Data Visualization Research Articles

Framework for tailoring consumer-centric communication to boost solar energy adoption in U.S. households

This review outlines a framework for tailoring consumer-centric communication to boost solar energy adoption in U.S. households. In response to growing environmental concerns and the need for sustainable energy solutions, this framework is designed to enhance residential solar adoption rates by customizing communication strategies to diverse consumer profiles. By leveraging consumer behavior insights, the framework addresses critical factors such as individual perceptions, social influences, financial considerations, and technological barriers that affect consumers' solar adoption decisions. The proposed framework emphasizes understanding various consumer segments, including early adopters, environmentally conscious individuals, and budget-focused households, to design targeted communication strategies. It integrates key elements like demographic analysis, psychographic profiling, and attitudinal segmentation to build a comprehensive picture of potential solar consumers. Using these insights, the framework proposes tailored messaging that resonates with each segment’s unique motivations and concerns, such as energy cost savings, environmental impact, and long-term investment value. To maximize engagement, the framework recommends deploying these tailored messages across a mix of digital and traditional media channels, ensuring optimal reach and relevance. Social media, for instance, can be effectively used to reach environmentally conscious and tech-savvy segments, while community events and local partnerships may better engage older or budget-focused demographics. Moreover, the framework advocates for the use of testimonials, data visualization, and interactive content to enhance credibility and user experience, further breaking down perceived barriers such as installation costs, maintenance concerns, and technology reliability. By focusing on consumer-centric communication, this framework aims to increase awareness, interest, and trust in solar energy solutions, thereby fostering a supportive environment for adoption. It also highlights the importance of iterative feedback mechanisms to continuously refine strategies and address emerging consumer needs. Ultimately, this framework serves as a strategic guide for policymakers, marketers, and solar companies to drive meaningful consumer engagement, boost conversion rates, and contribute to the broader goal of achieving sustainable energy adoption across the U.S. residential sector.

Cognitive Query System Using Generative AI

Abstract: The Cognitive Query System using Generative AI is a cutting-edge tool designed to transform the way we retrieve and analyse data from a wide range of sources, including text, images, and documents. Powered by the Google Gemini API and deployed through Streamlit, this system allows users to easily ask questions, process images, and explore document contents via an intuitive, interactive interface. The system comprises three core modules: the Question-Answering Module, which uses advanced natural language processing (NLP) techniques to provide contextually accurate, conversational responses to user queries, facilitating seamless, real-time interactions. The Image Processing and Querying Module enables users to upload images and ask questions related to their content, leveraging computer vision algorithms and AI to interpret and analyse visual data, making it easier to extract valuable insights. The Document Exploration Module supports various formats, including PDFs, Word documents, and spreadsheets, allowing users to quickly ask questions and navigate through sections without manual reading. Deployed online via Streamlit, the Cognitive Query System offers a smooth, real-time experience that does not require installation, making it ideal for remote and collaborative work environments. This innovative solution is designed to streamline workflows and effective outputs in the model.

An analysis of the use of 3D game engine technology in visualising sustainability data

Purpose Responding to calls for accountants to engage with modern technologies and explore data visualisation within a three-dimensional (3D) environment, this study aims to explore whether social and environmental accounting (SEA) data visualisation is a promising use case for 3D game engine technology. Design/methodology/approach Drawing on visual perception and embodiment theories, this study uses photo-elicitation, a qualitative research method, to explore the usefulness of two-dimensional (2D) and 3D visualisations of sustainability information in a 3D virtual environment. This study provides three stimuli: numerical data, 2D visualisations and 3D visualisations, and asks open-ended questions regarding future applications. Twelve semi-structured interviews were conducted with academics, preparers and users of sustainability reports to obtain responses to these stimuli. Findings The key finding is that visualisation of SEA information may indeed be a strong use case for 3D game technology, but only for certain data and for certain audiences. Presenting information within a 3D virtual environment offered enhanced engagement and contextual understanding but reduced navigation speed and data clarity. Participants were enthusiastic about the potential of a museum-like experience, incorporating interactivity and community, but felt that the appropriate audience was more likely to be novices than experts. Practical implications This study suggests that deploying 3D game engine technology can be a powerful tool for presenting sustainability information but requires significant resources. The optimum audience is likely to be novices, and a key design principle is to ensure the virtual environment supports, rather than overwhelms, the information presented within that environment. Originality/value This study introduces a novel application of 3D visualisation technology within the SEA context, offering original insights into its potential to enhance user understanding and decision-making capabilities. This study highlights the technology’s value not as a replacement for traditional reporting but as a supplementary educational tool. The study also provides a novel setting for the photo-elicitation method, demonstrating this approach’s utility in a 3D environment.

