Poor Data Quality Research Articles

TPMv2: An end-to-end tomato pose method based on 3D key points detection

The automatic harvesting of tomatoes has been achieved for many years in the laboratory. The new research topic is harvesting the tomato more flexibly and nondestructively at any tomato bunch pose according to the agronomic demands. Although the tomato pose can be predicted by keypoints detection, the poor data quality of commercial RGBD cameras, occlusion between plant organs, various tomato poses, and unstructured working environments pose some challenges to the tomato bunch pose detection. Therefore, our research proposed an improved version of the Tomato Pose Method (TPM), namely TPMv2, which is a two-stage end-to-end multi-task network. This network provides comprehensive information on the tomato bunch, including the positions and poses of the stem, peduncle, and fruits, by predicting the two-dimensional bounding box (2D BBox), three-dimensional bounding box (3D BBox), two-dimensional key point (2D Kpt), and three-dimensional key point (3D Kpt). Aiming at the problems of occlusion and poor-quality point cloud, this paper specially designs a key point network (KPN) for tomatoes, where a keypoints processing pipeline was innovatively proposed, improving the accuracy of key point positioning and reducing abnormal prediction effectively. TPMv2 makes it possible to detect tomato bunch pose precisely with an economical camera, avoiding dangerous situations caused by abnormal prediction. The precision of 2D BBox and 3D BBox reached 0.9372 and 0.8700, and the Percentage of correct Keypoints (PCK) of 2D Kpt and 3D Kpt reached 0.8882 and 0.7836. About 78.36 % of 3D Kpts' positioning errors are less than 20 mm, sufficient to describe a correct pose trend based on the 3D Kpt, benefiting the manipulator to plan a more reasonable trajectory for non-destructive harvesting.

Experiences, barriers, and facilitators of health data use among performance monitoring teams (PMT) of health facilities in Eastern Ethiopia: A qualitative study.

Routine health data is crucial in decision-making and improved health outcomes. Despite the significant investments in improving Ethiopia's Performance Monitoring Team (PMT), there is limited evidence on the involvement, implementation strategies, and facilitators and barriers to data utilization by these teams responding to present and emerging health challenges. Therefore, this study aimed to explore the PMT experiences, facilitators, and barriers to information use in healthcare facilities in Eastern Ethiopia. This study employed a phenomenological study design using the Consolidated Framework for Implementation Research (CFIR) to identify the most relevant constructs, aiming to describe the data use approaches at six facilities in Dire Dawa and Harari regions in July 2021. Key informant interviews were conducted among 18 purposively selected experts using a semi-structured interview guide. Thematic coding analysis was applied using a partially deductive approach informed by previous studies and an inductive technique with the creation of new emerging themes. Data were analyzed thematically using ATLAS.ti. Study participants felt the primary function of PMT was improving health service delivery. This study also revealed that data quality, performance, service quality, and improvement strategies were among the major focus areas of the PMT. Data use by the PMT was affected by poor data quality, absence of accountability, and lack of recognition for outstanding performance. In addition, the engagement of PMT members on multiple committees negatively impacted data use leading to inadequate follow-up of PMT activities, weariness, and insufficient time to complete responsibilities. Performance monitoring teams in the health facilities were established and functioning according to the national standard. However, barriers to operative data use included PMT engagement with multiple committees, poor data quality, lack of accountability, and poor documentation practices. Addressing the potential barriers by leveraging the PMT and existing structures have the potential to improve data use and health service performance.

Environmental sustainability assessment of a polyester T-shirt – Comparison of circularity strategies

