Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.

confirmation bias can cause people to overweigh information that confirms their beliefs

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

chart-misinformation-detection/bar-graphs dataset hosted on Hugging Face and contributed by the HF Datasets community

Use the Chart Viewer template to display bar charts, line charts, pie charts, histograms, and scatterplots to complement a map. Include multiple charts to view with a map or side by side with other charts for comparison. Up to three charts can be viewed side by side or stacked, but you can access and view all the charts that are authored in the map. Examples: Present a bar chart representing average property value by county for a given area. Compare charts based on multiple population statistics in your dataset. Display an interactive scatterplot based on two values in your dataset along with an essential set of map exploration tools. Data requirements The Chart Viewer template requires a map with at least one chart configured. Key app capabilities Multiple layout options - Choose Stack to display charts stacked with the map, or choose Side by side to display charts side by side with the map. Manage chart - Reorder, rename, or turn charts on and off in the app. Multiselect chart - Compare two charts in the panel at the same time. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Charts, Histograms, and Time Series

• Create a histogram graph from band values of an image collection

• Create a time series graph from band values of an image collection

Counts of Part I committed in San Mateo County from 1985 on. This dataset also includes Part II crimes from 2013 on.

Part I crimes include: homicide, rape, robbery, aggravated assault, burglary, motor vehicle theft, larceny-theft, and arson. These counts include crimes committed at San Francisco International Airport (SFO), Unincorporated San Mateo County, Woodside, Portola Valley, San Carlos from 10/31/10 forward; Half Moon Bay from 6/12/11 forward; and Millbrae from 3/4/12 forward.

Part II crimes do not include San Francisco International Airport (SFO) cases and is an estimate only. An estimate is required because there are no specific data types used when keying in Type II crime types. Therefore, Records Manager judgment is used.

The global bar graph displays market is anticipated to experience remarkable growth in the coming years, driven by increasing demand from various end-user industries. The market size was valued at USD XXX million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. This growth can be attributed to factors such as technological advancements, rising demand for visual data representation, and increasing adoption in sectors like electronics, medical, and aerospace. Among the key segments, the LED and LCD display types are expected to witness significant growth, owing to their superior brightness, clarity, and energy efficiency. The major regions driving the market include North America, Europe, and Asia Pacific. North America holds a dominant market share, with the United States being a notable contributor. The Asia Pacific region is projected to grow at a higher rate during the forecast period, driven by the rapidly expanding electronics and semiconductor industries in countries like China, India, and Japan. Key players in the bar graph displays market include akYtec, Everlight Electronics, Kingbright, Sifam Tinsley, and Texmate, among others. These companies are focusing on innovation, strategic partnerships, and geographical expansion to enhance their market presence.

These files represent the state and regional summaries of sensitivities to formaldehyde, acetaldehyde and ozone to various sources and compounds. This dataset is associated with the following publication: Luecken, D., S. Napelenok, M. Strum, R. Scheffe, and S. Phillips. Sensitivity of Ambient Atmospheric Formaldehyde and Ozone to Precursor Species and Source Types Across the United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(8): 4668–4675, (2018).

Matplotlib is a tremendous visualization library in Python for 2D plots of arrays. Matplotlib may be a multi-platform data visualization library built on NumPy arrays and designed to figure with the broader SciPy stack. It had been introduced by John Hunter within the year 2002.

A bar plot or bar graph may be a graph that represents the category of knowledge with rectangular bars with lengths and heights that’s proportional to the values which they represent. The bar plots are often plotted horizontally or vertically.

A bar chart is a great way to compare categorical data across one or two dimensions. More often than not, it’s more interesting to compare values across two dimensions and for that, a grouped bar chart is needed.

IntroductionIn the realm of next-generation sequencing datasets, various characteristics can be extracted through k-mer based analysis. Among these characteristics, genome size (GS) is one that can be estimated with relative ease, yet achieving satisfactory accuracy, especially in the context of heterozygosity, remains a challenge.MethodsIn this study, we introduce a high-precision genome size estimator, GSET (Genome Size Estimation Tool), which is based on k-mer histogram correction.ResultsWe have evaluated GSET on both simulated and real datasets. The experimental results demonstrate that this tool can estimate genome size with greater precision, even surpassing the accuracy of state-of-the-art tools. Notably, GSET also performs satisfactorily on heterozygous datasets, where other tools struggle to produce useable results.DiscussionThe processing model of GSET diverges from the popular data fitting models used by similar tools. Instead, it is derived from empirical data and incorporates a correction term to mitigate the impact of sequencing errors on genome size estimation. GSET is freely available for use and can be accessed at the following URL: https://github.com/Xingyu-Liao/GSET.

