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 This paper presents the results of the statistical graphs’ analysis according to the curricular guidelines and its implementation in eighteen primary education mathematical textbooks in Perú, which correspond to three complete series and are from different editorials. In them, through a content analysis, we analyzed sections where graphs appeared, identifying the type of activity that arises from the graphs involved, the demanded reading level and the semiotic complexity task involved. The textbooks are partially suited to the curricular guidelines regarding the graphs presentation by educational level and the number of activities proposed by the three editorials are similar. The main activity that is required in textbooks is calculating and building. The predominance of bar graphs, a basic reading level and the representation of an univariate data distribution in the graph are observed in this study.

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 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

LLM Distribution Evaluation Dataset

This dataset contains 50,000 synthetic graphs with questions and answers about statistical distributions, designed to evaluate large language models' ability to analyze data visualizations.

  Dataset Description





  Dataset Summary

This dataset contains diverse statistical visualizations (bar charts, line plots, scatter plots, histograms, area charts, and step plots) with associated questions about:

Normality testing Distribution… See the full description on the dataset page: https://huggingface.co/datasets/robvanvolt/llm-distribution.

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

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).

This bar chart displays books by publication date using the aggregation count. The data is filtered where the book publisher is Library & Information Statistics Unit, Loughborough University. The data is about books.

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.

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

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.

This bar chart displays books by publication date using the aggregation count. The data is filtered where the book publisher is Department of Agriculture for Northern Ireland, Economics and Statistics Division. The data is about books.

{"Description This code analyzes the impact of chanting the Hare Krishna mantra on the body's chakras using Python. It visualizes the cumulative energy distribution across the chakras during the chant and provides statistical analysis of the energy levels. Imports: numpy: For numerical operations and array handling. matplotlib.pyplot: For plotting the heatmap and bar charts. seaborn: For creating aesthetically pleasing statistical graphics. Define the Mantra and Chakra Associations: mantra_sequence: A list representing the sequence of words in the Hare Krishna mantra. chakra_association: A dictionary mapping each word in the mantra to its associated chakras, represented as binary lists (1 for activation, 0 for no activation). Initialize Chakra Energy Levels: chakra_levels: A 2D array initialized with zeros to store the energy levels for each chakra throughout the mantra sequence. Calculate Cumulative Impact on Chakras: Iterate through each word in the mantra sequence. For each word, update the corresponding chakra energy levels by accumulating the impact values. Generate Heatmap for Chakra Energy Distribution: Create a heatmap to visualize the energy distribution across the chakras for each word in the mantra sequence. Customize the heatmap with labels for the chakras and chant sequence. Statistical Analysis: Compute the mean and standard deviation of the energy levels for each chakra. Plot these statistical measures using a bar chart to compare the average energy levels and their variability across different chakras."}

(E, F) Extracellular acidification rates (ECAR) measurements of naïve CD4+ T cells from LN and OB mice upon TCR (1/1 μg/ml) stimulation for 24 h (n = 3 or 4, biological replicates). The statistical results are presented as a bar graph (F). Data information: All data are representative of at least 3 individual experiments. Statistics, two-tailed Student's t test; ns: not significant, *p < 0.5, **p < 0.01, ***p < 0.001. Error bars represent SD.. List of tagged entities: , ,

This bar chart shows the number of heat wave episodes in France from 2004 to 2018. It displays that 2016 and 2017 were the years with the highest number of heat wave episodes since 2004. There have been **** heat wave episodes during these two years.

A web-based tool and database for the gene ontology analysis. Its focus is on agricultural species and is user-friendly. The agriGO is designed to provide deep support to agricultural community in the realm of ontology analysis. Compared to other available GO analysis tools, unique advantages and features of agriGO are: # The agriGO especially focuses on agricultural species. It supports 45 species and 292 datatypes currently. And agriGO is designed as an user-friendly web server. # New tools including PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA) were developed. The arrival of these tools provides users with possibilities for data mining and systematic result exploration and will allow better data analysis and interpretation. # The exploratory capability and result visualization are enhanced. Results are provided in different formats: HTML tables, tabulated text files, hierarchical tree graphs, and flash bar graphs. # In agriGO, PAGE and SEACOMPARE can be used to carry out cross-comparisons of results derived from different data sets, which is very important when studying multiple groups of experiments, such as in time-course research. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Bar chart showing the zero-emission truck sales phase-in year for states like CA, NY, NJ, WA, and others.

This bar chart presents the results of a survey on the most stressful areas of life according to the French in 2019, distributed by age. It reveals that ** percent of respondents aged 35 to 49 years old declared that their work was the most stressful area of their life, while financial situation was mentioned by ** percent of interviewees aged 25 to 34 years old.

Code and raw data used for visualization of molecular composition (from FTICR-MS data processed by MetaboDirect) of Brady paleosol and modern soils using van Krevelen diagrams (occurence of group-specific compounds in chemical space defined by H:C vs O:C ratios) and stacked bar charts, as well as statistical comparisons of molecular class abundance amongst groups.

This dashboard provides graphs with Consumer Price Index (CPI) information for food categories in Manitoba and other provincial jurisdictions in Canada. This dashboard provides graphs with Consumer Price Index (CPI) information for food categories in Manitoba, and other provincial jurisdictions in Canada. Food prices are increasing at a pace not seen before in the last 20 years. Requests for information have been received by Manitoba Agriculture from the general public. This dashboard focuses strictly on food and food categories, showing price changes through time, starting in 2002 until the present. The food categories shown in the dashboard, either in a graph or in the selection option, are: Meat Fish, seafood and other marine products Dairy products Eggs Bakery and cereal products Fruit, fruit preparations and nuts Vegetables and vegetable preparations Other food products and non-alcoholic beverages All Foods The dashboard contains three tabs: Manitoba: This chart provides a graph with the option of plotting the food CPI for All Foods (average of all food categories), or for a specific food category for Manitoba. The chart can be filtered to show year-to-date data, or data for the last one, five, 10, or all years going back to 2002. By Food Category: This chart provides a bar graph with the CPI of all the food categories for Manitoba. Information is available for the past 12 months of available data, so the chart shows one-year variation. By Province: This chart provides a bar graph with the CPI for all the provinces, and Canada. Each province is represented by one bar in the graph. The user can select the food category of interest or All Foods (average of all categories). Information is available for the past 12 months of available data, so the chart shows one-year variation. The data table used for this dashboard is the Consumer Price Index Food Product Statistics table. The source of the information is the Statistics Canada Table 18-10-0004-01 Consumer Price Index, monthly, not seasonally adjusted. Data are updated monthly by Manitoba Agriculture from Statistics Canada sources.