Source data files used to generate visualizations presented in Figs 4 and 6–11, along with URLs of the corresponding generated visualizations.

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

The abundance of both taxonomic groups and gene categories in microbiome samples can now be easily assayed via various sequencing technologies, and visualized using a variety of software tools. However, the assemblage of taxa in the microbiome and its gene content are clearly linked, and tools for visualizing the relationship between these two facets of microbiome composition and for facilitating exploratory analysis of their co-variation are lacking. Here we introduce BURRITO, a web tool for interactive visualization of microbiome multi-omic data with paired taxonomic and functional information. BURRITO simultaneously visualizes the taxonomic and functional compositions of multiple samples and dynamically highlights relationships between taxa and functions to capture the underlying structure of these data. Users can browse for taxa and functions of interest and interactively explore the share of each function attributed to each taxon across samples. BURRITO supports multiple input formats for taxonomic and metagenomic data, allows adjustment of data granularity, and can export generated visualizations as static publication-ready formatted figures. In this paper, we describe the functionality of BURRITO, and provide illustrative examples of its utility for visualizing various trends in the relationship between the composition of taxa and functions in complex microbiomes.

Field collection of surface and groundwaters across Canada for characterization of dissolved organic matter (DOM). DOM was characterized using UV-visible absorbance, size-exclusion chromatography, and elemental ratios (dissolved organic carbon to dissolved organic nitrogen). Measures were compared in all samples to determine which select measures give us the best representation of DOM composition. These were then used to develop a simple visualization tool (Composition Wheel).

Uijeongbu City, Gyeonggi Province, provides population status data as public data at the end of each month. Uijeongbu City's population status public data can be used to produce various analysis data and visualization results. These data are useful for policy making, urban planning, and community research. Urban planning and policy making: By analyzing population density and the number of people per household, you can establish policies for housing, transportation, and welfare. Market research and business strategy: You can develop marketing strategies by understanding the population structure of a specific region. Academic research and statistical analysis: You can study various social phenomena by utilizing population statistics data.

This dataset contains Python scripts and associated input data for generating figures used in the manuscript titled "Reference compositions for bismuth telluride thermoelectric materials for low-temperature power generation." It includes visualizations related to compositional analysis, the thermoelectric properties of reference compositions, and the distributions of TEP properties in BiTe-based systems. Literature data used in Sections 1 and 3 is sourced from starrydata2.org.Compositional Analysis of p-type and n-type BiTe Systems:The scripts p_type_BiSbTe_composition_analysis.py and n_type_BiTeSe_composition_analysis.py use 2021-12-14_atom_composition.csv, 2021-12-16_p_type_BiTe_sampleid.csv, 2021-12-16_n_type_BiTe_sampleid.csv, and 20211124_rawdata_03_1_dev_fast_run_eff_check_20211209_205849.csv as inputs. These scripts generate bar charts of dopant frequency, violin plots of dopant atomic fraction, KDE plots of Te vs Se and Bi vs Sb, and histograms of host atom ratios and Sb atom counts. The input composition data was extracted from the literature using Starrydata2.org.Thermoelectric Properties of Reference Compositions:The scripts TE properties_p-type_Bi0.46Sb1.54Te3.py and TE properties_n-type_Bi2Te2.7Se0.3.py visualize direction-resolved TE properties of Bi₀.₄₆Sb₁.₅₄Te₃ and Bi₂Te₂.₇Se₀.₃ using the input files TE properties_Bi0.46Sb1.54Te3_HP_A-axis.csv, TE properties_Bi0.46Sb1.54Te3_HP_C-axis.csv, TE properties_Bi0.46Sb1.54Te3_SPS_A-axis.csv, TE properties_Bi0.46Sb1.54Te3_SPS_C-axis.csv, TE properties_Bi2Te2.7Se0.3_HP_A-axis.csv, TE properties_Bi2Te2.7Se0.3_HP_C-axis.csv, TE properties_Bi2Te2.7Se0.3_SPS_A-axis.csv, and TE properties_Bi2Te2.7Se0.3_SPS_C-axis.csv. These scripts produce plots of σ, S, PF, κ, κₚₕ, and zT along A- and C-axis directions.TEP Property Distribution in BiTe Systems:The scripts p-type_Bi0.46Sb1.54Te3_TEP distribution.py and n-type_Bi2Te2.7Se0.3_TEP distribution.py utilize literature data from Starrydata2.org (20211124_rawdata_alpha.csv, 20211124_rawdata_sigma.csv, 20211124_rawdata_kappa.csv, and 20211124_rawdata_zT.csv), along with sample ID filters and experimental overlay files such as TE properties_Bi0.46Sb1.54Te3_SPS_A-axis.csv or TE properties_Bi2Te2.7Se0.3_SPS_A-axis.csv. These scripts generate KDE plots that show the distributions of S, σ, κ, and zT, with reference composition data overlaid for comparison. All output figures are saved in .tif format

