Drug Cost-Effectiveness Assessments Require Standards for Rigor and Inclusion by Tia Goss Sawhney, Neil Thakur

“…ICER should re-evaluate their reliance on quality adjusted life-years and equal value of life years gained as measures of drug effectiveness, establish data quality and inclusiveness minimum standards, produce cost-effectiveness assessments only when the minimum data is available, and prominently report data quality and inclusion limitations. …”
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Application of random forest in big data completion by Zheng WANG, Hua REN, Yanping FANG

“…The construction of completion data subsystem in the system log retained end-to-end data quality guaranteed and improved quality, completed and improved the real-time data and historical data, and ultimately met the requirements of data processing and mining operators, improved data quality and value.…”
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Guidelines for Research Data Integrity (GRDI) by Gregor Miller, Elmar Spiegel

“…In addition to raising awareness about potential pitfalls and establishing standardization in research data usage, the proposed guidelines serve as a reference for researchers to provide a consistent standard of data quality. Furthermore, they improve the robustness and reliability of the scientific landscape by emphasising the critical role of data quality in research.…”
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Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh

“…Several factors can impact data quality, such as the presence of duplicates, data leakage across train-test partitions, mislabeled images, and the absence of a well-defined test partition. …”
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Reliability Evaluation of Pavement Life-Cycle Assessment Model by Jiale Huang, Fei Xiao, Yang Zhang

“…Inventory reliability of the life-cycle assessment (LCA) model highly depends on the data quality and normally exhibits significant uncertainty. …”
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Using Participatory Crowdsourcing in South Africa to Create a Safer Living Environment by Bhaveer Bhana, Stephen Flowerday, Aharon Satt

“…This study proposes public safety data quality criteria which were developed to assess and identify the problems affecting data quality. …”
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Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data by Pengfei Zhang, Zhenliang Ma, Xiaoxiong Weng

“…Data quality is essential for its authentic usage in analysis and applications. …”
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Situational analysis of the quality of maternal, child, and adolescent health data in the health districts of Thiès, Mbour, Kédougou, and Saraya in Senegal by Fatoumata Binetou Diongue, Adama Faye, Cheikh Loucoubar, Ndèye Marème Sougou, Ibrahima Sy, Adama Tall, Youssoupha Ndiaye, Samba Cor Sarr

“…Quality data strengthens the validity and reliability of research results. Common areas of data quality include accuracy, completeness, consistency, credibility, and timeliness. …”
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Application and Legal Governance of Computational Technologies in Credit Investigation: An Interdisciplinary Perspective by Ying Zhang, Zhen Xie, Xiuwei Qu

“…By analyzing the computational technologies and algorithms most commonly used in credit data collection and data storage, data transmission and data access, data analysis and processing, data calculation, result output and effect evaluation, this paper summarizes and proposes a unified general process of modern credit investigation, pointing out that in this general process, low data quality, privacy violation, algorithmic bias are the main challenges in the big data era, and countermeasures like data quality control, privacy protection, and algorithm governance need to be to be taken seriously into account in order to further explore the great potential of the credit investigation under the legal framework.…”
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A survey on key technologies of privacy protection for machine learning by Zishan LIU, Qiang CHENG, Bo LV

“…With the development of information and communication technology,large-scale data collection has vastly promoted the application of machine learning in various fields.However,the data involved in machine learning often contains a lot of personal private information,which makes privacy protection face new risks and challenges,and has attracted more and more attention.The current progress of the related laws,regulations and standards to the personal privacy protection and data safety in machine learning were summarized.The existing work on privacy protection for machine learning was presented in detail.Privacy protection algorithms usually have influence on the data quality,model performance and communication cost.Thus,the performance of the privacy protection algorithms should be comprehensively evaluated in multiple dimensions.The performance evaluation metrics for the privacy protection algorithms for machine learning were presented,given with the conclusion that the privacy preservation on machine learning needs to balance the data quality,model convergence rate and communication cost.…”
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Database quality assessment in research in paramedicine: a scoping review protocol by Dean Kriellaars, Gordon Giesbrecht, Neil McDonald, Malcolm Doupe, Rob T Pryce