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium

Objectives To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. Methods Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. Results A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an ‘intracellular protein score’ (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. Conclusions We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

Delineating bacterial genera based on gene content analysis: a case study of the Mycoplasmatales-Entomoplasmatales clade within the class Mollicutes.

Genome-based analysis allows for large-scale classification of diverse bacteria and has been widely adopted for delineating species. Unfortunately, for higher taxonomic ranks such as genus, establishing a generally accepted approach based on genome analysis is challenging. While core-genome phylogenies depict the evolutionary relationships among species, determining the correspondence between clades and genera may not be straightforward. For genotypic divergence, the percentage of conserved proteins and genome-wide average amino acid identity are commonly used, but often do not provide a clear threshold for classification. In this work, we investigated the utility of global comparisons and data visualization in identifying clusters of species based on their overall gene content and rationalized that such patterns can be integrated with phylogeny and other information such as phenotypes for improving taxonomy. As a proof of concept, we selected 177 representative genome sequences from the Mycoplasmatales-Entomoplasmatales clade within the class Mollicutes for a case study. We found that the clustering patterns corresponded to the current understanding of these organisms, namely the split into three above-genus groups: Hominis, Pneumoniae and Spiroplasma-Entomoplasmataceae-Mycoides. However, at the genus level, several important issues were found. For example, recent taxonomic revisions that split the Hominis group into three genera and Entomoplasmataceae into five genera are problematic, as those newly described or emended genera lack clear differentiations in gene content from one another. Moreover, several cases of misclassification were identified. These findings demonstrated the utility of this approach and its potential application to other bacteria.

Navigation methods using airlandscape data for small UAVs

The purpose of the research. UAVs are widely used in such fields as military, intelligence, and research. As a result, the issue of their use in electronic warfare conditions and the presence of various interference, both man-made and natural, is acute. Thus, the need for UAV navigation using visual data increases.The purpose is study of the main negative factors affecting the quality of aerial photography images and methods for eliminating them to construct a correct orthomosaic for UAV visual navigation.Methods. Mathematical methods are proposed to eliminate negative distortions in UAV camera images using various image transformation approaches. After applying these methods to the original images, corrected versions are obtained, which are used to construct the tile covering. Tiled coverage created from processed images provides continuous and uniform coverage of the area collected during the UAV’s flight. This allows you to obtain exact coordinates of images and objects on them.Results. An analysis of the main methods for eliminating negative factors that distort images during aerial photography for the visual navigation of UAVs, as well as a brief overview of the visual navigation methods themselves.Conclusion. To successfully implement visual navigation of a UAV, it is necessary to apply a number of methods for converting aerial photography images, as well as the use of certain algorithms for visual navigation. It is concluded that in addition to the use of mathematical and software algorithms, it will also be necessary to analyze and study the necessary computing power for the use of the entire hardware and software complex on board the UAV, taking into account its weight-dimensional properties.

Enhanced visualization of microbiome data in repeated measures designs

IntroductionRepeated measures microbiome studies, including longitudinal and clustered designs, offer valuable insights into the dynamics of microbial communities and their associations with various health outcomes. However, visualizing such multivariate data poses significant challenges, particularly in distinguishing meaningful biological patterns from noise introduced by covariates and the complexities of repeated measures.MethodsIn this study, we propose a framework to enhance the visualization of repeated measures microbiome data using Principal Coordinates Analysis (PCoA) adjusted for covariates through linear mixed models (LMM). Our method adjusts for confounding variables and accounts for the repeated measures structure of the data, enabling clearer identification of microbial community variations across time points or clusters.ResultsWe demonstrate the utility of our approach through simulated scenarios and real datasets, showing that it effectively mitigates the influence of nuisance covariates and highlights key axes of microbiome variation.DiscussionThis refined visualization technique provides a robust tool for researchers to explore and understand microbial community dynamics in repeated measures microbiome studies.

