Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Repository that contains alerts that will be sent to SSA employees when certain conditions exist, to inform them of work that needs to be done, is being reviewed, or has been completed.

About this dataset

Context

The dataset tabulates the Ocean View population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Ocean View. The dataset can be utilized to understand the population distribution of Ocean View by age. For example, using this dataset, we can identify the largest age group in Ocean View.

Key observations

The largest age group in Ocean View, DE was for the group of age 75 to 79 years years with a population of 521 (18.88%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Ocean View, DE was the Under 5 years years with a population of 29 (1.05%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Ocean View is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Ocean View total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Ocean View Population by Age. You can refer the same here

This dataset contains 10,000 synthetic records simulating the migratory behavior of various bird species across global regions. Each entry represents a single bird tagged with a tracking device and includes detailed information such as flight distance, speed, altitude, weather conditions, tagging information, and migration outcomes.

The data was entirely synthetically generated using randomized yet realistic values based on known ranges from ornithological studies. It is ideal for practicing data analysis and visualization techniques without privacy concerns or real-world data access restrictions. Because it’s artificial, the dataset can be freely used in education, portfolio projects, demo dashboards, machine learning pipelines, or business intelligence training.

With over 40 columns, this dataset supports a wide array of analysis types. Analysts can explore questions like “Do certain species migrate in larger flocks?”, “How does weather impact nesting success?”, or “What conditions lead to migration interruptions?”. Users can also perform geospatial mapping of start and end locations, cluster birds by behavior, or build time series models based on migration months and environmental factors.

For data visualization, tools like Power BI, Python (Matplotlib/Seaborn/Plotly), or Excel can be used to create insightful dashboards and interactive charts.

Join the Fabric Community DataViz Contest | May 2025: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/%EF%B8%8F-Fabric-Community-DataViz-Contest-May-2025/ba-p/4668560

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Presentation slides from the Interagency Data Team meeting. The Interagency Data Team is a community of data analysts, or agency liaisons, who convene regularly with representation from DC agencies of all persuasions. Participants engage in discussions regarding the team’s core mission and priorities for a better kind of data culture – collection, application, sharing, classification and governance to name a few. The team is coordinated by the Office of the Chief Technology Officer (OCTO), lead by the Chief Data Officer (CDO), and directly supports the District of Columbia's Data Policy.

These data show the geographic representation of Federal and State Waters for the purpose of display in the MarineCadastre.gov OceanReports application. The boundary between state and federal waters was determined by consulting The Submerged Lands Act (43 U.S.C. §§ 1301 et seq.), 48 U.S.C. §§ 1705 and The Abandoned Shipwreck Act (43 U.S.C. §§ 2101). Some boundary delineations based on the SLA were approximated in this data set, including areas in Hawaii, Alaska, and Washington State. Although state boarders do not extend over water, it was necessary to approximate these borders to produce this data set. The boundaries depicted in this data set are for visual purposes only. The placement of these boundaries was extrapolated from the Federal Outer Continental Shelf (OCS) Administrative Boundaries as described here http://edocket.access.gpo.gov/2006/pdf/05-24659.pdf. The delineation between waters under US sovereign territory jurisdiction and that of federal governance is also approximate. Although based upon legislation, these data do not represent legal boundaries, especially in the case of Navassa Island, The Northern Mariana Islands, Baker Island, Howland Island, Johnston Atoll, Kingman Reef, Palmyra Atoll, Wake Islands and Jarvis Island.The seaward limit of this data set is the boundary of the 200nm US Exclusive Economic Zone. The EEZ is measured from the US baseline, recognized as the low-water line along the coast as marked on NOAA's nautical charts in accordance with articles of the Laws of the Sea. These limits are ambulatory and subject to revision based on changes in coastline geometry. This dataset was produced based on an update to the Maritime Limits published in September, 2013. To view the most up-to-date Maritime Limits, please see http://www.nauticalcharts.noaa.gov/csdl/mbound.htm. Navassa Island does not have an EEZ around it, so the seaward extent of the federal waters surrounding it were based on the 12 mile offshore boundary of the USFWS National Wildlife Refuge established on the island. All data is displayed in WGS_1984_World_Mercator. Area calculations for all states except Alaska were completed in the same projection. Area calculations for Alaska were completed in Alaska Albers Equal Area Conic.

