Currently, in the field of chart datasets, most existing resources are mainly in English, and there are almost no open-source Chinese chart datasets, which brings certain limitations to research and applications related to Chinese charts. This dataset draws on the construction method of the DVQA dataset to create a chart dataset focused on the Chinese environment. To ensure the authenticity and practicality of the dataset, we first referred to the authoritative website of the National Bureau of Statistics and selected 24 widely used data label categories in practical applications, totaling 262 specific labels. These tag categories cover multiple important areas such as socio-economic, demographic, and industrial development. In addition, in order to further enhance the diversity and practicality of the dataset, this paper sets 10 different numerical dimensions. These numerical dimensions not only provide a rich range of values, but also include multiple types of values, which can simulate various data distributions and changes that may be encountered in real application scenarios. This dataset has carefully designed various types of Chinese bar charts to cover various situations that may be encountered in practical applications. Specifically, the dataset not only includes conventional vertical and horizontal bar charts, but also introduces more challenging stacked bar charts to test the performance of the method on charts of different complexities. In addition, to further increase the diversity and practicality of the dataset, the text sets diverse attribute labels for each chart type. These attribute labels include but are not limited to whether they have data labels, whether the text is rotated 45 °, 90 °, etc. The addition of these details makes the dataset more realistic for real-world application scenarios, while also placing higher demands on data extraction methods. In addition to the charts themselves, the dataset also provides corresponding data tables and title text for each chart, which is crucial for understanding the content of the chart and verifying the accuracy of the extracted results. This dataset selects Matplotlib, the most popular and widely used data visualization library in the Python programming language, to be responsible for generating chart images required for research. Matplotlib has become the preferred tool for data scientists and researchers in data visualization tasks due to its rich features, flexible configuration options, and excellent compatibility. By utilizing the Matplotlib library, every detail of the chart can be precisely controlled, from the drawing of data points to the annotation of coordinate axes, from the addition of legends to the setting of titles, ensuring that the generated chart images not only meet the research needs, but also have high readability and attractiveness visually. The dataset consists of 58712 pairs of Chinese bar charts and corresponding data tables, divided into training, validation, and testing sets in a 7:2:1 ratio.

Ceramic building material quantification data by context type for Illus. 5.21.

Location, type, and access information for electric vehicle charging stations in San Mateo County

This horizontal bar chart displays companies by company type using the aggregation count in Stanford. The data is about companies.

Blockchain data query: Stacked Bar Chart by Hour

This horizontal bar chart displays companies by company type using the aggregation count in Athens. The data is about companies.

Blockchain data query: bar chart

Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

Blockchain data query: Bar Chart IL

The dataset includes demographic information setting forth the number of filings made by business entities with the Department of State’s Division of Corporations. Such filings are categorized by type and filer.

This horizontal bar chart displays companies by employee type using the aggregation count in Dearborn. The data is about companies.

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.

Problem Statements for Data Visualization – Supermarket Sales Dataset 1. Sales Performance Across Branches Management wants to understand how sales performance varies across supermarket branches in Lagos, Abuja, Ogun, and Port Harcourt to identify the best-performing locations and areas that need improvement. • Suggested Visualizations: • Bar chart comparing total sales and profit by branch • Map chart showing sales by city • KPI cards: Total Sales, Profit, and Average Transaction Value per branch 2. Customer Purchase Behavior The marketing team needs insights into how different customer types (Member vs Normal) and genders influence purchase trends and average spending. • Suggested Visualizations: • Pie chart for customer type distribution • Bar chart for average spend by gender • Segmented comparison of total sales by customer type 3. Product Line Performance The business wants to know which product categories drive the highest revenue, quantity sold, and customer satisfaction to optimize stock levels and marketing focus. • Suggested Visualizations: • Bar chart showing total sales by product line • Column chart comparing average rating per product line • Profit margin chart by product line 4. Sales Trends Over Time The management team wants to monitor sales trends over time to identify peak periods, track seasonal variations, and plan future promotions accordingly. • Suggested Visualizations: • Line chart showing monthly or weekly sales trend • Seasonal decomposition (sales by month) • Trendline showing revenue growth 5. Payment Method Analysis The finance department needs to evaluate payment method usage (Cash, E-wallet, Credit Card) across cities to improve payment convenience and reduce transaction delays. • Suggested Visualizations: • Donut or bar chart showing share of payment methods • City-level breakdown of preferred payment type • Correlation between payment method and average transaction value 6. Customer Satisfaction Insights The customer experience team wants to explore how customer ratings relate to sales amount, product type, and branch performance to identify drivers of customer satisfaction. • Suggested Visualizations: • Scatter plot of rating vs total purchase amount • Heat map of average rating by branch and product line • KPI card showing average customer rating

Blockchain data query: Current Supply APY — bar chart

Blockchain data query: Fee Bar Chart

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 horizontal bar chart displays employees (people) by revenue type using the aggregation sum. The data is filtered where the industry is Broadline Retail. The data is about companies.

Blockchain data query: 2. Bar chart — Unique minters per day:

This bar chart displays employees (people) by revenue type using the aggregation sum in Richland. The data is about companies.