Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Avg Hourly Earnings: sa: FA: Activities Related to Real Estate data was reported at 27.240 USD in May 2018. This records an increase from the previous number of 27.090 USD for Apr 2018. Avg Hourly Earnings: sa: FA: Activities Related to Real Estate data is updated monthly, averaging 23.010 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 27.240 USD in May 2018 and a record low of 19.250 USD in Mar 2006. Avg Hourly Earnings: sa: FA: Activities Related to Real Estate data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G033: Current Employment Statistics Survey: Average Weekly and Hourly Earnings: Seasonally Adjusted.

Introduction

Robotic Process Automation Statistics: RPA is a transformative technology that leverages robot software to automate rule-based tasks within digital systems. It operates by identifying repetitive tasks and developing software bots to execute them.

Seamlessly integrating these bots with existing software applications. RPA offers numerous benefits, including cost efficiency, accuracy, scalability, and enhanced productivity.

Its adoption is on the rise across industries, with the global RPA market poised for significant growth. This technology has the potential to revolutionize business operations.

By reducing costs, improving efficiency, and allowing human employees to focus on more strategic activities. Ultimately enhancing overall productivity and competitiveness.

Introduction

Social Media Marketing Statistics: Social media marketing is a key part of any digital marketing plan today. With over 50% of the world’s population using social media, brands need to be active on these platforms. But it’s not just about making profiles and posting content. Effective social media marketing involves keeping up with changing algorithms and trends and understanding the behaviors of your target audience. Social media’s interactive and engaging nature helps businesses connect with their audience in ways they couldn’t before.

This opens up new opportunities for engaging with people, building the brand, and doing direct marketing. We shall shed more light on Social Media Marketing Statistics through this article.

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

I really don’t want to spend time talking about how big social media has become. While some of us are still in denial, the impact of social media platforms is so profound. Thus, it’s not surprising when social media trends and statistics go in sync with societal changes. Understanding these...

United States Avg Hourly Earnings: PB: Legal Services data was reported at 41.840 USD in May 2018. This records a decrease from the previous number of 42.420 USD for Apr 2018. United States Avg Hourly Earnings: PB: Legal Services data is updated monthly, averaging 36.930 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 42.420 USD in Apr 2018 and a record low of 31.530 USD in Aug 2006. United States Avg Hourly Earnings: PB: Legal Services data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G032: Current Employment Statistics Survey: Average Weekly and Hourly Earnings.

Data Guide: Premier League Match Statistics

Welcome to the Premier League Match Statistics dataset! ⚽ This guide will help you understand the structure of the dataset, key variables, and how to make the most of the data for analysis and predictions.

This dataset contains detailed match statistics from the English Premier League, including final scores, player statistics, team performance, goals, yellow cards, red cards, and more. It is ideal for analyzing team performance, predicting match outcomes, and exploring trends in football. This dataset is valuable for football enthusiasts, data analysts, and predictive model developer.

1. Dataset Overview

This dataset provides comprehensive match statistics from the English Premier League, including team performance, player stats, goals, assists, yellow/red cards, and more. It is ideal for football enthusiasts, analysts, and machine learning projects.

2. Data Structure

The dataset consists of multiple columns, each representing different aspects of a match:

Additional columns may provide more in-depth insights.

3. How to Use This Dataset?

Here are some ideas to explore using this dataset:
✅ Analyze team performance trends over different seasons.
✅ Predict match outcomes using machine learning models.
✅ Identify key players based on goals, assists, and ratings.
✅ Explore disciplinary records (yellow/red cards) for fair play analysis.

4. Data Limitations

• This dataset focuses only on the English Premier League.
• Some matches may have missing or incomplete data.
• Real-time updates may not be available immediately after matches.

Beginning in 2016, the Law Enforcement Management and Administrative Statistics (LEMAS) survey adopted a core and supplement structure. The LEMAS core has been conducted every 3 to 4 years since 1987 with approximately 3,200 local, county and state law enforcement agencies across the United States. Due to the breadth of the survey, detailed analysis of any specific law enforcement topic cannot be done with the LEMAS core. The LEMAS supplements are designed to fill this void by allowing for a more comprehensive examination on a key topic in law enforcement and are administered in between core years. The 2016 LEMAS Body-Worn Camera Supplement (LEMAS-BWCS) is the first supplement administered under the new structure.

This dataset is a polygon coverage of counties limited to the extent of the Pond Creek coal bed resource areas and attributed with statistics on the thickness of the Pond Creek coal zone, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

The median income indicates the income bracket separating the income earners into two halves of equal size.

