Interactive Data Visualization Research Articles

Interactive Visualization on Large High‐Resolution Displays: A Survey

AbstractIn the past few years, large high‐resolution displays (LHRDs) have attracted considerable attention from researchers, industries and application areas that increasingly rely on data‐driven decision‐making. An up‐to‐date survey on the use of LHRDs for interactive data visualization seems warranted to summarize how new solutions meet the characteristics and requirements of LHRDs and take advantage of their unique benefits. In this survey, we start by defining LHRDs and outlining the consequence of LHRD environments on interactive visualizations in terms of more pixels, space, users and devices. Then, we review related literature along the four axes of visualization, interaction, evaluation studies and applications. With these four axes, our survey provides a unique perspective and covers a broad range of aspects being relevant when developing interactive visual data analysis solutions for LHRDs. We conclude this survey by reflecting on a number of opportunities for future research to help the community take up the still‐open challenges of interactive visualization on LHRDs.

COLUMBUS 7-year update: A randomized, open-label, phase III trial of encorafenib plus binimetinib versus vemurafenib or encorafenib in patients with BRAF V600E/K-mutant melanoma

BackgroundTreatment with encorafenib plus binimetinib and encorafenib monotherapy is associated with improved progression-free survival (PFS) and overall survival (OS) compared with vemurafenib in patients with BRAF V600E/K-mutant metastatic melanoma. We report results from the 7-year analysis of COLUMBUS part 1 (NCT01909453) at 99.7 months (median duration between randomization and data cutoff). Methods577 patients with locally advanced unresectable or metastatic BRAF V600E/K-mutant melanoma who were treatment-naive or progressed after first-line immunotherapy were randomized 1:1:1 to encorafenib 450 mg once daily (QD) plus binimetinib 45 mg twice daily (BID) (n = 192), vemurafenib 960 mg BID (n = 191), or encorafenib monotherapy 300 mg QD (n = 194). No prior BRAF/MEK inhibitor was allowed. ResultsSeven-year PFS and OS rates (95 % CI) were 21.2 % (14.7–28.4 %) and 27.4 % (21.2–33.9%) in the encorafenib plus binimetinib arm and 6.4 % (2.1–14.0 %) and 18.2 % (12.8–24.3 %) in the vemurafenib arm, respectively. Median melanoma-specific survival (95 % CI) was 36.8 months (27.7–51.5 months) in the encorafenib plus binimetinib arm and 19.3 months (14.8–25.9 months) in the vemurafenib arm. Thirty-four long-term responders (complete/partial response ongoing at 7 years) were identified across arms. ConclusionsThis is the longest follow-up from a phase III trial of BRAF/MEK inhibitor combination in BRAF V600E/K-mutant metastatic melanoma. Safety results were consistent with the known tolerability profile of encorafenib plus binimetinib. Results support the long-term efficacy and known safety of encorafenib plus binimetinib in this population and provide new insights on long-term responders. Interactive data visualization is available at the COLUMBUS dashboard (https://clinical-trials.dimensions.ai/columbus7/).

Visual Guideline - Newer Pharmacologic Treatments in Adults With Type 2 Diabetes: A Visual Clinical Guideline From the American College of Physicians.

The purpose of this visual clinical guideline is to provide a visual summary of ACP's clinical guideline, "Newer Pharmacologic Treatments in Adults With Type 2 Diabetes." In addition, this visual clinical guideline features an interactive data visualization of findings associated with ACP's clinical recommendations for this topic.

From static snapshots to dynamic panoramas: the evolution and future vision of palliative care atlas in cross-national perspectives

Palliative care is essential to global health services as it improves the quality of life of patients, their families and caregivers. The ATLANTES Global Observatory of Palliative Care (University of Navarra) was created a decade ago to promote a positive attitude towards patients with advanced illness in society and medicine. To do so, and over the past 15 years, ATLANTES has mapped palliative care data worldwide using public health, macro and comparative perspectives in different atlases. These have enabled data to be contextualised and good examples to be identified concisely and graphically. Atlases have been widely employed as advocacy tools within international institutions and ministries of health. While the aim and the perspective have remained unalterable over time, the methods and design have evolved throughout the publications from sober cartography and static infographics to big interactive data visualisation web tools. By embracing technology, ATLANTES has developed an open-access web mapping tool reuniting information from regional atlases, favouring global access to data. In 2022, matching the increasingly recognised need for robust monitoring of palliative care worldwide, ATLANTES became a WHO Collaborating Centre for the Global Monitoring of Palliative Care Development. This attempt to bridge the gap and ensure equitable care information in countries with limited palliative care access has resulted today in more accessible, self-explanatory, and visually appealing palliative care data.

A Basketball Big Data Platform for Box Score and Play-by-Play Data.

