This dataset tracks annual total students amount from 2009 to 2023 for Academy For Social Action-a College Board School

This dataset tracks annual total classroom teachers amount from 2009 to 2023 for Academy For Social Action-a College Board School

This dataset tracks annual total students amount from 2009 to 2023 for Cornerstone Academy For Social Action

In 2019, everyday retail brands such as Forever21 or Fashion Nova generated a total of 1.7 total social media actions. Luxury retail brands generated the second-most user engagement on social media with 416 million actions. Online retail or big box retail had the smallest social media engagement, earning only 74.6 million social media actions in the year.

Whereas the amount of social media content posted by U.S. publishers has declined by eight percent in 2019, user engagement per post has increased. Overall, U.S. content publishers in the family genre a 50 percent increase on cross-platform social media actions per post. The finance and sporting industry performed best in terms of overall user actions, increasing overall audience engagement by 35 percent each.

Global Total Internal R&D Personnel in Human Health and Social Work Activities by Country, 2023 Discover more data with ReportLinker!

Global Total Researchers in Human Health and Social Work Activities by Country, 2023 Discover more data with ReportLinker!

This dataset tracks annual total classroom teachers amount from 2009 to 2023 for Cornerstone Academy For Social Action

How many people use social media? Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

Who uses social media? Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

What are the most popular social media platforms? Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Israel Total Wages: sa: Human Health and Social Work Activities data was reported at 3,896.983 ILS mn in Jul 2018. This records an increase from the previous number of 3,852.497 ILS mn for Jun 2018. Israel Total Wages: sa: Human Health and Social Work Activities data is updated monthly, averaging 3,219.200 ILS mn from Jan 2012 (Median) to Jul 2018, with 79 observations. The data reached an all-time high of 3,896.983 ILS mn in Jul 2018 and a record low of 2,598.600 ILS mn in Jan 2012. Israel Total Wages: sa: Human Health and Social Work Activities data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G033: Total Wages.

Credits: Jambi: Total: Industrial: Health and social work activities data was reported at 147.702 IDR bn in Jan 2025. This records a decrease from the previous number of 150.405 IDR bn for Dec 2024. Credits: Jambi: Total: Industrial: Health and social work activities data is updated monthly, averaging 133.695 IDR bn from Jun 2020 (Median) to Jan 2025, with 56 observations. The data reached an all-time high of 150.405 IDR bn in Dec 2024 and a record low of 93.187 IDR bn in Mar 2021. Credits: Jambi: Total: Industrial: Health and social work activities data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Banking Sector – Table ID.KBC008: Credits: by Industry: by Province.

Abstract copyright UK Data Service and data collection copyright owner.

The National Youth Social Action Survey was established in 2014 to measure the extent to which 10 to 20 year olds are taking part in social action in the UK. The survey was commissioned by the Cabinet Office in 2014 and 2015 and by the Department for Digital, Culture, Media and Sport (DCMS) in 2016 to 2019, and conducted by Ipsos Mori on behalf of the #iwill campaign.

The #iwill campaign, launched in 2013, aims to close the socio-economic gap in social action participation amongst 10 to 20 year olds while increasing overall participation. For the purposes of the campaign, social action is defined as 'practical action in the service of others to create positive change' and covers a wide range of activities that help other people or the environment, such as fundraising, campaigning, tutoring/mentoring and giving time to charity.

The survey captures a yearly snapshot of the beliefs, attitudes and behaviours linked with social action of a representative sample of over 2,000 young people in the UK. The same methodology has been used each year to allow comparisons over time. Data are weighted by age within gender, region and the family socio-economic status to reflect the known profile of the UK population.

The National Youth Social Action Survey, 2018 is the fifth survey in the series and is also known as Wave 5.

Main Topics:

Young people were asked about their participation in social action (e.g. taking part in fundraising; campaigning for causes), as well as the frequency and duration of participation, their motivations for participating or not participating, and the ways in which they participated.

This data set consists of multi-modal temporal team behaviors as well as learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore evolution of log actions, speech behavior, affective states, and gaze patterns for students to model constructs such as engagement, motivation, collaboration, etc. in educational settings.

In this data set, team level data is collected from 34 teams of two (68 children) where the children are aged between 9 and 12. There are two files:

PE-HRI_learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:

• last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.

• T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available.

• T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.

• T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity

PE-HRI_behavioral_timeseries.csv: In this file, for each team, the interaction of around 20-25 minutes is organized in windows of 10 seconds; hence, we have a total of 5048 windows of 10 seconds each. We report team level log actions, speech behavior, affective states, and gaze patterns for each window. More specifically, within each window, 26 features are generated in two ways:

1. non-incremental
2. incremental

A non-incremental type would mean the value of a feature in that particular time window while an incremental type would mean the value of a feature until that particular time window. The incremental type is indicated by an "_inc" at the end of the feature name. Hence, in the end, within each window, we have 52 values:

• T_add/(_inc): The number of times a team added an edge on the map in that window/(until that window).

• T_remove/(_inc): The number of times a team removed an edge from the map in that window/(until that window).

• T_ratio_add_rem/(_inc): The ratio of addition of edges over deletion of edges by a team in that window/(until that window).

• T_action/(_inc): The total number of actions taken by a team (add, delete, submit, presses on the screen) in that window/(until that window).

• T_hist/(_inc): The number of times a team opened the sub-window with history of their previous solutions in that window/(until that window).

• T_help/(_inc): The number of times a team opened the instructions manual in that window/(until that window). Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.

• T1_T1_rem/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I add an edge, I then delete it in that window/(until that window).

• T1_T1_add/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I delete an edge, I add it back in that window/(until that window).

• T1_T2_rem/(_inc): The number of times the members of the team followed the pattern consecutively: I add an edge, you then delete it in that window/(until that window).

