In 2023, adults in the United States spent more time reading on weekends than weekdays, according to recent data. The average time spent reading in the U.S. amounted to **** hours (almost ** minutes) on weekends and holidays, while daily time spent reading on weekdays in 2023 dropped back to pre-pandemic levels at a ******* of an hour.

The statistic shows data on share of parents who read aloud to their child 5-7 days a week in the United States in 2018, by child's age. According to the source, 52 percent of parents with a child under the age of two read to their child aloud five to seven times a week.

The statistic presents data on the daily time children spent reading in the United States in 2013, by platform. It was found that a two to ten year old spent nearly half an hour reading print books daily.

Female Color Morph CountsThe file reports the counts in female color morphs of damselflies (I. elegans) in 12 ponds of southern Sweden, for 12 consecutive years (2000 to 2011). The number of females of each morph (A, I, and O) is reported in three distinct tables (years as lines, populations as columns). The file is formatted such that it can be read by our statistical program.data.datfinal_scriptsThis ZIP file contains all scripts and code to reproduce the statistical analyses of our paper. Programs R and ADMB should be installed on the computer. Scripts were run on a Linux/Ubuntu operating system. Directory 'analysis': scripts to generate the figures (there could be minor differences compared to the published version). Directory 'data' contains the data (same file as already published on Dryad). Directory 'model' contains the ADMB code to run the model (should be compiled with ADMB). Directory 'suppl' contains the material necessary to run the supplementary analyses, including simulat...

This table contains data from the December release of Canadian Business Counts for 2007 until the latest complete year. The data includes the year, 2-digit North American Industry Classification System (NAICS) code, and a count of the number of businesses by number of employees. The table data shows the number of businesses categorized by the number of employees they have. Please ensure you read the notes provided below, as there is very important information on classification and comparability. NotesStatistics Canada advises users not to use these data as a time series. Further, the counts may reflect some of the business openings and closures caused by the COVID-19 pandemic, although they will not be fully represented as the evolving resumption or permanent closure of businesses may not yet be fully processed and confirmed by Statistics Canada's Business Register (The Daily — Canadian business counts, December 2021 (statcan.gc.ca)).Changes in methodology or in business industrial classification strategies used by Statistics Canada's Business Register can create increases or decreases in the number of active businesses reported in the data on Canadian business patterns. As a result, these data do not represent changes in the business population over time. Statistics Canada recommends users not to use these data as a time series. Beginning in December 2014, there were several important changes that were made:

The data appear in two separate series, one covering locations with employees, the other covering locations without employees. The second series corresponds to locations previously coded to the employment category called "indeterminate." A new North American Industrial Classification System (NAICS) category has been added to include locations that have not yet received a NAICS code: unclassified. It represents an additional 78,718 locations with employees and 313,107 locations without employees. The second series, locations without employees, also includes locations that were not previously included in tables but that meet the criteria used to define the Business Register coverage. The impact of the change will be the inclusion of approximately 600,000 additional locations.

Before 2014, the following notes apply:

The establishments in the "Indeterminate" category do not maintain an employee payroll, but may have a workforce which consists of contracted workers, family members or business owners. However, the Business Register does not have this information available, and has therefore assigned the establishments to an "Indeterminate" category. This category also includes employers who did not have employees in the last 12 months. Please note that the employment size ranges are based on data derived from payroll remittances. As such, it should be viewed solely as a business stratification variable. Its primary purpose is to improve the efficiency of samples selected to conduct statistical surveys. It should not be used in any manner to compile industry employment estimates. Employment, grouped in employment size ranges, is more often than not an estimation of the annual maximum number of employees. For example, a measure of "10 employees" could represent "10 full-time employees", "20 part-time employees" or any other combination.For more information refer to Statistics Canada's Definitions and Concepts used in Business Register.

Publication changes: Please read the section on 'Notes on changes to publications' within the PDF report as this highlights changes to data currently published and potentially future reports. This report shows monthly numbers of NHS Hospital and Community Health Service (HCHS) staff groups working in Trusts and CCGs in England (excluding primary care staff). Data is available as headcount and full-time equivalents. This data is an accurate summary of the validated data extracted from the NHS's HR and Payroll system. In addition to the regular monthly reports there are a series of quarterly reports which include statistics on staff in Trusts and CCGs and information for NHS Support Organisations and Central Bodies. The quarterly analysis is published each September (June data), December (September data), March (December data) and June (March data). Additional healthcare workforce data relating to GPs and the Independent Healthcare Provider workforce are also available via the Related Links below. This publication of April 2020 data features a supplementary file which shows trends in HCHS workforce data observed during the NHS response to the Covid-19 pandemic. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678

