Data Visualization Methods Research Articles

Identification of key narratives to assess students’ perception of social reality

Introduction. The article presents the results of the research investigation on identifying key narratives in the perception of the external social environment by university students on the example of St. Petersburg Peter the Great Polytechnic University (hereinafter referred to as SPbPU). The aim of the article is to identify key narratives (images of the present, past, and future) to assess students’ perception of social reality, to substantiate key needs of students in the field of soft skills development and to summarize the practices in the field of teaching these skills in SPbPU. Materials and Methods. The key methods of obtaining research data include the survey, participant observation of the research team members within the framework of professional activities, analysis of documents on the issues of teaching and assessment of teaching Soft Skills disciplines. The methods of semantic analysis, generalization, comparison, data visualization, and verification were used in order to collect data, process and present them. Results. The authors identified key narratives in the perception of the external social environment by university students, including peculiarities of the perception of enlarged groups in social, humanitarian and technical fields of study. The data on the key needs of young people within the framework of Soft Skills acquisition and the experience of implementing such disciplines on the example of SPbPU were generalized. During the study, a search semantic analysis of existing evaluative acronyms was applied to investigate the existing image of reality perception in the target group, and the DICE acronym was introduced to summarize the desired image of the future in university students. The practical results of the research consist in the possibilities to assess the potential of practices in the disciplines within the framework of «Soft Skills» in SPbPU and modernization of similar practices in other universities. Conclusions. The article concludes about the need to build an effective feedback system with university students, which will allow, based on specialized Soft Skills practices, to respond flexibly to needs in the field of deficit skills and competencies of students and ensure their acquisition. In addition, the importance of identifying the existing narratives in the youth environment in the field of perception of social environment and social time to realize the educational, communicative and moral functions of higher education institutions is shown.

Role of innovation culture and post-Soviet mindset as factors determining speed of economic development in a small open economy: Case of Latvia

This research seeks to identify the value of a few common factors determining the speed of economic growth in Baltic states and analyzes their impact in detail on Latvia’s lagging. Latvia’s economic starting point after regaining independence because of the collapse of the Soviet Union was at least comparable to its neighbors. Still, after the implementation of liberal reforms towards a free market’ economy and 20 years of operation as an EU full member, Latvia is lagging in growth, prosperity, and innovation. Within the analysis, this scientific paper pays special attention to the three less discussed factors, namely, the impact of post-Soviet mind-set effects as a part of local innovation culture, lasting since regaining independence in 1991; the importance of the availability of talent pull, its density, diversity, and accessibility; and readiness and capability to capture external knowledge and technology adoption. The overall approach is the systemic assessment of the national innovation system and/or innovation ecosystem, trying to understand the differences between these two models. Research is performed by analysis of the performance of the local innovation ecosystem in connection with export- and Foreign Direct Investment (FDI) policies. The authors present a novel method for visually representing economic growth and its application in analyzing process development within transitional economic nations. The study uses an analytical and synthetical literature review. It offers a new GDP data visualization method useful for monitoring economic development and forecasting potential economic crises—the outcomes from aggregative literature analysis in a consolidated concept are provided for required talent policy proposals. The post-Soviet mindset is seen as a heritage and devious underdog that has left incredibly diverse consequences on today’s society, power structures, economic growth potential, and the emergence of healthy, well-managed, and sustainable innovation ecosystems. The post-Soviet mindset is a seemingly hidden and, at the same time, an intriguing factor that has a significant impact on the desire to make and implement the right decisions related to innovation, education, and other policies promoting business development. The key outcome of the article is that sociocultural aspects and differences in innovation culture led to a slow-down of Latvia’s economic growth compared to Estonia’s and Lithuania’s slightly more successful economic reforms.

A Study on the Identification of the Water Army to Improve the Helpfulness of Online Product Reviews

Based on the perspective of identifying the water army, this paper uses the methods of machine learning and data visualization to analyze the helpfulness of online produce reviews, portray product portraits, and provide real and helpful product reviews. In order to identify and eliminate the water army, the Term Frequency-Inverse Document Frequency Model (TF-IDF) and Latent Semantic Index Model (LSI) are used. After eliminating the water army, three classification methods were selected to perform sentimental analysis, including logistics, SnowNLP, and Convolutional Neural Network for text(TextCNN). The TextCNN has the highest F1 score among the three classification methods. At the same time, the Latent Dirichlet Allocation mode (LDA) is used to extract the topics of various reviews. Finally, targeted countermeasures are proposed to manufacturers, consumers, and regulators.

From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England.

The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on "cold or flu" calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization.

ScBubbletree: computational approach for visualization of single cell RNA-seq data

BackgroundVisualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space.ResultsWe present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as “bubbles” at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells.ConclusionsTo facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/

Aardvark: Composite Visualizations of Trees, Time-Series, and Images.

