In 2024, there were 301,623 cases filed by the National Crime Information Center (NCIC) where the race of the reported missing person was white. In the same year, 17,097 people whose race was unknown were also reported missing in the United States. What is the NCIC? The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide. Missing people in the United States A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

The dataset contains the state-wise number of persons reported missing in a particular year, the total number of persons missing including those from previous years, the number of persons recovered/traced and those unrecovered/untraced. The dataset also contains the percentage recovery of missing persons which is calculated as the percentage share of total number of persons traced over the total number of persons missing. NCRB started providing detailed data on missing & traced persons including children from 2016 onwards following the Supreme Court’s direction in a Writ Petition. It should also be noted that the data published by NCRB is restricted to those cases where FIRs have been registered by the police in respective States/UTs.

Note: Figures for projected_mid_year_population are sourced from the Report of the Technical Group on Population Projections for India and States 2011-2036

NamUs is the only national repository for missing, unidentified, and unclaimed persons cases. The program provides a singular resource hub for law enforcement, medical examiners, coroners, and investigating professionals. It is the only national database for missing, unidentified, and unclaimed persons that allows limited access to the public, empowering family members to take a more proactive role in the search for their missing loved ones.

Ministry of Home Affairs, Government of India has defined missing child as 'a person below eighteen years of age, whose whereabouts are not known to the parents, legal guardians and any other persons who may be legally entrusted with the custody of the child, whatever may be the circumstances/causes of disappearance”. The dataset contains the state wise and gender-wise number of children reported missing in a particular year, total number of persons missing including those from previous years, number of persons recovered/traced and those unrecovered/untraced. The dataset also contains the percentage recovery of missing persons which is calculated as the percentage share of total number of persons traced over the total number of persons missing. NCRB started providing detailed data on missing & traced persons including children from 2016 onwards following the Supreme Court’s direction in a Writ Petition. It should also be noted that the data published by NCRB is restricted to those cases where FIRs have been registered by the police in respective States/UTs.

Number of homicide victims, by Indigenous identity (total, by Indigenous identity; Indigenous identity; First Nations (North American Indian); Métis; Inuk (Inuit); Indigenous person, Indigenous group unknown; non-Indigenous identity; unknown Indigenous identity) and missing person status (total, by missing person status; missing; not missing; missing person status unknown), Canada, 2015 to 2024.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

This is the dataset used and analyzed in the paper "How can we Better Use Twitter to find a Person who Got Lost due to Dementia?".A total of five tables are included. 1. raw_tweets.rds: All tweets that mentioned (i) "Dementia" or "Alzheimer"; and (ii) "Lost" or "Missing", which were crawled from Twitter from April to May 2017. 2. raw_userinfo.rds: The corresponding Twitter user info of Tweets.3. filtered_tweets.csv: Tweets that were included in the study. Details (age, gender, place, etc.) of the corresponding lost person mentioned in each tweet were appended in this table. 4. filtered_userinfo.csv: The corresponding Twitter user info of Tweets that were included in the study. Occupation (police / media / others) of each user were appended in this table. 5. cleansed_lostcases.csv: A cleansed table that shows several features of the lost cases.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool

Data includes:

• Date on which the death occurred
• Age group
• 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster

Additional notes

As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm.

As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON.

“Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

The primary data source for this study is the Northern Ireland Longitudinal Study (NILS), which in 2001 defined a representative cohort of c.28% of the population. It is formed from the linkage of the universal Health Card registration system, 2001 Census returns, and vital statistics data. NILS contains a unique Health and Care Number that enables linkage to other health service databases. It is maintained by the Northern Ireland Statistics and Research Agency (NISRA). The 2001 Census records provided most of the attributes of the NILS cohort members, also contextual information relating to household composition and interpersonal relationships, and characteristics of the household and area of residence. The vital events linked to NILS were used to determine whether a cohort member had been bereaved between April 2001 (the time of the Census) and the end of December 2009. The 2001 Census asked questions about relationship to other people living in the household, these questions were used to determine who a cohort member lived with, and the vital events records identified co-resident family members’ deaths. Approximately 96% of death records are routinely linked to the NILS dataset using a mixture of exact and probabilistic matching. Data relating to medications that have been prescribed by a General Practitioner and dispensed from community pharmacies have been collated centrally in an Enhanced Prescribing Database (EPD) since 2009. Each prescription record contains the individual’s Health and Care Number, a General Practice (GP) identifier, the drug name and British National Formulary (BNF) category. Information was extracted for antidepressant and anxiolytic medications (BNF categories 4.1.2 and 4.3) for the period January 1st to February 28th 2010. Health and Care Number allowed exact matching between prescribing and NILS records. The linkage process was carried out by the EPD and NILS data custodians. The linked dataset was then anonymised before being supplied to the researchers, and was held in a secure setting (9). At no time were patient identifiable data available. The data used for the Grief study is not publicly available, but researchers can make a request to link data for themselves by contacting the Northern Ireland Longitudinal Study Research Support Unit Everybody will face bereavement at some stage; but for some people, this can be a more difficult process. There are many factors that can influence how people cope with the loss of a loved one, including level of family support, financial resources, stress, and the circumstances surrounding death.By studying use of prescription medications to help with mental health, we can get a better understanding of how factors such as age, gender, family support, employment and religion affect how people cope after bereavement. By looking at circumstances of bereavement this study will also discover if the factors that help people cope - such as family support - are more or less important depending on how they lost their loved ones.The Grief Study is based on data from the Northern Ireland Longitudinal Study, this holds information on around 500,000 people. By linking this data with the Northern Ireland Mortality Study and Health and Social care information on prescriptions, the Grief Study aims to learn more about bereavement, mental health, complicated grief, and longer term outcomes for people who have lost a loved one.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON AUG. 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

