A qualitative exploration of the early adoption of an electronic medical record system for type 1 diabetes management in Rwanda by Nathalie Bille, Dirk Lund Christensen, Stine Byberg, Crispin Gishoma, Sarah Fredsted Villadsen, Michael Calopietro

“…The system was described as user-friendly and improved workflows, data quality, and patient care. The main concerns to successful adoption were related to the maintenance and long-term sustainability of the system. …”
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COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic by Yo Yehudi, Lukas Hughes-Noehrer, Carole Goble, Caroline Jay

“…These are: (1) Knowing data exists, (2) being able to access that data, (3) data quality, and (4) ability to share data onwards. …”
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Big Data Cleaning Based on Improved CLOF and Random Forest for Distribution Networks by Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng

“…In order to improve the data quality, the big data cleaning method for distribution networks is studied in this paper. …”
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Digitalized Optical Sensor Network for Intelligent Facility Monitoring by Esther Renner, Lisa-Sophie Haerteis, Joachim Kaiser, Michael Villnow, Markus Richter, Torsten Thiel, Andreas Pohlkötter, Bernhard Schmauss

“…The realized network services promote sensor data quality, fusion, and modeling, expanding opportunities using digital twin technology.…”
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Integrating artificial intelligence in energy transition: A comprehensive review by Qiang Wang, Yuanfan Li, Rongrong Li

“…Despite these advancements, challenges remain, including data quality and accessibility, system interoperability, scalability, and concerns regarding privacy and ethics. …”
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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…The surge in its use has sparked new research interests in understanding its data quality. Many studies have employed field calibration to improve sensor agreement with co-located reference monitors. …”
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A bird’s-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides by Maduravani Ramasundaram, Honglae Sohn, Thirumurthy Madhavan

“…Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. …”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. …”
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A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy by Chetan Shetty, S. Seema, B. J. Sowmya, Rajesh Nandalike, S. Supreeth, Dayananda P., Rohith S., Vishwanath Y., Rajeev Ranjan, Venugopal Goud

“…Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. …”
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Evaluation of care and clinical outcomes after the implementation of an electronic medical record system for type 1 diabetes management in Rwanda by Nathalie Bille, Dirk Lund Christensen, Knut Borch-Johnsen, Crispin Gishoma, Stine Byberg

“…However, the impact on data quality, quality of care and clinical outcomes for type 1 diabetes (T1D) in sub-Saharan Africa (SSA) has not yet been explored. …”
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Matched pairs demonstrate robustness against inter-assay variability by Jochem Nelen, Horacio Pérez-Sánchez, Hans De Winter, Dries Van Rompaey

“…These results establish a benchmark for expected noise in matched molecular pair data from the ChEMBL database, offering practical metrics for data quality assessment.…”
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Species distribution modelling using MaxEnt: overview and prospects by Yuliia Novoseltseva

“…In particular, it analyzes the impact of data quality on modeling results and considers various approaches to assessing the importance of ecological factors for species distribution. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…In the preprocessing phase, super-resolution is applied to input data using LapSRN to effectively enhance the data quality, aiming to achieve optimal performance. …”
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User avoidance behavior in pharmaceutical e-commerce intelligent customer service: a stressor-strain-outcome perspective by Jing Jia, Lu Chen, Chengzhen Wu, Manling Xiao

“…It expands the boundaries of technology acceptance research by focusing on ICS use and avoidance in high-risk environments.MethodA total of 418 valid questionnaires were collected from participants, ensuring data quality through rigorous screening. The study employed SPSS for data normality tests and SmartPLS for structural equation modeling to analyze the relationships between emotional stress, system overload, and user avoidance behavior.ResultsThe findings indicate that system overload, information overload, and service overload significantly contribute to user emotional stress, which in turn drives avoidance behavior. …”
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Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing by Amruta Awasthi, Lenka Krpalkova, Joseph Walsh

“…The importance of including stakeholders in the data review process to enhance the data quality is accentuated in this paper, specifically when big data analysis is to be integrated into corporate strategies. …”
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Artificial Intelligence in Dyslexia Research and Education: A Scoping Review by Jia Rong Yap, Thirishankari Aruthanan, Mellisa Chin

“…Additionally, five significant challenges were identified: data quality and quantity, accessibility of artificial intelligence-powered support, bias and validity in assessments, balancing artificial intelligence assistance with human expertise, and transparency in artificial intelligence decision-making. …”
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scATAC-seq generates more accurate and complete regulatory maps than bulk ATAC-seq by E. Ravza Gur, Jim R. Hughes

“…However, scATAC-seq provides substantially higher data quality compared to bulk ATAC-seq improving the sensitivity to detect relatively weak, but functionally important ATAC-seq signals. …”
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BanglaBlend: A large-scale nobel dataset of bangla sentences categorized by saint and common form of bangla languageMendeley Data by Umme Ayman, Chayti Saha, Azmain Mahtab Rahat, Sharun Akter Khushbu

“…To achieve the best data quality, rigorous pre-processing techniques such as anonymization and duplication removal were used. …”
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An evaluation of the severe acute respiratory infection surveillance system in Ireland by Louise Marron, Roisin Duffy, Joan O’Donnell, Lisa Domegan

“…However, improvement in the timeliness of WGS data and recording of vaccination status and equity stratifiers e.g. ethnicity, would strengthen data quality. Priorities for expansion include process automation and adequate resource allocation, to enable reliable SARI surveillance at all times.…”
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Deep(er) reconstruction of imaging Cherenkov detectors with swin transformers and normalizing flow models by C Fanelli, J Giroux, J Stevens

“…Fast reconstruction algorithms are essential for near real-time alignment, calibration, data quality control, and efficient analysis. At the future electron–ion collider (EIC), the ePIC detector will feature a dual Ring Imaging Cherenkov (RICH) detector in the hadron direction, a Detector of Internally Reflected Cherenkov (DIRC) in the barrel, and a proximity focus RICH in the electron direction. …”
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