Legal dissemination protections in community-based participatory health equity research.

There are legal protections for nurse researchers at public universities who employ community-based participatory research (CBPR) in research about social or health inequities. Dissemination of CBPR research data by researchers or participants may divulge unjust laws and create an imperative for university involvement. What are United States-based legal dissemination protections for CBPR health equity nurse researchers? Three case examples employing CBPR are examined: 1) a mixed methods study with participants reporting illegal discrimination in a municipal initiative about capacity building in community-based organizations serving children; 2) a visual methods study exposing potential clean air law violations in environmental justice research; and 3) a study examining workload violations and illegal discrimination among hotel workers. The cases involved participants from protected social class backgrounds. The research is described with respect to: background, funding, research purpose, and research team; research participants' power and legal vulnerability; dissemination of relevant research information balancing vulnerability and power; research dissemination issues; potential legal issues involved; and laws researchers may use. IRB approval was obtained for the studies. Using a social justice ethical framework, studies highlight actual or potential legal aspects of data dissemination in the context of gathering data about injustice. Legal protections for research data dissemination, whistleblower protection, research advancement protection, anti-harassment protection, false claims, defamation, and visual data liabilities are described. Knowledge of legal research data dissemination protections is an essential competency for nurse researchers invested in uplifting social justice.

AltGosling: Automatic Generation of Text Descriptions for Accessible Genomics Data Visualization.

Biomedical visualizations are key to accessing biomedical knowledge and detecting new patterns in large datasets. Interactive visualizations are essential for biomedical data scientists and are omnipresent in data analysis software and data portals. Without appropriate descriptions, these visualizations are not accessible to all people with blindness and low vision, who often rely on screen reader accessibility technologies to access visual information on digital devices. Screen readers require descriptions to convey image content. However, many images lack informative descriptions due to unawareness and difficulty writing such descriptions. Describing complex and interactive visualizations, like genomics data visualizations, is even more challenging. Automatic generation of descriptions could be beneficial, yet current alt text generating models are limited to basic visualizations and cannot be used for genomics. We present AltGosling, an automated description generation tool focused on interactive data visualizations of genome-mapped data, created with the grammar-based genomics toolkit Gosling. The logic-based algorithm of AltGosling creates various descriptions including a tree-structured navigable panel. We co-designed AltGosling with a blind screen reader user (co-author). We show that AltGosling outperforms state-of-the-art large language models and common image-based neural networks for alt text generation of genomics data visualizations. As a first of its kind in genomic research, we lay the groundwork to increase accessibility in the field. The source code, examples, and interactive demo are accessible under the MIT License at https://github.com/gosling-lang/altgosling. The package is available at https://www.npmjs.com/package/altgosling. Supplementary data are available at Bioinformatics online.

State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer

Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift. To tackle this problem, this paper introduces a state estimation algorithm that integrates an invariant extended Kalman filter (InEKF) with a disturbance observer, aiming to estimate the motion state of quadruped robots on non-stationary terrains. Firstly, foot-end slippage is modeled as a deviation in body velocity and explicitly included in the state equations, allowing for a more precise representation of how slippage affects the state. Secondly, the state update process integrates both foot-end velocity and position observations to improve the overall accuracy and comprehensiveness of the estimation. Lastly, a foot-end contact probability model, coupled with an adaptive covariance adjustment strategy, is employed to dynamically modulate the influence of the observations. These enhancements significantly improve the filter’s robustness and the accuracy of state estimation in non-stationary terrain scenarios. Experiments conducted with the Jueying Mini quadruped robot on various non-stationary terrains show that the enhanced InEKF method offers notable advantages over traditional filters in compensating for foot-end slippage and adapting to different terrains.

The California collaborative network to promote data driven care and improve outcomes in early psychosis (EPI-CAL) project: rationale, background, design and methodology.

