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.

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.

The Department of Public Safety and Correctional Services (DPSCS) submits these data to the Governor's Office each month for each of Maryland's prisons and jails. This dataset shows totals across those facilities: population totals, contraband seizures, searches, assaults, hearing officer reports, disciplinary action, identification document issuance, and IWIF statistics. Statistical analyses and data formatting are performed by Department of Information Technology (DoIT).

The dataset includes 15 visual diagrams (pie and bar charts) comparing the distribution of agricultural residues, OFMSW, and used cooking oil across each state in Nigeria, province in South Africa, and county in Kenya. These summaries provide a comparative overview of regional feedstock strengths. The charts complement quantitative analyses by providing visual summaries of feedstock availability.

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.

Data DescriptionThe layers on this map contain population, employed labour force counts, private dwelling counts, and employment counts at Census Subdivision and Census Tract geographies from the 2006, 2011, and 2016 Census. The definition of each variable is described next:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.InstructionsZoom in and out of the map to update the bar charts. Use the Select Tool to select specific geographies to display on the bar chart.“Select by rectangle” allows you to draw a rectangle and select multiple geography to view in the chart.“Select by point” allows you select an area by clicking on its geography."Add Data" allows you add separate public data as need from ArcGIS Online, URL (an ArcGIS Server Web Service, a WMS OGC Web Service, a KML file, a GeoRSS file, a CSV file), and local files (shapefile, csv, kml, gpx, geojson)Project lead: A.MaruicioDevelopers: C.Riccardo, W.Huang, D.Robbin

This bar chart shows the percentage of French people trusting or not statistics in 2019. It reveals that more than half of respondents declared that they rather trusted statistics.

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

This statistic bar chart shows the results of a survey asking Millennials to rate their cooking level in France in 2018. It reveals that the majority of responding Millennials, 64 percent of them, declared they had a rather good cooking level.

Seychelles's Air transport cargo volume statistics is 2.41[Million ton-km] which is the 97th highest in the world ranking. Transition graphs on Air transport cargo volume statistics in Seychelles and comparison bar charts (USA vs. China vs. Japan vs. Seychelles), (Sao Tome vs. Principe vs. Antigua vs. Barbuda vs. Seychelles) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

This bar chart presents the results of a survey on the most stressful areas of life according to French people in 2019, broken down by gender. It shows that the financial situation was the most stressful area of life among responding women, who were ** percent to mention it. On the other hand, the majority of men surveyed, ** percent, stated that the situation in the country was the most stressful life situation in 2019, according to them.

The Venue dataset includes yearly show and ticket figures ranging from 2019 to present day. The data presented consists of the following columns:YearEvent TypeShow CountTicket CountThe Venue music hall which hosts concerts in downtown Aurora is operated by the Fox Valley Music Foundation, a 501(c)3 non-profit dedicated to music education and preservation. The dashboard above depicts number of shows and tickets sold from 2019 to present.It is important to note that prior to 2022, show types were not captured, only number of shows. Thus the stacked bar chart representing show types and counts only begins at 2022.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Czech Republic's Net Outflow of Foreign Direct Investment (Balance of Payments Statistics) is US$3,277,827,384 which is the 29th highest in the world ranking. Transition graphs on Net Outflow of Foreign Direct Investment (Balance of Payments Statistics) in Czech Republic and comparison bar charts (USA vs. China vs. Japan vs. Czech Republic), (Greece vs. Sweden vs. Czech Republic) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

This bar chart presents the structure of the working population in the Ile-de-France region, in France in 2017, by business sector. It appears that approximately 85 percent of the employed people in the Ile-de-France region were employed in the tertiary sector.

The study aims to determine the differences in miRNA expression, particularly miRNA-21 and miRNA-221/222, of acute ischemic stroke patients relative to controls and determine its relationship with inflammatory cytokines, clinical severity, and outcome

Data used for the bar charts in Fig. 3B, 3C and S1A

Brunei Darussalam's Air transport cargo volume statistics is 122.46[Million ton-km] which is the 55th highest in the world ranking. Transition graphs on Air transport cargo volume statistics in Brunei Darussalam and comparison bar charts (USA vs. China vs. Japan vs. Brunei Darussalam), (Malta vs. Iceland vs. Brunei Darussalam) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.