Data gathered in August 2019 showed that 59 percent of surveyed U.S. adults spent less than one hour per week watching, listening to, or reading about sports. Conversely, a small share of respondents (seven percent) engaged with sports media for 14 hours or more each week.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in West Reading. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In West Reading, the median income for all workers aged 15 years and older, regardless of work hours, was $46,465 for males and $35,761 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 23% between the median incomes of males and females in West Reading. With women, regardless of work hours, earning 77 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in theborough of West Reading.

- Full-time workers, aged 15 years and older: In West Reading, among full-time, year-round workers aged 15 years and older, males earned a median income of $60,566, while females earned $51,650, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the borough of West Reading.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in West Reading.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 West Reading median household income by race. You can refer the same here

Armenia AM: Literacy Rate: Adult Male: % of Males Aged 15 and Above data was reported at 100.000 % in 2020. This stayed constant from the previous number of 100.000 % for 2017. Armenia AM: Literacy Rate: Adult Male: % of Males Aged 15 and Above data is updated yearly, averaging 100.000 % from Dec 1989 (Median) to 2020, with 6 observations. The data reached an all-time high of 100.000 % in 2020 and a record low of 99.000 % in 1989. Armenia AM: Literacy Rate: Adult Male: % of Males Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in West Reading. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In West Reading, the median income for all workers aged 15 years and older, regardless of work hours, was $42,760 for males and $28,053 for females.

These income figures highlight a substantial gender-based income gap in West Reading. Women, regardless of work hours, earn 66 cents for each dollar earned by men. This significant gender pay gap, approximately 34%, underscores concerning gender-based income inequality in the borough of West Reading.

- Full-time workers, aged 15 years and older: In West Reading, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,538, while females earned $36,867, leading to a 30% gender pay gap among full-time workers. This illustrates that women earn 70 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in West Reading.

https://i.neilsberg.com/ch/west-reading-pa-income-by-gender.jpeg" alt="West Reading, PA gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 West Reading median household income by gender. You can refer the same here

Guyana GY: Literacy Rate: Adult Female: % of Females Aged 15 and Above data was reported at 85.035 % in 2014. This records a decrease from the previous number of 87.252 % for 2009. Guyana GY: Literacy Rate: Adult Female: % of Females Aged 15 and Above data is updated yearly, averaging 86.143 % from Dec 2009 (Median) to 2014, with 2 observations. The data reached an all-time high of 87.252 % in 2009 and a record low of 85.035 % in 2014. Guyana GY: Literacy Rate: Adult Female: % of Females Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guyana – Table GY.World Bank: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; 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).

Descriptive statistics of sentence-level predictors (n = 160; all measures summed per sentence).

Live feed of current travel time along key corridors on York Region's regional road network. Data is read from the Bluetooth Travel Time System maintained by the Traffic Signal Operations Division, Transportation Services. This feed generates an XML file every 5 minutes consisting of the live traffic statistics. Data is for travel time on Regional roads only, data for local municipalities is not included.Select "Open" on this dataset to view the standard XML link.Field Definitions are available: Travel Time Feed - Field Definitions

Georgia GE: Literacy Rate: Adult: % of People Aged 15 and Above data was reported at 99.586 % in 2014. This records a decrease from the previous number of 99.652 % for 2002. Georgia GE: Literacy Rate: Adult: % of People Aged 15 and Above data is updated yearly, averaging 99.619 % from Dec 2002 (Median) to 2014, with 2 observations. The data reached an all-time high of 99.652 % in 2002 and a record low of 99.586 % in 2014. Georgia GE: Literacy Rate: Adult: % of People Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank.WDI: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; 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).

