How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when individuals in states and territories were subject to executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require or recommend people stay in their homes. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from the publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require or recommend individuals stay at home found by the CDC, COVID-19 Community Intervention and At-Risk Task Force, Monitoring and Evaluation Team & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 15, 2020 through May 31, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. These data do not include mandatory business closures, curfews, or limitations on public or private gatherings. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our mobility statistics program.

The "Trips by Distance" data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

These data are made available under a public domain license. Data should be attributed to the "Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland and the United States Bureau of Transportation Statistics."

Daily data for a given week will be uploaded to the BTS website within 9-10 days of the end of the week in question (e.g., data for Sunday September 17-Saturday September 23 would be updated on Tuesday, October 3). All BTS visualizations and tables that rely on these data will update at approximately 10am ET on days when new data are received, processed, and uploaded.

The methodology used to develop these data can be found at: https://rosap.ntl.bts.gov/view/dot/67520.

IH156 - All persons aged 15 and over, time spent walking on a typical day. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).All persons aged 15 and over, time spent walking on a typical day...

This dataset contains information about the total numbers of hours and minutes per day spent on unpaid work, disaggregated by sex in 1997 and 2006. (a) Person aged 15 years and over. (b) Data based …Show full descriptionThis dataset contains information about the total numbers of hours and minutes per day spent on unpaid work, disaggregated by sex in 1997 and 2006. (a) Person aged 15 years and over. (b) Data based on person's primary activity. For more information on definition of primary activity see the ABS Work and Family Balance glossary. (c) Some differences between 1997 and 2006 may partially be due to coding changes in 2006 rather than actual changes. For further information see Explanatory Notes in How Australians Use Their Time, 2006 (cat. no. 4153.0). (d) Aggregated time for primary activity averaged across all persons. (e) For definition of unpaid work see Work and Family Balance glossary. Source: ABS How Australians Use Their Time, (cat. no. 4153.0); ABS data available on request, Time Use Survey.

Analysis of ‘IH157 - All persons aged 15 and over, time spent walking on a typical day’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/6a5821f7-2b12-4038-acde-1bdbbc189852 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

All persons aged 15 and over, time spent walking on a typical day

--- Original source retains full ownership of the source dataset ---

State, territorial, and county executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance.

Data were collected to determine when individuals in states, territories, and counties were subject to executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require or recommend people stay in their homes.

These data are derived from the publicly available state, territorial, and county executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require or recommend individuals stay at home found by the CDC, COVID-19 Community Intervention and At-Risk Task Force, Monitoring and Evaluation Team & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 15 through May 5, 2020. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. These data do not include mandatory business closures, curfews, or limitations on public or private gatherings. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Daily average time and proportion of day spent on various activities, by age group and gender, 15 years and over, Canada, Geographical region of Canada, province or territory, 2022.

The online overview offers comprehensive metadata on the EVS datasets and variables.

The variable overview of the four EVS waves 1981, 1990, 1999/2000, and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

This overview can be found at: Online Variable Overview.

Moral, religious, societal, political, work, and family values of Europeans.