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

Mintel Reports

Mintel Reports provides solid market intelligence for both academic and commercial institutions. The reports offer in-depth analyses of consumer demographics and psychographics, market status, trend forecasts, competitive strategies, and case studies, all backed by interactive data visualizations. The 2023 upgrade formats these analyses and data into a clean, easy-to-read, easy-to-use layout, with smart infographics and multiple options for saving, sharing, and creating. The more mobile-friendly navigation, despite some accessibility issues, is much improved from the legacy product. Mintel Reports, already a staple for business, marketing, and advertising students, is an even more attractive resource with the new look and features.

A Mathematica-Based Interface for the Exploration of Inter- and Intra-Regional Financial Flows

This work surveys the use of directed weighted graphs in conducting comparative static analyses. The paper discusses the implementation of a computer-aided process for building spreadsheet-based graph models for inter- and intra-regional financial flows. The graph-theoretic techniques are programmed to enable the interactive visualization and analysis of financial data using Wolfram technologies (i.e., Mathematica software v. 11.3 or later, Wolfram player v. 12 or later). The paper describes the workflow for several interactive visualizations applicable to financial networks. The author provides four programs, written in the Wolfram language, that customize input–output financial models by combining the Manipulate command with built-in Mathematica functions and functions of the IGraph package (IGraph/M). The study is a tutorial article for the generation of a suite of visual schemes that provide patterns, practices, and roadmaps of the financial markets across the globe.

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool.

We report here the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package that processes data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data visualization stack, including DataShader and Param, to create an interactive visualization of the mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS and that the visualization methods could be useful for other open-source mass spectrometry tools.

An Interactive Dashboard for Statistical Analysis of Intensive Care Unit COVID-19 Data

Background: COVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19’s evolution, yielding insights for this pandemic and for future pandemics.

Pelatihan Tableau Untuk Visualisasi Data Secara Cepat dan Mudah

Human consumption of data and information has become very large due to the enormous growth of data. On the other hand, the increasing need for data visualization in various information media (online or offline) has also become very interesting. Data visualization is not only about beautifying the appearance of data but also about making data more quickly understood and engaging. Tableau specializes in data visualization, data storytelling and dashboards. Training to operate Tableau can answer the needs of pupils, students, the general public, teachers and lecturers as participants. This training aims for participants to apply interactive data visualization quickly and easily using Tableau in dashboard creation activities to explain data in visual form. The methods used in implementing this training are preparation methods and implementation methods as well as training. As a result of this training, 34 participants learned about the importance of data visualization. They learned Tableau as a data visualization tool and visualization training using Tableau to create self-ordered dashboards for examples of COVID-19 cases worldwide.

Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes.

Developments in biotechnologies enable multi-platform data collection for functional genomic units apart from the gene. Profiling of non-coding microRNAs (miRNAs) is a valuable tool for understanding the molecular profile of the cell, both for canonical functions and malignant behavior due to complex diseases. We propose a graphical mixed-effects statistical model incorporating miRNA-gene target relationships. We implement an integrative pathway analysis that leverages measurements of miRNA activity for joint analysis with multimodal observations of gene activity including gene expression, methylation, and copy number variation. We apply our analysis to a breast cancer dataset, and consider differential activity in signaling pathways across breast tumor subtypes. We offer discussion of specific signaling pathways and the effect of miRNA integration, as well as publish an interactive data visualization to give public access to the results of our analysis.

INFORMATION AND ANALYTICAL SYSTEM FOR COLLECTING, PROCESSING AND ANALYZING DATA ON AIR POLLUTION

The presented information and analytical system is designed to provide convenient management of the processes of collecting, processing and analyzing air pollution data. The system is a web application built on the basis of the R programming language using the Shiny web service and works with data from Vaisala monitoring stations. The created service receives data from the monitoring stations on various air parameters such as wind speed, temperature, and levels of pollutants such as PM2.5, CO, and NO2.The main functions of the web service include uploading data to a dedicated server, processing data with the ability to round time ranges, interactive data visualization using graphs and charts, building air quality charts, and presenting AQI, which can be used to classify pollution levels and generate recommendations for different sensitivity groups of the population.The system allows users to download data from the server in various formats or access the original data from the Vaisala server. In addition, users can convert data to different units of measurement. The web service can be of interest to both household users and professionals in various fields, including environmental researchers in Ukraine, governmental environmental control organizations, municipal and public organizations interested in a clean environment. By improving the availability and analysis of air quality data, this service contributes to the preservation of the environment and public health.

Web-based visualization and rendering of aerial LiDAR point cloud for urban flood simulation

Purpose In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management. Design/methodology/approach This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed. Findings The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making. Originality/value The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

Preclinical exploration of the DNA damage response pathway using the interactive neuroblastoma cell line explorer CLEAN.