A prolonged first episode of psychosis (FEP) without adequate treatment is a predictor of poor clinical, functional, and health outcomes and significant economic burden. Team-based "coordinated specialty care" (CSC) for early psychosis (EP) has established effectiveness in promoting clinical and functional recovery. However, California's CSC program implementation has been unsystematic and could benefit from standardizing its processes and data collection infrastructure. To address this, we established a consortium of EP clinics across the state via a Learning Health Care Network (LHCN) framework to develop the Early Psychosis Intervention Network of California (EPI-CAL). EPI-CAL's LHCN developed a core battery of evidence-based measures for service users and family members and linked them together using a unique data collection and visualization application, Beehive. EPI-CAL's LHCN collects, visualizes, and aggregates data at the individual and clinic level for EP programs across California via Beehive. Beehive was designed to: (1) collect outcomes data from service users receiving care at EP programs and their support persons, (2) provide the data to providers on a secure web-based dashboard to support measurement-based care, and (3) allow data to be used for program or research analysis. We will (1) determine the feasibility of implementing an LHCN across a diverse, decentralized network of early psychosis programs, (2) determine if the implementation of an LHCN increases the delivery of measurement-based care, and (3) determine if the implementation of measurement-based care is associated with significant improvements in key service user outcomes. EPI-CAL's network will contribute data to the Early Psychosis Intervention Network (EPINET) program. The current study aims to establish an LHCN of EP clinics in California that implements harmonized data collection using Beehive and assesses the feasibility of establishing such a network. Our goal is for this harmonized data collection approach to be used to inform decisions and develop learning opportunities for service users, staff, and administrators, and to improve outcomes for service users and their supporters in CSC care. Further, the data will enable programs and research teams to examine what elements of care lead to program success and improved treatment outcomes for service users. www. gov , identifier NCT04007510; registered 07/05/2019.

GTO: a comprehensive gene therapy omnibus.

Gene therapy, which involves the delivery of genetic material into cells to correct an underlying genetic problem, has emerged as a promising approach for treating various conditions. To promote research in this rapidly evolving field, we developed the Gene Therapy Omnibus (GTO) (http://www.inbirg.com/gto/), a comprehensive resource containing detailed clinical trial data and molecular information related to gene therapy. The GTO includes 6333 clinical trial records and 3466 transcriptome profiles, with information on 614 altered genes and 22 types of gene therapy, including DNA therapies, RNA therapies and genetically-modified cell therapies. For each gene therapy product in a clinical trial, detailed information, such as altered gene name, structural components, indication, vector information, phase of the clinical trial, clinical outcomes and adverse effects, is provided when available. Additionally, 345 comparison datasets, including 29 single-cell RNA-sequencing datasets comprising information on both gene therapy and control samples, were established. Differential gene expression and downstream functional enrichment analyses were performed through standardized pipelines to elucidate the molecular alterations induced by gene therapy. The user-friendly interface of the GTO supports efficient data retrieval, visualization and analysis, making it an invaluable resource for researchers and clinicians performing clinical research on gene therapy and the underlying mechanisms.

RiboSeq.Org: an integrated suite of resources for ribosome profiling data analysis and visualization.

Ribosome profiling (Ribo-Seq) has revolutionisedour understanding of translation, but the increasing complexity and volume of Ribo-Seq data present challenges for its reuse. Here, we formally introduce RiboSeq.Org, an integrated suite of resources designed to facilitate Ribo-Seq data analysis and visualisation within a web browser. RiboSeq.Org comprises several interconnected tools: GWIPS-viz for genome-wide visualisation, Trips-Viz for transcriptome-centric analysis, RiboGalaxy for data processing and the newly developed RiboSeq data portal (RDP) for centralised dataset identification and access. The RDP currently hosts preprocessed datasets corresponding to 14840 sequence libraries (samples) from 969 studies across 96 species, in various file formats along with standardised metadata. RiboSeq.Org addresses key challenges in Ribo-Seq data reuse through standardised sample preprocessing, semi-automated metadata curation and programmatic informationaccess via a REST API and command-line utilities. RiboSeq.Org enhances the accessibility and utility of public Ribo-Seq data, enabling researchers to gain new insights into translational regulation and protein synthesis across diverse organisms and conditions. By providing these integrated, user-friendly resources, RiboSeq.Org aims to lower the barrier to reproducible research in the field of translatomics and promote more efficient utilisation of the wealth of available Ribo-Seq data.

