A complete list of live websites using the Pie And Donut Chart technology, compiled through global website indexing conducted by WebTechSurvey.

Donut graph depicting ratio of lead-free to lead materials.

Venkatachalam/donut-chart dataset hosted on Hugging Face and contributed by the HF Datasets community

Welcome to the Sales Insights Dashboard, a comprehensive analysis of sales data with interactive visualizations and key insights. Follow the steps below to explore the dashboard:

Overview:

The dashboard consist of charts representing Monthly Sales Trends, Regional Profitability, Top 5 Products, Sales by Category and Product Wise Sales and Quantity. Line chart, Area chart, Clustered Bar chart and Doughnut chart can be found in the dashboard.

Monthly Sales Trend

Explore the "Monthly Sales Trend" to understand how sales have evolved over time. Dynamic line charts showcase monthly trends, helping you spot patterns and seasonality.

Product-wise Sales and Quantity:

Delve into the "Product-wise Sales and Quantity" section for a granular view. Clustered bar charts display sales and quantity metrics for each product.

Top 5 Products by Sales:

Identify the "Top 5 Products by Sales" to focus on high-performing items. Doughnut chart offer insights into the top-selling products.

Regional Profitability:

Evaluate "Regional Profitability" to understand which regions contribute the most to profits. Area charts visually represent regional performance.

Sales by Category:

Dive into the "Sales by Category" to identify the most lucrative product categories. Interactive Doughnut charts reveal sales performance, aiding in strategic decision-making.

How to Use:

Interact with dropdowns, sliders, and buttons to customize your view. Hover over charts for detailed tooltips and information. Click on specific elements to filter data and uncover specific insights.

Feedback:

We welcome your feedback to enhance the dashboard further. Share your thoughts in the comments section. Explore the Sales Insights Dashboard now and transform your sales data into actionable insights!

This report provides information on expenditures (i.e., cash transactions/payments) for the agencies that utilize the State Financial Management Application (SFMA) issued for the fiscal year 2011 (July 1, 2010 - June 30, 2011). See the Oregon Transparency Website Expenditure page for more detail: http://oregon.gov/transparency/expenditures.page

This statistic shows the consumption of donuts / doughnuts in the United States from 2011 to 2020 and a forecast thereof until 2024. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ****** million Americans consumed donuts / doughnuts in 2020. This figure is projected to increase to ****** million in 2024.

Comprehensive YouTube channel statistics for Donut, featuring 9,220,000 subscribers and 2,977,019,452 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Autos-&-Vehicles category and is based in US. Track 1,738 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub (https://github.com/feigl/gipht). We apply temporal adjustment to the data set described in Lu et al. (2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is (6.2 +/- 0.6) x 10^6 m^3/yr. Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as wrapped phase or wrapped residuals. Date of final oral examination: 05/19/2016 This thesis is approved by the following members of the Final Oral Committee: Kurt L. Feigl, Professor, Geoscience Michael Cardiff, Assistant Professor, Geoscience Clifford H. Thurber, Vilas Distinguished Professor, Geoscience

The Radar Chart collection is an archived product of summarized radar data. The geographic coverage is the 48 contiguous states of the United States. These hourly radar charts were prepared by the National Weather Service (NWS) and the National Centers for Environmental Prediction. Data contains analyzed areas, lines, and cells of cloud formations that include the base, tops, movement, and precipitation intensity. Precipitation types and change of intensity are also depicted. These charts are prepared from radar observations taken by NWS weather radar stations throughout the country.

No description found

This statistic shows the sales growth of Dunkin' Donuts worldwide from 2012 to 2019, by region. Dunkin' Donuts reported a sales growth of **** percent in the United States in 2019, compared to the previous year.

The global marine chart radar market is expected to reach a value of $1506 million by 2033, growing at a CAGR of 6.3% during the forecast period 2025-2033. The market is driven by the increasing demand for safety and navigation systems in the maritime industry. Growing concerns about maritime safety, the need for precise navigation, and the rising adoption of advanced technologies in the marine sector are fueling the market growth. The market is segmented into various sectors based on application, type, and region. By application, the market is divided into merchant marine, fishing vessels, military, and others. By type, the market is divided into X-band radars and S-band radars. The X-band radars segment is expected to witness significant growth due to its superior performance in short-range applications. Geographically, the market is divided into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. The Asia Pacific region is expected to dominate the market due to the increasing number of shipbuilding activities and growth in the fishing industry.

🚀 Project Summary

This dataset supports RoboFUSE-GNN, an uncertainty-aware Graph Neural Network designed for real-time collaborative perception in dynamic factory environments. The data was collected from a multi-robot radar setup in a Cyber-Physical Production System (CPPS). Each sample represents a spatial-semantic radar graph, capturing inter-robot spatial relationships and temporal dependencies through a sliding window graph formulation.

