The dataset contains year-wise number of cases, injured and deaths in train accidents, categorized by their types such as Explosion/Fire, Collisions, Fall From Train/Collision with People at Tracks, Derailments, and others

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

This is the number of people of all ages killed or seriously injured (KSI) in road traffic accidents, in an area, adjusted. This indicator includes only casualties who are fatally or seriously injured and these categories are defined as follows:

Fatal casualties are those who sustained injuries which caused death less than 30 days after the accident; confirmed suicides are excluded.

Seriously injured casualties are those who sustained an injury for which they are detained in hospital as an in-patient, or any of the following injuries, whether or not they are admitted to hospital: fractures, concussion, internal injuries, crushings, burns (excluding friction burns), severe cuts and lacerations, severe general shock requiring medical treatment and injuries causing death 30 or more days after the accident.

An injured casualty is recorded as seriously or slightly injured by the police on the basis of information available within a short time of the collision. This generally will not reflect the results of a medical examination, but may be influenced according to whether the casualty is hospitalised or not. Hospitalisation procedures will vary regionally.

Slight injuries are excluded from the total, such as a sprain (including neck whiplash injury), bruise or cut which are not judged to be severe, or slight shock requiring roadside attention.

Police forces use one of two systems for recording reported road traffic collisions; the CRaSH (Collision Recording and Sharing) or COPA (Case Overview Preparation Application). Estimates are calculated from figures which are as reported by police. Since 2016, changes in severity reporting systems for a large number of police forces mean that serious injury figures, and to a lesser extent slight injuries, are not comparable with earlier years. As a result, both adjusted and unadjusted killed or seriously injured statistics are available. Further information about the reporting systems can be found here.

Areas with low resident populations but have high inflows of people or traffic may have artificially high rates because the at-risk resident population is not an accurate measure of exposure to transport. This is likely to affect the results for employment centres e.g. City of London and sparsely populated rural areas which have high numbers of visitors or through traffic. Counts for Heathrow Airport are included in the London Region and England totals only.

From the publication of the 2023 statistics onwards, casualty rates shown in table RAS0403 to include rates based on motor vehicle traffic only. This is because the department does not consider pedal cycle traffic to be robust at the local authority level.

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

BackgroundGlobally, road traffic accidents (RTAs) cause over 1.35 million deaths each year, with an additional 50 million people suffering disabilities. Ethiopia has the highest number of road traffic accidents, with over 14,000 people killed and over 45,000 injured annually. This study aimed to assess survival status and predictors of mortality among road traffic accident adult patients admitted to intensive care units of Referral Hospitals in Tigray, 2024.MethodsAn institution-based retrospective follow-up study design was conducted from January 8, 2019, to December 11, 2023, on 333 patient charts. A bivariable Cox-regression analysis was performed to estimate crude hazard ratios (CHR). Subsequently, a multivariable Cox regression analysis was performed to estimate the Adjusted Hazard Ratios (AHR). Finally, AHR with p-value less than 0.05 was used to measure the association between dependent and independent variables.ResultThe incidence of mortality for road traffic accident victims, was 21 per 1000 person-days observation with (95% CI: 16, 27.6) and the median survival time was 14 days. The predictors of mortality in this study were the value of oxygen saturation on admission ≤ 89% (AHR = 4.9; 95%CI: 1.4–17.2), Intracranial hemorrhage (AHR = 3.3; 95% CI: 1.02–11), chest injury (AHR = 3.2; 95%CI: 1.38–7.59), victims with age catgories of 31–45 years (AHR = 0.3; 95% CI: 0.1–0.88) and 46–60 years (AHR = 0.22; 95% CI: 0.06–0.89).ConclusionA concerningly high mortality rate from car accidents were found in Referral Hospitals of Tigray. To improve the survival rates, healthcare providers should focus on victims with very low oxygen levels, head injuries, chest injuries, and older victims.

The monthly data on the number of accidents with personal injuries and the corresponding number of people injured are collected according to the city circle in which the event occurred. In cases where the indication of the circle is not available, the wording: "without indications" is found. The historical series is available from 2001 to 2021. NB: the circles are aggregations of the former 20 decentralization areas, which cannot be superimposed on the current municipalities (former decentralization areas). Therefore, the data reported in this dataset provide complementary but not comparable information with that of the dataset by municipality (former decentralization area). NOTE: The data on road accidents collected by the Local Police in the Municipality of Milan concern (as per ISTAT indications) only accidents with injuries to people. Those who have not caused deaths or injuries are excluded. Persons injured in the accident who died within 30 days of the event are considered to have died as a result of the accident.

Incident-based fire statistics, by type of casualty, age group of casualty, status of casualty and type of structure, Canada, Nova Scotia, New Brunswick, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, Yukon, Canadian Armed Forces, 2005 to 2021.