How do cancer cells grow, divide, proliferate, and die? How do drugs infuence these processes? These are diffcult questions that we can attempt to answer with a combination of time-series microscopy experiments, classifcation algorithms, and data visualization. However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative. State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory. To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

Enhancing Manufacturing Processing Stability and Efficiency with Linear-Regression Analysis: Modeling on a Flow-Drill Screw (FDS) Joining Process

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time.

Technical note: A biological anthropologist's guide for applying microbiome science to studies of human and non-human primates.

A central goal of biological anthropology is connecting environmental variation to differences in host physiology, biology, health, and evolution. The microbiome represents a valuable pathway for studying how variation in host environments impacts health outcomes. While there are many resources for learning about methods related to microbiome sample collection, laboratory analyses, and genetic sequencing, there are fewer dedicated to helping researchers navigate the dense portfolio of bioinformatics and statistical approaches for analyzing microbiome data. Those that do exist are rarely related to questions in biological anthropology and instead are often focused on human biomedicine. To address this gap, we expand on existing tutorials and provide a "road map" to aid biological anthropologists in understanding, selecting, and deploying the data analysis and visualization methods that are most appropriate for their specific research questions. Leveraging an existing dataset of fecal samples and survey data collected from wild geladas living in Simien Mountains National Park in Ethiopia (Baniel et al., 2021), this paper guides researchers toward answering three questions related to variation in the gut microbiome across host and environmental factors. By providing explanations, examples, and a reproducible workflow for different analytic methods, we move beyond the theoretical benefits of considering the microbiome within anthropological research and instead present researchers with a guide for applying microbiome science to their work. This paper makes microbiome science more accessible to biological anthropologists and paves the way for continued research into the microbiome's role in the ecology, evolution, and health of human and non-human primates.

Air target intent recognition method combining graphing time series and diffusion models

Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making. Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models. Inspired by the success of diffusion models in addressing visual domain sample imbalances, this paper introduces a new approach that utilizes the Markov Transfer Field (MTF) method for time series data visualization. This visualization, when combined with the Denoising Diffusion Probabilistic Model (DDPM), effectively enhances sample data and mitigates noise within the original dataset. Additionally, a transformer-based model tailored for time series visualization and air target intent recognition is developed. Comprehensive experimental results, encompassing comparative, ablation, and denoising validations, reveal that the proposed method achieves a notable 98.86% accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities. This approach represents a promising avenue for advancing air target intent recognition.

Housing data visualization and analysis

Data visualization is a powerful tool that can assist individuals and organisations in comprehending vast amounts of data and extracting valuable insights from it. The most significant function of data visualization is to make recommendations by figuring out the essence of the occurrence of the data. This paper will take housing data as an example, raise relevant questions, and reveal the logic behind the data and the relationship between variables through data visualization, linear regression, and other statistical methods.

Advanced Data Visualization Methods and Tools in Cloud Platforms

Advanced Data Visualization Methods and Tools in Cloud Platforms

Enhancing School Waste Management with EcoViber Using the Waterfall Approach

The research focuses on the development and implementation of the EcoViber application to address plastic waste management in school environments, specifically at SDN Perdopo 02. The study aims to tackle the issue of plastic waste accumulation through technological solutions, enhancing environmental awareness and operational efficiency. The application employs the waterfall method for development and features data collection, visualization, and educational tools to promote transparency and accountability. Comprehensive observations and interviews with seven teachers revealed significant improvements in waste management, with approximately 54.95 kg of plastic waste collected from January to April 2024. The findings highlight the practical implications of EcoViber in fostering a culture of environmental stewardship among school stakeholders. Despite its success, the study acknowledges potential biases in data reporting and the limited generalizability of results due to the focus on a single school. Future research should explore diverse settings and refine methodologies to enhance the scalability and long-term impact of EcoViber. Overall, the study demonstrates EcoViber's value as an innovative and effective solution for sustainable plastic waste management in educational settings.

Leveraging systems’ non-linearity to tackle the scarcity of data in the design of intelligent fault diagnosis systems

Deep transfer learning (DTL) allows for the efficient building of intelligent fault diagnosis systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of labelled data. Obtaining such an amount of data can be challenging when dealing with machines or structures faults. This document proposes a novel approach to the design of vibration-based IFDS using DTL in condition of strong data scarcity. A periodic multi-excitation level procedure leveraging intrinsic non-linearities of real-world systems is used to produce images that can be conveniently analysed by pre-trained Convolutional Neural Networks to diagnose faults. A new data visualization method and its augmentation technique are proposed in this paper to tackle the typical lack of data encountered during the design of IFDS. Experimental validation on a railway pantograph structure provides effective support for the proposed method.

EzSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell’s streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application (https://immunesinglecell.org/ezsc/) or a software package with a shinyApp interface (https://github.com/JinmiaoChenLab/ezSingleCell2) for offline analysis.