It is now well recognised that trust in sources of advice is important for effective risk communication. To maintain some influence over peoples opinions and risk-taking behaviours, government departments, medical bodies and other agencies must endeavour to maintain the trust that people have in them. When trust is lost, so is influence. As an example of this, commentators have pointed to effects of poor advice during the bovine spongiform encephalopathy (mad cow) crisis. The government assured people that it was safe to eat beef but later this behaviour was later linked to a brain disease that is always fatal. This reduced trust in the government as a source of advice about risk for many people. As a result, there appears to be a reluctance to take government advice on other matters. For example, the number of measles cases has increased in many parts of the country: fewer people are having their children vaccinated against the disease because they do not believe the governments assurances that the MMR vaccine is safe. Recently, Onora ONeill, in her 2002 Reith lectures, has argued that we should be cautious about accepting peoples claims that they have lost trust in some agency as evidence that they have actually lost their trust in it. For example, she suggests that many people who say they no longer trust supermarket food still shop in supermarkets rather than elsewhere. For her, it is important to make a distinction between stated trust and actual trust. Our aim is to find out more about trust by answering a number of questions about it while keeping ONeills distincion in mind. What determines trust? How should it be measured? Does it have similar effects on peoples estimates about levels of risk and their risk-related behaviours? How valid is the view that it is easier to destroy than to create trust? Is trust in advisors modified in a rational way? If not, what determines how it is modified? Does trust (or lack of it) transfer across domains? Answers to these questions should be relevant to many of the concerns of those responsible for the development of effective risk communication strategies in government, business and non-profit-making organizations. This is a proposal for systematic studies to answer these questions. In simulated situations, people will be required to assess levels of risk associated with various hazards on the basis of information received from advisors. Advice will be attributed to different sources, such as government departments and consumer organizations. Its quality will be under our control. We shall be able to measure actual trust in an advisor (in terms of the degree to which peoples own risk judgments depend on the risk estimate provided by that advisor) and stated trust in that advisor (in terms of a trust rating). We shall be able to determine how these measures depend on quality of previous advice from that source and on other variables, such as the degree to which judges perceive their values to be similar to those of the advisor Experimental. All 22 studies were empirical experiments run on students at University College London. Participants saw stimuli on a computer screen and entered their responses. Data set includes 22 experiments, with one folder per experiment, each containing a brief summary of the experiment, a sheet of raw data, and a copy of all experimental instructions – all in .txt format. Also included are a general summary of all 22 experiments, a key explaining column labels in the data summary files, and an additional ‘infosheet’ in the Experiment 10 folder describing the questionnaire used in that study.

Abstract

Looking for a job as Data Analyst? Maybe this dataset can help you.

About this dataset

Amidst the pandemic many people lost their jobs, with this dataset it is possible to hone the job search so that more people in need can find employment. This dataset was created by picklesueat and contains more than 2000 job listing for data analyst positions, with features such as: - Salary Estimate - Location - Company Rating - Job Description - and more.

How to use

• Find the best jobs by salary and company rating
• Explore skills required in job descriptions
• Predict salary based on industry, location, company revenue
• Your kernel can be featured here!
• Data Engineer Jobs
• Business Analyst Jobs
• Data Scientist Jobs
• More Datasets

Acknowledgements

If you use this dataset, please support the author.

License

License was not specified at the source

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The number of years of life lost by every 100,000 persons dying from a condition which is usually treatable, measured in a way which allows for comparisons between populations with different age profiles and over time. Please note that this indicator has been retired as of May 2015. This is because the PYLL from causes considered amenable to healthcare for all ages does not appear in the 2015/16 NHS Outcomes Framework. For Potential years of life lost (PYLL) from causes considered amenable to healthcare for adults or children and young people please see indicators 1a.i and 1a.ii. Purpose To ensure that the NHS is held to account for doing all that it can to prevent amenable deaths. Deaths from causes considered ‘amenable’ to healthcare are premature deaths that should not occur in the presence of timely and effective healthcare. Current version updated: May-15 Next version due: Will not be updated

This dataset contains descriptions of unidentified remains whose cases have been processed by the Medical Examiner’s Office.

Call 312-666-0500 to speak to Deputy Chief Investigator, Earl Briggs, about matching one of these unidentified bodies to the identity of a missing person. Descriptions of cases can also be found at NAMUS.gov

Please note that images posted in this section may be graphic in nature and may not be appropriate for all users.

