Visualization Tools Research Articles

ChatKG: Visualizing time-series patterns aided by intelligent agents and a knowledge graph

Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using Intelligent Agents (IA), Visual Analytic tools can automatically uncover explicit knowledge related information to said patterns. Yet, visualizing the association of data, patterns, and knowledge is not straightforward. In this paper, we present ChatKG, a novel visual analytics strategy that allows exploratory data analysis of a Knowledge Graph that associates temporal sequences, the patterns found in each sequence, the temporal overlap between patterns, the related knowledge of each given pattern gathered from a multi-agent IA, and the IA’s suggestions of related datasets for further analysis visualized as annotations. We exemplify and informally evaluate ChatKG by analyzing the world’s life expectancy. For this, we implement an oracle that automatically extracts relevant or interesting patterns, populates the Knowledge Graph to be visualized, and, during user interaction, inquires the multi-agent IA for related information and suggests related datasets to be displayed as visual annotations. Our tests and an interview conducted showed that ChatKG is well suited for temporal analysis of temporal patterns and their related knowledge when applied to history studies.

Developing Statistical Thinking in Schoolchildren: From Theory to Practice

Since 2022, the school course «Probability and Statistics» has been taught as a compulsory subject in all schools in Russia, and questions on it are included in the Unified State Exam. However, international experience in teaching the basics of statistics shows that this is not an easy task. It includes not only teaching students how to prove the theorems of mathematical statistics and explain the calculation of the median and variance, but also contributes to the development of statistical thinking in them – the ability to make decisions based on the analysis of empirical data. According to the authors, the statistical community cannot and should not replace teachers who promote the teaching of statistics at school, but it has the opportunity to make a significant contribution to this process.The article presents the experience of the All-Russian School competition on statistics «Trend», the All-Russian teacher competition for the best practices in teaching statistics «We train the best!», with the support of the territorial bodies of state statistics in St. Petersburg, Kirov and Rostov regions; master classes for hundreds of teachers teaching statistics in grades 7–11. In addition, the article presents plans for organizing a competition on civil statistics aimed at developing skills in working with statistical data, as well as teaching the basics of statistical analysis, the use of visualization tools, and the formation of citizenship through familiarity with objective statistical information

NcPlantDB: a plant ncRNA database with potential ncPEP information and cell type-specific interaction.

The field of plant non-coding RNAs (ncRNAs) has seen significant advancements in recent years, with many ncRNAs recognized as important regulators of gene expression during plant development and stress responses. Moreover, the coding potential of these ncRNAs, giving rise to ncRNA-encoded peptides (ncPEPs), has emerged as an essential area of study. However, existing plant ncRNA databases lack comprehensive information on ncRNA-encoded peptides (ncPEPs) and cell type-specific interactions. To address this gap, we present ncPlantDB (https://bis.zju.edu.cn/ncPlantDB), a comprehensive database integrating ncRNA and ncPEP data across 43 plant species. ncPlantDB encompasses 353 140 ncRNAs, 3799 ncPEPs and 4 647 071 interactions, sourced from established databases and literature mining. The database offers unique features including translational potential data, cell-specific interaction networks derived from single-cell RNA sequencing and Ribo-seq analyses, and interactive visualization tools. ncPlantDB provides a user-friendly interface for exploring ncRNA expression patterns at the single-cell level, facilitating the discovery of tissue-specific ncRNAs and potential ncPEPs. By integrating diverse data types and offering advanced analytical tools, ncPlantDB serves as a valuable resource for researchers investigating plant ncRNA functions, interactions, and their potential coding capacity. This database significantly enhances our understanding of plant ncRNA biology and opens new avenues for exploring the complex regulatory networks in plant genomics.

Lustrous silk and dark wool: materiality, colour and the refashioning of St. Augustine in the medieval imagination

Abstract Male bodies dressed in brilliant silk clothing conveyed messages of power in late medieval Italy. Previous scholars examined how these glamorous bodies reflected contemporary ideals of gentility and whiteness in a courtly context. This article shifts the attention from secular to religious contexts through examining the fictive materiality of the attire of St. Augustine. Silk and wool were two driving forces of the pre-industrial economy. Yet they are rarely considered together, and the discussion has focussed on production. Much less thought has been put into the social implications. By pulling together the threads of silk and wool, this article illuminates how St. Augustine’s dual silk-woollen attire transformed his body in the medieval imaginary and became a powerful visual tool to communicate messages of leadership, legitimacy and kinship.