Themes: Feeling of happiness; state of health; ever felt: very excited or interested, restless, proud, lonely, pleased, bored, depressed, upset because of criticism; when at home: feeling relaxed, anxious, happy, aggressive, secure; respect and love for parents; important child qualities: good manners, politeness and neatness, independance, hard work, honesty, felling of responsibility, patience, imaginantion, tolerance, leadership, self-control, saving money, determination perseverance, religious faith, unselfishness, obedience, loyalty; attitude towards abortion; way of spending leisure time: alone, with family, with friends, in a lively place; frequency of political discussions; opinion leader; volentary engagement in: welfare service for elderly, education, labour unions, polititcal parties, human rights, environment, professional associations, youth work, consumer groups; dislike being with people with different ideas; will to help; characterisation of neighbourhood: people with a ciminal record, of a different race, heavy drinkers, emotionally unstable people, immigrants or foreign workers, left-wing or right-wing extremists, people with large families, students, unmarried mothers, members of minority religious sects or cults; general confidence; young people trust in older people and vice versa; satisfaction with life; freedom of choice and control; satisfaction with financial situation of the household; financial situation in 12 months; important values at work: good pay, not too much pressure, job security, a respected job, good hours, opportunity to use initiative, generous holidays, responsibility, interesting job, a job that meets one´s abilities, pleasant people, chances for promotion, useful job for society, meeting people; look forward to work after weekend; pride in one´s work; exploitation at work; job satisfaction; freedom of decision taking in job; behaviour at paid free days: find extra work, use spare time to study, spend time with family and friends, find additional work to avoid boredom, use spare time for voluntary work, spend time on hobbies, run own business, relaxing; fair payment; preferred management type; attitude towards following instructions at work; satisfaction with home life; sharing attitudes with partner and parents: towards religion, moral standards, social attitudes, polititcal views, sexual attitudes; ideal number of children; child needs a home with father and mother; a woman has to have children to be fulfilled; sex cannot entirely be left to individual choice; marriage as an out-dated institution; woman as a single parent; enjoy sexual freedom; important values for a successful marriage: faithfulness, adequate income, same social background, respect and appreciation, religious beliefs, good housing, agreement on politics, understanding and tolerance, apart from in-laws, happy sexual relationship, sharing household chores, children, taste and interests in common; accepted reasons for divorce; main aim of imprisonment; willingness to fight for the own country; fear of war; expected future changes of values; opinion about scientific advances; interest in politics; political action: signing a petition, joining in boycotts, attending lawful demonstrations, joining unofficial strikes, occupying buildings or factories, damaging things and personal violence; prefence for freedom or equality; self-positioning on a left-right scale; basic kinds of attitudes concerning society; confidence in institutions: churches, armed forces, education system, the press, labour unions, the police, parliament, the civil services, major companies and the justice system; living day to day because of uncertain future; party preference and identification; regularly reading of a daily newspaper; frequency of TV watching; opinion on terrorism; thinking about meaning and purpose of life; feeling that life is meaningless; thoughts about dead; good and evil in everyone; regret having done something; worth risking life for: country, anoth...

All persons aged 15 and over, time spent walking on a typical day

All persons aged 15 and over, time spent walking on a typical day by Disability status, Year and Statistic

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VBA EDUCATION BENEFITS PROGRAM to provide educational assistance to persons entering the Armed Forces after December 31, 1976, and before July 1, 1985; to assist persons in obtaining an education they might otherwise not be able to afford; and to promote and assist the all volunteer military program of the United States by attracting qualified persons to serve in the Armed Forces. The participant must have entered on active duty on or after January 1, 1977, and before July 1, 1985, and either served on active duty for more than 180 continuous days receiving an other than dishonorable discharge, or have been discharged after January, 1, 1977 because of a service-connected disability. Also eligible are participants who serve for more than 180 days and who continue on active duty and have completed their first period of obligated service (or 6 years of active duty, whichever comes first). Participants must also have satisfactorily contributed to the program. (Satisfactory contribution consists of monthly deduction of $25 to $100 from military pay, up to a maximum of $2,700, for deposit in a special training fund.) Participants may make lump-sum contributions. No individuals on active duty in the Armed Forces may initially begin contributing to this program after March 31, 1987.

Percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission: indirectly standardised by age, sex, method of admission and diagnosis/procedure. The indicator is broken down into the following demographic groups for reporting: ● All years and female only, male only and both male and female (persons). ● <16 years and female only, male only and both male and female (persons). ● 16+ years and female only, male only and both male and female (persons) ● 16-74 years and female only, male only and both male and female (persons) ● 75+ years and female only, male only and both male and female (persons) Results for each of these groups are also split by the following geographical and demographic breakdowns: ● Local authority of residence. ● Region. ● Area classification. ● NHS and private providers. ● NHS England regions. ● Deprivation (Index of Multiple Deprivation (IMD) Quintiles, 2019). ● Sustainability and Transformation Partnerships (STP) & Integrated Care Boards (ICB) from 2016/17. ● Clinical Commissioning Groups (CCG) & sub-Integrated Care Boards (sub-ICB). All annual trends are indirectly standardised against 2013/14.

📊 Sleep Cycle & Productivity Dataset Overview

This dataset tracks sleep habits and their impact on productivity, mood, and stress levels. It includes 5000 records covering multiple individuals across different ages and lifestyles.

📌 Dataset Details

🔍 Key Insights from the Dataset

• Helps analyze the relationship between sleep duration and productivity.
• Examines how exercise, caffeine, and screen time affect sleep quality.
• Identifies patterns in stress levels and mood based on sleep habits.
• Useful for data analysis, machine learning models, and health research.

Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv

Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.

This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.