Re-wilding the Night: Understanding How Darkness Is Valued Through the Nighttime Light Ecology of Bonn Botanical Gardens

Rewilding the Night is an interdisciplinary research project that aims to reimagine the urban night by capturing and communicating the qualities and rhythms of both artificial and natural light at night. Through a collaborative and experimental approach, the project seeks to make the urban night legible by employing sensor technologies and data visualization techniques, thus fostering a deeper engagement with and appreciation for urban darkness. The project has three aims—first, to capture and analyse environmental light data in Bonn Botanical Gardens, Germany to create an accessible understanding of the variations in natural and artificial nighttime light. Second, to use this groundwork to inform subsequent workshops to capture public perceptions and values regarding darkness; and third, to develop lighting prototypes that respond to light data and the insights gained through the workshops. Central to the project is the re-evaluation of urban darkness by questioning and expanding the normative frameworks surrounding urban lighting. The project underscores the importance of interdisciplinary research and design in addressing complex urban challenges, offering a model for future research aimed at creating more livable, sustainable, and inclusive urban night environments for both humans and non-humans.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

Framing theory insights: the co-existence of frame coupling and frame mirroring in social media and online news about a disaster-affected destination

ABSTRACT Through the lens of framing theory, this paper explores social media and news media online narratives about a destination affected by a natural disaster. It presents original insights into the co-existence of frame coupling and frame mirroring in online content. Online news media, and local and overseas social media user-generated content (UGC) was examined. The aim of such triangulation of data, was to understand how different online sources framed the destination in the context of the disaster. 24,985 Twitter (now X) posts were sourced using Salesforce Social Studio and 1,248 online news media articles from Factiva. Leximancer-driven thematic analysis via data visualisation was used to explore the data. This study is the first to show the interplay of frame coupling and frame mirroring of a disaster-affected destination. It also uncovers the existence of online brand advocacy (OBA) in online framing. Our findings emphasise the importance of social media UGC in defending a destination brand, spreading positivity and activism during a disaster, and this has clear implications for the tourism industry, specifically marketing professionals.

Python For Data Analytics: A Systematic Literature Review Of Tools, Techniques, And Applications

In the era of big data, the ability to collect, process, and analyze data efficiently has become a vital component for decision-making across various industries. Python, as a versatile programming language, has emerged as a powerful tool for data analytics due to its extensive libraries and user-friendly nature. This systematic literature review explores Python’s role in streamlining data analytics by examining its applications across various stages of the data analysis process, including data collection, cleaning, manipulation, and visualization. Key Python libraries such as NumPy, Pandas, and Matplotlib are discussed, highlighting their functionality in handling large datasets and enabling accurate and efficient analysis. Real-world examples demonstrate how Python can be applied in diverse sectors, from retail to healthcare, enhancing decision-making processes through data-driven insights. Furthermore, the limitations of Python, as well as alternative data analysis tools such as R and RapidMiner, are explored to provide a comprehensive view of Python’s place in modern data analytics. The review concludes that while Python offers significant advantages in data analysis, a combination of tools may often be necessary to meet the complex demands of today’s data-driven industries.

Global burden of low back pain and its attributable risk factors from 1990 to 2021: a comprehensive analysis from the global burden of disease study 2021

BackgroundThis study aimed to examine the evolving trends in the global burden of low back pain (LBP) from 1990 to 2021 and predicted disease burden until 2035.Materials and methodsLBP-related data were obtained from the Global Health Data Exchange (GHDx) query tool. All estimates and their 95% uncertainty intervals (UIs) were generated using DisMod-MR 2.1, a Bayesian meta-regression tool in Global Burden of Disease, Injury, and Risk Factor Study (GBD) 2021. Data processing and visualization were conducted using The R Programming Language software version 4.3.2 and Joinpoint 4.7.ResultsIn 2021, approximately 628.8 million people worldwide were affected by LBP, with approximately 266.9 million new incident cases and age-standardized incidence rate (ASIR) of 3176.6 per 100,000. Compared with 1990, although the ASIR and age-standardized disability-adjusted life years rate (ASDALYsR) decreased, absolute numbers increased significantly. Projections for 2035 reveal a continued decline in ASIR and ASDALYsR for LBP. The LBP burden varied by the sociodemographic index quintile and GBD region, with the highest ASIR and ASDALYsR observed in Central Europe and the greatest decrease in East Asia. Globally, women bear a higher burden of LBP than men, with middle-aged populations experiencing the heaviest burden. Occupational ergonomic factors, high body mass index, and smoking remain the primary risk factors for LBP, with occupational ergonomic factors contributing the most to the overall burden.ConclusionDespite a projected decline in incidence, the global burden of LBP persists, exhibiting significant regional and gender disparities. To mitigate its future burden, precise and effective prevention and control strategies targeting high-risk factors are imperative.