A method for visualizing urban road events using distributed acoustic sensing

This study presents the construction of an urban underground sensing system using distributed acoustic sensing (DAS) technology, which utilizes the existing optical fiber infrastructure around urban roads for communication. To address the challenges posed by the complexity and variability of DAS data in infrastructure monitoring environments such as urban roads, as well as the difficulty and poor effectiveness of raw data visualization, a novel method for visualizing DAS data is proposed. This method involves preprocessing the data through wavelet threshold denoising, combining it with the root-mean-square (RMS) energy index to generate a visualization, and applying the dynamic threshold method to remove and suppress abnormal data indicators. Finally, this paper tested the visualization performance to assess the effectiveness of the proposed method in improving urban road safety management. The study focused on three typical urban road safety risk events: vehicle driving, construction, and road subsurface cavity incidents. The results demonstrate the efficacy of the data visualization method, showing improved visualization of vehicle trajectory directions and numbers, construction segment behaviors, and approximate road subsurface cavity locations in the time domain compared to the original data.

A review: Data visualization for COVID-19

As COVID-19 raged across the globe, it seriously disrupted social health security, daily work, and the rest of the people, which aroused great panic among all classes. These facts extensively affected the regional circulation and economic growth of the epidemic area. Data visualization, an advanced technology, can help humans understand epidemic transmission trends, identify high-risk areas of outbreak, and evaluate prevention and control policies. Previous essays immediately established databases to organize and build datasets. Other researchers built new visualization models to analyze the causes of virus transmission and pointed out the low efficiency of the existing COVID-19 system response. Although various research and data visualization methods have been used on COVID-19, there has not been a comprehensive, systematic essay that covers the entire span of COVID-19 and its data visualization usage. Therefore, this paper will provide a new systematic and comprehensive perspective to explore the analysis of data visualization to solve COVID-19 data challenges and provide research methods and applications for COVID-19. To be specific, this paper first introduces the characteristics and challenges of COVID-19 epidemic data people may face during the data visualization process, which includes data complexity problems, data timeliness problems, data regional problems, and data quality problems. Then, this paper discusses data visualization methods used by predecessors in previous research. After that, this paper describes the urgent data visualization analysis requirements of COVID-19, including regional differences in virus spread, the influence of temperature on the virus, transmission trends, and death rates for different genders and ages, etc. Finally, this paper reviews applications of data visualization in COVID-19 analysis, followed by earlier-stage prevention, mid-term containment, and later-term reduction of impact. This paper provides an important reference for further research and application of data visualization technology in COVID-19 prevention and control, which brings new ideas for strengthening future epidemic surveillance and data visualization for global epidemic prevention and control.

Developing Dashboard Analytics and Visualization Tools for Effective Performance Management and Continuous Process Improvement

Performance management and continuous process improvement are essential for businesses to monitor how well their operations are running, find ways to improve them, and bring about good change. However, making decisions based on data in these areas can be challenging because performance data is complicated. This research looks into how panel analytics and data visualization tools can help manage performance well and make processes better all the time. A literature study is done on performance management frameworks, process improvement methods, data visualization techniques, and dashboard solutions that are already out there. The Lean, Six Sigma, and balanced scorecard methods are looked at for success indicators and metrics. It also looks into data visualization tools' design principles and methods like dashboards, tables, charts, and interactive features. Interviews, observations, document reviews, and internal files are ways a case organization gathers both primary and secondary data. Key performance indicators for quality, delivery, productivity, safety, and customer happiness are found in several different business roles and processes. To look at performance trends and relationships, SPSS is used for descriptive statistics and hypothesis testing. Interactive dashboard examples that use different data visualization methods are created by thinking about how the user will experience it, how it will work with other systems, and how it will help them make decisions. Dashboards let you dig deeper into processes, determine why problems happen, and compare actuals to goals. Usability testing checks how easy the tools are and how well decision-makers can learn from them. The results show that dashboard analytics can make handling performance and improvement projects easier based on data. Insights made it possible to keep track of KPIs, find methods that were not working well, and work together across teams. There are also talks about problems with data quality and change management.

Prospective iterative data visualization (DV) study to enhance health literacy in prostate cancer (PC).