Materials qualification of reactor structural materials is a critical step in rapid implementation of advanced nuclear reactor technologies, particularly to assess the corrosion performance in these designs. Accelerated qualification of reactor structural materials requires incorporating powerful computational toolsets, such as phase field modelling in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to predict the evolution of structural materials due to corrosion. Accordingly, computational toolsets will require experimental data generated at appropriate length scales to validate accuracy. Focused ion beam (FIB) provides a high degree of control over manipulation of materials for analytical purposes, including capturing data on the evolution in the microstructure and elemental composition of materials at the mesoscale, an appropriate length scale for phase field modelling of intergranular diffusion phenomena using the MOOSE framework. For instance, the FEI Helios G4 UX dual beam plasma FIB microscope at the Irradiated Materials Characterization Laboratory (IMCL) is capable of backscatter diffraction (EBSD) and energy-dispersive x-ray spectroscopy (EDS) documenting the evolution in the microstructure and elemental composition, respectively. The Helios can perform EDS and EBSD three-dimensionally (3D) using tomography, which is then combined using different software packages to visualize 3D volumes correlating elemental composition to microstructural data. The purpose of this investigation was to develop a streamlined characterization and data processing workflow for 3D tomography studies on the FEI Helios G4 plasma FIB. The investigation is segmented into three parts: 1) Optimizing the data collection workflow, 2) identifying appropriate data processing and visualization software (i.e. DREAM.3D, MIPAR, and VGStudioMax), and 3) establishing an infrastructure for public release. The optimization of the data collection workflow is in collaboration with members of the U220 department to setup formal training on the tomography operation of the G4, through ThermoFisher Scientific, and exploring DREAM.3D, MIPAR, and VGStudioMax data processing/visualization software packages. VGStudioMax currently demonstrates the most promise for future use. Optimization of the data collection and processing workflow is still ongoing. A collaboration with INL High Performance Computing (HPC) established an open-source license for expediting the public release of FIB tomography datasets through HPC. FIB tomography data generated by the G4 will provide comprehensive data for validating 3D phase field mesoscale modelling tools within the MOOSE framework for accelerated qualification of reactor structural materials. label::after { content: "" !important; }

This repository contains data, analysis files, and other supplementary materials related to the article "The Impact of Data Reliability and Semantic Color Congruence on Trusting and Reading Visualizations".The word document outlines most of the results and additional information.There are two folders in this repository. The "Data Analysis" folder contains an R Markdown file used to analyze the data in E1 and E2. It also contains data files for Experiments 1 and 2. The "ColorPretest_Analyzed" file contains the processed pretest data.The "Stimuli Development" folder contains two folders, each of which have all the individual stimuli used in Experiments 1 and 2. It also contains the file "Map Composition.xlsx" that outlines how many map units are in each condition and data class.

IVI Graphics Composition Engine Market Outlook

According to our latest research, the global IVI Graphics Composition Engine market size reached USD 1.92 billion in 2024, reflecting robust demand across automotive digitalization initiatives. The market is expanding at a CAGR of 13.7% during the forecast period, with projections indicating that the market will reach USD 6.07 billion by 2033. This rapid growth is primarily driven by the accelerating adoption of advanced infotainment systems and digital cockpit solutions in both passenger and commercial vehicles, as automotive manufacturers and suppliers strive to enhance in-vehicle user experiences and integrate next-generation safety and convenience features.

