This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

This statistic shows the opinions of human resources (HR) practitioners by if online recruiting is faster at employee recruitment, as of January 2017. During the survey, 47.3 percent of the respondents agreed that online recruiting brings them candidates faster.

Introduction

Reality TV Statistics: Many people guilty pleasure themselves with reality TV shows. Some watch them for the drama or to escape their issues, while others feel a real connection with the stars. No matter the reason, reality TV is very popular, and many know about famous figures like Kim Kardashian. Plus, reality TV has a strong influence. We’ve gathered key reality TV statistics, such as the percentage of reality TV shows and the money reality stars earn.

Surprisingly, reality TV has been around since 1948, even though it became popular in the 2000s. This will help you see how common reality TV is, what people think about it, and its impact on society. It took a lot of effort, but it's worthwhile.

Investigator(s): Bureau of Justice Statistics Data in this collection examine the processing of federal offenders. The Cases Terminated files (Parts 1-3 and 25-28) contain information about defendants in criminal cases filed in the United States Federal District Court and terminated in the calendar year indicated. Defendants in criminal cases may either be individuals or corporations, and there is one record for each defendant in each case terminated. Information on court proceedings, date the case was filed, date the case was terminated, most serious charge, and reason for termination are included. The Docket and Reporting System files (Parts 4-7 and 31-34) include information on suspects in investigative matters that took an hour or more of a United States Attorney's time with one of the following outcomes: (1) the United States Attorney declined to prosecute, (2) the case was filed in Federal District Court, or (3) the matter was disposed by a United States magistrate. Codes for each disposition and change of status are also provided.The Pretrial Services data (Parts 8 and 22) present variables on the circuit, district, and office where the defendant was charged, type of action, year of birth and sex of the defendant, major offense charge, and results of initial and detention hearings. The Parole Decisions data (Part 9) contain information from various parole hearings such as court date, appeal action, reopening decision, sentence, severity, offense, and race and ethnicity of the defendant. The Offenders Under Supervision files (Parts 15-16 and 37-40) focus on convicted offenders sentenced to probation supervision and federal prisoners released to parole supervision. The Federal Prisoner files (Parts 18 and 20) supply data on when an offender entered and was released from confinement, as well as the amount of time served for any given offense.Years Produced: Annually.NACJD has prepared a resource guide for the Federal Justice Statistics Program.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

Twitter user statistics show a varying degree of how often users login to the platform. Here’s what it looks like.

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 51.59% of total population being female. 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.

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 Show Low is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Show Low 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 Show Low Population by Gender. You can refer the same here

Investigator(s): United States. Bureau of Justice Statistics Produces annual national- and state-level data on the number of prisoners in state and federal prison facilities. Aggregate data are collected on race and sex of prison inmates, inmates held in private facilities and local jails, system capacity, noncitizens, and persons age 17 or younger. Findings are released in the Prisoners series and the Corrections Statistical Analysis Tool (CSAT) - Prisoners. Data are from the 50 states departments of correction, the Federal Bureau of Prisons, and until 2001, from the District of Columbia (after 2001, felons sentenced under the District of Columbia criminal code were housed in federal facilities).

These Minecraft user statistics show just how big Minecraft has become and why it’s still so popular today.

The Dataset series contains historical versions of the data set “Statistic Units Base Circuits” up to and including 1.1.2022 and does not contain the current version. Latest and current version is available in the entry “Statistic units basic circuits” and can be found here: https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway. Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB). The Dataset series contains historical versions of the data set “Statistic Units Base Circuits” up to and including 1.1.2022 and does not contain the current version. Latest and current version is available in the entry “Statistic units basic circuits” and can be found here:https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway. Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB). Latest and current version is available in the entry “Statistic units basic circuits” and can be found here:https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway. Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB).The Dataset series contains historical versions of the data set “Statistic Units Base Circuits” up to and including 1.1.2022 and does not contain the current version. Latest and current version is available in the entry “Statistic units basic circuits” and can be found here: https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway. Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB).The Dataset series contains historical versions of the data set “Statistic Units Base Circuits” up to and including 1.1.2022 and does not contain the current version. Latest and current version is available in the entry “Statistic units basic circuits” and can be found here: https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway.Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB). https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway.Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB).https://kartkatalog.geonorge.no/metadata/statistiske-enheter-grunnkretser/51d279f8-e2be-4f5e-9f72-1a53f7535ec1 The data set shows the basic district division in Norway. Basic circuits are stable statistical units that were established in cooperation between the municipality and Statistics Norway (SSB).