Abstract copyright UK Data Service and data collection copyright owner.The main aim of the United Kingdom Day Visits Survey, the Great Britain Day Visits Survey (GBDVS), and latterly the England Leisure Visits Survey (ELVS), is to measure the extent of participation in day visits, and to estimate the scale and value of visits taken. In particular the principal investigators are interested in the extent of participation in different kinds of day trips, how frequently particular types of trip are undertaken, and associated expenditure. The survey also seeks to provide information on a number of other trip details, such as activities undertaken, areas visited, time spent at the main destination, modes of transport, distance travelled, number of people involved and the trip party composition. Respondents to the survey are generally asked to recall trips taken within the past two weeks. The 1998 survey covered only home-based trips (i.e. trips made from home for leisure activities, which start and finish on the same day), and did not cover business trips or holiday-based trips. Main Topics: The dataset provides a record of all trips from home in the last two weeks to: a town, seaside, countryside, wood/forest, water (with) boats, or water (without) boats. Details are asked for up to seven of the most recent trips undertaken by respondents within the last two weeks. If a seaside, wood/forest, water (with) boats or water (without) boats trip has not been undertaken in the past two weeks, respondents are asked to recall the most recent trip of this type in the last twelve months and to give details of this trip instead. If a town or countryside trip has not taken place in the last two weeks, respondents are not asked in detail about this trip. Instead they are only asked to recall the last time a trip of this type occurred and the frequency of this trip type in the last year. Standard Measures Occupational coding of the chief income earner was carried out using the 1991 Standard Occupational Classification (SOC) published by OPCS. This was used to derive a social grade classification on the basis of the Market Research Society (MRS) Dictionary of Occupations. Multi-stage stratified random sample Face-to-face interview CAPI

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

This repository contains 523 human feedback messages sent to daily smokers and vapers who were preparing to quit smoking/vaping with a virtual coach.

Study

Daily smokers and vapers recruited through the online crowdsourcing platform Prolific interacted with the text-based virtual coach Kai in up to five sessions between 1 February and 19 March 2024. The sessions were 3-5 days apart. In each session, participants were assigned a new preparatory activity for quitting smoking (e.g., listing reasons for quitting smoking, envisioning one's desired future self after quitting smoking, doing a breathing exercise). Between sessions, participants had a 20% chance of receiving a feedback message from one of two human coaches. More information on the study can be found in the Open Science Framework (OSF) pre-registration: https://doi.org/10.17605/OSF.IO/78CNR. The implementation of the virtual coach Kai can be found here: https://doi.org/10.5281/zenodo.11102861.

Feedback messages

All feedback messages were written by one of two Master's students in psychology. The two human coaches were directed to craft messages incorporating feedback, argument, and either a suggestion or reinforcement. They were also instructed to connect with individuals by referencing aspects of their lives, express empathy toward those with low confidence, and provide reinforcement when people were motivated.

When writing the feedback, the human coaches had access to data on people's baseline smoking and physical activity behavior (i.e., smoking/vaping frequency, weekly exercise amount, existence of previous quit attempts of at least 24 hours, and the number of such quit attempts in the last year), introduction texts from the first session with the virtual coach, previous preparatory activity (i.e., activity formulation, effort spent on the activity and experience with it, return likelihood), current state (i.e., self-efficacy, perceived importance of preparing for quitting, human feedback appreciation), and new activity formulation. Notably, the human coaches only had access to anonymized versions of the introduction texts and activity experience responses (e.g., names were removed). Except for the free-text responses describing participants' experiences with their previous activity and their introduction texts, all of this information is provided together with the feedback messages. For the previous and new activities, we just provide the titles and not also the entire formulations that the human coaches had access to.

Before sending the messages to participants on Prolific, we added a greeting (i.e., "Best wishes, Karina & Goda on behalf of the Perfect Fit Smoking Cessation Team"), a disclaimer that the messages were not medical advice, and a link to confirm having read the message at the end. We also added "This is your feedback message from your human coaches Karina and Goda for preparing to quit [smoking/vaping]:" at the start of the message.

The human coaches approved publishing these feedback messages.

Additional data from the study

Additional data from the study such as participants' free-text descriptions of their experiences with their activities and their introductions from the first session with the virtual coach will also be published and linked to the OSF pre-registration of the study.

In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).

Average time use of persons 20-64 years by day, activity, sex, table content and year, divided

The ultimate goal of HIV treatment is to achieve viral suppression, which means the amount of HIV in the body is very low or undetectable. This is important for people with HIV to stay healthy, have improved quality of life, and live longer. People living with HIV who maintain viral suppression have effectively no risk of passing HIV to others. Texas DSHS is the source of this data. Diagnosed- received a diagnosis of HIV Linked to care-visited an HIV heath care provider within 1 month (30 days) after learning they were HIV positive Received- or were retained in care** received medical care for HIV infection Viral suppression- their HIV “viral load” – the amount of HIV in the blood – was at a very low level.