Link to the Open Data site for the United States Census Bureau.

Ce jeu de donnée est une version géocodée du fichier des bureaux de vote publié par l'INSEE. Il a été géocodé à l'aide de: la Base Adresse Nationale (février 2020) la BANO d'OpenStreetMap France (février 2020) les points d'intérêt extraits d'OpenStreetMap ATTENTION: Le géocodage est processus imparfait qui ne peut pas toujours localiser un bureau de vote, leurs adresses n'étant pas normalisée et parfois très imprécises. Sur les 70000 bureaux du fichier original, 60000 ont pu être géocodés de façon plus ou moins précise lors d'un premier géocodage. La colonne geo_type indique la précision géographique: housenumber: précision à l'adresse interpolation: position interpolée entre 2 adresses street: position "à la rue" locality: position "au lieu dit" municipality: position "à la commune" poi: position liée à un point d'intérêt (mairie, école, etc) geo_score: indique le taux de correspondance du libellé initial recherché et de celui trouvé, plus il est élevé, plus l'adresse trouvée a de chance d'être la bonne.

This dataset contains metrics that measure the operational performance of the Bureau of Street Lighting. These metrics are used on a regular basis by the department and the Mayor to evaluate progress and inform decision making. Performance management forms the foundation of a data-driven culture of innovation and excellence in the City of Los Angeles.

Comprehensive dataset of 11 Convention information bureaus in New York, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

Cette donnée (surfacique) renseigne l’attachement aux bureaux de vote de la Ville de Montpellier.

Comprehensive dataset of 321 Citizen information bureaus in Mexico as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

A list of 4-digit GSA federal agency bureau codes used to identify federal agencies. The primary source is the GSA published list.

Comprehensive dataset of 22 Convention information bureaus in Georgia, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Australia Private Credit Bureau Coverage: % of Adults data was reported at 100.000 % in 2019. This stayed constant from the previous number of 100.000 % for 2018. Australia Private Credit Bureau Coverage: % of Adults data is updated yearly, averaging 100.000 % from Dec 2004 (Median) to 2019, with 16 observations. The data reached an all-time high of 100.000 % in 2019 and a record low of 95.400 % in 2004. Australia Private Credit Bureau Coverage: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Businesses Registered Statistics. Private credit bureau coverage reports the number of individuals or firms listed by a private credit bureau with current information on repayment history, unpaid debts, or credit outstanding. The number is expressed as a percentage of the adult population.;World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme;Unweighted average;Data are presented for the survey year instead of publication year.

WBMS provides trade, production, consumption, and stock data for metals globally, on a country, regional, and worldwide data classification. Learn more.

Well Information includes information on borehole activities such as drilling activity, counts on the number of boreholes completed, and number of shut-in's.Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary.Additional files are available on well completions and well tests. All surveys are referenced to grid north in the native map projection of the block containing the surface location of the survey.For some old surveys the north reference was not marked and was assumed by BOEM to be grid north. In an effort to improve the quality of our downloadable data, we are now exporting bottom hole locations as calculated from the directional survey associated with the well. The coordinate values were derived using the North American Datum (NAD) 1927.

About this dataset

Context

The dataset tabulates the data for the Prue, OK population pyramid, which represents the Prue population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Prue, OK, is 34.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Prue, OK, is 30.8.
• Total dependency ratio for Prue, OK is 65.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Prue, OK is 3.2.

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 for the Prue population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Prue for the selected age group is shown in the following column.
• Population (Female): The female population in the Prue for the selected age group is shown in the following column.
• Total Population: The total population of the Prue for the selected age group is shown in the following column.

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 Prue Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Silver Lake, MN population pyramid, which represents the Silver Lake population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Silver Lake, MN, is 30.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Silver Lake, MN, is 27.0.
• Total dependency ratio for Silver Lake, MN is 57.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Silver Lake, MN is 3.7.