The dataset contains monthly data relating to road accidents that occurred in the Municipality of Milan, from 2001 to 2021, which caused injuries to people. The data concern the number of accidents (distinguished by fatal accidents and accidents with injuries only) and the number of injured people, distinguished by outcome of the accident (injured/dead). NOTE: The data on road accidents collected by the Local Police in the Municipality of Milan concern (as per ISTAT indications) only accidents with injuries to people. Those who have not caused deaths or injuries are excluded. Persons injured in the accident who died within 30 days of the event are considered to have died as a result of the accident.

This is a subset of a larger dataset. This dataset includes pedestrians and cyclists killed in traffic collisions in 2021.

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system.

The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

The numbers reflect incidents that were reported to and tracked by the Ministry of Labour. They exclude death from natural causes, death of non- workers at a workplace, suicides, death as a result of a criminal act or traffic accident (unless the OHSA is also implicated) and death from occupational exposures that occurred in the past.

Data from the Ministry of Labour reflects Occupational Health and Safety (OHS) and Employment Standards (ES) information at a point in time and/or for specific reporting purposes. As a result, the information above may not align with other data sources.

Notes on critical injuries :

For the purposes of the data provided, a critical injury of a serious nature includes injuries that:

1. "Place life in jeopardy"
2. "Produce unconsciousness"
3. "Result in substantial loss of blood"
4. "Involve the fracture of a leg or arm but not a finger or toe"
5. "Involve the amputation of a leg, arm, hand or foot but not a finger or toe"
6. "Consist of burns to a major portion of the body"
7. "Cause the loss of sight

Only critical injury events reported to the ministry are included here. This represents data that was reported to the ministry and may not represent what actually occurred at the workplace. The critical injury numbers represent critical injuries reported to the ministry and not necessarily critical injuries as defined by the Occupational Health and Safety Act (OHSA). Non- workers who are critically injured may also be included in the ministry's data. Critical injuries data is presented by calendar year to be consistent with Workplace Safety and Insurance Board harmonized data;

Data is reported based on calendar year

Individual data for the Health Care program is available for Jan. 1 to Mar. 31, 2011 only. From April 2011 onwards Health Care data is included in the Industrial Health and Safety numbers.

Notes on Fatalities :

Only events reported to the ministry are included here. The ministry tracks and reports fatalities at workplaces covered by the OHSA. This excludes death from natural causes, death of non-workers at a workplace, suicides, death as a result of a criminal act or traffic accident (unless the OHSA is also implicated) and death from occupational exposures that occurred many years ago. Fatalities data is presented by calendar year to be consistent with Workplace Safety and Insurance Board harmonized data. Fatality data is reported by year of event.

*[OHSA]: Occupational Health and Safety Act *[Mar.]: March *[Jan.]: January

This dataset contains Emergency Medical Services (EMS) information for reported emergency response incidents in Virginia that involve heat-related illness (HRI), as defined using heat-related protocols; heat-related ICD-10-CM codes in the cause of injury, primary impression, and secondary impressions fields; and key terms in the patient complaint and narrative text fields. The full case definition is available on this Virginia Department of Health Office of EMS data requests webpage under ‘Case Definitions’: https://www.vdh.virginia.gov/emergency-medical-services/ems-trauma-data/data-requests/. These data only represent HRI patients who interacted with the EMS system and do not represent HRI patients who reported directly to an emergency room or did not seek medical care. Therefore, these data should not be interpreted as the total number of HRI incidents in a community.

Data in this dataset have been provided by ESO on behalf of the Office of EMS. Please be advised that the accuracy of the data within the EMS patient care reporting system is limited by system performance and the accuracy of data submissions received from EMS agencies.

Counts of less than 5 have been suppressed, denoted by an asterisk, to prevent individual identification and protect patient confidentiality. This dataset has been classified as a Tier 0 asset by the Commonwealth Data Trust. Tier 0 classifies a data resource as information that is neither sensitive nor proprietary and is intended for public access.

The dataset contains monthly data on road accidents that occurred in the territory of the Municipality of Milan, which caused injuries to people. The data cover the number of accidents (distinguished by fatalities and injuries only) and the number of injured persons, broken down by accident outcome (injuries/deaths).

NOTE: The data on road accidents collected by the Local Police on the territory of the Municipality of Milan concern (as indicated by ISTAT) only accidents with injuries to persons. Excluded are those who have not caused death or injury. Persons injured in the accident who died within 30 days of the accident shall be considered to have died as a result of the accident.

Monthly data on the number of accidents with personal injuries and the corresponding number of people injured are collected according to the number of vehicles involved (from one up to 7 and more). The historical series is available from 2001 to 2021. ** NOTE **: The data on road accidents collected by the Local Police in the Municipality of Milan concern (as per ISTAT indications) only accidents with injuries to people. Those who have not caused deaths or injuries are excluded. Persons injured in the accident who died within 30 days of the event are considered to have died as a result of the accident.

Data from the national TRAxy application (information system of the national interministerial road safety observatory (ONISR))

For each bodily injury accident (i.e. an accident occurring on a road open to public traffic, involving at least one vehicle and causing at least one victim requiring treatment), information entries describing the accident are carried out by the law enforcement unit (police, gendarmerie, etc.) which intervened at the scene of the accident. These entries are collected in a form entitled injury accident analysis bulletin. All of these files constitute the national traffic accident file known as the “BAAC File” administered by the National Interministerial Road Safety Observatory “ONISR”.