Scenario

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26959588%2Fd0bd6be20d25441ddff17727f999f372%2Fphysical_setup_compressed-1.png?generation=1747997891229706&alt=media" alt=""> layout_01: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26959588%2F7706eca75693ebc76de2a49e6d49d7bb%2FLayout_01_setup-1.png?generation=1747997919528516&alt=media" alt=""> layout_02: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26959588%2Ffca4f039613cd25dc4fafb1bc03a529d%2FLayout_02_setup-1.png?generation=1747997995497196&alt=media" alt=""> layout_03: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26959588%2Faf235ecd458b371645f170ccbd72bf31%2FLayout_03_setup-1.png?generation=1747998010218223&alt=media" alt="">

📚 Dataset Description

Each sample in the dataset represents a radar graph snapshot composed of:

Nodes: Radar detections over a temporal window

Node features: Position, radar-specific attributes, and robot ID

Edges: Constructed using spatial proximity and inter-robot collaboration

Edge attributes: Relative motion, SNR, and temporal difference

Labels:

Node semantic classes (e.g., Robot, Workstation, Obstacle)

Edge labels indicating semantic similarity and collaboration type

📁 Folder Structure

RoboFUSE_Graphs/split/ ├── scene_000/ │ ├── 000.pt │ ├── 001.pt │ └── scene_metadata.json ├── scene_001/ │ ├── ... ├── ... └── scene_split_mapping.json

Each scene_XXX/ folder corresponds to a complete scenario and contains:

NNN.pkl: A Pickle file for the N-th graph frame

scene_metadata.json: Metadata including:

scene_name: Scenario identifier

scenario: Scenario Description

layout_name: Layout name (e.g., layout_01, layout_02, layout_03)

num_frames: Number of frames in the scene

frame_files: List of graph frame files

🧠 Graph Details

Each .pkl file contains a dictionary with the following:

🔧 Graph Construction Pipeline

The following steps were involved in creating the datatset:

1. Preprocessing:

   - Points are filtered using SNR, Z height, and arena bounds
   - Normalized radar features include SNR, range, angle, velocity
   

2. Sliding Window Accumulation:

   - Temporal fusion over a window W improves robustness
   - Used to simulate persistence and reduce sparsity
   

3. Nodes:

   - Construct node features xi = [x, y, z, ŝ, r̂, sin(ϕ̂), cos(ϕ̂), sin(θ̂), cos(θ̂), robotID]
   - Label nodes using MoCap-ground-truth footprints.
   

4. Edges:

   - Built using KNN 
   - Edge attributes eij = [Δx, Δy, Δz, ΔSNR, Δt]
   - Edge Labels: 
       - 1 if nodes are of the same class and within a distance threshold
       - Includes **intra-robot** and **inter-robot** collaborative edges
   

🧪 Use Cases

• Multi-robot perception and mapping
• Semantic object detection
• Graph-based reasoning in radar domains
• Uncertainty-aware link prediction

A complete list of live websites using the Wp Radar Chart technology, compiled through global website indexing conducted by WebTechSurvey.

This contains the data associated with a paper submission titled "Radar-Inertial ICP-based Pose Graph SLAM". The datasets contains IMU, cameras (2x), lidar, and fmcw radars (3x) which is sufficient for reproducing the results of the paper. See readme for more details.

The size of the Marine Chart Radar Dome market was valued at USD 563 million in 2023 and is projected to reach USD 776.49 million by 2032, with an expected CAGR of 4.7% during the forecast period.

📊 Road Accident Data Analysis: Interactive Excel Dashboard 🚗

Excited to share my Kaggle project focusing on road accident data analysis. Leveraging Excel's power, I've developed an interactive dashboard offering comprehensive insights for safer roads.

Key Aspects:

Data Processing & Cleaning: Ensured data reliability through meticulous processing. KPIs: Primarily focused on Total Casualties, with detailed breakdowns for Fatal, Serious, Slight, and by Car type. Visualizations: Engaging charts - Doughnuts, Line, Bar, and Pie - offering a holistic view of accident trends. Interactivity: User-friendly features include Urban/Rural and Year filters for dynamic exploration. Unique Insights:

Monthly Trends: Line chart for a nuanced comparison of current vs. previous year casualties. Road Type Breakdown: Bar chart to showcase casualties distributed across different road types. Geospatial Analysis: Doughnut charts detailing casualties by location and area. Call for Collaboration: Seeking Kaggle community input for refinement and optimization. Let's collectively contribute to making our roads safer through data-driven insights!

DataAnalysis #RoadSafety #InteractiveDashboard #KaggleProject #Excel #DataVisualization #CollaborationOpportunity

Looking forward to your feedback and contributions! 🚀🌐

The marine chart radar market is booming, projected to reach $2.46 billion by 2033 with a 6.3% CAGR. Discover key growth drivers, market trends, and leading companies shaping this vital navigation technology sector. Learn more about the latest innovations and regional market analysis.

Imports - Parts For Television, Radio & Radar Apparatus in Mexico increased to 257435 USD Thousand in January from 225389 USD Thousand in December of 2023. This dataset includes a chart with historical data for Mexico Imports of Parts For Television, Radio & Radar Ap.

Comparing the functional performance of biological systems often requires comparing multiple mechanical properties. Such analyses, however, are commonly presented using orthogonal plots that compare N ≤ 3 properties. Here, we develop a multidimensional visualization strategy using permutated radar charts (radial, multi-axis plots) to compare the relative performance distributions of mechanical systems on a single graphic across N ≥ 3 properties. Leveraging the fact that radar charts plot data in the form of closed polygonal profiles, we use shape descriptors for quantitative comparisons. We identify mechanical property-function correlations distinctive to rigid, flexible, and damage-tolerant biological materials in the form of structural ties, beams, shells, and foams. We also show that the microstructures of dentin, bone, tendon, skin, and cartilage dictate their tensile performance, exhibiting a trade-off between stiffness and extensibility. Lastly, we compare the feeding versus singing performance of Darwin’s finches to demonstrate the potential of radar charts for multidimensional comparisons beyond mechanics of materials.