The considerable environmental burden of textiles is currently globally recognized. This burden can be mitigated by applying circular economy (CE) strategies to the commonly linear, short garment life cycles that end with incineration or landfill disposal. Even though all CE strategies strive to promote environmental sustainability, they might not be equally beneficial. Environmental data on different textile products is insufficiently available, which leads to complications when assessing and deciding on different CE strategies to be implemented. This paper studies the environmental impacts of a polyester T-shirt's linear life cycle through life cycle assessment (LCA) and evaluates the benefits attainable by adopting different CE strategies, and their order of priority, while noting uncertainty arising from poor data quality or unavailability. The LCA is complemented by assessing health and environmental risks related to the different options. Most of the linear life cycle's LCA-based impacts arise from use-phase washing. Hence, it is possible to reduce the environmental impact notably (37 %) by reducing the washing frequency. Adopting a CE strategy in which the shirt is reused by a second consumer, to double the number of uses, enables an 18 % impact reduction. Repurposing recycled materials to produce the T-shirt and recycling the T-shirt material itself emerged as the least impactful CE strategies. From the risk perspective, reusing the garment is the most efficient way to reduce environmental and health risks while washing frequency has a very limited effect. Combining different CE strategies offers the greatest potential for reducing both environmental impacts as well as risks. Data gaps and assumptions related to the use phase cause the highest uncertainty in the LCA results. To gain the maximum environmental benefits of utilizing CE strategies on polyester garments, consumer actions, design solutions, and transparent data sharing are needed.

Physics-informed hierarchical echo state network for predicting the dynamics of chaotic systems

Echo state network (ESN), a type of special recurrent neural network, has gained attention for its simplicity and low computational cost, making it commonly used for data-driven prediction of complex dynamical systems. However, in cases of insufficient or poor-quality data, data-driven approaches can suffer from low prediction accuracy caused by overfitting. To address this problem, a physics-informed hierarchical echo state network (Pi-HESN) is proposed for predicting the dynamics of chaotic systems. Firstly, the Pi-HESN can capture the latent evolutionary patterns hidden in the dynamical systems by processing data layer by layer in stacked reservoirs. Secondly, the Pi-HESN integrates data and physical laws in a unified way, incorporating prior physical knowledge into the objective function to ensure basic physical principles are respected. The combination of data-based and knowledge-based approaches in Pi-HESN improves model generalization, alleviates the shortage of training data, and ensures physical consistency of results. Experiments on four classical chaotic systems illustrate that the proposed Pi-HESN outperforms the original ESN and existing hierarchical ESN-based models in accuracy and predictability horizon.

Making China’s water data accessible, usable and shareable

Water data are essential for monitoring, managing, modelling and projecting water resources. Yet despite such data—including water quantity, quality, demand and ecology—being extensively collected in China, it remains difficult to access, use and share them. These challenges have led to poor data quality, duplication of effort and wasting of resources, limiting their utility for supporting decision-making in water resources policy and management. In this Perspective we discuss the current state of China’s water data collection, governance and sharing, the barriers to open-access water data and its impacts, and outline a path to establishing a national water data infrastructure to reform water resource management in China and support global water-data sharing initiatives. This Perspective characterizes the major challenges to open-access water data in China. A potential data management infrastructure to improve the collection, sharing, and use of water data is outlined.

Challenges and Opportunities of Secondary Use of health Data in Developing Countries

Open and reliable health data platforms can assist developing countries in responding to the many pressing issues confronting their society and economy today. Many countries’ cultural and economic structures have already been affected by the global pandemic, and data has played an important role in addressing these global challenges as well as cross-sector collaboration in public health response. Even before the pandemic, there was a pressing need to improve healthcare systems and better manage limited resources to meet the requirements of rising, and frequently aging, populations. We can boost the value of health data and assist to achieve these advancements by reusing it in diverse ways. Clinical data, such as healthcare incidents and clinical trial data, can be merged with information gathered from other sources, such as illness and insurance claims records, as well as gadgets and wearable technology. This data may then be anonymized and pooled to develop new insights and optimize population health, enhance the health and experiences of patients, build more efficient healthcare systems, and stimulate innovation. While many advanced countries have taken the advantage of secondary use of health data, developing countries like Nigeria are lagging behind due to challenges ranging from inadequate infrastructure, manual data collection and processing, siloed electronic health records, poor data quality, inefficient skills in data use core competencies, just to mention but few. This paper takes a deeper look at the inherent opportunities in the use of secondary health data and barriers toward its use in developing countries.