Ex vivo magnetic resonance imaging of the human fetal brain.

The fetal brain undergoes a dynamic process of development during gestation, marked by well-orchestrated events such as neuronal proliferation, migration, axonal outgrowth, and dendritic arborization, mainly elucidated through histological studies. Ex vivo magnetic resonance imaging (MRI) has emerged as a useful tool for 3D visualization of the developing fetal brain, serving as a complementary tool to traditional histology. In this review, we summarized the commonly employed ex vivo MRI techniques and their advances in fetal brain imaging, as well as a standard protocol for postmortem fetal brain specimen collection and fixation. We then provided an overview of ex vivo MRI-based studies on the fetal brain. According to our review, ex vivo T1- or T2-weighted structural MRI has contributed to the characterization of the anatomy of transient neuronal proliferative zones, the basal ganglia, and the cortex. Diffusion MRI related techniques, such as diffusion tensor imaging and tractography, have helped to investigate the microstructural patterns of fetal brain tissue, as well as the early emergence and development of neuronal migration pathways and white matter bundles. Ex vivo MRI findings have shown strong histological correlations, supporting the potential of MRI in evaluating the developmental events in the fetal brain. Postmortem MRI examinations have also demonstrated comparable, and in certain cases, superior performance to traditional autopsy in revealing fetal brain abnormalities. In conclusion, ex vivo fetal brain MRI is an invaluable tool that provides unique insights into the early stages of brain development.

Sparsified simultaneous confidence intervals for high-dimensional linear models

AbstractStatistical inference of the high-dimensional regression coefficients is challenging because the uncertainty introduced by the model selection procedure is hard to account for. Currently, the inference of the model and the inference of the coefficients are separately sought. A critical question remains unsettled; that is, is it possible to embed the inference of the model into the simultaneous inference of the coefficients? If so, then how to properly design a simultaneous inference tool with desired properties? To this end, we propose a notion of simultaneous confidence intervals called the sparsified simultaneous confidence intervals (SSCI). Our intervals are sparse in the sense that some of the intervals’ upper and lower bounds are shrunken to zero (i.e., [0, 0]), indicating the unimportance of the corresponding covariates. These covariates should be excluded from the final model. The rest of the intervals, either containing zero (e.g., $$[-1,1]$$ [ - 1 , 1 ] or [0, 1]) or not containing zero (e.g., [2, 3]), indicate the plausible and significant covariates, respectively. The SSCI intuitively suggests a lower-bound model with significant covariates only and an upper-bound model with plausible and significant covariates. The proposed method can be coupled with various selection procedures, making it ideal for comparing their uncertainty. For the proposed method, we establish desirable asymptotic properties, develop intuitive graphical tools for visualization, and justify its superior performance through simulation and real data analysis.

Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

The UCSC Genome Browser database: 2025 update.

The UCSC Genome Browser (https://genome.ucsc.edu) is a widely utilized web-based tool for visualization and analysis of genomic data, encompassing over 4000 assemblies from diverse organisms. Since its release in 2001, it has become an essential resource for genomics and bioinformatics research. Annotation data available on Genome Browser includes both internally created and maintained tracks as well as custom tracks and track hubs provided by the research community. This last year's updates include over 25 new annotation tracks such as the gnomAD 4.1 track on the human GRCh38/hg38 assembly, the addition of three new public hubs, and significant expansions to the Genome Archive[GenArk) system for interacting with the enormous variety of assemblies. We have also made improvements to our interface, including updates to the browser graphic page, such as a new popup dialog feature that now displays item details without requiring navigation away from the main Genome Browser page. GenePred tracks have been upgraded with right-click options for zooming and precise navigation, along with enhanced mouseOver functions. Additional improvements include a new grouping feature for track hubs and hub description info links. A new tutorial focusing on Clinical Genetics has also been added to the UCSC Genome Browser.

