How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 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 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 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.

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.

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

As of the third quarter of 2024, internet users spent six hours and 38 minutes online daily. This is a slight increase in comparison to the previous quarter. Overall, between the third quarter of 2015 and the third quarter of 2024, the average daily internet use has increased by 19 minutes. Most online countries Internet users between 16 and 64 years old in South Africa spent the longest time online daily, nine hours and 27 minutes, followed by Brazil and the Philippines. These figures include the time spent using the internet on any device. In Japan, internet users spent around three hours and 57 minutes online per day. Users in Denmark also spent relatively less time on the internet, reaching about five hours daily. Most common online activities According to a 2024 survey, more than six in 10 people worldwide used the internet to find information. Furthermore, the usage of communication platforms was also a common reason for going online, followed by online content consumption, such as watching videos, TV shows, or movies.

Consumer Spending in the United States increased to 16445.70 USD Billion in the second quarter of 2025 from 16345.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Average daily time spent by adults on activities including paid work, unpaid household work, unpaid care, travel and entertainment. These are official statistics in development.

As of the third quarter of 2024, female users between 16 and 24 years were the demographic group that spent the most time online, using the internet for around seven hours and 35 minutes daily. Male users, of the same age group, spent seven hours and 11 minutes daily online. Among the less active demographic groups were internet users aged 65 and older, who reported spending, on average, up to four hours online daily.

The dataset consists of 5,000 customer records, capturing various aspects of their demographic, behavioral, and transactional information. It includes details such as customer age, gender, subscription length, region, and payment method. Additionally, the dataset tracks customer interactions, such as the number of support tickets raised and satisfaction scores, as well as financial data like monthly spend and discounts offered. The "Last_Activity" field indicates how recently a customer has engaged with the service. Finally, the dataset includes a churn indicator, showing whether a customer has ended their subscription. Some variables, like Age and Satisfaction_Score, contain missing values. This dataset provides valuable insights into customer behavior and can be used for analyzing churn patterns, satisfaction levels, and spending trends.

The dataset contains information about 5,000 customers and includes the following 12 variables:

Customer_ID: A unique identifier for each customer (object type). Age: The age of the customer (float64), with some missing values. Gender: The gender of the customer (object type). Subscription_Length: The length of time (in months) the customer has been subscribed (int64). Region: The region where the customer is located (object type). Payment_Method: The method used by the customer to make payments (object type). Support_Tickets_Raised: The number of support tickets raised by the customer (int64). Satisfaction_Score: A score indicating customer satisfaction (float64), with some missing values. Discount_Offered: The discount offered to the customer (float64). Last_Activity: The number of days since the customer last interacted with the service (int64). Monthly_Spend: The amount spent by the customer per month (float64). Churned: Indicates whether the customer has churned (1 = yes, 0 = no) (int64).

During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis.

              Social media: trust and consumption

              Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media.

              What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis.
              Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers.
              Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

This is a dataset from a 16ha paddock containing 20 Nelore breed animals with brachiaria Decumbens. The dataset contains weight and supplementation data for each animal and for a specific period of time (15 periods in total) as well as Hyperspectral data and environmental data for each period.

During 12 months, every 12-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (amount per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each period of days, the dates (START_DATE and FINAL_DATE) and day range (AMOUNT_DAYS) was registered, as well as the animal identification (ANIMAL), its weight at the start and end of the period (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (GMD), average daily supplementaion (SMD), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically for the time, attendance and days data, a couple more specific filter were applied to them in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

For the paddock, hyperspectral data was also acquired. The data was collected using Google Earth Engine API, based on Sentinel-2 hyperspectral images. Twenty sentinel-2 bands (B01, B02, B03, B04, B05, B06, B07, 8A, B09, B11, and B12) were acquired, and eight well-known spectral indices (NDVI, NDWI, EVI, LAI, DVI, GCI, GEMI, and SAVI) were calculated and integrated into the dataset.