One of the primary growth drivers for the IVI Graphics Composition Engine market is the increasing consumer demand for sophisticated in-vehicle infotainment (IVI) systems. As vehicles become more connected and digitally enabled, consumers expect seamless integration of navigation, entertainment, connectivity, and driver assistance features within a unified digital interface. This demand has compelled automakers and Tier 1 suppliers to invest heavily in high-performance graphics composition engines, which are essential for rendering complex, real-time multimedia content and ensuring fluid user interactions. Furthermore, the proliferation of touchscreens, digital instrument clusters, and head-up displays in modern vehicles is fueling the adoption of advanced graphics engines, as these components require robust processing capabilities to deliver high-resolution, interactive graphical content.

Another significant factor contributing to the market's growth is the evolution of automotive safety and convenience technologies, particularly in the realm of Advanced Driver Assistance Systems (ADAS). As regulatory bodies worldwide introduce stringent safety standards and as consumers prioritize safety features, automakers are integrating more ADAS functionalities that rely on real-time data visualization and intuitive human-machine interfaces. IVI graphics composition engines play a critical role in aggregating and displaying information from various sensors and cameras, enabling features such as lane departure warnings, adaptive cruise control, and collision avoidance alerts. This trend is further amplified by the shift toward semi-autonomous and fully autonomous vehicles, which demand even more sophisticated graphical interfaces to communicate complex driving scenarios to occupants.

The ongoing electrification of the automotive industry is also propelling the growth of the IVI Graphics Composition Engine market. Electric vehicles (EVs) typically feature more advanced digital dashboards and infotainment systems compared to traditional internal combustion engine vehicles, as OEMs leverage digitalization to differentiate their EV offerings and educate drivers about battery management, energy consumption, and charging infrastructure. As a result, the need for powerful graphics composition engines capable of handling dynamic, data-rich displays is becoming increasingly pronounced in the EV segment. Additionally, the growing adoption of cloud-based deployment models and over-the-air (OTA) software updates is enabling automakers to enhance and personalize IVI experiences throughout the vehicle lifecycle, further boosting demand for scalable and flexible graphics engine solutions.

Regionally, Asia Pacific continues to dominate the IVI Graphics Composition Engine market, accounting for the largest share in 2024, followed by North America and Europe. The strong presence of leading automotive OEMs, the rapid pace of vehicle electrification, and the high penetration of digital technologies in countries such as China, Japan, and South Korea are key factors driving regional growth. North America is witnessing significant traction due to the early adoption of connected vehicle technologies and the presence of major technology providers, while Europe remains a critical market owing to its focus on automotive safety, sustainability, and premium vehicle segments. Emerging markets in Latin America and the Middle East & Africa are also expected to contribute to future growth, albeit from a smaller base, as automotive digitalization initiatives gain momentum.

The residential body composition scale market is experiencing robust growth, driven by increasing health consciousness among consumers and the rising prevalence of obesity and related health issues globally. The market's expansion is fueled by technological advancements leading to more accurate and user-friendly scales, offering insights beyond basic weight measurement. Smart scales, integrating with mobile apps and offering detailed body composition analysis including body fat percentage, muscle mass, bone mass, and hydration levels, are gaining significant traction. This trend is further propelled by the rising adoption of wearable technology and fitness tracking apps, fostering a holistic approach to health management. The market is segmented by application (online vs. offline sales) and type (smart vs. normal scales), with smart scales commanding a larger and rapidly growing share due to their advanced features and data-driven insights. While the initial cost of smart scales might be higher than traditional scales, the long-term value proposition of personalized health data and continuous monitoring is driving adoption. Competition is intense, with established players like Tanita and Omron alongside newer entrants offering innovative features and competitive pricing. Geographic growth is diverse, with North America and Europe currently holding significant market shares, but Asia-Pacific is predicted to experience substantial growth in the coming years due to rising disposable incomes and increasing health awareness in developing economies. Despite challenges such as potential inaccuracies in certain measurement technologies and the need for user compliance in achieving accurate data, the market outlook remains positive, anticipating strong growth throughout the forecast period. The ongoing market expansion will likely be influenced by factors such as evolving consumer preferences towards personalized health solutions, increased integration with health and fitness apps, and further advancements in sensor technology leading to more precise and reliable measurements. Regulatory changes concerning data privacy and security are also expected to play a role in shaping the market landscape. Companies are likely to focus on product innovation, strategic partnerships, and expansion into emerging markets to maintain a competitive edge. The increasing availability of affordable, yet feature-rich, body composition scales is expected to further broaden market penetration across diverse demographics. The growth in online sales channels presents significant opportunities for companies to reach a wider audience and offer personalized recommendations based on user data. Future trends point to increased focus on user experience through intuitive app interfaces and more accessible data visualization tools.

