Blockchain data query: PIE Chart for Top Senders

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: AAVE Supply Pie Chart for time intervals

Blockchain data query: AAVE borrow Pie Chart for time intervals

Create three charts/graphs. Each should illustrate an important fact/relationship from the Grades data. Each with a short explanatory narrative of appropriate graph types (except a pie chart) to visualize data in the grades dataset. To each chart/graph, append a rationale for choosing the fact/relationship in the form of a Big Idea statement.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows six condensed maps of the distribution of plants producing the following: leather footwear, womens and childrens factory made clothing, synthetic textiles and silks, mens factory made clothing, cotton textiles, and rubber products. All data for these maps is for 1954 with the exception of the rubber products map which is for 1955. Each map is accompanied by a bar graph and pie chart. The bar graphs show the value of production by major categories of products. The pie charts show the percentage distribution of persons employed in each manufacturing industry by province.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows two condensed maps illustrating the distribution of the labour force engaged in manufacturing circa early 1950s. The map has a dot representing every 100 people in the manufacturing labour force, with places of 1000 or more people in manufacturing being shown as proportional circles, instead. There are additional data for the 18 census metropolitan areas. This consists of a pie graph for each of these places showing the breakdown of the manufacturing labour force into each of 16 manufacturing industry types. The total manufacturing labour force in each of the census metropolitan areas is also given.

This dataset contains anatomically labeled centerline graphs, surface meshes, and derived morphometric features of the Circle of Willis (CoW). The data is based on the TopCoW 2024 training set, which consists of 250 (=125 paired) CTA and MRA images displaying brain vasculature, including the CoW.

The dataset complements the available TopCoW multi-class segmentations and supports research in vascular morphology, graph-based modeling, and automated centerline extraction. The dataset itself, along with the accompanying baseline algorithm for CoW centerline and feature extraction, is described in our manuscript:

Musio et al., “Circle of Willis Centerline Graphs: A Dataset and Baseline Algorithm” (2025)

A detailed description of the dataset structure and contents is provided in the README file included in the downloadable ZIP archive.

For access to the raw imaging data and multi-class masks, please refer to the TopCoW dataset repository:
https://zenodo.org/records/15692630

Note: The baseline algorithm can be applied on top of the TopCoW segmentation models to extract centerlines and features. The code is available on GitHub: https://github.com/fmusio/CoW_Centerline_Extraction

Contents

For each patient and modality (n=250), the dataset includes:

• CoW Graph (cow_graphs/): Labeled CoW centerline graph (.vtp)
• CoW Mesh (cow_meshes/): Labeled CoW surface mesh (.vtp)
• CoW Variant (cow_variants/): Variant description encoding presence/absence of segments and fetal-type PCA (.json)
• CoW Nodes (cow_nodes/): Node descriptions for start, end, bifurcation and segment boundary points (.json)
• CoW Features (cow_features/): Morphometric feature descriptions for segments and bifurcations (.json)

The files follow the same naming pattern as introduced by TopCoW: topcow_{modality}_{pat_id}.{suffix}
Where modality is ct for CTA or mr for MRA, and pat_id is the patient ID (e.g., 001, 002, ...).

Citation

If you use this dataset in your work, please cite:

Musio et al., “Circle of Willis Centerline Graphs: A Dataset and Baseline Algorithm” (2025)

License

This dataset is released under the CC BY-NC (Attribution-NonCommercial) license.
By downloading the data, you agree to the license terms.

Contact

For questions or feedback, please contact:

• Fabio Musio – fabio.musio@zhaw.ch
• Prof. Dr. Sven Hirsch – sven.hirsch@zhaw.ch
  Zurich University of Applied Sciences (ZHAW)

Acknowledgements

The work on this dataset was supported by the EU project GEMINI (funded by the EU Horizon Europe R&I programme, Grant No. 101136438).