This dataset contains our most up to date list of Unclaimed Persons found in Cook County.

If you feel you are the next of kin to any of the individuals listed, Please contact Rebeca Perrone, Indigent Coordinator, at 312-997-4480. During regular business hours: Monday through Friday, 7 a.m. to 3 p.m.

Statement of Public Notice: Unclaimed indigents will be cremated or buried at County expense within 90 days of admission to our facility as dictated by the Cook County Medical Examiner Ordinance

List of every shooting incident that occurred in NYC during the current calendar year.

This is a breakdown of every shooting incident that occurred in NYC during the current calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a shooting incident in NYC and includes information about the event, the location and time of occurrence. In addition, information related to suspect and victim demographics is also included. This data can be used by the public to explore the nature of police enforcement activity. Please refer to the attached data footnotes for additional information about this dataset.

Abstract: Larry and Sophie long for a romantic relationship, even if they have to fight the social and legal restrictions that society imposes on them because of their intellectual disability. But, as they secretly convince their care worker to arrange some time alone, the plan runs less smoothly than they think. While enjoying some unsupervised liberty and fulfilling their romantic aspirations, some troubles and concerns await everyone along the way. Details: Larry and Sophie are two adults with intellectual disabilities. Larry has Down syndrome and lives with his mother, who still treats him like a child, while Sophie has severe epilepsy and lives at a housing facility. They know each other because they spend time at a centre caring for people with disabilities. They were employed to do some minor work until now, but they received the terrible news that they won’t be receiving any work. So instead, some “fun activities” will take place, for which they will not be paid anymore. Disappointed, they protest and ask if they were not doing a good job, and Tom, their care worker, answers straight forward that the problem was that no one was willing to pay them properly. Larry and Sophie are attracted to each other and want to spend an afternoon alone in a hotel room, so he saves some money and turns to Tom for the arrangement. He reluctantly takes the bribe and agrees to arrange it for them on the same day, on which he is taking the rest of the group to the movie theatre. The entire group goes off to the movie theatre. During the movie, Sophie, Larry, and Tom find an excuse to get out of the theatre and head to the hotel, where Tom knows a friend at the reception that gets them a room. Once they are up there, Larry discloses to Tom his intentions of having sex with Sophie and asks him for a condom. However, the law in Ireland forbids people with intellectual disabilities to have sex outside of wedlock; therefore, Tom tries to convince him not to. Larry gets angry and questions the law, asking if it was different if he was “normal.” In the end, he manages to convince Tom to give him a condom, asks him for instructions on how to use it, and sends him away. Meanwhile, one of Larry and Sophie’s colleagues, Andrew, gets out and starts stealing candies from the bar at the theatre. Others follow him outside, alarmed by the fact that their supervisor has gone missing, and begin wandering the streets. Only two of them, a man and a woman also affected by intellectual disabilities, stayed and watched the movie. She’s attracted to him, but he’s too shy and doesn’t like flirting. However, he makes her smile by telling her that she doesn’t need to be a movie star and that she’s much better just as she is. At the hotel, Sophie surprises Larry with a beautiful dress. Then, they start dancing, but suddenly, Sophie sits down with a sad expression and confesses to Larry that she was abused at her previous housing facility. After her confession, they start kissing. However, when she realizes that Larry wants to have sex with her, she gets angry and wants to leave, even though he apologizes and tells her that he loves her. Finally, he gets her to forgive him by making her laugh with some dance moves. After that, she changes her mind and tells him she wants to make love. Tom gets to the theatre and discovers that many people from the group are gone. He tries to call them, but they don’t answer, so he looks for them. While two of them are walking around at a mall, the other two are at a bar drinking beer. Claire, Tom’s friend from the hotel, joins him during his desperate search. Unfortunately, he misunderstands her intentions, and she rejects him when he tries to kiss her. Meanwhile, the other two, still at the movies, finally kiss. By the time Tom gathered the other four group members, the movie was over, and he lost track of the two who had remained there. Then, finally, he finds them with a guard, who accuses him of leaving people with disabilities wandering off the streets without supervision. Meanwhile, Larry and Sophie get into a discussion at the hotel because they have just had unprotected sex. For instance, Larry starts overthinking the consequences of getting Sophie pregnant. They reflect on how they would like to start a family together if all the legal and social obstacles against their relationship disappeared. As the group starts wondering where Larry and Sophie are, Tom takes everyone to pick them up to the hotel room. When they arrive at the room, they find out that the couple had unprotected sex. The situation becomes even more severe when Andrew starts accusing Sophie of having loose morals, and she has a seizure due to the champagne she drank, which messed with her medication. Despite the many complications, the group had fun enjoying the liberty. Therefore, they all decide to keep this episode to themselves to be able to do it again. However, while Tom is gone to look for the seventh group’s component, the others call the hospital to help Sophie with her seizure. The end scene shows everyone quiet on the bus on the way back home, especially Tom, who looks very concerned about the trouble he’s about to face.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name