The pyroptosis and fibrotic diseases: a bibliometric analysis from 2010 to 2024.

Fibrosis is the ultimate, common pathological ending of most chronic inflammatory diseases and increases the chances of developing life-threatening illnesses. Pyroptosis, a newfound form of lytic programmed cell death initiated by the inflammasome, has received more and more attention because of its association with fibrotic diseases. Therefore, this study visualizes the connection between pyroptosis and fibrosis research through bibliometric methods, aimed at providing global research hits and tendencies in the field. We collected and analyzed the articles on pyroptosis and fibrosis from 2010 to 2024 via Web of Science. Visual data analysis was performed for countries, institutions, authors, references, and keywords in the field using VOSviewer, CiteSpace software, the "Bibliometrix" R package, the bibliometric website ( https://bibliometric.com/ ), and Excel software. We analyzed the data by utilizing the bibliometric review method. A total of 566 articles and reviews relating to pyroptosis and fibrosis were identified in the Web of Science. The number of publications in the domain has continued to grow since 2010. These scientific outputs were mainly from 129 countries/regions and 1919 institutions, particularly China (n = 423) and the USA (n = 83). More importantly, although China publishes a vast majority of articles, its centrality is lower than that of the USA (0.59 vs 0.61). Among the 3833 authors involved in this field, Feldstein, A. E. is the most prolific author. Shi, J. J. is the world's most-cited author among the 12,143 authors in these academic journals. Frontiers in Immunology was a prolific contributor, and Nature was the most frequently cited journal. After analysis, Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death were the top-cited articles. The analysis of keywords displayed that pyroptosis, fibrosis, and pathways were the main research hotspots and frontier directions in recent years. We analyzed the characteristics of published articles and drew a fundamental knowledge structure on pyroptosis and fibrosis research via bibliometric analysis. The potential mechanism between fibrosis and pyroptosis is deeply tied to the current moment. Our findings can help researchers make clear the research status and value of fibrosis and pyroptosis and provide new directions for future research as soon as possible.

Analytical skills for accounting students in a data-driven job market: Australian evidence

Purpose The purpose of this study is to investigate the essential analytical skills required by accounting students to succeed in a rapidly evolving data-driven job market. Specifically, the study explores the role of data analytics, visualisation and software proficiency (e.g. Excel, Power BI) in the contemporary Australian accounting industry. The scope extends to providing recommendations for academia, professional bodies and employers on how accounting education can better align with the dynamic needs of the business environment by integrating these skills into curricula and professional development programmes. Design/methodology/approach Data was collected from Australian job listings incorporating accounting and data analytics tasks from popular job advertisement websites such as Seek, Indeed and Monster. Using the keyword “Data Analyst”, information was gathered on skills demanded, technology proficiency, industry sectors, professional qualifications and salary expectations. The data set comprised 73 observations across various job requirements and characteristics variables. Findings Critical analytical skills sought by accounting professionals in the current job market include extracting insights from big data, solving complex business problems, effective communication and data visualisation. Proficiency in tools like Microsoft Excel, SQL and Power BI is paramount. Originality/value This study enriches the literature by providing comprehensive insights into the contemporary demands for accounting analytics skills. It highlights the importance of integrating technology into accounting education and the varying skill requirements across industries. Additionally, it offers implications for professional qualifications and underscores the necessity for continuous monitoring and adaptation of accounting curricula to meet evolving industry needs.