The data contain records of sentenced offenders released from the custody of the Bureau of Prisons (BOP) during fiscal year 1996. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. Records of offenders who exit federal prison temporarily, such as for transit to another location, to serve a weekend sentence, or for health care, are not included in the exiting cohort. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database, as well as "SAF" variables that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 7.9-7.16. Variables containing identifying 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.

This dataset is about book series. It has 1 row and is filtered where the books is Statistics : a guide for therapists. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. \r \r The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public. \r \r The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose. \r \r Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.\r \r The Crime Statistics Agency publishes location type data for all offences where a location type was recorded by Victoria Police. There are three main location types; Residential, Community and Other. These types are further broken down into Subdivisions which show an intermediate level of information, and further into Groups which show a finer level of detail\r \r Data Classification - https://www.crimestatistics.vic.gov.au/about-the-data/classifications \r \r Glossary and Data Dictionary - https://www.crimestatistics.vic.gov.au/about-the-data/glossary-and-data-dictionary\r

The data contain records of sentenced offenders committed to the custody of the Bureau of Prisons (BOP) during fiscal year 2007. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database, as well as "SAF" variables that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 7.9-7.16. Variables containing identifying 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.

Introduction

Stack Overflow Statistics: The 2024 Stack Overflow Developer Survey offers a comprehensive snapshot of the global developer community, compiling insights from 65,437 respondents across 185 countries. Conducted between May 19 and June 20, 2024, the survey had a median completion time of approximately 21 minutes.

A significant 76% of developers reported using or planning to use AI tools in their development processes, marking an increase from 70% in 2023. However, trust in AI tool accuracy remains divided, with only 43% expressing confidence in their outputs. Despite this, 81% of developers identified increased productivity as the primary benefit of integrating AI tools into their workflows.

Educational backgrounds among respondents show that 66% hold a Bachelor's or Master's degree, even though only 49% learned to code through formal education.

Geographically, the United States accounted for 18.9% of respondents, followed by Germany at 8.4% and India at 7.2%, highlighting the survey's extensive international reach.

This year's survey underscores the evolving landscape of software development, emphasizing the growing integration of AI tools, the shift towards self-directed learning, and the diverse global composition of the developer community.

This article will highlight the Stack Overflow statistics and its performance.

These tables show discovery statistics for NEAs and comets discovered by NASA's WISE mission - now renamed to NEOWISE. The first small table shows the number of NEAs, PHAs (a sub-group of NEAs), and comets discovered to-date (within a day or two). The second table shows each object discovered, sorted by designation, with selected parameters describing the object's orbit. http://neo.jpl.nasa.gov/stats/wise/

Benefit Payments: Single Employer Program (SE) data was reported at 5,486,182.000 USD th in 2015. This records an increase from the previous number of 5,436,297.000 USD th for 2014. Benefit Payments: Single Employer Program (SE) data is updated yearly, averaging 3,883,565.000 USD th from Sep 1996 (Median) to 2015, with 20 observations. The data reached an all-time high of 5,486,182.000 USD th in 2015 and a record low of 790,500.000 USD th in 1996. Benefit Payments: Single Employer Program (SE) data remains active status in CEIC and is reported by Pension Benefit Guaranty Corporation. The data is categorized under Global Database’s USA – Table US.G079: Single Employer Program Statistics.

The CMS Program Statistics - Medicare Inpatient Hospital tables provide use and payment data for all inpatient hospitals, including short-stay hospitals, critical access hospitals, long term care hospitals, inpatient psychiatric facilities, inpatient rehabilitation facilities, religious nonmedical health care institutions, children’s hospitals, and other hospitals.

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 INPT HOSP 1. All Medicare Inpatient Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR INPT HOSP 2. All Medicare Inpatient Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR INPT HOSP 3. All Medicare Inpatient Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR INPT HOSP 4. All Medicare Inpatient Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Hospital MDCR INPT HOSP 5. Medicare IPPS Short Stay Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR INPT HOSP 6. Medicare IPPS Short Stay Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment MDCR INPT HOSP 7. Medicare IPPS Short Stay Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR INPT HOSP 8. Medicare IPPS Short Stay Hospitals: Utilization and Program Payments for Original Medicare Beneficiaries, by Type of Entitlement and Total Days of Care MDCR INPT HOSP 9. Medicare IPPS Short Stay Hospitals: Utilization and Program Payments for Original Medicare Beneficiaries, by Location and Bedsize of Hospitals, by Medical School Affiliation, and Type of Control MDCR INPT HOSP 10. Special-Category Hospitals: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Hospital

In 2024, the Korean drama Marry My Husband was the most streamed TV show on Amazon Prime Video in Indonesia, with an index score of 100. This was followed by an Indonesian drama series Induk Gajah, with an index score of **.