A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes–Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity.

Rectified (level 1.5) Meteosat SEVIRI image data. The data is transmitted as High Rate transmissions in 12 spectral channels. Level 1.5 image data corresponds to the geolocated and radiometrically pre-processed image data, ready for further processing, e.g. the extraction of meteorological products. Any spacecraft specific effects have been removed, and in particular, linearisation and equalisation of the image radiometry has been performed for all SEVIRI channels. The on-board blackbody data has been processed. Both radiometric and geometric quality control information is included. Images are made available with different timeliness according to the latency: quarter-hourly images with a latency of more than 3 hours and hourly images if latency is less than 3 hours (for a total of 87 images per day). To enhance the perception for areas which are on the night side of the Earth a different mapping with increased contrast is applied for IR3.9 product. The greyscale mapping is based on the EBBT which allows to map the ranges 200 K to 300 K for the night and 250 K to 330 K for the day.

From 1 June 2022, Meteosat-9 at 45.5° E is the prime satellite for the IODC service, replacing Meteosat-8 (located at 41.5° E while in operation).

A selection of single channel data are visualised in our EUMETView service.

MJ-Bench Dataset

This dataset contains image pairs generated from different models (GPT-4o Vision and FLUX) across multiple categories and subcategories for evaluation.

  Dataset Structure

The dataset is organized into several categories, all located in the data folder:

Composition: Images related to composition aspects like physics laws, perspective, and occlusion/depth ordering Visualization: Images focused on visualization techniques Quality: Images demonstrating… See the full description on the dataset page: https://huggingface.co/datasets/MJ-Bench/MJ-Bench.

Communicating and understanding climate induced environmental changes can be challenging, especially using traditional representations such as graphs, maps or photos. Immersive visualizations and experiences offer an intuitive, visceral approach to otherwise rather abstract concepts, but creating them scientifically is challenging. In this paper, we linked ecological modeling, procedural modeling, and virtual reality to provide an immersive experience of a future forest. We mapped current tree species composition in northern Wisconsin using the Forest Inventory and Analysis (FIA) data and then forecast forest change 50 years into the future under two climate scenarios using LANDIS-II, a spatially-explicit, mechanistic simulation model. We converted the model output (e.g., tree biomass) into parameters required for 3D visualizations with analytical modeling. Procedural rules allowed us to efficiently and reproducibly translate the parameters into a simulated forest. Data visualization, environment exploration, and information retrieval were realized using the Unreal Engine. A system evaluation with experts in ecology provided positive feedback and future topics for a comprehensive ecosystem visualization and analysis approach. Our approach to create visceral experiences of forests under climate change can facilitate communication among experts, policy-makers, and the general public.

This folder contains the supplementary data visualizations of Phenylketonuria in a microbial world, chapter 3: Gut-microbiome composition in response to phenylketonuria depends on dietary phenylalanine in BTBR Pahenu2 mice.

This dataset contains the results of a chemical analysis of wines. The dataset is widely used for classification, clustering, and regression tasks in Machine Learning and Data Science. Each record represents a wine sample, including its chemical composition and a class label that identifies the type of wine.