Environmental data was acquired using a weather API (Open-Meteo). The climate data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

Aineistossa selvitetään 15-74-vuotiaiden suomalaisten rahapelaamista ja ongelmapelaamisen laajuutta, sekä niihin liittyviä tekijöitä. Kyselyn on toteuttanut Veikkaus Oy pyrkimyksenään tuottaa ajantasaista tietoa rahapelaamisen tuottamien terveyshaittojen laajuudesta, sekä arvioida suunnittelemiensa vastuullisuustoimien vaikuttavuutta. Aluksi kysyttiin, olivatko vastaajat pelanneet elämänsä aikana jotain rahapeliä. Myöntävästi vastanneilta kysyttiin, olivatko he pelanneet jotain rahapeliä viimeisen 12 kuukauden aikana. Tämän jälkeen kartoitettiin, pelattiinko rahapelejä internetissä, ja pelattiinko kotimaisten vai ulkoimaisten palveluntarjoajien pelejä. Lisäksi tiedusteltiin syitä ulkomaisten rahapelien pelaamiseen. Seuraavaksi kysyttiin, kuinka usein erilaisia rahapelejä, kuten nettipokeria, arvontapelejä ja vedonlyöntipelejä pelattiin, ja pyydettiin arvioimaan kuinka paljon rahaa rahapeleihin käytettiin keskimäärin yhden viikon aikana. Kyselyn loppuosa käsitteli vastaajien suhdetta rahapelaamiseen. Kartoitettiin, kuinka usein hävittyjä rahoja yritettiin voittaa takaisin, ja oliko omasta menestyksestä rahapelaamisessa valehdeltu muille ihmisille. Lisäksi kysyttiin, oliko pelejä pelattu enemmän kuin oli alkuperin ollut aikomus, ja oliko pelaaminen aiheuttanut syyllisyydentunteita. Kysyttiin myös, oliko rahapelaamiseen liittynyt lopettamisen vaikeutta, poissaoloja töistä tai opiskelusta, pelaamisen piilottelua läheisiltä, tai kiistelyä rahankäytöstä läheisten kanssa. Rahankäyttöön liittyen kysyttiin myös, oltiinko rahapelien pelaamista varten lainattu rahaa läheisiltä, pankilta tai muilta luottolaitoksilta, myyty omaa tai perheen omaisuutta tai otettu pikavippejä. Lisäksi kysyttiin, koettiinko oma rahapelaaminen ongelmalliseksi, ja oliko kukaan ulkopuolinen puuttunut pelaamiseen. Lopuksi pyydettiin kertomaan, millaisten pelien pelaamisen koettiin aiheuttavan ongelmia pelaamisen hallinnan kanssa. Tutkimuksessa on käytetty Suomen oloihin sovitettua, ongelmapelaamista kuvaavaa South Oaks Gambling Screen (SOGS-R)-mittaria. Taustamuuttujina aineistossa ovat ikäryhmä, sukupuoli, tilastollinen kuntaryhmä, maakunta, suuralue, koulutusaste, pääasiallinen toiminta ja keskimääräiset kuukausitulot. The survey studied gambling behaviour and problem gambling in Finland. The study was conducted by the state-owned gambling monopoly company Veikkaus Oy to provide up-to-date information on gambling habits and the extent of problems caused by gambling. Many questions used in the survey were based on the South Oaks Gambling Screen (SOGS-R), with some adaptations to Finnish circumstances. First, the respondents were asked whether they had ever gambled, whether they had gambled in the past 12 months and whether they had used Veikkaus or foreign gambling company games. Those who had gambled on foreign websites were asked which service providers they had used and why they had used foreign ones. Next questions charted how often the respondents did certain types of gambling (lottery, online poker, slot machines, gambling onsite in casinos, scratch cards etc.) Problems caused by gambling during the past 12 months were investigated by asking how much money the respondents usually spent on gambling a week, how often they went back another day to win back money lost, claimed to others to be winning money when were in fact losing, or gambled more than they had intended. Other questions explored whether the respondents had felt guilty when gambling, had wanted to stop gambling but hadn't thought they could, had hidden betting slips, documents related to debts and other signs of gambling from important people in their life. Financial consequences were studied with questions focusing on arguments with others over the way the respondents handled money, had these arguments been centered on gambling, had the respondents borrowed money from someone and not paid back because of gambling, had they taken time from work or study to gamble, and had they borrowed or acquired money to gamble or pay gambling debts and from whom or where. Finally, the respondents were asked which types of gambling they felt might cause them problems in managing their gambling. Background variables included the respondent's gender, age group, education level, average monthly income, socio-economic status, degree of urbanisation of the municipality of residence, region (NUTS3) and major region.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - October, 2023).

Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset.

Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).

