training, this approach offers a robust solution for accurate SOH estimation, particularly in scenarios with limited access to labeled data. Conflict of Interest Statement The authors declare no conflicts of interest.

Human Vital Signs Signal Monitoring and Repairment with an Optical Fiber Sensor Based on Deep Learning

Optical fiber sensors have been widely applied for their advantages such as small size, lightweight, and strong electronic interference robustness. Compared with current electronic sensors, optical fiber sensors perform better in measuring parameters in harsh environments, which makes them suitable for more and more applications, such as target tracing and detection and monitoring of health signs in medical services. However, due to fiber optic sensor failure, improper transmission and storage, or other reasons, missing data occur from time to time. Therefore, effective missing value processing methods are desirable as they can be used to facilitate data processing or analysis. In the present study, gated recurrent unit (GRU) interpolation is performed by using the generative adversarial network (GAN) model to process the irregular delay relationship between the data before and after the collection of incomplete vital signs data. Furthermore, a data interpolation model based on VS-E2E-GAN is proposed to reconstruct vital signs signals. The ROC curve (AUC), metrics including mean squared error (MSE), and accuracy (ACC) of experiments reach 0.901, 0.777, and 0.908, respectively, which indicates that the proposed VS-E2E-GAN model performs well in terms of vital signs data imputation and repairment, has strong robustness when compared with other works, and has potential clinical application in health monitoring, smart home, and so on.

Neonatal Riga-Fede disease: A systematic review of 84 cases collected over 120 years

Background: Traumatic ulceration of the tongue due to congenital teeth is known as Riga-Fede disease (RFD). The literature on RFD consists largely of anecdotal case reports, thereby lacking valid clinical recommendations. While managing a newborn with RFD complicated by Ludwig’s angina, this knowledge gap was recognized. Therefore, this systematic review was conducted to gain insight into the optimal management and the possible complications of RFD. Methods: A literature search was conducted using PubMed, Google Scholar, and Science Direct, supplemented with African Journals Online, Indmed, and manual searches. Papers published between 1900 and July 2023 in any language were retrieved. Neonates with natal or neonatal teeth and ulceration of the oral mucosa were included. Papers with incomplete data and review articles were excluded. Results: A total of 84 newborns with RFD from 62 published reports (including the case reported herein) were analyzed. Most of the reports were from the Indian subcontinent, and the frequency of reporting has increased since the year 2000. Tongue ulceration in all cases was due to mandibular incisors. There was no significant difference in the median duration of healing between the tooth extraction group (14 days) and the conservative management group (18 days). There was only a very weak positive correlation between the duration of symptoms and healing time, which was statistically insignificant. Conclusion: Healing of mucosal ulcers in RFD is often protracted and does not appear to be influenced by the duration of symptoms or by the modality of treatment. Extraction of the offending teeth is more common (75% of cases) than conservative management, such as grinding (ameloplasty) or resin capping of teeth. RFD is mostly a benign disorder; however, serious complications such as meningitis, Ludwig’s angina, and soft tissue abscesses can rarely occur with Staphylococcus aureus infections.

Breast self-examination among female medical students at Damascus University: A cross-sectional study

IntroductionBreast cancer (BC) is the leading cause of death in Syria. In young females, it is a serious complication, making it crucial to raise awareness about the disease and its early detection methods. Given the challenging circumstances that Syrians face, regular visits to medical centers for mammography are often not feasible. Therefore, breast self-examination (BSE) is a valuable tool for detecting cancer. Educating girls in medical colleges is key to disseminating knowledge about BSE among women, as they will become future healthcare providers and can share accurate information with their families. Materials and methodsA cross-sectional study was conducted in the medical faculties of Damascus University (medicine, dentistry, and pharmacy), Syria. The study utilized a structured questionnaire on Google Forms with four sections. The questionnaire was prepared electronically, and shared as an online link in formal university groups on social media. Data collection started on September 19, 2022, and ended on October 19 of the same year. Participation was accepted from female students in medical colleges, ranging from second to fifth year (for dental and pharmacy students) and sixth year (for medical students). Data from other students and incomplete data were excluded. Results589 female medical students from three faculties (medicine, dentistry, and pharmacy) filled out the questionnaire. Only 57 % of the students demonstrated a good attitude towards BSE, and 79.6 % of them agreed or strongly agreed that they avoid BSE due to fear of the consequences of breast cancer. Regarding BSE knowledge, 89.6 % of the participants reported previously hearing about BSE, and 55.7 % of them had performed BSE before. The factors that were accompanied by better knowledge about BSE were: a good attitude towards BSE; previously performed BSE; studying in a medical faculty; study years for medical and pharmacy students; and higher college average grades. Whereas, having a medical relative and having a family member with a history of BC surprisingly do not impact the knowledge. ConclusionThe study, conducted at Damascus University in Syria, assessed the knowledge and practice of breast self-examination among female medical college students. The results indicate a moderate level of knowledge and limited practical experience in performing breast self-examination. These findings highlight the urgent need to emphasize the importance of promoting and teaching this method to a wider population of women in society.

