This dataset includes County spending data for Montgomery County government. It does not include agency spending. Data considered sensitive or confidential and will be encrypted before it is posted.

For more data on Austin demographics please visit austintexas.gov/demographics.

These files represent the state and regional summaries of sensitivities to formaldehyde, acetaldehyde and ozone to various sources and compounds. This dataset is associated with the following publication: Luecken, D., S. Napelenok, M. Strum, R. Scheffe, and S. Phillips. Sensitivity of Ambient Atmospheric Formaldehyde and Ozone to Precursor Species and Source Types Across the United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(8): 4668–4675, (2018).

The source data includes original data on all the plotted figures involved in the main content and supplementary information.

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.

A complete list of live websites using the pie-chart technology, compiled through global website indexing conducted by WebTechSurvey.

These pie charts show the distribution of biomass quantities (in percentage terms) per municipality in the following categories, roadside clippings, reeds and heaths, stems and leaves. The size of the pie chart shows the cumulative quantity. This dataset is used in the "biomass" map as part of the research into the natural capital in Overijssel.

Data set containing information on the facilities licensed by DMV in accordance with Vehicle and Traffic Law.

DBEDT Pie Chart Of Electric Hybrid Fossil Cars

Three interactive dashboards have been created using Tableau to provide in-depth insights into sales, customer behaviour, and product performance, offering a comprehensive view of key business metrics and drivers.

The Sales Analysis dashboard focuses on top-level metrics, such as total revenue and customer count over a year. A geographic map with color-coded and size-adjusted bubbles shows sales distribution across regions, aiding in inventory and marketing planning. A bar chart ranks top and bottom-selling products by category, while a pie chart illustrates category-wise sales contributions, and a horizontal bar chart displays seasonal sales trends.

The second dashboard delves into customer analytics, highlighting potential markets with a shaded map that shows areas with varying customer densities. A lollipop chart displays customers’ delivery preferences, while bubble charts, histograms, and pie charts offer insights into customers' age groups, past purchases, seasonal footfall, gender, payment methods, subscriptions, discounts, and preferred product sizes. These visualizations support the development of targeted marketing strategies tailored to specific customer segments.

The third dashboard examines product-specific insights. A tabular visualization ranks top-selling products by state, and a color-coded table presents average product ratings for quality assessment. Bar charts detail sales volume across categories for inventory planning, analyze sales of discounted products, and identify seasonal trends for strategic pricing and promotional decisions.

In summary, these interactive visualizations facilitate big data analysis and reveal hidden patterns, equipping decision-makers with a comprehensive understanding to guide strategy across marketing, merchandising, and operations.

Liquor Authority quarterly list of all active licensees in NYS filtered by Winery and Brewery specific License Types.

The Medical Expenditure Panel Survey (MEPS) Household Component collects data on all members of sample households from selected communities across the United States. With the MEPS-HC Data Tools, users can explore trends and cross-sectional bar charts for nationally representative estimates of household medical utilization and expenditures, demographic and socioeconomic characteristics, health insurance coverage, accessibility and quality of care, treated medical conditions, and prescribed medicine purchases.

This pie chart displays companies per website using the aggregation count in Seattle. The data is about companies.

This pie chart displays books per BNB id using the aggregation count. The data is filtered where the author is Bruce Fader. The data is about books.

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

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.

These pie charts represent the distribution of biomass quantities (percentually) per municipality in the following categories, berm mower, reed and heath, stem and leaf. The size of the pie chart shows the cumulative amount. This dataset is used in the ‘biomass’ map as part of the research into natural capital in Overijssel.

The "Restoration Projects" 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.

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.