The "CountiesStatesInfo" feature layer is a component of the "Pollinator Restoration 2022" map which is itself a component of the "USFWS Pollinator Restoration Projects Mapper" which is a dashboard showing management projects that benefit pollinators across the Western U.S. See below for a description of the "USFWS Pollinator Restoration Projects Mapper."The "USFWS Pollinator Restoration Projects Mapper" is under development by the Region 1 (Pacific Northwest) USFWS Science Applications program. Completion is anticipated by Winter 2023. Contact: Alan Yanahan (alan_yanahan@fws.gov).The purpose of the "USFWS Pollinator Restoration Projects Mapper" is to inform future pollinator conservation efforts by providing a way to identify geographic areas where additional pollinator conservation may be needed.The "USFWS Pollinator Restoration Projects Mapper" maps the locations of where on-the-ground projects that are beneficial to pollinators have taken place. Its primary focus is projects on public lands. The majority of records included in this tool come from internal databases for the USFWS, US Forest Service, and the Bureau of Land Management, which were queried for relevant projects. The tool is not intended as a database for reporting projects to. Rather, the tool synthesizes records from existing databases.The geographic scope of the tool includes the western states of Arizona, California, Idaho, Nevada, Oregon, Utah, and Washington.When possible, the tool includes projects from 2014 to the present. This timespan was chosen because it matches the timespan of the USFWS Monarch Conservation Database For consistency, the tool groups pollinator beneficial projects into the following four activity types:Restoration: Actions taken after a disturbance, such as planting native forbs after a wildfireMaintenance: Actions taken outside the growing season that maintain habitat quality through regular disturbance using manual or chemical means. Examples: mowing, spraying weeds, prescribed fireConservation: Acquiring land or creating easements that are managed for biodiversityEnhancement: Actions that increase forb diversity and nectar resources, such as planting native milkweedThe tool includes a map that aggregates project point locations within 49 square mile sized hexagon grid cells. Users can click on individual grid cells to activate a pop-up menu to cycle through the projects that occurred within that grid cell. Information for each project include, but are not limited to, acreage, type of activity (i.e., restoration, maintenance, conservation, enhancement), data source, and lead organization.The tool also includes a dashboard to view bar graphs and pie charts that display project acreages and project number based on location (i.e., state), project activity type (i.e., restoration, maintenance, conservation, enhancement), data source, and management type. Data can be filtered by data source, activity type, and year. Data filtering will update the map, bar graphs, and pie charts.

Scholarly figures are data visualizations like bar charts, pie charts, line graphs, maps, scatter plots or similar figures. Text extraction from scholarly figures is useful in many application scenarios, since text in scholarly figures often contains information that is not present in the surrounding text. This dataset is a corpus of 121 scholarly figures from the economics domain evaluating text extraction tools. We randomly extracted these figures from a corpus of 288,000 open access publications from EconBiz. The dataset resembles a wide variety of scholarly figures from bar charts to maps. We manually labeled the figures to create the gold standard. We adjusted the provided gold standard to have a uniform format for all datasets. Each figure is accompanied by a TSV file (tab-separated values) where each entry corresponds to a text line which has the following structure: X-coordinate of the center of the bounding box in pixel Y-coordinate of the center of the bounding box in pixel Width of the bounding box in pixel Height of the bounding box in pixel Rotation angle around its center in degree Text inside the bounding box In addition we provide the ground truth in JSON format. A schema file is included in each dataset as well. The dataset is accompanied with a ReadMe file with further information about the figures and their origin. If you use this dataset in your own work, please cite one of the papers in the references.

This data is made to generate a pie chart or other graph on the number of internet users in Africa.

It is used in my Today, the Underdogs... Tomorrow, the Challengers notebook which analyzes underdogs like Africa and shows why they should have updated technology and data science because of their great potential.

How many internet users are there in Africa? Try to plot it out👍 .

In 2024, the most popular foreign single on the Circle Digital Chart in South Korea was the track "APT." by Rosé and Bruno Mars. All of the leading foreign singles that charted that year were released in English, thought "APT." also had some Korean lines. English language music is popular within South Korea.

Exploring a Clothing Dataset with Web Conversion Metrics

The dataset at hand is a comprehensive collection of different types of clothing items, each paired with specific color information. Alongside these clothing attributes, the dataset includes vital web metric conversion funnel statistics. These metrics encompass visitor sessions, Add-to-Cart (ATC) interactions, and conversion rates, providing a rich source of information for analysis and insights.

This dataset presents a wide array of opportunities for exploration and analysis using Python. Here are some possible operations and insights that can be derived from this dataset:

1. Descriptive Statistics:

   • Basic summary statistics such as mean, median, and standard deviation can provide an overview of the performance of different clothing types and colors in the conversion funnel.

2. Visualization:

   • Create visually appealing charts and graphs using libraries like Matplotlib or Seaborn to depict trends in visitor sessions, ATC events, and conversions over time.
   • Explore color preferences by generating pie charts or bar graphs to represent color distribution within each clothing type.

3. Segmentation:

   • Segment the dataset by clothing type and color to analyze how each category performs in terms of conversion metrics. Are certain clothing types more likely to convert than others?
   • Analyze the impact of color on conversion rates. Do specific colors lead to higher or lower conversion rates for particular clothing types?

4. Time Series Analysis:

   • Investigate the time-based trends in visitor sessions, ATC events, and conversions. Are there seasonal patterns or significant changes over time?

5. Predictive Modeling:

   • Build predictive models using machine learning techniques (e.g., regression, classification) to forecast future conversion rates based on clothing type, color, and historical data.

6. Dashboard Creation:

   • Design interactive dashboards using tools like Plotly or Dash to provide stakeholders with real-time insights into web conversion metrics.