Neuroblastoma (NB) is the most common cancer in infancy with an urgent need for more efficient targeted therapies. The development of novel (combinatorial) treatment strategies relies on extensive explorations of signaling perturbations in neuroblastoma cell lines, using RNA-Seq or other high throughput technologies (e.g. phosphoproteomics). This typically requires dedicated bioinformatics support, which is not always available. Additionally, while data from published studies are highly valuable and raw data (e.g. fastq files) are nowadays released in public repositories, data processing is time-consuming and again difficult without bioinformatics support. To facilitate NB research, more user-friendly and immediately accessible platforms are needed to explore newly generated as well as existing high throughput data. To make this possible, we developed an interactive data centralization and visualization web application, called CLEAN (the Cell Line Explorer web Application of Neuroblastoma data; https://ccgg.ugent.be/shiny/clean/). By focusing on the regulation of the DNA damage response, a therapeutic target of major interest in neuroblastoma, we demonstrate how CLEAN can be used to gain novel mechanistic insights and identify putative drug targets in neuroblastoma.

ClusterV-Web: a user-friendly tool for profiling HIV quasispecies and generating drug resistance reports from nanopore long-read data.

Third-generation long-read sequencing is an increasingly utilized technique for profiling human immunodeficiency virus (HIV) quasispecies and detecting drug resistance mutations due to its ability to cover the entire viral genome in individual reads. Recently, the ClusterV tool has demonstrated accurate detection of HIV quasispecies from Nanopore long-read sequencing data. However, the need for scripting skills and a computational environment may act as a barrier for many potential users. To address this issue, we have introduced ClusterV-Web, a user-friendly web-based application that enables easy configuration and execution of ClusterV, both remotely and locally. Our tool provides interactive tables and data visualizations to aid in the interpretation of results. This development is expected to democratize access to long-read sequencing data analysis, enabling a wider range of researchers and clinicians to efficiently profile HIV quasispecies and detect drug resistance mutations. ClusterV-Web is freely available and open source, with detailed documentation accessible at http://www.bio8.cs.hku.hk/ClusterVW/. The standalone Docker image and source code are also available at https://github.com/HKU-BAL/ClusterV-Web.

Impact of Lossy Compression Errors on Passive Seismic Data Analyses

Abstract New technologies such as low-cost nodes and distributed acoustic sensing (DAS) are making it easier to continuously collect broadband, high-density seismic monitoring data. To reduce the time to move data from the field to computing centers, reduce archival requirements, and speed up interactive data analysis and visualization, we are motivated to investigate the use of lossy compression on passive seismic array data. In particular, there is a need to not only just quantify the errors in the raw data but also the characteristics of the spectra of these errors and the extent to which these errors propagate into results such as detectability and arrival-time picks of microseismic events. We compare three types of lossy compression: sparse thresholded wavelet compression, zfp compression, and low-rank singular value decomposition compression. We apply these techniques to compare compression schemes on two publicly available datasets: an urban dark fiber DAS experiment and a surface DAS array above a geothermal field. We find that depending on the level of compression needed and the importance of preserving large versus small seismic events, different compression schemes are preferable.

Lexcube: Interactive Visualization of Large Earth System Data Cubes.

Many subsystems of Earth are constantly monitored in space and time and undergo continuous anthropogenic interventions. However, research into this transformation remains largely inaccessible to the public due to the complexity of the Big Data generated by models and Earth observation. To overcome this barrier, we present the Leipzig Explorer of Earth Data Cubes (lexcube.org), an interactive Earth data visualization that allows users to explore terabyte-scale datasets with minimal latency through space, time, variables, and model variants. With over 2800 users and 163,000 API requests since its public release in May 2022, lexcube.org is a novel interactive data cube visualization that embraces the concept of "data cubes," enabling an equal treatment of space and time. We expect this development to be particularly relevant for the emerging exascale Digital Twins of Earth, as interactive visualizations in real-time could remove access barriers and help democratize Earth system sciences.

De-cluttering Scatterplots with Integral Images.

Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalability issues, since growing data sizes eventually lead to overplotting and visual clutter on a screen with a fixed resolution, which hinders the data analysis process. We propose an algorithm that compensates for irregular sample distributions by a smooth transformation of the scatterplot's visual domain. Our algorithm evaluates the scatterplot's density distribution to compute a regularization mapping based on integral images of the rasterized density function. The mapping preserves the samples' neighborhood relations. Few regularization iterations suffice to achieve a nearly uniform sample distribution that efficiently uses the available screen space. We further propose approaches to visually convey the transformation that was applied to the scatterplot and compare them in a user study. We present a novel parallel algorithm for fast GPU-based integral-image computation, which allows for integrating our de-cluttering approach into interactive visual data analysis systems.

Creating Interactive Visualizations with Plotly

This lesson demonstrates how to create interactive data visualizations in Python with Plotly's open-source graphing libraries using materials from the Historical Violence Database.