РАЗРАБОТКА МЕТОДИКИ УПРАВЛЕНИЯ КАЧЕСТВОМ ДАННЫХ НА ОСНОВЕ ЛОГИКО-СЕМАНТИЧЕСКОГО ЦИФРОВОГО ДВОЙНИКА

Data quality is a key issue in decision support systems. The urgency of the problem is due to the fact that the introduction of corporate information systems, digital technologies and digital twins requires strict synchronization and updating of a large amount of data. Management decisions depend on big data. The use of data containing one or more logical errors can lead to incorrect decisions and large losses for the enterprise. Aim. The aim of the study is to develop a classification of data that takes into account the production profile, types of data in production, the formal structure of business processes for each type of data based on the proposed logical-semantic architecture of the digital twin for data quality control. The theoretical and methodological basis of the study are the works of domestic and foreign scientists on the problems of managing digital platforms and digital transformation and digital twins of the enterprise. Results. In the course of the study, the classification of the main logical-semantic factors of poor-quality data provision was generalized and systematized, and a methodology for assessing data quality was presented. Business processes for collecting, storing and processing are formalized for each type of data and one responsible person is identified for the relevant business processes, as well as competency requirements for individual responsible persons and a tool for data quality control are developed. As a result, an approach based on the logical-semantic architecture of the digital twin for data quality control is proposed. Conclusion. In modern realities, logical filtering of a huge amount of redundant data in decision support systems (DSS) is necessary. Digital twins (DTs) in decision support systems can provide high quality of initial digital data for PPR. This article presents an integrated approach to data quality management based on their logical-semantic digital twins when creating an effective decision support system using the example of oil and gas companies. Data quality management based on the use of logical-semantic digital twins ensures the usefulness of digital data.

Data Quality Improvement Strategies for Data Analysis Specific to the IT Industry

In the ever-evolving landscape of the IT industry, data acts as the foundational element driving decisions, strategies, and innovations. The quality of data significantly influences the operational efficiency, decision-making accuracy, customer satisfaction, and overall success of IT initiatives. Poor data quality leads to erroneous insights, and ineffective strategies, and could potentially cost companies millions, making data quality improvement not just an option but a critical necessity. This paper explores the multifaceted importance of maintaining high data quality in the IT industry and outlines various strategies for improvement. It talks about big-picture methods, including sorting out data, keeping it up-to-date, using technology to improve accuracy and trustworthiness, and ensuring timeliness. This article explains methods IT specialties and groups should use to check and fix their data quality. These steps will help keep the information accurate and valuable for better results in the future

State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation

For flood simulation in small- and medium-sized catchments, discharge observations may be used to update model states of a distributed hydrological model to improve performance. The ensemble Kalman filter (EnKF) has been widely used for hydrological assimilation due to its relative simplicity and robustness. An advantage of the EnKF is that it is easy to include different sources of uncertainty, therefore the choice of error model is crucial for the application of the EnKF assimilation. This paper describes an EnKF assimilation scheme for estimating error models using the maximum a posteriori estimation method (MAP). We test this scheme in two small and medium-sized catchments in China with different characteristics, and in addition compared the performance differences under two kinds of rainfall forcing. We show that MAP is beneficial in specifying error models and providing reliable ensemble spread. The assimilation scheme can effectively ameliorate the degradation of distributed hydrological model performance due to uncalibrated model parameters and/or poor quality of input data.

Exploring the Use of Geographic Information Systems to Identify Spatial Patterns of Remote UAS Pilots and Possible National Airspace Risk

The proliferation of Unmanned Aircraft Systems (UAS) in the United States National Airspace System (NAS) has resulted in an increasing number of close encounters between manned aircraft and UAS, which correlates with the increasing number of remote pilots in the Federal Aviation Administration (FAA) airmen database. This research explores spatial patterns of registered airmen using Geographic Information Systems (GIS) analyses that provide notable spatial distribution patterns of pilots and how they relate to UAS sightings and airspace categories. The application of GIS to these aviation data may assist safety practitioners with identifying geographic patterns, areas of higher risk, and ultimately improve safety management. The authors analyzed publicly available airmen data to examine spatial distribution patterns, data correlations, and inferences. Airmen addresses were first geocoded into ArcPro 10.4 GIS software as a vector data layer containing attribute values of the database. The spatial analysis tool set was then utilized to establish clustering, density patterns, and spatial relationships between various categories of registered airmen. These density analyses revealed implicitly that commercial registered pilots tend to have the highest clustering near major commercial use controlled airspace, yet registered remote (UAS) pilots are also clustered in these and other densely populated areas. UAS sighting data were also geocoded using zip code values of the reported city to potentially correlate UAS sighting with registered remote pilots, yet the lack of spatial precision in the database made establishing any type of spatial relationship ineffective. The implicit spatial relationships between commercial and remote registered pilots revealed further research is needed to integrate UAS safely and effectively into the national airspace. The poor quality of UAS sighting data also demonstrates the need to better utilize GIS to monitor and track UAS flights within the context of an Unmanned Traffic Management System.