By leveraging the power of Python and its rich ecosystem of data analysis libraries, this dataset can yield valuable insights for optimizing web conversion strategies, enhancing user experiences, and ultimately increasing the conversion rates of the clothing items in question.

Please upvote! also let me know what more we can do with this dataset

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows six condensed maps of the distribution of plants producing the following: leather footwear, womens and childrens factory made clothing, synthetic textiles and silks, mens factory made clothing, cotton textiles, and rubber products. All data for these maps is for 1954 with the exception of the rubber products map which is for 1955. Each map is accompanied by a bar graph and pie chart. The bar graphs show the value of production by major categories of products. The pie charts show the percentage distribution of persons employed in each manufacturing industry by province.

Comparison of CT values of samples with discrepant results between the UVRI COVID19 National Reference Laboratory and the primary testing laboratories.

Real time PCR testing platforms being used for SARS CoV2 detection by various Ugandan laboratories.

Introduction

Objective:

Improve understanding of real estate performance.

Leverage data to support business decisions.

Scope:

Track property sales, visits, and performance metrics.

Technical Steps

Step 1: Creating an Azure SQL Database

Action: Provisioned an Azure SQL Database to host real estate data.

Why Azure?: Scalability, security, and integration with Power BI.

Step 2: Importing Data

Action: Imported datasets (properties, visits, sales, agents, etc.) into the SQL database.

Tools Used: SQL Server Management Studio (SSMS) and Azure Data Studio.

Step 3: Data Transformation in SQL

Normalized Data: Ensured data consistency by normalizing the formats of dates and categorical fields.

Calculated Fields:

Time on Market: DATEDIFF function to calculate the difference between listing and sale dates.

Conversion Rate: Aggregated sales and visits data using COUNT and SUM to calculate conversion rates per agent and property.

Buyer Segmentation: Identified first-time vs repeat buyers using JOINs and COUNT functions.

Data Cleaning: Removed duplicates, handled null values, and standardized city names and property types.

Step 4: Connecting Power BI to Azure SQL

Action: Established a live connection to Azure SQL Database in Power BI.

Benefit: Real-time data updates and efficient analysis.

Step 5: Data Modeling in Power BI

Relationships:

Defined relationships between tables (e.g., Sales, Visits, Properties, Agents) using primary and foreign keys.

Utilized active and inactive relationships for dynamic calculations like time-based comparisons.

Calculated Columns and Measures:

Time on Market: Created a calculated measure using DATEDIFF.

Conversion Rates: Used DIVIDE and CALCULATE for accurate per-agent and per-property analysis.

Step 6: Creating Visualizations

Key Visuals:

Sales Heatmap by City: Geographic visualization to highlight sales performance.

Conversion Rates: Bar charts and line graphs for trend analysis.

Time on Market: Boxplots and histograms for distribution insights.

Buyer Segmentation: Pie charts and bar graphs to show buyer profiles.

Step 7: Building Dashboards

Structure:

Page 1: Overview (Key Metrics and Sales Heatmap).

Page 2: Performance Analysis (Conversion Rates, Time on Market).

Page 3: Buyer Insights (First-Time vs Repeat Buyers, Property Distribution).

Insights Gained

Insight 1: Sales Performance by City

Cities highest sales volume.

City low performance, requiring further investigation.

Insight 2: Conversion Rates

Agent highest conversion rate.

Certain properties (e.g., luxury villas) outperform others in conversion.

Insight 3: Time on Market

Average time on market.

Insight 4: Buyer Trends

Repeat Buyers make up 60% of purchases.

First-Time Buyers prefer apartments over villas.

Recommendation

Focus on High-Performing Cities Recommendation 2: Support Low-Performing Areas

Investigate challenges to develop targeted marketing strategies.

Enhance Conversion Rates

Train agents based on techniques used by top performers.

Prioritize marketing for properties with high conversion rates.

Engage First-Time Buyers

Create specific campaigns for apartments to attract first-time buyers.

Offer financial guidance programs to boost their confidence.

Summary:

Built a robust data solution from Azure SQL to Power BI.

Derived actionable insights that can drive real estate growth.

In 2024, the best-selling digital single by a boy group, band or duo was "plot twist" by K-pop boy group TWS with *********** index points. The six-member group debuted that same year under a sublabel of BTS' label HYBE. Meanwhile, K-rock band DAY6, who debuted in 2015, enjoyed a resurgence in popularity, with several old as well as newly released singles ranking highly.

Unemployment Rate in India - nationwide, state-wise, rural and urban employment, and comparison with global peers.

In 2024, the most successful digital single by a female soloist in South Korea was "Love wins all" by IU, with an index score of *********** points. Several of the singles were released prior to 2024, and have achieved high index scores for multiple years.