Suomalaisten rahapelaaminen 2011 -kyselyssä selvitettiin 15-74 -vuotiaiden suomalaisten (n=4484) rahapelaamista, pelaamisen määrää, siihen käytettyjä rahamääriä, mielipiteitä rahapelaamisesta ja ongelmapelaamista. Puhelinhaastattelun ensimmäinen osio käsitteli rahapelejä yleisesti. Aluksi selvitettiin, mitä rahapelejä vastaaja oli pelannut viimeisen 12 kuukauden ja koko elämänsä aikana. Samoin tiedusteltiin, mitä pelejä vastaaja oli pelannut internetissä. Edelleen selvitettiin, kuinka usein vastaaja pelasi pelejä, sekä kuinka paljon aikaa ja rahaa hän oli niihin käyttänyt viimeisen 30 päivän aikana. Vastaajat arvioivat, kuinka paljon rahaa he käyttivät tavallisesti peleihin viikossa, mikä oli heidän suurin voittonsa viimeisen vuoden aikana, minkä ikäisenä vastaaja oli pelannut ensimmäisen kerran ja mitä peliä hän oli tuolloin pelannut. Seuraava osio käsitteli rahapelaamista koskevia mielipiteitä. Vastaajilta tiedusteltiin muun muassa, oliko rahapelien pelaaminen vakava ongelma Suomessa, olivatko pelaamisongelmat heidän mielestään lisääntyneet, oliko valtion ohjaama monopoli hyvä tapa rajoittaa pelaamisen haittoja, ja oliko 18 vuoden ikäraja pelaamiselle hyvä keino ongelmien vähentämiselle. Lisäksi otettiin kantaa väitteisiin kuten, ihmisillä pitäisi olla oikeus pelata rahapelejä milloin vain he haluavat, rahapelaamiseen pitäisi kannustaa ja rahapelien pelaaminen on vaaraksi perhe-elämälle. Kolmas osio käsitteli internetin käyttöä ja muuta kuin rahapelaamista. Vastaajilta tiedusteltiin, oliko heillä internetyhteys, kuinka monta tuntia he olivat viimeisen 7 päivän aikana käyttäneet internetiä muihin kuin työasioihin, pelasivatko he video- tai tietokonepelejä, ja kuinka monta tuntia he olivat pelanneet viimeisen viikon ja kuukauden aikana. Seuraavaksi käsiteltiin suhdetta rahapelaamiseen. Vastaajilta tiedusteltiin esimerkiksi, kuinka usein vastaaja yritti voittaa häviämänsä rahat takaisin jonain toisena päivänä, ja oliko hän väittänyt muille voittaneensa, vaikka oli todellisuudessa hävinnyt. Lisäksi tiedusteltiin, oliko vastaaja tuntenut syyllisyyttä rahapelejä pelatessaan, oliko hän halunnut lopettaa pelaamisen, mutta ei ole uskonut kykenevänsä siihen, oliko hän kiistellyt rahankäytöstä läheistensä kanssa ja oliko hän esimerkiksi menettänyt työ- tai opiskelupaikkansa rahapelaamisen vuoksi. Vastaajilta tiedusteltiin keneltä tai mistä hän oli lainannut rahaa pelaamista varten tai pelivelkojen maksamiseen. Samoin tiedusteltiin, oliko vastaaja pelannut suuremmilla summilla kuin hänellä olisi ollut varaa hävitä, kuinka usein hänestä oli tuntunut, että rahapelaaminen oli hänelle ongelma, kuinka usein hänelle oli sanottu, että pelaaminen oli hänelle ongelma, oliko hän etsinyt pelaamiseen apua, mistä hän oli etsinyt apua, ja oliko vastaajan omaisilla ollut ongelmia rahapelaamisessa. Valtaosa tämän osion kysymyksistä käsitteli tilannetta viimeisien 12 kuukauden ajalta. Viimeisessä osiossa käsiteltiin terveyttä ja hyvinvointia. Vastaajat arvioivat nykyistä terveydentilaansa, sekä kuinka usein olivat tunteneet viimeisien neljän viikon aikana itsensä hermostuneeksi, tyyneksi, alakuloiseksi ja onnelliseksi. Seuraavaksi kysyttiin, oliko vastaajalla ollut viimeisen 12 kuukauden aikana vähintään kahden viikon jaksoja, jolloin hän oli ollut mieli maassa, alakuloinen tai masentunut, menettänyt kiinnostuksensa harrastuksiin, työhön tai muihin asioihin, joista yleensä koki mielihyvää. Lisäksi selvitettiin vastaajien tupakointia ja alkoholin käyttöä. Taustatietoina kerättiin sukupuoli, syntymävuosi, siviilisääty, kuinka monta vuotta vastaaja oli opiskellut, paljonko tällä oli kuukausituloja, ja mitä vastaaja teki päätoimisesti. The survey charted Finnish gambling habits, frequency of gambling, amount of money gambled and views on problem gambling. The term