Number of Instances: 178

Number of Attributes: 13 chemical properties + 1 class label

Class Labels: 3 types of wine

Key Features:

Alcohol

Malic Acid

Ash

Alcalinity of Ash

Magnesium

Total Phenols

Flavanoids

Nonflavanoid Phenols

Proanthocyanins

Color Intensity

Hue

OD280/OD315 of diluted wines

Proline

Use Cases:

Classification (e.g., predicting wine type)

Clustering and unsupervised learning

Feature selection and dimensionality reduction (PCA, LDA)

Data visualization and exploratory data analysis

This dataset is a popular benchmark in the Machine Learning community and is often used for testing algorithms in supervised learning.

The present study explores the development and evaluation of a mixed reality music visualization for a live music performance. Real-time audio analysis and crossmodal correspondences were used as design guidelines for creating the visualization, which was presented through a head-mounted-display. To assess the impact of the music visualization on the audience’s aesthetic experience, a baseline visualization was designed, featuring the same visual elements but with random changes of color and movement. The audience’s aesthetic experience of the two conditions (i.e., listening to the same song with different visualizations) was assessed using the Aesthetic Emotions Scale (AESTHEMOS) questionnaire. Additionally, participants answered questions regarding the perceived audiovisual congruence of the stimuli and questionnaires about individual musicality and aesthetic receptivity. The results show that the visualization controlled by real-time audio analysis was associated with a slightly enhanced aesthetic experience of the audiovisual composition compared to the randomized visualization, thereby supporting similar findings reported in the literature. Furthermore, the tested personal characteristics of the participants did not significantly affect aesthetic experience. Significant correlations between these characteristics and the aesthetic experience were observed only when the ratings were averaged across conditions. An open interview provided deeper insights into the participants’ overall experiences of the live music performance. The results of the study offer insights into the development of real-time music visualization in mixed reality, examines how the specific audiovisual stimuli employed influence the aesthetic experience, and provides potential technical guidelines for creating new concert formats.

Population pyramids provide a way to visualize the age and sex composition of a geographic region, such as a nation, state, or county. A standard population pyramid has two bar charts or histograms, one for the male population and one for the female population. The two charts mirror each other and are divided into 5-year age cohorts. The shape of a population pyramid provide insights into a regions fertility, mortality, and migration patterns. When a region has high fertility and mortality, but low migration the visualization will look like a pyramid. In many regions fertility and mortality have decreased between 1970 and 2017, as people live longer and women have fewer children. With lower fertility and mortality population pyramids are shaped more like a pillar. When interpreting population pyramids for smaller areas (like counties) the most important force that shapes the pyramid is in- and out-migration. (Wang and vom Hofe, 2006, p. 65) For smaller regions population pyramids can have unique shapes.

This data archive provides the resources needed to generate population pyramids for the United States, individual states, and any county within the United States. Population pyramids usually require significant data cleaning and graph making skills to generate one pyramid. With this data archive the data cleaning has been completed and the R script provides reusable code to quickly generate graphs. The final output is an image file with six graphs on one page. The final layout makes it easy to compare changes in population age and sex composition for any state and any county in the US for 1970, 1980, 1990, 2000, 2010, and 2017.

The Land Information System (LIS) provides collection of sample data for demonstration of LIS code. The original source of each data set is given whenever possible. LIS does not endorse the validity, completeness, or usefulness of the data sets. Users are strongly recommended to compile and validate their own data sets based on their scientific goals. LIS has the built-in flexibility for users to easily incorporate their own data sets.

The sample data are archived and served in three categories: land surface parameters, atmospheric forcing, LIS model output.

They are available either by: HTTP, which enables you to directly download data files; or by GrADS-DODS server (GDS), which enables you to access a user-defined subset of the sample data.

A web-based visualization system called "Land Explorer" (LE) has been developed to visualize the data sets. LE tightly integrates with the LIS Grads-DODS server. It is designed to let users interactively visualize and explore the sample and other data sets at all resolutions, featuring an intuitive web interface and fast response.

[Summary provided by NASA.]