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.

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

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.

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.

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.

Set of annual MDOT perfromance data including port, transit, bridge and highway condition, and MVA branch office wait time data.

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

Raw Data for:Figure3d, Figure 3g, Figure 4d, Figure 4f, Figure 7c, Figure 8b, Extended Figure 3c, Extended Figure 5d, and Extended Figure 5e

This data supports the assertions made in a paper (same name as this project) which surveyed 3 Second Language Acquisition journals (Modern Language Journal, Language Learning, and Studies in Second Language Acquisition) from the time of their inception to 2011/2012. The raw data used for calculations about the number of graphics and which type of graphics were published is included in the attached Excel file.

Abstract The aim of this work was to analyze the statistical graphs included in the two most frequently series of textbooks used in Costa Rica basic education. We analyze the type of graph, its semiotic complexity, and the data context, as well as the type of task, reading level required to complete the task and purpose of the graph within the task. We observed the predominance of bar graphs, third level of semiotic complexity (representing a distribution), second reading level (reading between the data), work and school context, reading and computation tasks and analysis purpose. We describe the differences in the various grades and between both editorials, as well as differences and coincidences with results of other textbook studies carried out in Spain and Chile.

This dataset contains data about the highest grade completed by residents of San Mateo County by city. Grade levels include less than high school graduate, high school graduate, some college or associate's degree, and bachelor's degree or higher. This data was extracted from the United States Cenus Bureau's American Community Survey 2014 5 year estimates.

Open data bar graph depicting lead service line replacements by month & year.

Emergency Medical Service ambulance dispatch incidents in Marin County, CA, for the period beginning March 1, 2013 through June 30, 2017. Data is updated quarterly. Data includes time stamps of events for each dispatch, nature of injury, and location of injury. Data also includes geocoding of most incident locations, however, specific street address locations are "obfuscated" and are generally shown within a block and are not, therefore, exact locations. Geocoding results are also based on the quality of the address information provided, and should therefore not be considered 100% accurate.

Some of the data may be interpreted incorrectly without adequate knowledge of the clinical context. Please contact EMS@marincounty.org if you have any questions about the interpretation of fields in this dataset.

This dataset was developed within an analysis of research data generated and managed within the University of Bologna, with respect to the differences and commonalities between disciplines and potential challenges for institutional data support services and infrastructures. We are primarily mapping the type (e.g., image), content (e.g., scan of a manuscript) and format (e.g., .tiff) of managed data, thus sustaining the value of FAIR data as granular resources.

Cases created since 7/1/2008 with location information

Animal shelter data

ABSTRACT: This paper shows how to apply the lattice package of R to create effective scientific graphs. The readers will learn basic notions of the package and ways to work with it in an easy way. The R code the paper provides will help them create various graphs, including a scatter plot, a box plot, a density plot, and a bar plot; with a little work, the code can be changed to make other graphs. The paper emphasizes the trellis display, a useful but still undervalued technique in scientific visualization.

The Bar Graph Meters market has emerged as a pivotal segment within the broader instrumentation and control industry, enabling clear and efficient visualization of data across various sectors. Bar graph meters, or analog and digital indicators that represent data in the form of bars, are widely utilized in industrie

Open data bar graph depicting lead service line replacements by month & year.

Details about the Data * Outdoor experiment was conducted at the open plaza in SDE4

• Indoor experiment was conducted at the education resource centre in SDE4

Context

Thermal comfort affects the well-being of different genders differently. However, due to practical and time limitations, the number of studies that we are able to conduct is limited. Since our studies involves the study of both indoor and outdoor conditions, longitudinal data are therefore a valuable resource to understand how different genders perceive temperature. For our analysis, we chose to use bar graphs to showcase our data instead of other graphs, particularly bubble graphs as the size of the bubbles will not be impactful in showing the differencd between both genders.

Content For both Indoor and Outdoor datasets, it is done based on longitudinal subjective feedback of the different genders' preference to determine the differing levels of thermal comfort. The experiment was conducted with 6 participants (3 female, 3 male) over the course of 8 hours in a day. This produced 360 surveys for thermal preference.

For the whole duration of the study, each survey was conducted at 15 minute intervals. To make sure that the experiment is fair and constant, we ensured that the location was kept constant for each dataset, leading to environmental variables (e.g. temperature, relative humidity) being kept constant as well. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics was collected using an on-boarding survey administered at the beginning of the experiment.