Downscaling and Merging of Daily Scale Satellite Precipitation Data in the Three River Headwaters Region Fused with Cloud Attributes and Rain Gauge Data

Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, based on the relationship between cloud optical and physical properties and precipitation, a daily geographically weighted regression (GWR) precipitation downscaling model was constructed for the Three Rivers Source region, China, for the period from 2010 to 2014. The GWR precipitation downscaling model combined three different satellite precipitation datasets (CMORPH, IMERG, and ERA5) which were downscaled from a coarse resolution (0.25° and 0.1°) to a fine resolution (1 km). At the same time, the preliminary downscaling results were calibrated and verified by employing the geographic difference analysis (GDA) and geographic ratio analysis (GRA) methods combined with rainfall data. Finally, the analytical hierarchy process (AHP) and the entropy weight method (EW) were adopted to fuse the three downscaled and calibrated satellite precipitation datasets into the merged satellite precipitation dataset (MSP), which provides a higher quality of data (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm). In summary, the downscaling calibration and precipitation fusion scheme proposed in this study is suitable for obtaining high-resolution daily precipitation data in the Three Rivers Source region with a complex climate and topography.

Comparison between droplet digital PCR and reverse transcription-quantitative PCR methods to measure ecotoxicology biomarkers

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is currently the gold-standard technique for detecting and quantifying messenger RNA. However, without proper validation, the method may produce artefactual and non-reproducible cycle threshold values generating poor-quality data. The newer droplet digital PCR (ddPCR) method allows for the absolute quantification of targeted nucleic acids providing more sensitive and accurate measurements without requiring external standards. This study compared these two PCR-based methods to measure the expression of well-documented genes used in ecotoxicology studies. We exposed Mediterranean mussels (Mytilus galloprovincialis) to copper and analyzed gene expression in gills and digestive glands using RT-qPCR and ddPCR assays. A step-by-step methodology to optimize and compare the two technologies is described. After ten-fold serial complementary DNA dilution, both RT-qPCR and ddPCR exhibited comparable linearity and efficiency and produced statistically similar results. We conclude that ddPCR is a suitable method to assess gene expression in an ecotoxicological context. However, RT-qPCR has a shorter processing time and remains more cost-effective.

The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm

Abstract A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers. Significance Statement This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.

An approach for assessing industrial IoT data sources to determine their data trustworthiness

Trustworthy data in the Industrial Internet of Things are paramount to ensure correct strategic decision-making and accurate actions on the shop floor. However, the enormous amount of industrial data generated by a variety of sources (e.g. machines and sensors) is often of poor quality (e.g. unreliable sensor readings). Research suggests that certain characteristics of data sources (e.g. battery-powered power supply and wireless communication) contribute to this poor data quality. Nonetheless, to date, much of the research on data trustworthiness has only focused on data values to determine trustworthiness. Consequently, we propose to pay more attention to the characteristics of data sources in the context of data trustworthiness. Thus, this article presents an approach for assessing Industrial Internet of Things data sources to determine their data trustworthiness. The approach is based on a meta-model decomposing data sources into data stores (e.g. databases) and providers (e.g. sensors). Furthermore, the approach provides a quality model comprising quality-related characteristics of data stores to determine their data trustworthiness. Moreover, a catalogue containing properties of data providers is presented to infer the trustworthiness of their provided data. An industrial case study revealed a moderate correlation between the data source assessments of the proposed approach and experts.

Drug Cost-Effectiveness Assessments Require Standards for Rigor and Inclusion.