11061 Background: Methods used to visualize and present comparative effectiveness research (CER) impact patient (pt) and caregiver (CG) understanding and ultimately treatment decisions. Evidence-based approaches to optimize DV and comprehension in PC are lacking. We studied the presentation and DV of CER with key stakeholders aiming to overcome health disparity/inequity barriers related to understanding of CER DV. Methods: A panel representing pt and advocate, industry, and health care provider viewpoints provided qualitative feedback on multiple CER DV methods and generated a survey for US PC pts and CGs. Respondents were identified by a market research agency (buzzback, n=192) or a pt advocacy group (ZERO Prostate Cancer, n=58). Respondents first reported on comfort with and understanding of scientific data on PC using a Likert scale, then evaluated a randomly assigned graphic (1 of 2 versions each depicting the same information) for each of 4 topics. For efficacy and prostate-specific antigen (PSA) data, figures showed superiority of treatment over placebo; for safety, figures positively or negatively represented outcomes. For quality of life (QoL), overall QoL and specific QoL domains were illustrated. For each graphic, respondents first suggested their own key takeaway, then selected the best from 4 options, and reason for selection. Graphics were then assessed side by side with better key takeaway selected to create an overall story. Results: Respondents (pts, n=199; CGs, n=51) enrolled between Aug and Sep 2023; 54% were non-White; 52% had an annual income <$75,000/yr; 45% had no college degree. Some respondents reported being uncomfortable/neutral toward PC scientific data (26%) or found data difficult to understand (35%) or overwhelming (29%). >70% identified correct takeaways for Efficacy and PSA. Figures with numerical data showing a difference between treatment groups (Efficacy, PSA) more effectively communicated key takeaways than those without (Safety, QoL). Figures representing pts without toxicity (positive reporting) versus pts with toxicity (negative reporting) were better understood (62% vs 37%) and communicated (67% vs 33%). Respondents (~55-60%) chose the correct key takeaway for QoL figures, not containing quantitative data, and were divided as to which version best communicated the key takeaway. There were no consistent, robust patterns of differences in accuracy of interpretation based on respondent characteristics, especially for top performing Efficacy and PSA graphics. Conclusions: This is one of the first prospective multistakeholder studies in PC to explore comprehension and preferences from a diverse group of pts and CGs. Figures showing a difference between treatments were more accurately interpreted than those conveying parity. We provide a benchmark for best practices for CER DV for pts and CGs to assist in their education and optimize shared decision-making.

Global Overview of the Application of the Braun-Blanquet Approach in Research

Environmental classifications are of paramount importance for assessing the impacts of land-use changes, for prioritizing conservation efforts, and for developing effective management strategies to mitigate the negative impacts of human activities. The aim of our research was to provide as complete an analysis as possible of the studies that have been carried out using the Braun-Blanquet approach. The global review of studies based on the Braun-Blanquet approach includes 1168 papers and was conducted using the PRISMA 2009 methodological recommendations, strict criteria for the selection/quality of papers, and modern methods of data analysis and visualization using VOSviewer software developed by Nees Jan van Eck and Ludo Waltman (Centre for Science and Technology Studies (CWTS) of Leiden University in the Netherlands) (version 1.6.18), which ensures a representative sample, minimization of subjective judgements, and reliability of conclusions. It was noted that the number of publications on Braun-Blanquet is growing exponentially. This is an indication of the scientific interest in this methodology and its continuous further development. Based on a detailed analysis of the keywords, the main research directions and challenges are identified. These include improving the conceptual and methodological foundations of the Braun-Blanquet approach; improvement in regional vegetation classifications, synthesizing them and producing a comprehensive classification for large areas as a basis for biodiversity conservation and sustainable land use; expansion of the geography; compilation and updating of databases of phytosociological data; management of dynamics and vegetation; discussion of the important problem of continuity and discreteness of vegetation in the context of ecological classifications; and vegetation mapping. The top 20 journals publishing the most cited articles were identified, as well as the top 20 most cited journals whose high citation rate is due to the large number of high-quality articles. The analysis of the bibliographic network of papers in dynamics has shown that the structure of relationships is not constant and has changed significantly. The analysis of the authors’ publication activity showed that the vast majority of researchers have a low publication activity and have published only one to three papers. A peculiarity also emerges: if all the most cited authors are concentrated in Eurasia, then most of the most actively published authors are outside Eurasia. The importance of the Braun-Blanquet approach for the study and classification of forest vegetation should be emphasized. In this case, the Braun-Blanquet approach is integrated into forest typologies, increasing their ecological validity and environmental relevance.

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

here is a growing concern among consumers and the wine industry regarding the quality of wine. Traditionally, wine experts determined its quality through tasting, which was time-consuming. Therefore, there is a need to predict wine quality based on specific key features to streamline these tasks. Technological developments like machine learning (ML) approaches have replaced human assessments with computational methods. However, some of these methods have faced criticism due to their low accuracy and lack of interpretability for humans. In this paper, a stacking ensemble method is introduced and demonstrates superior predictive performance when compared to other classification techniques like Logistic Regression (LR), Decision Trees (DT), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Random Forest (RF). This evaluation is based on classification metrics such as accuracy, precision, recall, and F1-Score, all under the same conditions. Additionally, outlier detection algorithms were employed to identify exceptional or subpar wines, though their results did not match the accuracy of classification approaches. Lastly, a feature analysis study was conducted to assess the significance of each feature in the model's performance.