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 for the Silver Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Silver Lake for the selected age group is shown in the following column.
• Population (Female): The female population in the Silver Lake for the selected age group is shown in the following column.
• Total Population: The total population of the Silver Lake for the selected age group is shown in the following column.

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 Silver Lake Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Rice Lake, WI population pyramid, which represents the Rice Lake population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Rice Lake, WI, is 36.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Rice Lake, WI, is 39.1.
• Total dependency ratio for Rice Lake, WI is 75.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Rice Lake, WI is 2.6.

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 for the Rice Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Rice Lake for the selected age group is shown in the following column.
• Population (Female): The female population in the Rice Lake for the selected age group is shown in the following column.
• Total Population: The total population of the Rice Lake for the selected age group is shown in the following column.

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 Rice Lake Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Arbor Vitae, Wisconsin population pyramid, which represents the Arbor Vitae town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Arbor Vitae, Wisconsin, is 32.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Arbor Vitae, Wisconsin, is 56.4.
• Total dependency ratio for Arbor Vitae, Wisconsin is 89.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Arbor Vitae, Wisconsin is 1.8.

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 for the Arbor Vitae town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Arbor Vitae town for the selected age group is shown in the following column.
• Population (Female): The female population in the Arbor Vitae town for the selected age group is shown in the following column.
• Total Population: The total population of the Arbor Vitae town for the selected age group is shown in the following column.

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 Arbor Vitae town Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Jeffers, MN population pyramid, which represents the Jeffers population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Jeffers, MN, is 25.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Jeffers, MN, is 41.4.
• Total dependency ratio for Jeffers, MN is 67.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Jeffers, MN is 2.4.

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 for the Jeffers population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Jeffers for the selected age group is shown in the following column.
• Population (Female): The female population in the Jeffers for the selected age group is shown in the following column.
• Total Population: The total population of the Jeffers for the selected age group is shown in the following column.

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 Jeffers Population by Age. You can refer the same here

Europe's Collection Agencies and Credit Bureaux industry has contended with numerous challenges in recent years. Lending activity has been muted as businesses became cautious about borrowing in the face of turbulent economic conditions and rising interest rates, draining the pool of debt available for collection. Revenue is expected to fall at a compound annual rate of 3.8% over the five years through 2024 to €19.6 billion, including an estimated decline of 3.2% in 2024. In recent years, the industry has witnessed a significant transformation driven by digitalisation. Collection agencies and credit bureaux embraced digital platforms and automation tools to streamline processes, enhance data analysis efficiency and improve consumer communication. The integration of AI and alternative credit scoring models has revolutionised credit assessment practices, offering more inclusive evaluation methods and personalised debt collection strategies. The adoption of blockchain technology for secure data management has also gained traction, promising enhanced data security and transparency across operations. Revenue is slated to mount at a compound annual rate of 2.7% over the five years through 2029 to €22.5 billion, while profit is also expected to edge upwards. Looking ahead, Europe's collection agencies and credit bureaux are poised for further evolution and innovation. Expanding alternative data sources for credit assessment will provide more comprehensive credit profiles and improve risk assessment accuracy. Companies will also continue to integrate blockchain technology for secure data management, offering increased data security, fraud prevention and operational efficiencies.

The Bureau of Transportation Statistics (BTS), part of the Department of Transportation (DOT) is the preeminent source of statistics on commercial aviation, multimodal freight activity, and transportation economics, and provides context to decision makers and the public for understanding statistics on transportation. BTS assures the credibility of its products and services through rigorous analysis, transparent data quality, and independence from political influence. BTS promotes innovative methods of data collection, analysis, visualization, and dissemination to improve operational efficiency, to examine emerging topics, and to create relevant and timely information products that foster understanding of transportation and its transformational role in society. The Bureau’s National Transportation Library (NTL) is the permanent, publicly accessible home for research publications from throughout the transportation community; the gateway to all DOT data; and the help line for the Congress, researchers, and the public for information about transportation.

Comprehensive dataset of 14 License bureaus in West Virginia, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.