gambling is used here as an umbrella term for lotteries, slot machines, betting, bookmaking, the pools, roulette wheels, and card and dice tables as well as online variations of all of these. The first section of the survey focused on gambling games in general. The respondents were presented with a list of various games (e.g. lotto games and scratchcards of Veikkaus, the National Lottery of Finland, games of chance in a casino and slot machines of Finland's Slot Machine Association, RAY) and asked whether they had played them during the past 12 months or before. Other questions charted online gambling, the gambling websites visited, frequency of gambling activities, and money and time spent on gambling in the previous 30 days. The respondents were asked to estimate the average weekly sum spent on gambling, the largest win in the previous 12 months, their age and the game played when they gambled for the first time. The second section covered perceptions on gambling. The respondents were asked whether they thought gambling was a problem in Finland, whether the problems associated with gambling had increased or decreased and if the government monopoly and the age limit of 18 were effective ways of limiting problem gambling. The respondents were asked to what extent they agreed with statements relating to gambling, such as "people should have the right to gamble whenever they want" and "gambling is detrimental to family life." In the third section, the respondents' Internet use and habits of playing non-gambling games were charted. Questions covered whether they had an Internet connection, how many hours not relating to work they had spent on the Internet in the previous 7 days, whether they played video games and how many hours they had played them in the previous week and month. The respondents' relation to gambling was examined. They were asked how often they returned another day to try to win back the money they had lost, whether they had claimed to be winning while gambling even though they were actually losing money, whether they had gambled more than they intended to, and whether people had criticised their gambling or told them they had a gambling problem. Some questions explored whether the respondents had felt guilty while gambling, whether they had wanted to stop betting money or gambling but could not do so, and whether they had hidden their gambling from their family members. Some questions covered arguments with the people the respondents lived with over how the respondents handled money and whether those arguments had centred on their gambling. Other topics included whether the respondents had borrowed from someone and not paid them back as a result of their gambling, whether they had lost time from work or school due to betting or gambling, and whether they had borrowed or acquired money to gamble or to pay gambling debts. Finally, opinions were probed on whether the respondents themselves gambled or had gambled too much, whether they had gambled money borrowed for other purposes and whether they had tried to seek help for gambling addiction. Most of the questions in this section focused on the circumstances in the previous 12 months. The final section pertained to health and welfare. The respondents were asked to estimate their current status of health and how often they had felt nervous, calm, despondent and happy in the previous four weeks. They were also asked if they had, in the previous 12 months, had periods during which they had been discouraged, sad or depressed, or lost their interest in things that they usually found pleasing. Smoking and alcohol use were charted. Background variables included the respondent's gender, year of birth, marital status, number of years studied, monthly net income and employment status.