The Institute for Clinical and Economic Review (ICER), a nonprofit, nongovernmental organization, is the predominant independent price assessor in the United States. ICER's cost effectiveness assessments are increasingly being used to support health insurance coverage and healthcare policy decisions. ICER often does not apply rigorous data quality and inclusion criteria to either the assumptions embedded within their cost-effectiveness models or the data inputted into the models. Poor quality assumptions and data can lead to poor quality assessments. 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. These changes will increase the rigor and inclusiveness of drug price assessments and support sustainable access to high-value care for all Americans.

Disaggregating Racial and Ethnic Data: A Step Toward Diversity, Equity, and Inclusion

Disaggregating Racial and Ethnic Data: A Step Toward Diversity, Equity, and Inclusion

IMPLEMENTASI PENGOLAHAN SISTEM PENJUALAN FURNITURE MENGGUNAKAN METODE DESIGN THINKING (STUDI KASUS : FURNITURE JATI SUNGU BANDAR LAMPUNG)

Teak Sungu furniture in the process of processing sales transactions is still manual so that it produces inaccurate and irrelevant information. Staff also have difficulty in making monthly sales reports, because reports are recapitulated based on daily sales transactions, through sales notes, if left continuously it will take up data storage space and time only for notes and sales reports. Manual data processing is also vulnerable to data manipulation, so that the data or sales reports produced are not necessarily reliable. From these problems, the researchers developed a website-based sales information system. The sales information system was developed using the system development method, namely design thinking. A website-based sales information system can help Jati Sungu Furniture Bandar Lampung in recording and managing sales transaction data better than the previous system, so that previously poor quality data becomes accurate and relevant information. the system that has been developed also helps in making monthly sales reports more easily and quickly, the data is also difficult to manipulate because there are user access rights with a login system. the system has also been tested with ISO 25010. The test results of ISO 25010 have a percentage value of 92.9% and are very feasible to be implemented on Teak Sungu Furniture Bandar Lampung

Awareness and Impact of GST among Small Business Owners: A Study with Special Reference to Mysuru City, Karnataka

The transferring of the information from the source port to the destination port plays a vital role in our day to day life. The data which is confidential should be send in a platform where the exact data without any loss is received. The insufficient poor quality of data can have a negative impact on real time data processing. The data transmission path should be selected in such a way that the entire data should be received for the usage without any loss in the network link. The data should be received with the accuracy and with less delay. In this way a technology called Software Defined Networking (SDN) plays an important role in managing and controlling the various network conditions. In this way, an approach is being designed in this paper where SDN technology is being used to transfer the data with an appropriate path in order to receive the exact data. It is an application based approach so that the patient medical data is being send using SDN technology. Key Words: SDN technology, OpenFlow switch, Dijsktra’s Algorithm, Hyper Text Transfer Protocol

Factors contributing to poor healthcare data quality: qualitative study from Southern Ethiopia

Factors contributing to poor healthcare data quality: qualitative study from Southern Ethiopia

Mastering DNA Content Estimation by Flow Cytometry as an Efficient Tool for Plant Breeding and Biodiversity Research.

Flow cytometry gives a unique opportunity to analyze thousands of individual cells for multiple parameters in a course of minutes. The most commonly used flow cytometry application in plant biology is estimation of nuclear DNA content. This becomes an indispensable tool in different areas of plant research, including breeding, taxonomy, plant development, evolutionary biology, populational studies and others. DNA content analysis can provide an insight into natural ploidy changes that reflect evolutionary processes, such as interspecific hybridization and polyploidization. It is also widely used for processing samples with biotechnologically induced ploidy changes, for instance, plants produced by doubled haploid technology. Absolute genome size data produced by cytometric analysis serve as useful taxon-specific markers since genome size vary between different taxa. It often allows the distinguishing of species within a genus or even different subspecies. Introducing flow cytometry method in the lab is extremely appealing, but new users face a significant challenge of learning instrument management, quality sample preparation and data processing. Not only is flow cytometry a complex method, but plant samples have unique features that make plants a demanding research subject. Without proper training, researchers risk damaging the expensive instrument or publishing poor quality data, artifacts or unreproducible results. We bring together information from our experience, key papers and online resources to provide step by step protocols and give a starting point for exploring the abundant cytometry literature.