The Get Data Out programme from the National Disease Registration Service publishes detailed statistics about small groups of cancer patients in a way that ensures patient anonymity is maintained. The Get Data Out programme currently covers 15 cancer sites. This data release updates the incidence data for all 15 sites to cover 2013-2020 (previous data covered 2013-2019) and also adds new cancer sites ‘Liver and biliary tract’, 'Haematological malignancies' and 'Haematological malignancy transformations'. The 18 cancer sites now covered by Get Data Out are: ‘Bladder, Urethra, Renal Pelvis and Ureter’, ‘Bone cancer’, ‘Brain, meningeal and other primary CNS tumours’, ‘Eye cancer’, 'Haematological malignancies', 'Haematological malignancy transformations', ‘Head and neck’, ‘Kaposi sarcoma’, ‘Kidney’, 'Liver and Biliary tract', ‘Oesophageal and Stomach’, ‘Ovary, fallopian tube and primary peritoneal carcinomas’, ‘Pancreas’, ‘Prostate’, ‘Sarcoma’, ‘Skin tumours’, ‘Soft tissue and peripheral nerve cancer’, ‘Testicular tumours including post-pubertal teratomas’. Anonymisation standards are designed into the data by aggregation at the outset. Patients diagnosed with a certain type of tumour are divided into many smaller groups, each of which contains approximately 100 patients with the same characteristics. These groups are aimed to be clinically meaningful and differ across cancer sites. For each group of patients, Get Data Out routinely publish statistics about incidence, routes to diagnosis, treatments and survival. All releases and documentation are available on the Get Data Out main technical page. Before using the data, we recommend that you read the guide for first time users. The data is available in an open format for anyone to access and use. We hope that by releasing anonymous detailed data like this we can help researchers, the public and patients themselves discover more about cancer. If you have feedback or any other queries about Get Data Out, please email us at NDRSenquires@nhs.net and mention 'Get Data Out' in your email.

The "https://www.bls.gov/tus/" Target="_blank">American Time Use Survey (ATUS) is the nation's first federally administered, continuous survey on time use in the United States. The goal of the survey is to measure how people divide their time among life's activities. In the ATUS, individuals are randomly selected from a subset of households that have completed their eighth and final month of interviews for the "https://www.census.gov/programs-surveys/cps.html" Target="_blank">Current Population Survey (CPS). ATUS respondents are interviewed only one time about how they spent their time on the previous day, where they were and whom they were with. The survey is sponsored by the "https://www.bls.gov/tus/home.htm" Target="_blank">Bureau of Labor Statistics and is conducted by the "https://www.census.gov/" Target="_blank">U.S. Census Bureau. The data file available for download from the ARDA combines three files from the 2010 ATUS: the Respondent file, the Activity summary file and the Well-Being Module. Variables from the 2010 Well-Being Module have names that begin with the letter 'W.'

Note: The Bureau of Labor Statistics has reported that there was an error in the activity selection process for the 2010 Well-Being Module. Due to a programming error in the data collection software, certain activities were less likely than others to be selected for follow-up questions in the WB Module. As of October 2013, the Bureau of Labor Statistics and the Census Bureau were exploring ways to mitigate the error; more information on this error could be found at the following link: "https://www.bls.gov/tus/wbnotice.htm" Target="_blank">https://www.bls.gov/tus/wbnotice.htm.

The Office of Gun Violence Prevention (OGVP) shares real-time gun violence data to increase government transparency, improve the public's awareness, and support community-based gun violence prevention and reduction partners. All District crime data is available through Crime Cards. The dashboards below focus on gun violence only. The data in these dashboards is updated daily at 7:40AM with the incidents from the day before. View data covering 7-Day Look-back of Gun Violence and Year-to-date Gun Violence.All statistics presented here are based on preliminary DC criminal code offense definitions. The data do not represent official statistics submitted to the FBI under the Uniform Crime Reporting program (UCR) or National Incident Based Reporting System (NIBRS). All preliminary offenses are coded based on DC criminal code and not the FBI offense classifications. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. The MPD does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The MPD will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. Read complete data notes at buildingblocks.dc.gov/data.

This resource contains a Jupyter Notebook that is used to introduce hydrologic data analysis and conservation laws. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) calculate the residence time of water in land and rivers for the global hydrologic cycle; (2) quantify the relative and absolute uncertainties in components of the water balance; (3) navigate public websites and databases, extract key watershed attributes, and perform basic hydrologic data analysis for a watershed of interest; (4) assess, compare, and interpret hydrologic trends in the context of a specific watershed.

Please note that in problems 3-8, the user is asked to use an R package (i.e., dataRetrieval) and select a U.S. Geological Survey (USGS) streamflow gage to retrieve streamflow data and then apply the hydrological data analysis to the watershed of interest. We acknowledge that the material relies on USGS data that are only available within the U.S. If running for other watersheds of interest outside the U.S. or wishing to work with other datasets, the user must take some further steps and develop codes to prepare the streamflow dataset. Once a streamflow time series dataset is obtained for an international catchment of interest, the user would need to read that file into the workspace before working through subsequent analyses.