• T1_T2_add/(_inc): The number of times the members of the team followed the pattern consecutively: I delete an edge, you add it back in that window/(until that window).

• redundant_exist/(_inc): The number of times the team had redundant edges in their map in that window/(until that window).

• positive_valence/(_inc): The average value of positive valence for the team in that window/(until that window).

• negative_valence/(_inc): The average value of negative valence for the team in that window/(until that window).

• difference_in_valence/(_inc): The difference of the average value of positive and negative valence for the team in that window/(until that window).

• arousal/(_inc): The average value of arousal for the team in that window/(until that window).

• gaze_at_partner/(_inc): The average of the the two team member's gaze when looking at their partner in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).

• gaze_at_robot/(_inc): The average of the the two team member's gaze when looking at the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).

• gaze_other/(_inc): The average of the the two team member's gaze when looking in the direction opposite to the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).

• gaze_at_screen_left/(_inc): The average of the the two team member's gaze when looking at the left side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).

• gaze_at_screen_right/(_inc): The average of the the two team member's gaze when looking at the right side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).

• T_speech_activity/(_inc): The average of the two team member's speech activity in that window/(until that window). Each individual member's speech activity is calculated as a percentage of time that they are speaking in that window/(until that window).

• T_silence/(_inc): The average of the two team member's silence in that window/(until that window). Each individual member's silence is calculated as a percentage of time in that window/(until that window).

• T_short_pauses/(_inc): The average of the two team member's short pauses over their speech activity in that window/(until that window). Each individual member's short pause refers to a brief pause of 0.15 seconds and is calculated as a percentage of time in that window/(until that window).

• T_long_pauses/(_inc): The average of the two team members long pauses over their speech activity in that window/(until that window). Each individual member's long pause refers to a pause of 1.5 seconds and is calculated as a percentage of time in that window/(until that window).

• T_overlap/(_inc): The average percentage of time the speech of the team members overlaps in that window/(until that window).

• T_overlap_to_speech_ratio/(_inc): The ratio of the speech overlap over the speech activity of the team in that window/(until that window).

Apart from these 52 values, within each window, we also indicate:

• team: The team to which the window belongs to.
• time_in_secs: Time in seconds until that window.
• window: The window number.
• normalized_time: The time when this window occurred with respect to the total duration of the task for a particular team.

Lastly, we briefly elaborate on how the features are operationalised. We extract log behaviors from the recorded rosbags while the behaviors related to both gaze and affective states are computed through the open source library OpenFace [6] that returns both facial actions units (AUs) as well as gaze angles. For voice activity detection (VAD), that classifies if a piece of audio is voiced or unvoiced, we made use of the python wrapper for the open source Google WebRTC VAD. The literature that inspired our log, audio and video features as well as the tools used to extract them are described in more detail in [3,4]. However, in those papers, we make use of only the aggregate version of this data [5].

This is the total gross value added (GVA) generated from human health and social work activities in the area calculated using a balanced approach to GVA.These figures are presented in £ millions at current basic prices. They do not allow for different regional price levels or changes in prices over time (inflation). Balanced GVA estimates are produced by combining the existing income and production GVA estimates using weighted quality metrics.

Income GVA estimates are calculated by adding up the income generated by individuals or corporations in the production of goods and services whilst the Production Approach estimates GVA by calculating the total output of goods and services less the value of goods and services used up in the production process. A Balanced approach evaluates the strengths and weaknesses of these two opposing approaches and gives them an appropriate weighting in informing a single ‘balanced’ estimate of GVA.

GVA estimates are on a workplace basis, that being they are allocated to where the economic activity took place. Please note, figures can be rather volatile. If you see erratic movements in the time series, you should use caution in interpreting the data.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

In 2023, over 56 percent of the time spent on social media platforms for users in the United States was spent on social video activities. This represents an increase from the previous year, when the time spent engaging with social video among U.S. users was of around 53.3 percent, compared to 46.7 percent of the total social media time spent on other social network activities.

Customs records of are available for YOUNG POWER IN SOCIAL ACTION YPSA.Learn about its suppliers,trading situations,countries of origin of products and trading ports

Forecast: Total Internal R&D Personnel in Human Health and Social Work Activities in Italy 2024 - 2028 Discover more data with ReportLinker!

This dataset tracks annual overall school rank from 2010 to 2018 for Academy For Social Action-a College Board School

The survey on the social action of municipalities and intercommunalities (ASCO) carried out by DREES provides in-depth information on the whole of the social action implemented by the French municipalities and intercommunalities in 2014. Municipalities are bound by few legal obligations in the social field, but they can set up many so-called “optional” social actions for their citizens. This communal social action can be carried out both by the communal services or by the Communal Centres for Social Action (CCAS) and can also be entrusted to an inter-community (a public institution of inter-communal cooperation (EPCI)). Communal social action can cover many areas. It is aimed at the elderly, people with disabilities; it also covers the fight against poverty and exclusion, housing or housing policies, early childhood, youth and family policies, occupational integration, access to care and health prevention. Other areas may depend on social action: transport, urban planning, sport, environment, culture, etc. The main topics covered are the types of services set up in the municipalities, the establishments they manage (institutions for the elderly, disabled people, young children, etc.), the areas of action and the public targeted by communal social benefits, the arrangements for granting aid and the distribution of these actions between the municipalities’ services and their communal social action centre (CCAS), but also the transfer of these powers to the EPCIs and a possible intercommunal centre for social action (CIAS). Also mentioned are the reports of municipalities to departments and their other partners (in particular social security funds).

This statistic displays the total turnover of social work activities without accommodation for the elderly and disabled from 2008 to 2022, in the United Kingdom (UK). The graphic shows that in the year 2022, the total turnover was nearly 7.6 billion British pounds.