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App."

On the Census Data Hub, CSO Census 2022 and 2016 datasets have been combined with Tailte Éireann official boundary data. Almost 800 variables across 15 themes can be retrieved to make powerful visualisations for both statistical and statutory boundaries. This guide will show you how to view, and quickly visualise data, based on a variable of your choice.Topics covered include: View Census countsSearching for data Viewing Census count data on a map Filtering by location Multiple filters

About this dataset

Context

The dataset tabulates the Lake View population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Lake View. The dataset can be utilized to understand the population distribution of Lake View by age. For example, using this dataset, we can identify the largest age group in Lake View.

Key observations

The largest age group in Lake View, IA was for the group of age 65-69 years with a population of 108 (11.45%), according to the 2021 American Community Survey. At the same time, the smallest age group in Lake View, IA was the 0-4 years with a population of 12 (1.27%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Lake View is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Lake View total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lake View Population by Age. You can refer the same here

In this data set, 6 objects including 2 targets and 4 non-targets lay on the sea sand bottom. Upon this experiment, the transmitted signal is Wide-Band Linear Frequency Modulated Pulse (WLFM) which covers frequency range 5-110 KHz. Targets lay on the bottom rotate 180 degrees with 1 degree accuracy via electromotor. Off target to 10 meters backscattered echoes are accumulated. Fine dataset takes key role in sonar target classification. Regarding massive raw data obtained from previous stage, above massive calculation will be expected. To reduce calculation burden relating to classifying and extracting feature, it is essential to detect targets out of total received data. To implement this, the intensity of the received signal is used. It is inevitable to consider multi-path propagation, secondary reflections, and reverberation due to shoal of the region. The researcher attempts to eliminate artifact tract after detecting stage and before extracting feature by the use of a matched filter.

The global business data visualization tools market size was valued at USD 6.0 billion in 2025 and is projected to grow from USD 7.3 billion in 2026 to USD 19.5 billion by 2033, exhibiting a CAGR of 13.4% during the forecast period. The growing need for data-driven decision-making, increasing adoption of cloud-based solutions, and advancements in artificial intelligence (AI) and machine learning (ML) technologies are the primary factors driving the market growth. North America is expected to dominate the market throughout the forecast period due to the presence of a large number of established vendors and early adoption of advanced technologies. Asia Pacific is projected to grow at the highest CAGR during the forecast period, owing to the increasing adoption of data visualization tools in emerging economies, such as China and India. Key players in the market include Microsoft, Tableau (Salesforce), IBM, MicroStrategy, Oracle America, TIBCO Software, Domo, SAP, QlikTech, and SAS Institute.

A collection of files used for a data visualization project for the Digital Humanities Praxis course at the Graduate Center, CUNY. The files represent raw data (csv), data used for the visualization(s) (gephi), and the visualizations themselves (pdf). A write-up on the project can be located at the GC Academic Commons site: http://dhpraxis14.commons.gc.cuny.edu/2014/11/12/its-big-data-to-me-data-viz-part-2

These data sets were originally created for the following publications:

M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? In Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany, 2010.

H.-P. Kriegel, E. Schubert, A. Zimek Evaluation of Multiple Clustering Solutions In 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece, 2011.

The outlier data set versions were introduced in:

E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel On Evaluation of Outlier Rankings and Outlier Scores In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.

They are derived from the original image data available at https://aloi.science.uva.nl/

The image acquisition process is documented in the original ALOI work: J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, Int. J. Comput. Vision, 61(1), 103-112, January, 2005

Additional information is available at: https://elki-project.github.io/datasets/multi_view

The following views are currently available:

    Feature type
    Description
    Files


    Object number
    Sparse 1000 dimensional vectors that give the true object assignment
    objs.arff.gz


    RGB color histograms
    Standard RGB color histograms (uniform binning)
    aloi-8d.csv.gz aloi-27d.csv.gz aloi-64d.csv.gz aloi-125d.csv.gz aloi-216d.csv.gz aloi-343d.csv.gz aloi-512d.csv.gz aloi-729d.csv.gz aloi-1000d.csv.gz