Effect of General Surgery Resident Participation in Thoracic Surgery on Oncologic Outcomes: An Observational Cohort Study.

Background: Despite increasing sub-specialization, general surgeons continue to perform oncologic thoracic surgeries. Our objective was to determine whether general surgery resident participation in thoracic surgery affects surgical quality or oncologic outcomes. We hypothesized that patient outcomes with and without resident participation would be similar. Methods: We retrospectively reviewed the electronic health records of patients with stage 0-IV lung cancer undergoing oncologic pulmonary resection at BLINDED FOR REVIEW during an 11-year period (2012-2022). Patients younger than 18years or older than 85years were excluded, as were those who had incomplete follow-up data or were unregistered in our institutional cancer registry. Patients were divided into groups based on whether residents or staff surgeons completed >50% of the critical portions of the operation. We compared 30-day morbidity outcomes, overall survival (OS), and disease-free survival (DFS). Results: Three hundred thirteen patients met inclusion criteria. Demographic and clinical characteristics were similar between groups, as were types of surgical resection and median operative times. A statistical difference was found in the distribution of surgical approach. The odds of morbidity were 65% higher in the Staff group (OR=1.65; 95% CI, 1.007-2.71). Resident participation was not significantly associated with OS or DFS (P =.32 and P =.54, respectively). Discussion: General surgery resident involvement in lung cancer operations is not associated with longer operative times but is associated with a higher likelihood of a thoracotomy. General surgery resident involvement was associated with decreased postoperative morbidity and did not significantly affect OS or DFS.

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model

The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconstruct images at higher acceleration factors and incur long reconstruction time. Furthermore, end-to-end deep learning-based reconstruction algorithms suffer from large model parameters and lack robustness in the reconstruction results. Recently, unrolled deep learning models, have shown immense potential in algorithm stability and applicability flexibility. In this paper, we propose an unrolled deep learning network based on a second-order Half-Quadratic Splitting(HQS) algorithm, where the forward propagation process of this framework strictly follows the computational flow of the HQS algorithm. In particular, we propose a degradation-sense module by associating random sampling patterns with intermediate variables to guide the iterative process. We introduce the Information Fusion Transformer(IFT) to extract both local and non-local prior information from image sequences, thereby removing aliasing artifacts resulting from random undersampling. Finally, we impose low-rank constraints within the HQS algorithm to further enhance the reconstruction results. The experiments demonstrate that each component module of our proposed model contributes to the improvement of the reconstruction task. Our proposed method achieves comparably satisfying performance to the state-of-the-art methods and it exhibits excellent generalization capabilities across different sampling masks. At the low acceleration factor, there is a 0.7% enhancement in the PSNR. Furthermore, when the acceleration factor reached 8 and 12, the PSNR achieves an improvement of 3.4% and 5.8% respectively.

Role of GeneXpert in the diagnosis of extrapulmonary tuberculosis.

