Monthly data on federally administered Supplemental Security Income payments.

This table contains 11685 series, with data for years 1997 - 2011 (not all combinations necessarily have data for all years), and was last released on 2013-05-15. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Newfoundland and Labrador; Canada; Nova Scotia; Prince Edward Island ...), Sector (3 items: Total economy; Non-business sector; Business sector ...), Labour productivity measures and related measures (15 items: Total number of jobs; Number of employee jobs; Number of self-employed jobs; Hours worked for all jobs ...), North American Industry Classification System (NAICS) (19 items: All industries; Agriculture; forestry; fishing and hunting ...).

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.

Contains statistics on the UK's economy, industry, society and demography presented in easy to read tables and backed up with explanatory notes and definitions. It covers, among others, the following areas: area; parliamentary elections; defence; population and vital statistics; education; labour market; expenditure and wealth; health; crime and justice; lifestyles; environment, housing; transport and communications; government finance; agriculture, fisheries and food; production; banking and insurance and service industry.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: AA

DO NOT EDIT THIS DATASET. This dataset, which is automatically updated contains Bureau of Labor Statistics data. This dataset is updated by a Socrata process; please contact support@socrata.com if you encounter any questions or issues.

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).

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).

The CMS Program Statistics - Medicare Physician, Non-Physician Practitioner and Supplier tables provide use and payment data for physicians, other practitioners, limited-licensed practitioners, and durable medical equipment, prosthetic, and orthotic (DMEPOS) suppliers.

For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page.

These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data.

Below is the list of tables:

MDCR PHYSSUPP 1. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization, Program Payments, Cost Sharing, and Balance Billing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR PHYSSUPP 2. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization, Program Payments, Cost Sharing, and Balance Billing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR PHYSSUPP 3. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization, Program Payments, Cost Sharing, and Balance Billing for Original Medicare Beneficiaries, by Area of Residence MDCR PHYSSUPP 4. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization, Program Payments, and Balance Billing for Original Medicare Beneficiaries, by Type of Service MDCR PHYSSUPP 5. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization, Program Payments, and Balance Billing for Original Medicare Beneficiaries, by Place of Service MDCR PHYSSUPP 6. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization, Program Payments, and Balance Billing for Original Medicare Beneficiaries, by Physician Specialty MDCR PHYSSUPP 7. Medicare Physicians, Non-Physician Practitioners, and Suppliers: Utilization and Program Payments for Original Medicare Beneficiaries, by Berenson-Eggers Type of Service (BETOS) Classification

Introduction

PayPal Statistics: Paypal is a multinational American company focusing on online payments and money transfers. It was developed to serve as an alternative to traditional cash payments and money orders. The company has evolved to become a popular payment platform. As we go forward, we will learn about PayPal Statistics to garner a better understanding of relevant statistical data and gain essential information about the factors that have led to the growth of this company altogether. By the end of this, people can learn about the development of the online payment business.

List of footnotes, notes, and source information for NHIS Adult Summary Statistics. Each row of this dataset contains the accompanying text for a footnote found in the NHIS Adults Summary Statistics Dataset.

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

Google Fit Statistics: Google Fit, since its launch in 2014, formed the major platform of fitness and health for Google, enabling users to track several health metrics and pool data from several fitness apps and devices. In its continued evolution were added unique features like Heart Points, developed under the auspices of WHO and AHA, aimed at inducing physical activity.

Changes of much significance are due in 2024, marking a change in Google's very own approach to health data-keeping. In this article, we will enclose the Google Fit statistics.

The data contain records of defendants in criminal cases filed in United States District Court during fiscal year 2007. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables, or "SAF variables," that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 4.1-4.5 and 5.1-5.6. Variables containing information (e.g., name, Social Security number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

Statistics on Capital Markets Services Licence holders by Core Activity

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

ST: All Education Staff Compensation: Primary: % of Total Expenditure in Primary Public Institutions data was reported at 86.389 % in 2014. This records an increase from the previous number of 62.697 % for 2013. ST: All Education Staff Compensation: Primary: % of Total Expenditure in Primary Public Institutions data is updated yearly, averaging 62.697 % from Dec 2012 (Median) to 2014, with 3 observations. The data reached an all-time high of 86.389 % in 2014 and a record low of 44.452 % in 2012. ST: All Education Staff Compensation: Primary: % of Total Expenditure in Primary Public Institutions data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sao Tome and Principe – Table ST.World Bank: Education Statistics. All staff (teacher and non-teachers) compensation is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programs, and other allowances and benefits.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

This statistical release makes available the most recent monthly data on NHS-funded maternity services in England, using data submitted to the Maternity Services Data Set (MSDS). This is the latest report from the newest version of the data set, MSDS.v.2, which has been in place since April 2019. The new data set was a significant change which added support for key policy initiatives such as continuity of carer, as well as increased flexibility through the introduction of new clinical coding. This was a major change, so data quality and coverage initially reduced from the levels seen in earlier publications. MSDS.v.2 data completeness improved over time, and we are looking at ways of supporting further improvements. This publication also includes the National Maternity Dashboard, which can be accessed via the link below. Data derived from SNOMED codes is used in some measures such as those for birthweight, and others will follow in later publications. SNOMED data is also included in some of the published Clinical Quality Improvement Metrics (CQIMs), where rules have been applied to ensure measure rates are calculated only where data quality is high enough. System suppliers are at different stages of development and delivery to trusts. In some cases, this has limited the aspects of data that can be submitted in the MSDS. Since last month, this publication contains a new Clinical Quality Improvement Metric (CQIM) called CQIMReadmissions. This new metric reports the number of babies born in hospital then discharged home, who were then readmitted to hospital while still under 30 days old. This is supported by five new data quality metrics to ensure we only publish CQIMReadmissions figures where the underlying data is of sufficient completeness and quality. The new data quality metrics are CQIMDQ46 to CQIMDQ50. Further information about this new readmissions metric can found in this publication’s Data Quality Statement. This new data can be found in the Measures file available for download and in the CQIM and CQIM+ pages in the National Maternity Dashboard, and further information on the new metrics can be found in the accompanying Metadata file. To help Trusts understand to what extent they met the Clinical Negligence Scheme for Trusts (CNST) Maternity Incentive Scheme (MIS) Data Quality Criteria for Safety Action 2, we have been producing a CNST Scorecard Dashboard showing trust performance against this criteria. This dashboard has been updated following the release of CNST Y6 criteria, and can be accessed via the link below. The percentages presented in this report are based on rounded figures and therefore may not total to 100%.

This dataset reflects is for the Individual Shelter & Rescue Statistics that were reported in 2018 for the 2017 Calendar year. Although PACFA requires this data to be submitted and takes all care possible to ensure the validity of this data, we do not control, and therefore guarantee, the complete accuracy, completeness and availability of data. PACFA believes this information to be within ± 4% margin of error. The CDA-PACFA is not responsible for any issues that may arise from the use of this data.

IE: Adjusted Net Enrollment Rate: Primary: % of Primary School Age Children data was reported at 99.678 % in 2015. This records an increase from the previous number of 99.236 % for 2014. IE: Adjusted Net Enrollment Rate: Primary: % of Primary School Age Children data is updated yearly, averaging 94.999 % from Dec 1971 (Median) to 2015, with 42 observations. The data reached an all-time high of 99.942 % in 2007 and a record low of 84.722 % in 1986. IE: Adjusted Net Enrollment Rate: Primary: % of Primary School Age Children data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ireland – Table IE.World Bank.WDI: Education Statistics. Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).