Advanced Visualization Research Articles

Kajian Pemahaman Mahasiswa tentang Pengumpulan dan Analisis Data melalui Evaluasi Hasil Penelitian Skripsi

A deep understanding of data collection and analysis techniques is an essential competence for students to produce valid and high-quality research. This study employed a quantitative descriptive approach with 22 students as subjects who had studied these techniques. Each student was tasked with evaluating two quantitative theses relevant to the learning model, published within the last five years. The evaluation used a rubric covering seven key components: Data Identification and Classification, Frequency Distribution, Measures of Central Tendency and Data Variation, Coefficient of Variation, Standard Values, Inferential Statistical Procedures, and Data Integration and Synthesis. The study results revealed varying levels of student understanding across the components. Most students achieved high competence in Data Identification and Classification (95.45%) and Inferential Statistical Procedures (95.45%). However, comprehension of more complex components, such as Coefficient of Variation and Standard Values, remained weak, with only 54.55% of students fully competent. This study highlights the need for more structured teaching methods and intensive practical exercises, particularly in advanced statistical analysis and data visualization. The implications suggest that improving these areas can strengthen students' research skills, supporting better mastery of data analysis techniques in the future.

Real-time Analytics and Clinical Decision Support Systems: Transforming Emergency Care

In the rapidly evolving landscape of emergency medicine, real-time analytics and Clinical Decision Support Systems (CDSS) have emerged as transformative tools for enhancing patient care and clinical decision-making. This comprehensive article examines these technologies' integration, implementation, and impact in emergency care settings. The article covers the foundational aspects of real-time analytics, the intelligence layer provided by CDSS, and the sophisticated technology stack supporting these systems. The article explores various clinical applications, including sepsis detection, trauma care, and cardiac emergency management, while addressing implementation challenges and future opportunities. The findings demonstrate significant improvements in patient outcomes, operational efficiency, and clinical workflow optimization across emergency departments. Integrating artificial intelligence, machine learning, and advanced visualization technologies has revolutionized emergency care delivery, setting new standards for clinical excellence and patient safety

Words to unite nations: The complete United Nations General Debate Corpus, 1946–present

Every year since 1946, the General Debate has taken place at the beginning of the United Nations (UN) General Assembly session. Representatives from all UN member states deliver an address, discussing the issues that they consider most important in global politics, revealing their governments’ positions, and seeking to persuade other states of their perspectives. The annual UN General Debate statements provide invaluable information for scholars of international relations – comparable globally and over time. However, these texts are stored as poor quality images without relevant metadata, preventing researchers from applying data science methods. This paper introduces the complete UN General Debate Corpus (UNGDC). Building on a previous incomplete release of UNGDC, we have extended the corpus to cover the entire 1946–present period, included additional data on all speakers and provided advanced search and data visualization tools on a new website. The complete corpus contains over 10,000 speeches from 202 countries, including historical countries – making it the most comprehensive, unique and accessible collection of global political speeches. We discuss the complete UNGDC, provide relevant information for data users and present illustrative examples of how the corpus can be employed to address key questions in world politics.

Bioinspired Passive Cooling Hydrogel for Visualizing Hygroscopicity and Desorption Process

AbstractSelf‐hygroscopic hydrogels, characterized by high evaporation enthalpy, cooling efficiency, and self‐regulating properties, have garnered significant attention. However, most current research focuses on enhancing the hygroscopic and desorption performance, often overlooking the importance of monitoring the self‐regulation process, which limits its further application. Advanced visualization technologies, such as in situ electrical impedance tomography, low‐field nuclear magnetic resonance, and hyperspectral imaging, offer potential insights into this behavior, yet they often require additional devices, incur high costs, and involve complex sample preparation processes. Therefore, drawing inspiration from nature, humidity‐color‐sensitive hydrogels (HCSHs) strategy is proposed for visualized cooling. Benefiting from the strong polar responsiveness of the aggregation‐induced emission (AIE) molecules, the hydrogel's fluorescence significantly changes with varying interior water content, thereby its self‐regulation process is monitored easily. Further, the obtained hydrogel could be applied in the electronic device cooling owing to the polymer skeletons’ high swelling ratio, strong adhesion, and excellent self‐hygroscopic properties. This strategy overcomes current limitations in the visual technology of self‐hygroscopic materials and provides new insights into intelligent thermal management for electronic devices.