    HSV color histograms
    Standard HSV/HSB color histograms in various binnings
    aloi-hsb-2x2x2.csv.gz aloi-hsb-3x3x3.csv.gz aloi-hsb-4x4x4.csv.gz aloi-hsb-5x5x5.csv.gz aloi-hsb-6x6x6.csv.gz aloi-hsb-7x7x7.csv.gz aloi-hsb-7x2x2.csv.gz aloi-hsb-7x3x3.csv.gz aloi-hsb-14x3x3.csv.gz aloi-hsb-8x4x4.csv.gz aloi-hsb-9x5x5.csv.gz aloi-hsb-13x4x4.csv.gz aloi-hsb-14x5x5.csv.gz aloi-hsb-10x6x6.csv.gz aloi-hsb-14x6x6.csv.gz


    Color similiarity
    Average similarity to 77 reference colors (not histograms) 18 colors x 2 sat x 2 bri + 5 grey values (incl. white, black)
    aloi-colorsim77.arff.gz (feature subsets are meaningful here, as these features are computed independently of each other)


    Haralick features
    First 13 Haralick features (radius 1 pixel)
    aloi-haralick-1.csv.gz


    Front to back
    Vectors representing front face vs. back faces of individual objects
    front.arff.gz


    Basic light
    Vectors indicating basic light situations
    light.arff.gz


    Manual annotations
    Manually annotated object groups of semantically related objects such as cups
    manual1.arff.gz

Outlier Detection Versions

Additionally, we generated a number of subsets for outlier detection:

    Feature type
    Description
    Files


    RGB Histograms
    Downsampled to 100000 objects (553 outliers)
    aloi-27d-100000-max10-tot553.csv.gz aloi-64d-100000-max10-tot553.csv.gz



    Downsampled to 75000 objects (717 outliers)
    aloi-27d-75000-max4-tot717.csv.gz aloi-64d-75000-max4-tot717.csv.gz



    Downsampled to 50000 objects (1508 outliers)
    aloi-27d-50000-max5-tot1508.csv.gz aloi-64d-50000-max5-tot1508.csv.gz

This data set contains mapping and table of the location of all Pay and Display Meters in Fingal County Councils pay and display system around the county of Fingal. We Currently have 146 Meters in use .e have Pay and Display in the following areas: Malahide, Skerries, Balbriggan, Swords, Rush and Clonsilla.Pay & Display operates from 8.00 am to 6.00 pm Monday to Saturday inclusive. However, this can vary for particular areas, check the signage at your location.Charges can vary for different areas for both on-street and car parks. However, the charge is usually €1.20 per hour or €3 per day in the long-term parking areas.Traffic Wardens ticket illegally parked vehicles in a Pay and Display area. It is your responsibility to ensure that a valid parking ticket/permit/disc is displayed and clearly visible on your vehicle.

Displays the process' RACI in its conventional RACI format/chart

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

United States Employment: NF: PB: Display Advertising data was reported at 30.400 Person th in Mar 2025. This stayed constant from the previous number of 30.400 Person th for Feb 2025. United States Employment: NF: PB: Display Advertising data is updated monthly, averaging 30.400 Person th from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 38.900 Person th in Nov 2007 and a record low of 18.700 Person th in Jan 1990. United States Employment: NF: PB: Display Advertising data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Employment: Non Farm Payroll.

This web map is a component of the CrowdMag Visualization App.NOAA's CrowdMag is a crowdsourced data collection project that uses a mobile app to collect geomagnetic data from the magnetometers that modern smartphones use as part of their navigation systems. NCEI collects these data from citizen scientists around the world and provides quality control services before making them available through a series of aggregated maps and charts. These data have the potential to provide a high resolution alternative to geomagnetic satellite data, as well as near real-time information about changes in the magnetic field.This map shows data collected from phones around the world! Displayed are the Crowdsourced magnetic data within a tolerance level of prediction by World Magnetic Model. We have added some uncertainty to each data point shown to ensure the privacy of our contributors. The data points are grouped together (or "aggregated") into small areas , and we display the median data value across all the readings for each point.

This map is updated every day. Layers are available for Median Intensity, Median Horizontal Component (Y), and Median Vertical Component (Z).


Use the time slider to select the date range. Select the different layers under the "Crowdmag Observations" menu. View a color scale using the legend tool. Zoom to your location using the "Find my Location" tool. Click or tap on a data point to view a popup containing more information.