This deposit includes the data that was collected in an experimental study on debunking strategies for misleading bar charts, involving 2 surveys (one week delay) with a total of 24 unique bar charts each with two bars, filled in by 441 representative (age, ethnicity, gender) participants from the USA. De experiment compares four methods for correcting misleading bar charts with truncated vertical axes by measuring the participants evaluated difference between the bars at five time points. Measures were taken on a visual analogue scale. The first survey also included a short graph literacy scale and a question on highest completed educational level. Date Submitted: 2022-06-24

Vertical Bar Chart

## Overview

Vertical Bar Chart is a dataset for object detection tasks - it contains Figure Item X_axis Y_axis Legend annotations for 727 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Network of 30 papers and 64 citation links related to "Histogram-Based Image Retrieval Keyed by Normalized HSY Histograms and Its Experiments on a Pilot Dataset".

This bar chart displays depth (cm) by acquisition year using the aggregation sum. The data is about artworks.

These data are based on the latest Veteran Population Projection Model, VetPop2020, provided by the National Center for Veterans Statistics and Analysis, published in 2023.

Bar Chart Parser

## Overview

Bar Chart Parser is a dataset for object detection tasks - it contains Charts annotations for 304 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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 divides sex into two bar charts or histograms, one for the male population and one for the female population. The two charts mirror each other and are divide age into 5-year cohorts. The shape of a population pyramid provides insights into a region’s fertility, mortality, and migration patterns. When a region has high fertility and mortality, but low migration the visualization will look like a pyramid, with the youngest age cohort (0-4 years) representing the largest percent of the population and each older cohort representing a progressively smaller percent of the population.

In many regions fertility and mortality have decreased significantly since 1970, as people live longer and women have fewer children. With lower fertility and mortality, population pyramids are shaped more like a pillar.

While population pyramids can be made for any geographic region, when interpreting population pyramids for smaller areas (like counties) the most important force that shapes the pyramid is often 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.

This statistic describes the frequency of bar visits among respondents from the United Arab Emirates (UAE) as of the second quarter of 2016. The share of respondents from the UAE who stated that they visit bars only on special occasion totaled ** percent.

The global Bar Graph Array market, valued at $156 million in 2025, is projected to experience robust growth, driven by increasing demand across diverse applications. The Compound Annual Growth Rate (CAGR) of 8.5% from 2025 to 2033 indicates significant expansion potential. Key application segments include traffic indication, switch indication, and safety indication, reflecting the critical role of bar graph arrays in conveying visual information in various industries. The prevalence of bright red, high-performance green, high-performance yellow, super lime green, and super lime yellow types underscores the importance of color accuracy and visibility in different operational contexts. Market growth is further fueled by advancements in LED technology, enhancing brightness, energy efficiency, and lifespan. This leads to wider adoption across automotive, industrial automation, and consumer electronics sectors. Geographic growth is expected to be particularly strong in rapidly developing economies in Asia-Pacific and other regions, driven by increasing infrastructure development and industrialization. While specific restraining factors are not provided, potential challenges could include competition from alternative display technologies and fluctuations in raw material costs. However, continuous innovation in display technology and increasing demand for user-friendly interfaces are anticipated to offset these challenges. Established players like Broadcom, London Electronics Limited, and others are expected to lead the market, while smaller companies focusing on niche applications are likely to further drive innovation and competition. The market's success hinges on consistent technological improvements that enhance both the performance and cost-effectiveness of bar graph arrays. The ongoing development of more energy-efficient and brighter LEDs, alongside miniaturization efforts, will continue to drive adoption across diverse industries and geographical regions. This positive trajectory is expected to significantly impact the growth of the global market through the forecast period. The integration of bar graph arrays into sophisticated systems demanding high reliability and precise visual feedback will also contribute to the growth and ongoing demand.