A dataset of vehicle MPG ratings and fuel cost calculations based on manufacturer, model, and fuel type.

Lasten ja nuorten vapaa-aika 2016 haastattelututkimuksessa kerättiin tietoja lasten ja nuorten vapaa-ajasta, median käyttötavoista ja liikunnasta. Tutkimus on jatkoa aiemmin Nuorten vapaa-aikatutkimuksen nimellä kulkeneelle tutkimukselle, jonka nimi päivitettiin Lasten ja nuorten vapaa-aikatutkimukseksi kohderyhmän alaikärajan laskemisen vuoksi. Aluksi esitettiin kysymyksiä lapsen tai nuoren vanhemmille tai huoltajille. Kysymykset käsittelivät kotitalouden rakennetta, sisarusten lukumäärää ja ikää sekä ketä kotitalouteen kuuluu. Tässä yhteydessä kysyttiin myös vanhempien tai huoltajien koulutustasoa. Seuraavaksi kysymykset käsittelivät lapsen tai nuoren median käyttöä ja liikuntaa. Näihin kysymyksiin vastasivat alle 15-vuotiaiden vanhemmat tai huoltajat ja yli 15-vuotiaat itse. Tämän jälkeen lapset tai nuoret vastasivat kysymyksiin itse. Kysymykset käsittelivät vapaa-ajan määrää, harrastuksia, seurassa, kerhossa tai yhdistyksissä toimimista ja median käyttöä. Kysyttiin myös, mitä erilaisia laitteita lapsilla tai nuorilla oli käytössään vapaa-ajalla, kuinka usein he näitä käyttivät ja mihin tarkoituksiin. Lisäksi kysyttiin, millaisiin eri tarkoituksiin internetia käytettiin. Seuraavaksi kysymykset käsittelivät yhteydenpitoa ja yhteisöllisyyttä. Kysyttiin ystävien määrää, missä ystäviin on tutustuttu sekä median käytöstä yhteydenpitoon sukulaisten ja ystävien kanssa. Kysyttiin myös, kuinka usein vastaajat tapasivat ystäviään kasvokkain. Liikuntaa käsittelevissä kysymyksissä kysyttiin kaikesta vapaa-ajalla tapahtuvasta liikunnasta, ei pelkästään erilaisista urheilulajeista. Kysyttiin, harrastaako vastaaja mitä tahansa liikuntaa ja kuinka usein sekä mitä liikuntamuotoja harrastetaan ja olisiko vastaajalla joku mieluisa liikuntalaji, mitä hän haluaisi harrastaa. Tiedusteltiin myös syitä olla harrastamatta liikuntaa. Lopuksi vastaajia pyydettiin arvioimaan tyytyväisyyttään elämän eri osa-alueisiin. Taustamuuttujina olivat muun muassa ikä luokiteltuna, maakunta, sukupuoli, kieli, perhemuoto, vanhempien tai huoltajien koulutustaso, kotitalouden rakenne, asuinpaikan tyyppi, opiskelutilanne, työssä käynti ja korkein suoritettu tutkinto. The survey studied the leisure time activities of Finnish children and young people aged from seven to 29. Data collection is carried out every three years. In 2016 the survey was renamed as the Children and Youth Leisure Survey because the minimum age limit for participants was lowered. The parents/guardians of the respondents between 7 and 14 years of age were first asked how much time the child spent using media (including social media, computer games, books, the internet etc.) and exercising. Respondents over the age of 15 answered these questions themselves. The respondents were asked how much leisure time they had and whether they had any hobbies. Participation in organized leisure time groups, such as sports teams or scouts, was investigated. The use of media was measured by questions investigating the importance of different media and how often children used these media. Children under the age of 15 were also asked which devices they could use during their leisure time. Furthermore, Internet use was charted with questions about the things done online, and how often children did these things. The respondents were also asked about the device they used the most to access the Internet. Questions about media use continued by investigating the importance of different uses of media. The respondents evaluated their own use of media and compared it with people of the same age. Media use within the respondent's family and media as a hobby were also studied. Next set of questions dealt with relationships to other people. The respondents were asked how many friends they had, whether they had friends they had first met online, whether they felt they were a part of some group in social media, how often they used media to interact with friends and family, and how often they met face-to-face with their friends. The respondents' physical activity was charted by asking how often they exercised in their leisure time, if they would have liked to take part in a certain kind of exercise, if they played sports as a member of a sports club, and how often they exercised in the sports club. Reasons for not exercising were investigated. The respondents were also asked how much money they would be willing to spend on exercising. Questions on whether the respondents exercised alone or with friends, and whether their physical activity was comprised mainly of sports, or everyday physical activities were asked. Finally, the respondents were asked to evaluate their satisfaction on different areas of their life. These areas included leisure time spent on media, overall leisure time, human relationships, health, physical fitness, appearance, financial situation, and life in general. Background variables included the respondent's age, gender, mother tongue, household composition, number and ages of household members, education level of mother and father, financial situation, and whether they identified as a member of a minority. For those aged 15-29, further background variables included the type of educational institution, employment status, economic activity and education.

This dataset was found online at the Association of Religious Data Archives (ARDA) website. http://www.thearda.com/ . This data set shows information on religous groups throughout the United States. All data was uploaded as a polypoint centroids per county in the United States, in shapefile format. This Data set shows the Total congregations, Total Adherents, and Rate of Adherence per 1000 population for All religions in the United States and for the Mainline Religions.

This dataset displays the amount of hydroelectric power that was consumed on a nation level. The dataset covers the time period spanning from 1980 to 2005. Data is available for 200+ countries. This data is scalled at: Billion Kilowatt hours. Data references:Energy Information Administration International Energy Annual 2005 Table Posted: September 11, 2007 Next Update: June 2008 This data is available directly at: http://www.eia.doe.gov/fuelrenewable.html Access Date: November 8, 2007.

This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - October, 2023).

Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).

Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))

Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset.

Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG).