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.

This is a public release of Beiwe-generated data. The Beiwe Research Platform collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found here. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe.

As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".

How preschool teachers and children spend their time in preschool is important for children's engagement and learning. The study aims to give a broad description of how often children and staff spend time in different types of activities, interactions, and environments. Systematic momentary observations of individual children and teachers/staff were conducted continuously during a full day in 78 preschool units (mainly for children 3-5 years) during the autumn term. The observations resulted in frequency data for different types of activities for children and teachers. Frequency data were summarized at the unit level, and percentage distributions of activities were calculated.

Results showed that free play indoors was the main activity setting, followed by free play outdoors. Children interacted as much with other children as with teachers. The focus was dominated by non-pretend play, construction, art and music, followed by pretend play and academic contents. Child engagement was significantly higher in free play indoors compared to outdoors. Teachers engaged in varied tasks, but their central task was managing. Teachers were typically in proximity to small groups of children, or by themselves, and mostly talked to or listened to a single child.

Data was collected with systematic observations with the help of manual-based instruments Child Observation in Preschool (COP) and Teacher Observation in Preschool (TOP). The observations consists of snapshots of individual children/teachers across a preschool day. Several aspects of the individual's current activity are coded. Individual data was aggregated to preschool unit level, and proportions for different activity aspects were calculated. Aggregated frequency and proportionate data are available in the data set for child and teacher data, respectively. Some preschool unit background information is also available.

This dataset is released to encourage the study of ATLAS file transfers in the Worldwide LHC Computing Grid environment, to better understand the transfer processes in this particularly heterogeneous environment.

Joaquin Bogado (UNLP)

Mario Lassnig (CERN)

Fernando Monticelli (UNLP)

Thomas Beermann (University of Wuppertal)

Javier Díaz (UNLP)

2020-11-27

jbogado @ linti.unlp.edu.ar

Motivation

This dataset is released to encourage the study of ATLAS file transfers in the Worldwide LHC Computing Grid[1] environment, to better understand the transfer processes in this particularly heterogeneous environment.

Rucio[2] is a Distributed Data Management system. Data from the Rucio ATLAS instance from June and July 2019 was retrieved and summarized in the present dataset. The Rucio ATLAS instance is responsible to keep track of the files of the ATLAS Experiment[3] at CERN. These files are stored all around the world in 100+ data centers. In order to work with the files, physicists around the world need to move them across sites. Rucio delegates the file transfer to another subsystem called FTS[4]. The rules in Rucio are groups of file transfers that are done as a unit, i.e.: a physicist may need a set of files to do an analysis, then they create a rule specifying which files need to be moved to where, and when the rule is done the analysis can start.

If the Rule Time To Complete (RTTC) can be predicted with certain accuracy, this will allow the Rucio system and the ATLAS Experiment to schedule the transfers in a smarter way, eventually helping to optimize the resources the experiment has to do more and faster science.

State of the art

The metric used to calculate the accuracy is the Fraction of Good Predictions (FoGP). Formally, the FoGP is defined as in the equation that follows

FoGP(y, y, τ) = 1/n i = 1ng(yi, yi, τ)

Where y is the vector of observations, y is the vector of predictions, g is a function that returns 1 if the relative error | yi - yi | / yi < τ , and 0 otherwise. For a group of predictions we have that FoGP(y, y, τ = 0.1) = 0.5. This means that 50% of the predictions have less than 10% of relative error. This easy to understand metric allows to compare models directly, independently from their implementation, and only focus on the predictions the model made.

We estimate a FoGP(τ = 0.1) > 0.95 for a model to be useful. However, there are no known models that can predict the RTTC at the rule creation time with such a high accuracy. Models with FoGP(τ = 0.1) ~= 0.5 could be useful to give feedback to the users about how much time the transfers will take. Best models known have a FoGP(τ = 0.1) = 0.14.

Fields description

account

The hashed account name from the user that issued the transfer. This data has been anonymized and does not represent the real name of the user in the system.

state

The final state of the transfer. 'D' means the transfer is done, 'F' means the transfer has failed. Other states represent internal states from Rucio and are not important. Very few transfers showed other states than D or F.

activity

The activity of the transfer. It's related to the priority of the transfers inside the system. Priorities are based on shares and related to the 'share' field. As transfers requests are queued in Rucio and in FTS, transfers are picked to be served with a probability equal to its share among all the transfers that are in the queue at that time.

SIZE

The size in bytes of the file to be transferred.

src_rse/dst_rse

The source/destination Rucio Storage Element (RSE). An RSE is a logical unit inside Rucio that represents a dedicated storage location of a data center. Usually there are more than one physical machine. Rucio doesn't know how many storage nodes compose an RSE, so this is the minimum logical unit of storage for the system. Both fields have been anonymized.

id

The unique identifier of a transfer request. If a transfer needs to be retried, the next attempt will have a different id.

previous_attempt_id

If the transfer request is a retry, the id of the previous attempt is filled in. Otherwise, this field is empty.

retry_count

This is the number of times a transfer has been retried. If it is the first attempt, the field is 0.

rule_id

This is the id of the rule the transfer belongs to. All the transfers in the same rule share the same rule_id.

external_host

This is the hash of the FTS server that will trigger the actual transfer of files between the files. There are several FTS servers and some are shared with other Experiments outside ATLAS. It is known that the server with hash fe1d4db902b6271 is used by ATLAS Experiment exclusively, so this can be a good place to start.

RTIME

This is the time in seconds the transfer spends in the Rucio System, since it is created at the created timestamp, till the transfer is submitted to the FTS system, at the submitted timestamp. This can be calculated as submitted - created. This value is not available to the system until the transfer is submitted, that is the submitted timestamp.

QTIME

This is the time in seconds the transfer spends in the FTS System, since it is submitted by Rucio the submitted timestamp, till the transfer starts its network time at the started timestamp. This can be calculated as started - submitted. This value is not available to the system until the transfer ends, that is until the ended timestamp, because FTS does not propagate the started time of a transfer immediately, but only once the transfer ends or fails.

NTIME

This is the actual time in seconds the file is being transferred, using the network, since the transfer is started by FTS at the started timestamp, till the transfer ends at the ended timestamp. This can be calculated as ended - started. The value is not available to the system until the transfer ends, that is the ended timestamp.

RATE

This is the average rate in bytes per second of each transfer. It is calculated as SIZE/NTIME and is not available till the transfer ends.

link

This is the hash that represents a source/destination RSE pair. Links have peculiarities that make them unique, and likely affect the RTTC, e.g., some links have higher bandwidth, or the disks of the associated storages in the respective source and destination RSEs are faster than the ones on other links.

created

This is the time at which a transfer request is created in Rucio. For all the transfers that share the same rule_id, the minimum created timestamp is also the rule creation time, at which we want to know the RTTC. All date timestamps have a resolution of 1 second.

submitted

This is the time at which the transfer request is submitted from Rucio to FTS.

started

This is the time at which the transfer request starts the actual transfer, using the network. This data will not be known until the transfer ends because FTS doesn't publish this data immediately but only once the transfer ends.

ended

This is the time at which the transfer ends. For all the transfers that share the same rule_id, the maximum ended timestamp is also the ending time of the rule.

share

This is a number between 0 and 1 that represents the weighted probability of a transfer of being picked to be served given its activity.

Target

The target of the study is to know the Rule Time To Complete (RTTC) at the creation time of the rule. The creation time of the rule is the minimum created timestamp of those transfers that share the same rule_id. The RTTC can be computed as the ending time of the rule minus the starting time of the rule, being the ending time of the rule, the maximum ended timestamp of all the transfers that share the same rule_id.

References

1. Worldwide LHC Computing Grid. https://wlcg.web.cern.ch/ Retrieved 23/11/2020

2. Rucio Scientific Data Management. https://rucio.cern.ch/ Retrieved 23/11/2020

3. The ATLAS Experiment. https://atlas.cern/ Retrieved 23/11/2020

File Transfer Service. https://fts.web.cern.ch/fts/ Retrieved 23/11/2020

The project ´Quality of Life and Well-being of Very Old People in NRW (NRW80+)´, which is funded by the Ministry of Innovation, Science and Research of North Rhine-Westphalia and carried out by the CERES research association at the University of Cologne, is intended to provide representative statements on the living conditions of very old people in North Rhine-Westphalia. The aim is to obtain comprehensive information about the environment in which very old people live or would like to live, what their social role is and how satisfied they are with their living situation. Housing situation: type of housing; full inpatient care in the case of residential accommodation; number of rooms; duration of living in this apartment/house/home; tenure (owner, main tenant, subtenant, rent-free); always in this apartment/house or lived in this flat/house; barrier-reduced living: thresholds over 2 cm; doors at least 80 cm wide; stairs with handrail or stair lift; doors of bath and WC open to the outside; suitability of the living environment on foot or in a wheelchair (walkability); residential attachment; trust in people in the neighbourhood (social cohesion). 2. Family situation: marital status; currently stable partnership; children present; number of children; number of living children; number of grandchildren and great grandchildren; household size; household composition: sex of up to three persons and their relationship to the respondent; pets. 3. Financial situation: sources of income; net household income; costs: amount of the monthly rent for warmth; amount of the monthly rent for cold or rent without additional costs; amount of the monthly additional costs; housing loans or mortgages to be paid off and their amount; monthly costs for the stay in the home; debts from loans; amount of debts; assets: amount of the total assets. 4. Dealing with old age: autonomy; experience of ageing (e.g. greater appreciation of relationships and other people, more attention to one´s own health, decrease in mental capacity, etc.); appreciation by others (being needed, being appreciated for services, being treated as a burden, being appreciated more than before). 5. Health: cognitive tests on mental health (repeat ten selected words in two passes, convert numbers, mention as many things as possible that you can buy in the supermarket in one minute, repeat numbers in reverse order, remember the ten words at the beginning of the cognitive test); self-assessment of health; assessment of pain level in the last four weeks; height in cm; weight in kg; weight loss in the last twelve months; multimorbidity: medical treatment due to selected diseases; existence of care level or degree of care; designation of care level or degree of care; additional care level 0 (limited everyday competence); care use: use of an outpatient care service; use of day care; private care; hours of private care per week; respondent cares privately for another person and hours per week; functional health with regard to various activities of daily life (eating, dressing and undressing, personal hygiene, walking, looking up from bed and lying down, being bedridden, bathing or showering, reaching the toilet in time, frequency of problems with bladder and bowel control, using the telephone, organising routes outside the walking range (trips by taxi or bus), buying food and clothing yourself, preparing your own meals, doing housework, taking medication, regulating financial matters); use of assistive devices (hearing aid, wheelchair, home emergency call system, private car); health literacy (knowledge and compliance). 6. Everyday life and lifestyle: importance and frequency of: time spent together with other people, physical activity, rest and time for oneself, in-depth study of a topic and creative activity; preferred music style; preferences regarding clothing and food; leisure activities in the last 12 months (e.g. sports, participation in a coffee circle or regulars´ table, visiting a café, restaurant or pub, travelling, voluntary work, etc.); frequency and place of the respective activities; religious community, club membership; political participation: party affiliation; participation in the last federal election. 7. Technology setting and technology use: technology use in the last 12 months (computer or laptop, internet, smartphone, regular mobile phone, tablet computer, fitness wristband) and frequency of use; technology setting: interest, difficulties in using modern digital devices, ease of everyday life with modern digital devices); purpose of internet use in the last three months (emails, looking for information on health topics, participating in social networks, buying or selling goods or services). 8. Social inclusion: called social network; for the four most important persons the following was asked: sex, their relationship to the respondent, frequency of contact and attachment to these persons; number of other persons in the social network (size of the social network); frequency of loneliness in the last week; social support: larger gifts given or larger gifts received; frequency of social support given or received by the respondent (e.g. helped other people with their tasks, received help with tasks and tasks, received consolation, received consolation); Generativity (importance of passing on one´s own experiences to younger people, passing on social values to younger people, being a role model for younger people); Integration into society: Anomie (coping with today´s social way of life, one´s own values fit less and less with the values of today´s society, lack of orientation due to rapidly changing society). 9. Hand grip force: agreement with hand grip test; right- or left-handed; writing hand; test value 1st measurement right and left; test value 2nd measurement right and left; deviations exist. 10. Value system: Individual value system (doing things in one´s own way (self-determination), being wealthy (power), avoiding dangers and safe environment (security), spending good time (hedonism), doing good for society (benevolence), getting achievements recognized (achievement), taking risks (stimulation), avoiding teasing others (conformity), caring for nature and the environment (universalism), respecting traditions (tradition); spirituality: Importance of a connection with God or a higher power, with people and with nature; frequency of connection with God or a higher power, with people and with nature; importance of institutionalizing one´s own beliefs, e.g. in church; ; frequency of the feeling of community in institutionalized forms; orientation to the guidelines of religious institutions; importance of being part of a large entity; frequency of the feeling of being part of a larger entity; importance of practicing religious practices such as Praying or meditating, frequency of religious practices; reconciled relationship with God; God as support; desire to leave everything behind to go to God; God is threatening and punishing; importance of faith or spirituality in one´s own life; attitude towards dying and death: acceptance of one´s own mortality; death as an incriminating thought; fear of dying; frequency of thoughts about death; will written; dispositions (living will, precautionary power of attorney, care-giving will, general power of attorney). 11. Interpersonal personality: tendency to quarrel, losing control, feeling irritated and harassed); external and internal controlling life (life in one´s own hands, success through effort, life is determined by others, plans thwarted by fate). 12. Well-being and life satisfaction: frequency of selected feelings in the last year (PANAS: enthusiastic, attentive, joyfully excited/expectant, stimulated, determined); depressiveness during the last 14 days (depressed, difficult to pick up, enjoy life, even if some things are difficult, brooding a lot); Valuation of Life-Scale (and a. optimistic, consider current life as useful, life determined by religious or moral principles, etc.); Meaning in Life-Scale (satisfaction with what has been achieved in the past, with the past at peace); general life satisfaction. 13. Critical life events: perceived burden of life events in general; generally most stressful event; current burden of events related to World War II; most stressful event related to World War II; current burden of events outside World War II; most stressful event outside World War II; most stressful event outside World War II; most stressful event outside World War II Interpersonal conflicts and emotional consequences (INDICATE): Frequency of conflicts with known persons (other person has become louder/ abusive towards the respondent (intimidation), has spoken about weaknesses or impairments of the respondent (shame), blamed for an event, paternalism: Ignoring the respondent´s opinion, has caused the respondent to renounce his or her wish or right, neglect: no support given, no time given, financial exploitation: property or possessions of the respondent used for own purposes, has been kept by the respondent, physical violence: firm or rough handling, physically rough or inconsiderate handling, custodial measures restriction of freedom of movement, medication given without consent, sexualised violence: offensive behaviour, sexual harassment). 14. Biography: caregiver in childhood up to the age of 16; social status of parents: employment and occupational status of father and mother when the interviewee was 15 years old; number of siblings; occupational biography of the interviewee: end of full-time employment; occupational status; special designation of occupational status; occupational biography of spouse: end of full-time employment; occupational status; special designation of occupational status; request to politicians to improve one´s own quality of life (open). Demography: sex; age; origin: country, place of residence 1949-1990; education: country of last school attendance; highest

A dataset of airborne particulate matter (PM10 and PM2.5) readings (every 3 minutes) collected by participating households in Northeast England in their kitchens and living rooms over the course of one week, along with data from a linked questionnaire survey and metal(oid)s data from a corresponding household vacuum dust sample collected by the study participant. Matched air monitoring and dust sample collection took place between June 2020 and August 2021. We increasingly spend time indoors and household air pollution results in an estimated 4.25 million premature deaths globally each year. The majority of these deaths are associated with fine particulate matter (PM), or dust. Exposure to PM can initiate or enhance disease in humans, yet the nature of the hazard that house dust presents remains poorly characterized. The data was collected to provide concentrations of PM2.5 and PM10 in a range of Northeast England households and concentrations of metal(oid)s in their house dust. It will be of interest to those interested in human exposure to potentially toxic elements and environmental health. We used factory calibrated Aeroqual 500 units for PM monitoring. Metal(oid)s data were generated using a SPECTROSCOUT X-Ray fluorescence spectrometer on the <250um sieved fraction of household vacuum dust. This dataset was part of NERC Grant NE/T004401/1.

A dataset of trace metal concentrations (As, Cu, Cr, Mn, Ni, Pb and Zn) in indoor dust from homes from 11 countries, along with a suite of potentially contributory residential characteristics. A household vacuum dust sample, collected by the study participant using their regular vacuum cleaner, was submitted to the laboratory for analysis by X-Ray fluorescence spectrometry (XRF) on the <250um sieved fraction, along with the completion of an online questionnaire survey. Dust sample collection took place between 2018 – 2021. The Home Biome project is affiliated to the DustSafe community science programme (see mapmyenvironment.com). Sample location data are provided at town/city and Country level. Health risk from exposure to potentially contaminant-laden dust has been widely reported. Given the amount of time people spend indoors, residential environments are an important but understudied environment with respect to human exposure to contaminants. Indeed, the nature of the hazard that house dust presents remains poorly characterized. These data will be of interest to those interested in human exposure to potentially toxic elements and environmental health, as well as to the participants, who received a bespoke report on their sample data and information on key sources and ways to reduce exposure to trace elements in indoor dust.

The dataset consists of air temperatures recorded longitudinally and reported at hourly intervals using Hobo MX1101, Hobo MX1102A and Hobo MX2301 devices. The monitoring period covered 1st May 2022 to 30th September 2022 inclusive – the full non-heating season in England.

The devices were deployed in 30 care homes across England: eleven in Greater London, nine in the north of England as far north as Newcastle-upon-Tyne, six in the Midlands, and four in the south of England including on the Isle of Wight. The locations monitored consisted of 22 offices (staff-only areas such as manager’s offices, administrator offices, nurse stations), 30 lounges (communal areas such as lounges, dining rooms and lounge/diners), and 30 bedrooms (single rooms, with a range of occupancy – some vacant, some occupied only at night, others occupied 24/7 depending on resident needs). In addition, outdoor temperatures were monitored at each of the 30 care homes.

As a result of global climate change, the UK is expected to experience hotter and drier summers, and heatwaves are expected to occur with greater frequency, intensity and duration. In 2003 and 2018, 2,091 and 863 heat-related deaths, respectively, were reported in England alone as a result of heatwaves, meaning future temperature increases could lead to a parallel rise in heat-related mortality. The UK also currently has a rapidly ageing population, with people aged 75 or over expected to account for 13% of the total population by 2035. Older populations are more vulnerable to climate-induced effects as they are more likely to have underlying, chronic health complications, making them more vulnerable to heat stress. The indoor environment is a principle moderator of heat exposure in older populations, who tend to spend the majority of their time indoors. Poor building design, the lack of effective heat management and diverging needs and preferences between staff and residents in care settings may contribute to increased indoor heat exposure with detrimental health impacts falling on the most vulnerable residents. Maladaptation to a warming climate, such as the uptake of air conditioning, could increase fuel bills in care homes, increase operational costs for businesses in the already financially stretched care sector, and increase building carbon emissions, thus undermining government efforts to reduce greenhouse gas emissions.

The one-year pilot project 'Climate Resilience of Care Settings' and previous small-scale studies led by our research team have shown that UK care homes are already overheating even under non-extreme summers. A key target for climate adaptation in care settings is to limit such risks by introducing passive cooling strategies via building design. However, preliminary modelling as part of the pilot project also demonstrated that common passive cooling strategies may not adequately mitigate overheating risk in the 2050s and 2080s. Further research into advanced passive cooling strategies, combined with human behaviour and organisational change is required to identify optimum climate adaptation pathways for UK's care provision.

The main aim of the project is to quantify climate related heat risks in care settings nationwide and enhance understanding of human behaviour, organisational capacity and governance to enable the UK's care provision to develop equitable adaptation pathways to rising heat stress under climate change. Building on the foundations of the pilot project, this novel, interdisciplinary project will collect, for the first time in the UK, longitudinal temperature and humidity data in a panel of 50 care settings in order to quantify the recurring risk of summertime overheating. We will also identify and assess social, institutional and cultural barriers and opportunities underpinning the governance of adaptation to a warmer climate in care and extra-care homes through surveys with residents, frontline care staff, managers and policy stakeholders. Within sub-samples of this panel, we will use innovative measurement techniques to collect residents' physiological data and study their relation with heat exposure and health impacts. Also for the first time in the UK, we will create a building stock model of the UK's care provision able to predict future overheating risks in care settings under a range of future climate change scenarios. This will help evaluate the effectiveness of near, medium and long term future overheating mitigation strategies and policies on thermal comfort and health outcomes. Throughout the project, we will continue to develop and expand the stakeholder community that was created during the pilot project. Through ongoing dialogue with our diverse network of stakeholders, we will explore organisational capacity and structures, and how these influence action and policy, in order to generate best practice guidance for practitioners, businesses and policymakers.

How preschool teachers and children spend their time in preschool is important for children's engagement and learning. The study aims to give a broad description of how often children and staff spend time in different types of activities, interactions, and environments. Systematic momentary observations of individual children and teachers/staff were conducted continuously during a full day in 78 preschool units (mainly for children 3-5 years) during the autumn term. The observations resulted in frequency data for different types of activities for children and teachers. Frequency data were summarized at the unit level, and percentage distributions of activities were calculated. Results showed that free play indoors was the main activity setting, followed by free play outdoors. Children interacted as much with other children as with teachers. The focus was dominated by non-pretend play, construction, art and music, followed by pretend play and academic contents. Child engagement was significantly higher in free play indoors compared to outdoors. Teachers engaged in varied tasks, but their central task was managing. Teachers were typically in proximity to small groups of children, or by themselves, and mostly talked to or listened to a single child. Data was collected with systematic observations with the help of manual-based instruments Child Observation in Preschool (COP) and Teacher Observation in Preschool (TOP). The observations consists of snapshots of individual children/teachers across a preschool day. Several aspects of the individual's current activity are coded. Individual data was aggregated to preschool unit level, and proportions for different activity aspects were calculated. Aggregated frequency and proportionate data are available in the data set for child and teacher data, respectively. Some preschool unit background information is also available. Hur förskolepersonal och barn tillbringar sin tid i förskolan är viktigt för barns engagemang och lärande. Studien ämnar ge en bred beskrivning av hur ofta barn och personal spenderar tid i olika typer av aktiviteter, interaktioner, och miljöer. Systematiska ögonblicksobservationer av enskilda barn och lärare/barnskötare genomfördes kontinuerligt under en heldag på 78 förskoleavdelningar (främst 3-5 års avd.) under höstterminen. Observationerna resulterade i frekvensdata för olika typer av aktiviteter för barn resp. personal. Frekvensdata summerades på avdelningsnivå, och procentuell fördelning av aktiviteter räknades ut. Data har samlats in med hjälp av systematiska observationer genom de manualbaserade instrumenten Child Observation in Preschool (COP) och Teacher Observation in Preschool (TOP). Observationerna innebär att ögonblicksbilder av individuella barn/personal görs under en heldag i förskolan. Flera aspekter av individens aktuella aktivitetet kodas. Därefter har individuella data aggregerats till avdelningsnivå och procentuell fördelning av aktiviteter har kalkylerats. Aggregerad frekvensdata och proportionsdata för barn respektive personal återfinns i dataseten samt viss bakgrundsinformation om avdelningarna. Seventy-eight preschool units participated in the study. All units were inclusive in line with Swedish preschool norms. Preschool units in the TUTI project (n = 39) were selected through stratified convenience sampling based on municipality size and population density. Only public preschools were approached, and no limitations of unit target age was made. Preschool units in the PEPI project (n = 39) were selected by a combination of purposive and convenience sampling. Units where the majority of children were three years or older were approached. Public preschool units in a region of Sweden where children with disabilities currently were enrolled were initially approached, in line with overall project aims. Then, independent (non-profit) preschool units in a smaller region of Sweden was approached, without further criteria.Seventy-eight preschool units participated in the study. All units were inclusive in line with Swedish preschool norms. Preschool units in the TUTI project (n = 39) were selected through stratified convenience sampling based on municipality size and population density. Only public preschools were approached, and no limitations of unit target age was made. Preschool units in the PEPI project (n = 39) were selected by a combination of purposive and convenience sampling. Units where the majority of children were three years or older were approached. Public preschool units in a region of Sweden where children with disabilities currently were enrolled were initially approached, in line with overall project aims. Then, independent (non-profit) preschool units in a smaller region of Sweden was approached, without further criteria. Olika varianter av bekvämlighetsurval användes. Data från 78 förskoleavdelningar från två olika projekt, TUTI (n =39) och PEPI (n= 39) användes i studien.Olika varianter av bekvämlighetsurval användes. Data från 78 förskoleavdelningar från två olika projekt, TUTI (n =39) och PEPI (n= 39) användes i studien.

This dataset contains volatile organic compounds (VOC) and ultrafine particle measurements collected onboard several vehicles for the TRANSITION Clean air Network during May 2021 in the London and surrounding area. The particles measured include VOCs captured in the field using thermal desorption tubes, and then analysed into the component species using highly sensitive Markes International GCxGC-TOF-MS system, and ultrafines data captured using a V2000 sensor from National Air Quality Testing Services by Emissions Analytics. Therefore, a much wider range of pollutants have been tested than in standard air quality monitoring. The harm caused by emissions from vehicles to air quality and the health of humans outside is increasing well understood and It is generally accepted that it is a policy priority to remove high-emitting vehicles from the road and to swap for low-emission vehicles or public transport. What is less well understood is the exposure of the occupants in various transportation modes. Aggregate time spent in vehicles is significant, and can be measured in hours per day for certain commuters and professional drivers. Existing research by Emissions Analytics shows that the worst-performing cars can have particle number concentrations more than three times than in the ambient air. With Net Zero, particles are likely to be the dominant traffic pollutant. This dataset contains interior air quality measurements made on a range of modes of transport including diesel and electric trains, the London Underground, diesel and electric buses, and old and new cars, including a battery electric.

This table presents planned Full-time Equivalents (FTEs) by Program. This table also aligns planned FTEs with the Whole-of-Government by linking each Program line with its corresponding Government of Canada Spending Area and Activity. Notes: - Planned FTEs is a measure of the extent to which an employee represents a full person-year charge against the departmental budget for future spending years. Full-time equivalents are calculated as a ratio of assigned hours of work to scheduled hours of work. Scheduled hours of work are set out in collective agreements. - Internal Services supports all Government of Canada Activity and Spending Areas but cannot be disaggregated amongst them. Accordingly, for analytical purposes, it is assigned a virtual outcome and a spending area named “Internal Services”. - The data entries with “.” are intentional to distinguish no recorded value for a cell as opposed to an actual recorded value of zero. - This table consolidates 2017-18 Departmental Plans Planned FTE data submitted by federal institutions.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

In light of the rapid socio-economic development in this era, it is necessary to make data on household expenditure and income available, as well as the relationship between those statistics and various variables with direct or indirect impact. Therefore, most of the countries are nowadays keen to periodically carry-out Household Expenditure and Income surveys. Given the continuous changes in spending patterns, income levels and prices, as well as in population both internal and external migration, it was now mandatory to update data for household income and expenditure over time. The main objective of the survey is to obtain detailed data on HH income and expenditure, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, it was well considered that the sample should be representative on the sub-district level. Hence, the data collected through the survey would also enable to achieve the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 2- Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns. 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators. 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it. 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps.. 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

This survey was carried-out for a sample of 12678 households distributed on urban and rural areas in all the Kingdom governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size has been uniformly selected, and in the second stage, a systematic approach guaranteing a representative sample of all sub-districts (Qada) has been applied.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires:

(1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data

The design and implementation of this survey procedures are: 1. Sample design and selection. 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals. 3. Design the tables template to be used for the dissemination of the survey results. 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks. 5. Selection and training of survey staff to collect data and run required data checkings. 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results. 7. Data collection. 8. Data checking and coding. 9. Data entry. 10. Data cleaning using data validation programs. 11. Data accuracy and consistency checks. 12. Data tabulation and preliminary results. 13. Preparation of the final report and dissemination of final results.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.