EXPRESSO: a multi-omics database to explore multi-layered 3D genomic organization.

The three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation. EXPloration of Regulatory Epigenome with Spatial and Sequence Observations (EXPRESSO) is a novel multi-omics database for exploration and visualization of multi-layered 3D genomic features across 46 different human tissues. Integrating 1360 3D genomic datasets (Hi-C, HiChIP, ChIA-PET) and 842 1D genomic and transcriptomic datasets (ChIP-seq, ATAC-seq, RNA-seq) from the same biosample, EXPRESSO provides a comprehensive resource for studying the interplay between 3D genome architecture and transcription regulation. This database offers diverse 3D genomic feature types (compartments, contact matrix, contact domains, stripes as diagonal lines extending from a genomic locus in contact matrix, chromatin loops, etc.) and user-friendly interface for both data exploration and download. Other key features include REpresentational State Transfer application programming interfaces for programmatic access, advanced visualization tools for 3D genomic features and web-based applications that correlate 3D genomic features with gene expression and epigenomic modifications. By providing extensive datasets and tools, EXPRESSO aims to deepen our understanding of 3D genomic architecture and its implications for human health and disease, serving as a vital resource for the research community. EXPRESSO is freely available at https://expresso.sustech.edu.cn.

Simulating the Cellular Context in Synthetic Datasets for Cryo-Electron Tomography.

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane-bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

GraphCVAE: Uncovering cell heterogeneity and therapeutic target discovery through residual and contrastive learning

Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce GraphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. GraphCVAE employs a multi-layer Graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates GraphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, GraphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. GraphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies.

CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses.

In recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, despite technological advancements of HD-MEAs, there is a lack of effective data-analysis platforms that are capable of processing and analyzing the data, particularly in the context of cardiac arrhythmias and drug testing. To address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSCCMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks. We demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, userfriendly platform for studying cardiac diseases and assessing drug effects.

A dynamic platform for global pullorum disease and fowl typhoid

Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gallinarum, respectively. The prevalence of these diseases varies across regions and is affected by different risk factors that remain insufficiently documented. To fill this knowledge gap, we have compiled a global dataset for its prevalence, drawing upon a collection of literature from the last seven decades obtained from bilingual databases. However, a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures, limit disease transmission, and prevent future outbreaks at the global and local levels. Here, we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum, especially in China, which is categorized by geographical region and temporal span. The platform offers a user-friendly, efficient, and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different regions. Additionally, this platform allows users to understand the influence of various factors, such as breed, farm mode, economic usage and even the sex of the primary host, chickens, on the prevalence of this disease. We further provided a detailed overview of individual province within China. In particular, by selecting two different provinces on the interface, users can quickly visualize and grasp the disparities in disease prevalence between the chosen regions. This interactive toolkit enables a dynamic exploration of the patterns and factors contributing to the prevalence of Salmonella Pullorum and Gallinarum. This interactive platform is freely available open source at http://139.9.85.208/.

Shifting Sands: Examining and Mapping the Population Structure of Greece Through the Last Three Censuses

This paper aims to facilitate a more nuanced understanding of regional disparities in the population age structure at a local scale by applying a recent method for visualizing these disparities. Utilizing data from the three most recent population censuses in Greece, this method applies advanced data visualization techniques to map age distributions, highlighting significant variations in aging patterns across municipalities, towns, and districts. Traditional demographic analysis often overlooks local heterogeneities, leading to broad policies that often fail to address the unique needs of specific regions. Detailed maps are created by integrating geographic data with census data (using R and GIS), enabling policymakers to pinpoint areas with specific demographic challenges and opportunities. This localized approach reveals critical insights, such as regions experiencing rapid population aging, areas with younger population profiles, and zones undergoing demographic transitions. The visualization tool significantly improves the formulation of targeted strategies, enhancing the effectiveness of policies related to healthcare, workforce planning, and social services distribution. Through case studies and comparative analysis, we demonstrate the practical applications and advantages of this method in shaping public policy and strategic planning. This paper contributes to the field of geodemography by introducing and demonstrating a visualization method that enhances the accuracy of demographic analysis, providing policy makers with useful information to better address local demographic challenges and tailor strategies to specific regional needs.

Bibliometric and visualization analysis in the field of epigenetics and glioma (2009-2024).

Glioma represents the most prevalent primary malignant tumor in the central nervous system, a deeper understanding of the underlying molecular mechanisms driving glioma is imperative for guiding future treatment strategies. Emerging evidence has implicated a close relationship between glioma development and epigenetic regulation. However, there remains a significant lack of comprehensive summaries in this domain. This study aims to analyze epigenetic publications pertaining to gliomas from 2009 to 2024 using bibliometric methods, consolidate the extant research, and delineate future prospects for investigation in this critical area. For the purpose of this study, publications spanning the years 2009 to 2024 were extracted from the esteemed Web of Science Core Collection (WoSCC) database. Utilizing advanced visualization tools such as CiteSpace and VOSviewer, comprehensive data pertaining to various aspects including countries, authors, author co-citations, countries/regions, institutions, journals, cited literature, and keywords were systematically visualized and analyzed. A thorough analysis was conducted on a comprehensive dataset consisting of 858 publications, which unveiled a discernible trend of steady annual growth in research output within this specific field. The nations of the United States, China, and Germany emerged as the foremost contributors to this research domain. It is noteworthy that von Deimling A and the Helmholtz Association were distinguished as prominent authors and institutions, respectively, in this corpus of literature. A rigorous keyword search and subsequent co-occurrence analysis were executed, ultimately leading to the identification of seven distinct clusters: "epigenetic regulation", "DNA repair", "DNA methylation", "brain tumors", "diffuse midline glioma (DMG)", "U-87 MG" and "epigenomics". Furthermore, an intricate cluster analysis revealed that the primary foci of research within this field were centered around the exploration of glioma pathogenesis and the development of corresponding treatment strategies. This article underscores the prevailing trends and hotspots in glioma epigenetics, offering invaluable insights that can guide future research endeavors. The investigation of epigenetic mechanisms primarily centers on DNA modification, non-coding RNAs (ncRNAs), and histone modification. Furthermore, the pursuit of overcoming temozolomide (TMZ) resistance and the exploration of diverse emerging therapeutic strategies have emerged as pivotal avenues for future research within the field of glioma epigenetics.

Mapping the Path to Low-Carbon Behaviour: A Systematic Review of Trends, Gaps, and Future Directions

It is essential to conduct research on low-carbon behaviour in order to address global climate change and promote sustainable development. This study conducts a thorough examination of a bibliometric analysis of the existing literature in this field. We analysed 129 papers from the Web of Science Core Collection database to conduct a study on the concept of “low-carbon behaviour”. Review articles, proceedings papers, and editorial materials were all excluded. This investigation examines a diverse array of research environment factors, including the most highly ranked publications, publication trends, significant publishers, and patterns in citations and publications over time. The primary institutional contributors in the discipline and the most influential works worldwide are also mentioned. The application of advanced visualisation techniques, such as wordcloud representations, the spatial distribution of research output, and co-occurrence and overlay networks, is employed to generate maps of keyword relationships and collaboration networks using Bibliometrix Stable Version, VOSviewer 1.6.20, and Scimago Graphica Beta 1.0.45. This multi-modal methodology enables a comprehensive investigation of significant research issues and emerging directions in low-carbon behavioural studies. This study contributes to the ongoing endeavours to promote sustainable development and mitigate climate change by conducting a comprehensive evaluation of the current state of research and establishing a robust framework for future investigations into low-carbon behavioural patterns and behaviours.

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages.This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Deep Earth System Data Laboratory (DeepESDL)

Abstract. The Deep Earth System Data Lab (DeepESDL) provides an AI-ready, collaborative environment for researchers aiming to study the Earth's complex dynamics using various datasets and empirical approaches. Recently opened to Early Adopters, it builds on projects like CAB-LAB and ESDL, utilizing well-established Python and Julia technology stacks. DeepESDL offers programmatic access to extensive analysis-ready data cubes and computational resources, enabling researchers to focus on analysis without extensive preparations. Scientists can use persistently available data cubes or generate user-tailored cubes from own data or publicly available datasets. The goal is to streamline data processing through empirical or AI methods within high-dimensional Earth Observation workflows. DeepESDL addresses the complete research cycle, from discovery of earth data to powerful analyses, collaborative scientific research, advanced data visualisation and publication of results, promoting FAIR and Open Science. Apart from serving as a research environment, DeepESDL showcases scientific use cases and supports educational purposes through capacity building, academic programs, and Open Science initiatives. This paper presents an overview of DeepESDL.

Geospatial Data Enrichment through Address Geocoding: Challenges and Solutions

Abstract. Enriching geospatial data, particularly through address geocoding, is fundamental to many geospatial analytical solutions, facilitating resource allocation, accessibility assessment, and urban development planning. This paper explores the address geocoding approaches from traditional GIS-based methods to advanced data-driven solutions, emphasising their accuracy, efficiency, and complementarity. Geospatial data enrichment challenges are explored using the SemTUI framework and QGIS platform. SemTUI employs a data-driven approach that facilitates interactive semantic enrichment and address reconciliation. QGIS is utilised for address geocoding, using the MMQGIS plugin with two geocoding web services provided by OSM and Google. The comparison between SemTUI and QGIS workflows highlights their complementary strengths. SemTUI offers intuitive interfaces and an automated solution, while QGIS provides advanced geospatial visualisation and analysis capabilities. Practical applications in supply-demand analysis of social amenities and walkability index calculation demonstrate the significance of geocoding in urban planning. Further research efforts are focused on enhancing the integration between SemTUI and QGIS to improve the precision and performance of address geocoding and further spatial data analysis.

Developing a Workflow for Transforming BIM Models into Immersive Virtual Reality Experiences

Abstract. The integration of Extended Reality (XR) and Building Information Modelling (BIM) has emerged as a promising approach for enhancing interaction with spatial data in the built environment. While the potential of BIM for advanced visualization has been recognized, there remains a need for standardized workflows that facilitate the transformation of BIM models into immersive virtual reality (VR) experiences. This research contributes to the ongoing development of BIM-VR integration by presenting a workflow that leverages Revit, Twinmotion, and Datasmith to create visually rich and interactive VR environments. The established process focuses on enhancing textures, incorporating dynamic lighting and weather simulations, and populating the virtual space with realistic elements. The workflow also addresses the integration of interactable objects to elevate user engagement and the use of cutting-edge VR headsets for an immersive experience. By providing a structured approach to bridging complex BIM data with VR, this research aims to support architects, engineers, construction professionals, and clients in design collaboration, project review, and decision-making across the building lifecycle. The workflow's potential for facilities management and training applications is also explored. Through a case study, the research demonstrates the workflow's effectiveness in facilitating a deeper understanding of architectural spaces before construction, highlighting its potential for improving efficiency and reducing rework. The paper also discusses the challenges and opportunities associated with integrating BIM with GIS and sensor data to create comprehensive digital twins. This research contributes to the growing body of knowledge on BIM-VR integration and underscores the importance of continued development in this field to enhance the design, communication, and experience of the built environment.

Molecular Interactions between Kaempferol and Apolipoprotein A-I (APOA1) of Rattus Norvegicus of Using Drug Docking

Nowadays, pharmacological research investigations are essential to the development of new medication prospects. We have created high-resolution synteny and rearrangement breakpoint maps across the human, mouse, and rat genomes using paired-end sequences from bacterial artificial chromosomes. Apolipoprotein A-I (APOA1) is often associated with a markedly elevated risk of atherosclerosis and cardiovascular disease. Several studies in the field of clinico-genetics have confirmed this fact. In this work, we employ 3D Insilico drug docking techniques to simulate kaempferol interactions with the potential mutant target protein Apolipoprotein A-I (APOA1). Kaempferol is one of the primary phytochemicals present in Hibiscus rosa-sinensis. Numerous pharmacological effects of Hibiscus rosa-sinensis have been shown to be effective in treating a wide spectrum of human ailments. We look into the connection between APOA1 and kaempferol. In post-docking tests, advanced 3D molecular visualization capabilities were utilized. Kaempferol directly reduces amino acid mutational sites, as indicated by the docking study results. The molecular 3D H-bond interaction between APOA1 and Kaempferol is illustrated using concepts of molecular dynamics techniques based on 3D views. Docking studies play a key role in pharmaceutical researches for the production of novel drug candidates. Thus, investigations on Rattus norvegicus with kaempferol plays a significant role in the pharmacological industry in designing innovative medication candidates. Lastly, we conclude that kaempferol is protective against cardiovascular diseases associated with atherosclerosis.

Advanced monitoring of damage behavior and 3D visualization of fiber distribution in assessing crack resistance mechanisms in steel fiber-reinforced concrete

This study presents a novel approach to understanding the reinforcing mechanisms of steel fiber-reinforced concrete, particularly its crack resistance and toughening effects. By integrating dynamic surface strain monitoring with advanced three-dimensional visualization techniques, new insights are provided into how steel fibers enhance concrete's structural performance. X-ray computed tomography (CT) technology enabled detailed 3D visualization and analysis of steel fibers embedded within the concrete matrix, facilitating an in-depth examination of their distribution patterns, orientation, and positioning. Additionally, the Digital Image Correlation (DIC) three-dimensional full-field strain measurement system was utilized to dynamically monitor the loading process during three-point bending tests on semicircular concrete specimens. The findings indicate that the uniform distribution of steel fibers significantly reduces stress concentration and effectively delays crack initiation and propagation. Steel fibers bridge cracks, mitigate local stress concentrations, and substitute for the tensile components of concrete after macro-crack formation, thereby inhibiting further crack propagation. Quantitative analysis of fiber distribution through 3D image reconstruction and coordinate extraction confirmed a strong correlation between fiber orientation and crack resistance. This study contributes to the field by offering a comprehensive analysis of key parameters such as fracture process strain monitoring, 3D visualization, and quantification of fiber distribution, advancing the understanding of crack propagation mechanisms and failure processes in fiber-reinforced concrete.

A Comprehensive Guide to Generating SDTM Demographic Datasets Using R in Clinical Trials

R is a programming language widely used for statistical analysis and data visualization, offering a flexible and interactive environment supported by various packages for data cleaning, tidying, and analysis. It is particularly relevant for professionals in mathematics and statistics, including biostatisticians and programmers in the pharmaceutical and biotech industries. R provides a robust array of user-developed packages that can efficiently manipulate complex datasets, such as those based on the Study Data Tabulation Model (SDTM). The popularity of R in data-related fields has surged exponentially over the past decade due to its open-source nature, powerful statistical capabilities, and advanced visualization tools. In this paper, we demonstrate a step-by-step approach to generating an SDTM Demographic (DM) dataset using R. The process leverages R packages such as sas7bdat, tidyverse, haven, parsedate, dplyr, tidyr, and Hmisc. We also provide a detailed procedure for setting up the R environment required for this process. While R has been extensively used for exploratory analysis in the pharmaceutical and biotech industries, its application in creating and analyzing clinical trial datasets, such as SDTM, has been limited. Traditionally, SAS® has been the preferred tool for generating clinical trial datasets. This paper explores R’s potential as a viable alternative, offering enhanced flexibility and cost- effectiveness in clinical trial.