How urban renewal affects the sustainable development of public spaces: trends, challenges, and opportunities

The process of urbanization has spurred economic growth and social challenges, necessitating research on public spaces in urban renewal to optimize design, enhance functionality, promote sustainable urban development, and improve residents’ quality of life. However, existing studies lack in-depth discussions on development trends and research focal points. This study addresses the gap in existing literature, by conducting a bibliometric analysis using data from the Web of Science Core Collection database from 1 January 2000, to 1 April 2024. Using visualization tools such as VOSviewer and CiteSpace, the study examines publication trends, collaborative networks among countries, institutions, and authors, co-citation relationships among key journals and articles, and emerging research hotspots through keyword analysis. A total of 393 papers were analyzed, with China contributing the highest number (65), followed by the United States (51). Leading contributors include Zazzi Michele and Anguelovski Isabelle. The top three journals for publications are Sustainability, Cities, and Land. Key research trends highlight themes such as space syntax, nature-based solutions, and sustainable transportation. These findings have significant implications for urban planning and policy, suggesting that future urban development strategies should increasingly incorporate sustainable design practices and nature-based solutions to address both environmental and social challenges. By identifying global research trends and highlighting future challenges, this study provides a comprehensive overview that will help policymakers and practitioners in urban planning align their efforts with cutting-edge research and emerging best practices for more sustainable and resilient cities.

Research trends and hotspots in clinical trials of migraine in the past 20 years: bibliometric analysis

BackgroundMigraine is a widespread, recurrent primary headache disorder primarily characterized by severe pulsatile headache, typically on one or both sides. It is often accompanied by nausea, vomiting, and hypersensitivity to sound and light. Despite the availability of multiple drugs for migraine management, the condition often becomes chronic due to untimely or irrational drug use, significantly distressing patients and increasing the burden on families and society. Over the past two decades, numerous clinical studies on migraine have been published. This study aimed to provide a comprehensive summary of the current status and trends of migraine clinical trials through bibliometric analysis.MethodsWe used visual network tools such as CiteSpace and VOSviewer to perform a knowledge graph analysis of publications related to migraine clinical trials extracted from the WoSCC.ResultsThis study analyzed 1,129 articles published in 389 journals from 61 countries. The number of publications on migraine clinical trials has steadily increased from 2004 to 2023. The United States and Albert Einstein College of Medicine are the leading countries and institutions in this field, respectively. Richard B. Lipton is the most prolific author, making significant contributions to the research. The journal Headache has the highest number of publications and citations in this area. Keywords such as “efficacy,” “RCT,” “CGRP,” “prophylaxis,” “disability,” “depression,” “questionnaire,” and “real-world effectiveness” received significant attention.ConclusionThis study identified reliable research hotspots and provided directions for clinicians. The treatment of migraine continues to be challenging. Future trends may include continued growth in migraine classification, risk factor analysis, and comorbidity studies. Research on CGRP and epigenetics will advance the progress of precision medicine in the migraine field.

Green means go: The effect of a visualization tool towards increased use of renewable energy in households

There is a need to further develop digital and hardware tools to support consumers in the adaptation of their energy use to, for example, integrate renewable sources into it and/or reduce expenses. This paper presents an evaluation study of a tool, including its hardware and software versions, that supports consumers in their electricity consumption optimization, most notably concerning renewable energies for specific devices. We conducted a six-week study in Oldenburg, Germany involving 43 participants divided into three groups: one hardware and one digital variant each of an energy visualization tool, as well as a control group. We furthermore collected quantitative data on energy usage patterns and qualitative insights through semi-structured interviews. Here the participants received information about the percentage of renewable energy currently in the grid, and were asked to document their washing machine and dishwasher use. This paper provides insights into the perception of the proposed visualization tools achieved through semi-structured interviews, comparing the digital and hardware versions, while outlining the challenges of and possibilities for changing energy use behavior, especially regarding the time of day of an appliance's use. Even though the measurable influences of the tool were limited due to thresholds being reached too easily as a result of e.g. high wind levels generating higher-than-anticipated amounts of renewable energy, in terms of the amount of renewable energy use, slight improvements in the two test groups were observed compared to the control group. Regarding consumer behavior, changes in the appliances' use time show slightly higher improvement compared to the control group.

Mapping the evolution and future directions of creative tourism: a comprehensive bibliometric analysis

ABSTRACT The main objective of this study is to deepen the understanding of the knowledge structure in the field of creative tourism and thereby enhance communication with advanced international theories and experiences. We utilized two visualization tools, VOSviewer and CiteSpace, to analyze 138 platform research papers from the Web of Science Core Collection, comprising 7050 references. Combining quantitative analysis (such as scientometric analysis, knowledge mapping, and keyword clustering) with content analysis (including thematic reading), this study primarily focuses on three aspects: firstly, revealing the basic characteristics of the literature, such as publication metrics, authors, and research institutions, to outline the foundational landscape of the field; secondly, identifying key research areas, content, and development trends to provide profound insights into the advancement of creative tourism and suggest directions for future research; thirdly, constructing a comprehensive knowledge framework to consolidate existing information and insights, facilitating a more structured and accessible understanding of creative tourism.

A comprehensive Beyond-GDP database to accelerate wellbeing, inclusion, and sustainability research

“Beyond-GDP” metrics are essential for understanding societal progress. Yet despite their importance, these metrics are scattered across various databases, hindering accessibility and interdisciplinary analysis. Addressing this gap, we present the ‘WISE database’ – the first extensive collection of important Beyond-GDP metrics organized by Wellbeing, Inclusion, and Sustainability (WISE) dimensions. The WISE database consolidates data from a variety of sources, including international institutions and academic publications. It encompasses over one million data points across 244 metrics, covering 218 countries and 61 country groupings, from specific social and environmental indicators to the main Beyond-GDP indexes, and is augmented by essential metadata. The data primarily spans from 1995 to 2015, with some metrics extending back to the 19th century. To improve accessibility and data interpretation, a user-friendly online visualization tool has been developed. The WISE database aims to foster a broader view of societal progress, facilitate research on synergies and trade-offs of wellbeing, inclusion, and sustainability, and provide a foundation for interdisciplinary research.

Elucidating Heterogeneity of Intimate Partner Violence Among Women: Latent Class Analysis of Daily IPV, PTSD, and Substance Use Collected via Experience Sampling Methodologies.

The co-occurrence of physical, psychological, and sexual intimate partner violence (IPV) is associated with heightened risk for deleterious mental and physical health outcomes, including PTSD symptoms and substance use. However, few studies have investigated the heterogeneity of experiences with respect to this co-occurrence among women who experience IPV as it unfolds naturally in women's daily lives. In this study, we developed a novel approach to classify and visualize daily patterns of IPV experiences, PTSD-related distress, and substance use among women who experience IPV. Women (N = 244, Mage = 37.1 years) who reported experiencing IPV and using substances were recruited from the community. Micro-longitudinal data were collected four times per day for 14 days using automated telephone-based data collection. Analyses included latent class analysis (LCA) and multilevel analysis (interval-, day-, and person-level), complimented by novel data visualization tool (EventFlow). We identified three classes that significantly differed in the prevalence of and patterns among IPV types, PTSD, and substance use. Based on examination of LCA results and visualization of the latent classes in EventFlow. Different patterns of associations among IPV types, PTSD symptoms, and substance use were identified within classes at the interval-, day-, and person-level. Results from this study provide greater insight into the heterogeneity of women's lived experiences than do studies solely relying on cross-sectional survey data. Findings with this type of data collection can inform the development of interventions to increase the precision in clinical practice, identify new avenues for future research, and have important policy implications.

Sphaeroptica: A tool for pseudo-3D visualization and 3D measurements on arthropods.

Natural history collections are invaluable reference collections. Digitizing these collections is a transformative process that improves the accessibility, preservation, and exploitation of specimens and associated data in the long term. Arthropods make up the majority of zoological collections. However, arthropods are small, have detailed color textures and share small, complex and shiny structures, which poses a challenge to conventional digitization methods. Sphaeroptica is a multi-images viewer that uses a sphere of oriented images. It allows the visualization of insects including their tiniest features, the positioning of landmarks, and the extraction of 3D coordinates for measuring linear distances or for use in geometric morphometrics analysis. The quantitative comparisons show that the measures obtained with Sphaeroptica are similar to the measurements derived from 3D μCT models with an average difference inferior to 1%, while featuring the high resolution of color stacked pictures with all details like setae, chaetae, scales, and other small and/or complex structures. Shaeroptica was developed for the digitization of small arthropods but it can be used with any sphere of aligned images resulting from the digitization of objects or specimens with complex surface and shining, black, or translucent texture which cannot easily be digitized using structured light scanner or Structure-from-Motion (SfM) photogrammetry.

Early Diagnosis of Liver Injury and Real-Time Evaluation of Photothermal Therapy Efficacy with a Viscosity-Responsive NIR-II Smart Molecule.

The early diagnosis of liver injury and in situ real-time monitoring of tumor therapy efficacy are important for the enhancement of personalized precision therapy but remain challenging due to the lack of reliable in vivo visualization tools with integrated diagnostic, therapeutic, and efficacy monitoring functions. Herein, a smart second near-infrared window (NIR-II) molecule (BITX-OH) is rationally designed for diagnosis and therapy by vinyl-bridging hydroxyl diphenyl xanthine unit and benzo[cd]indolium skeleton. BITX-OH exhibits high selectivity and sensitivity toward viscosity, exhibiting a significant enhancement (1167-fold) in NIR-II fluorescence at 962nm. With the assistance of BITX-OH and NIR-II fluorescence imaging, early diagnosis and therapeutic evaluation of non-alcoholic fatty liver (NAFL), as well as in-site real-time monitoring of hepatic fibrosis (HF) in live mice have been successfully achieved, which is at least several hours earlier than the typical clinical test. Notably, BITX-OH displays excellent photothermal conversion efficiency when exposed to an 808nm laser, which can induce tumor ablation and increase viscosity, thereby enhancing NIR-II fluorescence for the real-time evaluation of photothermal therapy (PTT). This viscosity-based "self-monitoring" strategy provides a convenient and reliable platform for timely obtaining therapeutic feedback to avoid over- or under-treatment, thus enabling personalized precision therapy.

Molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction.

The construction, management, and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.

20 years of the Bio-Analytic Resource for Plant Biology.

The Bio-Analytic Resource for Plant Biology ('the BAR', at https://bar.utoronto.ca) is celebrating its 20th year in operation in 2025. The BAR encompasses and provides visualization tools for large 'omics data sets from plants. The BAR covers data from Arabidopsis, tomato, wheat, barley and 29 other plant species (with data for 2 others to be released soon). These data include nucleotide and protein sequence data, gene expression data, protein-protein and protein-DNA interactions, protein structures, subcellular localizations, and polymorphisms. The data are stored in more than 200 relational databases holding 186 GB of data and are presented to the researchers via web apps. These web apps provide data analysis and visualization tools. Some of the most popular tools are eFP ('electronic fluorescent pictograph') Browsers, ePlantsand ThaleMine (an Arabidopsis-specific instance of InterMine). The BAR was designated a Global Core Biodata Resource in 2023. Like other GCBRs, the BAR has excellent operational stability, provides access without login requirement, and provides an API for researchers to be able to access BAR data programmatically. We present in this update a new overarching search tool called Gaia that permits easy access to all BAR data, powered by machine learning and artificial intelligence.

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Effect of Visual Images on Digital Communication: Empirical Research Review

As mobile messengers gain popularity, visual images affect digital communication, leading to new interaction patterns. This review sums up scientific experience in describing digital communication facilitated by emoji and memes. The review covered scientific articles that reported empirical research on visual communication in digital environment in such open research databases as CyberLeninka, PubMed, Google Scholar, eLibrary, and ResearchGate in 2019–2024. The current rise in visual communication tools seems to reflect the general visual shift in modern culture, associated with the changes in human psychology brought about by total digitalization. Visual communication tools have a high information capacity, which hinders their interpretation, thus modifying the complex of individual and socio-psychological characteristics of interlocutors in an attempt to ensure mutual understanding. The review can be used to improve digital communication that employs alternative communication tools.