MSTools—Web based application for visualization and presentation of HXMS data

This article addresses the urgency of improving hydrological monitoring and effective water resource management, which is particularly crucial for flood prevention and rapid emergency response.The problem lies in the need to develop a tool for efficient collection, processing, and visualization of water level data in the rivers of the Lviv region. The research objective is to develop and implement an interactive web application based on ArcGIS for visualizing and analyzing water level dynamics. This tool is designed to display the location of hydrological posts, provide access to their current status information, and analyze time series of hydrological data. The key feature of this approach is the integration and visualization of data from both automated and manual (gauge) posts, significantly expanding the cover-age of hydrological information.Based on the stated objectives, the geoinformation approach has been implemented for collecting and visualizing hydro-logical post data in the Lviv region using ArcGIS tools. The data source for this system comprises observations provided by the Basin Water Resources Management of the Western Bug and Sian Rivers. As a result, the interactive ArcGIS monitoring dash-board has been developed. It visualizes spatial and temporal water level data, integrating a web map with tabular time series data. This ensures automatic synchronization between selecting a post on the map and displaying dynamic charts and analytical tables. The web application facilitates comprehensive analysis of hydrological data.The proposed solution significantly enhances hydrological monitoring and supports managerial decision-making regarding flood prevention and emergency response. Its practical value lies in its application for water resource management and flood risk assessment, including the visualization of indicators and access to operational information for specialists and au-thorities. Future research will focus on real-time data updates, integration with meteorological indicators, and development of models for forecasting water levels and potential flood zones.

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an American interactive data visualization software company focused on business intelligence and can be used across all disciplines. Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations.[1] Power BI (Business Intelligence) is a suite of business analytics tools developed by Microsoft that is widely used across various industries for business reporting, data analysis, and decision-making. [2] Future directions of this research are discussed that include using other data mining and visualization methods such as image processing techniques for applications to data for plant pathology and human diseases.

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

This paper proposes a dynamic analytical processing (DAP) visualization tool based on the Bubble-Wall Plot. It can be handily used to develop visual warning systems for visualizing the dynamic analytical processes of hazard data. Comparative analysis and case study methods are used in this research. Based on a literature review of Q1 publications since 2017, 23 types of data visualization approaches/tools are identified, including seven anomaly data visualization tools. This study presents three significant findings by comparing existing data visualization approaches. The primary finding is that no single visualization tool can fully satisfy industry requirements. This finding motivates academics to develop new DAP visualization tools. The second finding is that there are different views of Line Charts and various perspectives on Scatter Plots. The other one is that different researchers may perceive an existing data visualization tool differently, such as arguments between Scatter Plots and Line Charts and diverse opinions about Parallel Coordinate Plots and Scatter Plots. Users' awareness rises when they choose data visualization tools that satisfy their requirements. By conducting a comparative analysis based on five categories (Style, Value, Change, Correlation, and Others) with 26 subcategories of metric features, results show that this new tool can effectively solve the limitations of existing visualization tools as it appears to have three remarkable characteristics: the simplest cartographic tool, the most straightforward visual result, and the most intuitive tool. Furthermore, this paper illustrates how the Bubble-Wall Plot can be effectively applied to develop a warning system for presenting dynamic analytical processes of hazard data in the coal mine. Lastly, this paper provides two recommendations, one implication, six research limitations, and eleven further study topics.

In the modern business landscape, data visualization has become an indispensable element of business intelligence (BI), particularly within the context of US companies. The aim of this research paper is to examine how data visualization technologies might improve business intelligence and decision making in American businesses by examining adaptation, advantages and disadvantages. This research paper explores the transformative contribution of data visualization tools to improving business intelligence capabilities and their significant influence on the processes of decision-making. The study begins by examining the adoption of various data visualization tools and platforms, such as Tableau, Power BI, and Elkview within US companies. It delves into how these tools facilitate the conversion of complex data sets into visually intuitive formats, allowing stakeholders at all levels to grasp trends, patterns, and anomalies with greater ease. By integrating these visualization tools into their BI systems, companies can achieve a more nuanced understanding of their operational landscapes and market dynamics. The paper further evaluates the impact of data visualization on decision-making processes. Through an analysis of case studies and empirical data, it highlights several key benefits, including improved accuracy in data interpretation, enhanced ability to identify actionable insights, and accelerated decision-making timelines. The paper discusses strategies to overcome these challenges, such as ensuring data integrity and investing in user training to maximize the efficacy of visualization tools. Overall, the findings underscore that while data visualization is not a panacea, it significantly augments the decision-making capabilities of US companies by making data more accessible and actionable. The integration of advanced visualization techniques into BI practices is increasingly seen as a strategic advantage, driving both operational efficiencies and competitive differentiation. As data continues to proliferate, the role of data visualization in shaping business strategies and outcomes will likely become even more pronounced, making it a critical area of focus for future research and development in the field of business intelligence.

BackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

Changes in protein structure are closely related to gel strength. Dense phase carbon dioxide (DPCD) treatment is an excellent non-thermal food processing method that can be used to induce gel formation in surimi. The sensory, water holding capacity and gel strength of DPCD induced gels are superior to heat-induced gels. Fourier-transform infrared spectroscopy was used to investigate the role of DPCD in the quality of golden pompano surimi gels and changes in protein structure. The intermolecular forces of surimi gels were analyzed in terms of ionic and hydrogen bonds, disulfide covalent and non-disulfide covalent bonds, as well as hydrophobic interactions. Correlation analysis was used to investigate the relationship between the changes in advanced protein structure and gel strength during DPCD-induced gel formation in golden pompano surimi. The results showed that the α-helix and random coil levels of surimi gel were significantly decreased (p < 0.05), while the β-sheet and β-turn content was significantly increased (p < 0.05). The number of ionic and hydrogen bonds in gel proteins decreased significantly (p < 0.05), while the hydrophobic interactions, and disulfide and non-disulfide covalent bonds increased significantly (p < 0.05) after DPCD treatment. Correlation analysis showed that β-sheets, β-turns, hydrophobic interactions, and disulfide and non-disulfide covalent bonds were strongly positively correlated with gel strength, whereas α-helices, random coils, and ionic and hydrogen bonds were strongly negatively correlated with gel strength. Therefore, the α-helix and random coil structures of surimi gels were transformed into β-sheet and β-turn structures after DPCD treatment. Hydrophobic interactions, and disulfide and non-disulfide covalent bonds were the main intermolecular forces during the DPCD-induced gel formation of surimi. Ionic and hydrogen bonds were not the main intermolecular forces. The results provide fundamental data for elucidating the mechanism of DPCD-induced protein gel formation.

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Purpose With the deep integration of information technologies into urban governance, smart communities have emerged as pivotal platforms for advancing sustainable urban development. However, existing research has not offered a systematic analysis or clear presentation of the field’s academic evolution and thematic structure. This study examines the literature on smart communities published between 2000 and 2024. Employing data analysis and visualization tools, it aims to trace the evolution and development trends of smart community research, map its core themes and their interrelationships, and provide actionable insights for policymaking and practical implementation. Design/methodology/approach Based on 2,347 publications indexed in the Web of Science from 2000 to 2024, this study employed CiteSpace and VOSviewer to conduct co-citation analysis, keyword co-occurrence mapping, national collaboration network analysis, author and institutional contribution assessment, burst detection, and hotspot term analysis. The literature screening adhered to predefined publication-type criteria and citation-count thresholds to ensure that the results were representative and reliable. Findings This study, through literature analysis and data visualization in the field of smart communities, yields the following principal conclusions. First, the application of digital twin technology in optimizing smart community resources has attracted growing attention, demonstrating considerable potential in urban management, infrastructure maintenance, and resident services. Second, as technology advances, digitaltwin applications are evolving towards greater precision and efficiency, particularly by deepening their support for resource allocation and decision-making processes. Finally, the future development of smart communities will increasingly depend on the deep integration of digital twins with other cutting-edge technologies, thereby driving intelligent management and optimization of community resources. Research limitations This literature repository excludes grey literature and non-English publications, potentially underestimating the representativeness of grassroots innovation. Furthermore, the temporal analysis was constrained by the citation-lag effects of publications from 2000 to 2024. Practical implications This study proposes a decision-support toolkit tailored for municipal planners and policymakers. The toolkit comprises three core intervention strategies: multiscale environmental sensing, participatory governance protocols, and regenerative technology pathways. These measures are designed to advance the implementation of the United Nations Sustainable Development Goal 11: Sustainable Cities and Communities. Originality/value By explicitly defining and applying the “thematic knowledge framework,” this paper offers a concise roadmap for the evolution of smart community research. It also provides precise guidance for designing and implementing community development strategies that align with Sustainable Development Goals.

Open data published by various organizations is intended to make the data available to the public. All over the world, numerous organizations maintain a considerable number of open databases containing a lot of facts and numbers. However, most of them do not offer a concise and insightful data interpretation or visualization tool, which can help users to process all of the information in a consistently comparable way. Canadian Federal and Provincial Elections is an example of these databases. This information exists in numerous websites, as separate tables so that the user needs to traverse through a tree structure of scattered information on the site, and the user is left with the comparison, without providing proper tools, data-interpretation or visualizations. In this paper, we provide technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges. We hope that the methodology used here can help in developing similar tools to achieve some of the goals of publicly available datasets. The developed tool contains data visualization, trend analysis, and prediction components. The visualization enables the users to interact with the data through various techniques, including Geospatial visualization. To reproduce the results, we have open-sourced the tool.

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

With the advancement of Information technologies and applications, a copious amount of data is generated, which attracts both the research community to utilize this information for extracting knowledge and the industry for developing the knowledge-based system. Visualization of data, pattern mining from datasets and analyzing data drift for the different features are three highly used applications of machine learning and data science fields. A generic web-based tool integrated with such features will provide prodigious support for preprocessing the dataset and thus extracting accurate information. In this work, we propose such a data visualization tool, named VIM, which is a web-based comprehensive tool for generic data visualization, data preprocessing and mining suitable knowledge with drift analysis of data. Given a dataset, it can envisage the distribution of data with convenient statistical diagrams for different selected features. Moreover, users can employ VIM to generate association rules by selecting multiple features. We have developed VIM using Python Django framework and GraphLab library. We have deployed this tool to make this publicly usable, which can be accessed at http://210.4.73.237:9999/

Business owners, managers, and policymakers are overwhelmed by the volume of data available in their company’s internal databases and external sources. The ability to represent meaningful and concise information that facilitates decision-making is challenging. Consider a technology firm such as Company A, where a large quantity of data is generated across numerous data sources covering all the North American locations. Traditionally, these data come in different formats such as email attachments (.pdf, .xlsx, .docx, etc.) and at different time intervals, e.g., daily, weekly, and some non-periodic. The data arrives at the team’s folder on the Microsoft SharePoint platform, where team members calculate key metrics. This existing process is tedious, manual, and prone to computation or latency errors due to database refresh. Data visualization design and automation serve as a solution to these issues by creating a method through which large volumes of data can be easily aggregated, represented, consumed, and understood. When data is visualized correctly, it is easy to draw simple, actionable conclusions. This study will explore how modern data visualization tools can be used to help policymakers and managers understand supply chain operations and performance in a relatively short time, as well as make strategic plans by interacting with the dashboard. This would be achieved by creating a visualization dashboard for the real-time monitoring of Company A North America Supply Chain data across all departments using the Microsoft Power BI tool. This would help to see the big picture all at once and enable business owners, key managers, and policymakers to make sound business choices and judgments. The overarching goal of this project is to automate the data collection and visualization process to help key business owners make informed decisions. This will allow the team managers to spend less time compiling large volumes of data and leverage the visualization tool to better identify and mitigate risk as well as proactively uncover valuable opportunities that lie in their databases.

Software tools for visualization and analysis of human brain data for language mapping