“…Variable or unknown data quality can influence the validity of research in paramedicine. …”
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Shuffled differential privacy protection method for K-Modes clustering data collection and publication by Weijin JIANG, Yilin CHEN, Yuqing HAN, Yuting WU, Wei ZHOU, Haijuan WANG

“…Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.…”
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Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing by Zepei Li, Peng Zheng, Yanjia Tian

“…Experiments conducted on the NASA Turbofan Engine Degradation and UCI Hydraulic Systems datasets demonstrate that BD-IoTQNet outperforms existing models in accuracy, precision, and data quality improvement, with reduced latency and enhanced robustness under noisy and missing data conditions. …”
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Study on Missing Data Filling Algorithm of Nuclear Power Plant Operation Parameters by Tianshu Wang, Ren Yu, Qiao Peng

“…In order to improve the data quality, two parts of researches are carried on. …”
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Feasibility of Output-Only Modal Identification Using Wireless Sensor Network: A Quantitative Field Experimental Study by Wen-ai Shen, Ying Lei, Liang Hu, Yang Wang

“…This paper first assesses the accuracy of time synchronization performed by the WSN through a centralized beacon signal and then studies wireless data quality using two quantitative performance indexes. …”
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A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization by Binglin Liu, Qian Li, Zhihua Zheng, Yanjia Huang, Shuguang Deng, Qiongxiu Huang, Weijiang Liu

“…We found that multi-source data, including sensors, social media, citizen feedback, and GIS data, face challenges such as data quality and privacy security when being fused. Data fusion algorithms are diverse and have their own advantages and disadvantages. …”
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Blockchain based adaptive federated learning in computational power network by LIU Tianrui, WANG Lianhai, WANG Qizheng, XU Shujiang, ZHANG Shuhui, WANG Yingxiaochun

“…Concurrently, an adaptive weight aggregation algorithm was designed to alleviate the decrease in model performance and stability caused by differences in data quality, thereby improving the accuracy of the model and accelerating model convergence. …”
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Neonatal indicator data in Tanzania District Health Information System: evaluation of availability and quality of selected newborn indicators, 2015-2022 by Josephine Shabani, Nahya Salim, Christine Bohne, Louise Tina Day, Claud Kumalija, Ahmad Mohamed Makuwani, Felix Bundala, Habib Ismail, Joy E. Lawn, Eric O. Ohuma

“…Placing greater emphasis on using DHIS2 data to monitor progress will help identify gaps and drive improvements in data quality, ultimately supporting better maternal and newborn health outcomes.…”
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Prevalence of overweight/obesity and its association with fast food consumption among adolescents in Southern Ethiopia, 2022: a community-based cross-sectional study by Chernet Elias, Awoke Abraham, Chernet Asrat, Tagese Yakob, Daniel Girma

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Adaptive selection method of desensitization algorithm based on privacy risk assessment by Lijun ZU, Yalin CAO, Xiaohua MEN, Zhihui LYU, Jiawei YE, Hongyi LI, Liang ZHANG

“…The financial industry deals with a vast amount of sensitive data in its business operations.However, the conventional approach of binding financial data for desensitization and using desensitization algorithms is becoming inefficient due to the fast-paced growth of financial businesses and the proliferation of data types.Additionally, manual verification and assessment of desensitized data by security experts are time-consuming and may carry potential privacy risks due to the improper selection of desensitization algorithms.While prior research has emphasized desensitization methods and privacy-preserving technologies, limited work has been conducted on desensitization algorithms from the perspective of automation.To address this issue, an adaptive recommendation framework was propose for selecting desensitization strategies that consider various factors, such as existing privacy protection technologies, data quality requirements of business scenarios, security risk requirements of financial institutions, and data attributes.Specifically, a dual-objective evaluation function was established for privacy risk and data quality to optimize the selection of desensitization algorithm parameters for different algorithms.Furthermore, the desensitization algorithm and parameters were adaptively selected by considering the data attributes through a multi-decision factor system and desensitization effect evaluation system.Compared to traditional approaches, the proposed framework effectively tackle issues of reduced data usability and inadequate personal data privacy protection that derive from manual intervention.Testing on a dataset with multiple financial institution types, the experiments show that the proposed method achieves a recommendation accuracy exceeding 95%, while the desensitized privacy risk level differed by less than 10% from the expected level.Additionally, the recommendation efficiency is 100 times faster than expert manual processing.…”
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