Key Table Information.Table Title.Construction: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2223BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesConstruction workers annual wages($1,000)Construction workers for pay period including March 12Construction workers for pay period including June 12Construction workers for pay period including September 12Construction workers for pay period including December 12Construction, production and/or development and exploration workers annual hours (1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Other employees for pay period including June 12Other employees for pay period including September 12Other employees for pay period including December 12Total fringe benefits ($1,000)Employers cost for legally required fringe benefits ($1,000)Employers cost for voluntarily provided fringe benefits ($1,000)Total selected costs ($1,000) Cost of materials, components, packaging and/or supplies used, minerals received, or purchased machinery installed ($1,000)Cost of construction work subcontracted out to others ($1,000)Cost of purchased land ($1,000)Total cost of selected power, fuels, and lubricants ($1,000)Cost of gasoline and diesel fuel ($1,000)Cost of natural gas and manufactured gas ($1,000)Cost of on-highway use of gasoline and diesel fuel ($1,000)Cost of off-highway use of gasoline and diesel fuel ($1,000)Cost of all other fuels and lubricants ($1,000)Cost of purchased electricity ($1,000)Value of construction work ($1,000)Value of construction work on government owned projects ($1,000)Value of construction work on federally owned projects ($1,000)Value of construction work on state and locally owned projects ($1,000)Value of construction work on privately owned projects ($1,000)Value of other business done ($1,000)Value of construction work subcontracted in from others ($1,000)Net value of construction work ($1,000)Value added ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, beginning of year ($1,000)Materials and/or supplies, parts, fuels, etc. inventories, end of year ($1,000)Gross value of depreciable assets (acquisition costs), beginning of year ($1,000)Total capital expenditures for buildings, structures, machinery, and equipment (new and used) ($1,000)Total retirements ($1,000)Gross value of depreciable assets (acquisition costs), end of year ($1,000)Total depreciation during year ($1,000)Total rental payments or lease payments ($1,000)Rental payments or lease payments for buildings and other structures ($1,000)Rental payments or lease payments for machinery and equipment ($1,000)Total other operating expenses ($1,000)Temporary staff and leased employee expenses ($1,000)Expensed computer hardware and other equipment ($1,000)Expensed purchases of software ($1,000)Data processing and other purchased computer services ($1,000)Communication services ($1,000)Repair and maintenance services of buildings and/or machinery ($1,000) Refuse removal (including hazardous waste) services ($1,000)Advertising and promotional services ($1,000)Purchased professional and technical services ($1,000) Taxes and license fees ($1,000)All other operating expenses ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical locati...

About this dataset

Context

The dataset tabulates the population of Yetter by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Yetter across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.94% of total population being female. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Yetter is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Yetter 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 Yetter Population by Race & Ethnicity. You can refer the same here

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

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Employment: NF: PW: RT: FH: Furniture & Home Furnishings (FF) data was reported at 330.000 Person th in Mar 2025. This records a decrease from the previous number of 330.800 Person th for Feb 2025. Employment: NF: PW: RT: FH: Furniture & Home Furnishings (FF) data is updated monthly, averaging 394.500 Person th from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 515.800 Person th in Dec 2006 and a record low of 203.000 Person th in Apr 2020. Employment: NF: PW: RT: FH: Furniture & Home Furnishings (FF) 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: Production Worker: Non Farm Payroll.

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010.

The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars (USD).

Data are recorded on an end-quarter basis.

This statistical table contains two data worksheets - one presenting data expressed in Australian dollar (AUD) terms and the other in USD terms.

There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only consolidated data are reported in this statistical table.

‘Total banks and RFCs’ is also reported in USD equivalent amounts, using the end-quarter AUD/USD exchange rate from statistical table F11.

The consolidated data reported in this statistical table are on the international exposures of banks (and RFCs between March 2003 and September 2010) operating in Australia. The types of assets included here are consistent with the locational data in statistical table B12.1. However, the consolidated data differ from the locational data in three key ways: foreign currency positions with Australian residents are excluded (whereas they are included in the locational data); claims between different offices of the same institution (e.g. between the head office and its subsidiary) are netted (whereas positions, including intra-group positions, are reported on a gross basis in the locational data); and on-balance sheet derivatives are not included in international claims or foreign claims, but are included separately under ‘Derivatives’ in statistical table B13.2. Foreign-owned reporting entities report on an unconsolidated basis.

The consolidated data are split by type of exposure. ‘International claims’ refers to all cross-border claims plus foreign offices’ local claims on residents in foreign currencies; foreign claims refers to all cross-border claims plus foreign offices’ local claims on residents in both local and foreign currencies; immediate risk claims (expressed by the BIS as claims on an immediate borrower basis) cover claims based on the country where the immediate counterparty resides; and ultimate risk claims cover immediate exposures adjusted (via guarantees and other risk transfers) to reflect the location of the ultimate counterparty/risk.

Foreign offices include the overseas branches, subsidiaries and joint ventures of a bank (or RFC between March 2003 and September 2010).

Risk transfers are those transfers of risk from the country of the immediate borrower to the country of ultimate risk as a result of guarantees, collateral, and where the counterparty is a legally dependent branch of a bank headquartered in another country. The risk reallocation includes loans to Australian borrowers that are guaranteed by foreign entities and therefore represent outward risk transfers from Australia, which increase the ultimate exposure to the country of the guarantor. Similarly, foreign lending that is guaranteed by Australian entities is reported as an inward risk transfer to Australia, which reduces the ultimate exposure to the country of the foreign borrower. The risk reallocation also includes transfers between different economic sectors (banks, public sector and non-bank private sector) in the same country.

Foreign claims on an ultimate risk basis are shown for the following types of reporting entity: Australian-owned banks (i.e. those with their parent entity legally incorporated in Australia); foreign subsidiary banks; branches of foreign banks; RFCs; and Australian-owned entities (i.e. Australian-owned banks and RFCs). The RFC data are only available between March 2003 and September 2010.

‘Foreign claims (ultimate risk basis) – Aust-owned entities’ is also reported in USD equivalent amounts, using the end-quarter AUD/USD exchange rate from statistical table F11.