The World Health Organization endorsed the cartridge-based nucleic acid amplification test Xpert MTB/RIF (GXP) for the diagnosis of tuberculosis (TB). Studies about GXP efficiency in extrapulmonary TB (EPTB) are scarce. Hence, we decided to study the role of GXP in EPTB. This prospective observational study, conducted in the pulmonary medicine department of a tertiary care hospital after ethics committee permission, recruited 200 EPTB patients. The diagnosis of TB was achieved with the help of clinico-radiological correlation with microbiological test positivity. Acid-fast bacilli (AFB) culture was treated as the comparative gold standard. Patients who had no or incomplete data were excluded from the study. Data was analyzed to calculate the sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of TB and the detection of rifampicin resistance. The majority of cases were women (126 patients: 63%). The mean age was 23.71 years. On GXP, 130 (65%) had detected mycobacterium tuberculosis (MTB), and 70 (35%) did not. Adding AFB culture data, 168 (81.5%) showed microbiological evidence of TB, and 32 (18.5%) were negative. On the drug susceptibility test, 131 cases were rifampicin-sensitive, 32 were rifampicin-resistant TB, and in 5 cases, data was unavailable. The most common extrapulmonary site of involvement was the lymph node, with 94 patients (47%). The most common lymph node involved was the cervical lymph node, with 70 patients (74.5%). The sensitivity, specificity, positive predictive value, and negative predictive value of GXP in EPTB collectively were 76.68%, 86.48%, 96%, and 45.7%, respectively. GXP is useful for the rapid detection of EPTB and the identification of rifampicin resistance, especially in a high-prevalence country like India.

Identifying risk clusters for African swine fever in Korea by developing statistical models.

African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses. We observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rank-based statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities. As a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022. This approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease.

Enhancing the Autonomy of Establishing Indicator Informatization.

Quality indicators are essential for hospital management, clinical decision-making, and patient care. In today's technologically advanced era, it is increasingly critical to derive effective statistical data analysis from intricate datasets. However, the administrative department within our hospital often fails to prioritize information needs due to limited resources. Consequently, the collection of indicator data continues to rely on paper-based operations.To tackle common issues in collecting indicator data, such as incomplete or inaccurate data, time-consuming aggregation and statistical analysis, and a lack of timeliness, we have implemented appropriate Business Intelligence (BI) tools. We provide educational training to empower users to create visual templates for indicators that are tailored to their specific requirements. This approach saves time, reduces data duplication, and minimizes writing errors. It enhances supervisors' understanding of the current situation and future prospects in terms of data analysis and timeliness. Ultimately, this initiative improves healthcare quality and management effectiveness.

Use Mobile Apps to Link to Google Forms to Conduct Online Surveys.

The study aimed to evaluate changes in anxiety levels in patients with coronary artery disease before and after cardiac catheterization. The mobile applications LINE and GOOGLE were used to collect online data. A total of 188 patients participated in the study conducted at a regional teaching hospital in eastern Taiwan, and 51 of them completed the questionnaire twice, with a response rate of 27.1%. Although the second study noted the problem of incomplete data and low response rates, this study shows that online research methodology can still be improved and that using electronic questionnaires for data collection and statistical analysis reduces the risk of errors in online research and saves time in documentation. It is recommended to provide clear and detailed instructions when conducting online surveys and to review them carefully upon completion to ensure the completeness of the data collected.

Loved everywhere?: Netflix’s top 10 and the popularity of geographically diverse content

Using Netflix’s weekly Global Top 10 lists for English-language and non-English-language series and films, this article highlights some of the ways in which the world’s most popular streaming platform misrepresents both the production and consumption of its most popular geographically diverse global content. First, the Global Top 10 lists create a series of false equivalencies between the popularity of English-language and non-English-language content that mask the difference in production budgets between the two categories. Second, Netflix’s publicized data isolates the global audience distribution of non-English-language content from other economic, technological, cultural, and political factors affecting local media ecosystems as demonstrated through an analysis of South Korea. Third, in failing to account for genre-related differences, the Global Top 10 lists distorts audience behavior in the Ibero-American region. Despite the variety of limitations presented by the incomplete audience data from Global Top 10 lists, when properly contextualized with recent academic research and economic information extracted from the specialized trade press publications, this article argues that the relationship between global streaming audiences and geographically diverse content is far more complex than it appears in the Netflix’s industrial discourses.