Among the victims, we distinguish:

· people killed : people who die as a result of the accident, instantly or within thirty days following the accident,

· injured people: victims not killed.

Among the injured people it is necessary to differentiate:

· the injured so-called “hospitalized ”: victims hospitalized more than 24 hours,

· lightly injured: victims who have received medical treatment but have not been admitted as patients to hospital for more than 24 hours.

The indicators are illustrated on the page Blank angles

People killed or seriously injured (KSI) is used as the metric for roadway safety rather than simply fatalities because fatalities alone tend to be random in nature and can obscure long-term trends. Including serious injuries makes the data more robust and better highlights how the region is doing at preventing serious vehicle crashes. This approach has been promoted by the FHWA and embraced by both NJDOT and PennDOT. Because KSI can fluctuate from year to year, five-year rolling averages are used to identify trends, as seen in the first chart. The data separates pedestrians and bicyclists from motor vehicle occupants because these users are more vulnerable to death or serious injury when involved in a crash. Data for motor vehicle and combined bicyclist and pedestrian KSI can be looked at as a raw total, normalized based on population (per capita), or based on vehicle miles driven (per VMT).

Each year, transit agencies have to fulfill the Federal Transit Agency’s (FTA) TPM requirements by reporting data to the FTA’s National Transit Database (NTD) on passengers who are killed and injured (regardless of severity) on their services, employees who are injured at work, and safety events. Transit fatalities are defined as deaths confirmed within thirty days, excluding deaths from trespassing and suicide. SEPTA includes fatalities from trespassers and suicides in their TPM reporting and target setting, while New Jersey Transit and PATCO do not. To use consistent data for all three transit agencies, trespassing deaths and suicides are included in this analysis. Transit injuries are defined as harm to a person which requires immediate medical attention away from the scene. While crime-related injuries are reported to the NTD, they are excluded from the injury performance target. As with fatalities, these are included in the analysis for data consistency. The third table below shows employee injuries per 200,000 work hours, which is also a TPM requirement. Major safety events include collisions, derailments, fires, hazardous material spills, or evacuations. Major security events are excluded from this analysis, per federal guidance.

This dataset shows numbers of people Killed or Seriously Injured (KSI) in Road Traffic Collisions by calendar year for Lincolnshire and districts.

The dataset shows:

• Numbers of people KSI in road collisions

• KSI numbers of children age 0-15

• Numbers of KSI casualties from collisions involving drivers age 17-24 and age 60 and over

• Annual total numbers of fatalities from road collisions

Numbers below 5 have been removed, and where needed one or more further counts of 5 or greater have also been removed. This generally only affects district figures but means some figures for districts will not add up to the Lincolnshire total.

The data is updated annually in May. Source: Lincolnshire Road Safety Partnership (LRSP). For any enquiries about this publication contact stayingalive@lincolnshire.gov.uk

The monthly data on the number of accidents resulting in personal injury and the corresponding number of injured persons are collected by municipality (former decentralization zones). For some accidents, the indication of the town hall is not available: in these cases, the corresponding cell is empty in the "town hall" column. The time series is available from 2001 to 2021. NOTE: The data on road accidents collected by the Local Police in the territory of the Municipality of Milan concern (as per ISTAT indications) only accidents with injuries to people. Accidents that have not caused deaths or injuries are excluded. Persons injured in the accident who died within 30 days of the accident are considered to have died as a result of the accident. NOTE: The data on road accidents collected by the Local Police in the Municipality of Milan concern (as per ISTAT indications) only accidents with injuries to people, excluding those that have not caused deaths or injuries. Persons injured in the accident who died within 30 days of the event are considered to have died as a result of the accident.

Overview:

Information on location and characteristics of crashes in Queensland for all reported Road Traffic Crashes occurred from 1 January 2001 to 30 June 2024.

Fatal, Hospitalisation, Medical treatment and Minor injury:

This dataset contains information on crashes reported to the police which resulted from the movement of at least 1 road vehicle on a road or road related area. Crashes listed in this resource have occurred on a public road and meet one of the following criteria:

• a person is killed or injured, or
• at least 1 vehicle was towed away, or
• the value of the property damage meets the appropriate criteria listed below.

Property damage:

1. $2500 or more damage to property other than vehicles (after 1 December 1999)
2. $2500 or more damage to vehicle and/or other property (after 1 December 1991 and before 1 December 1999)
3. value of property damage is greater than $1000 (before December 1991).

Please note:

• This data has been extracted from the Queensland Road Crash Database.
• Information held in the Road Crash Database on events occurring within the last 12 months is considered preliminary as investigations into crashes can take up to 1 year to finalise.
• Property damage only crashes ceased to be reported/recorded by Queensland Police Service after 31 December 2010.
• These crash location coordinates reference the current Australian geodetic datum is GDA2020 (previously it was GDA94).

Road accidents are accidents in which persons have been killed or injured as a result of driving on public roads and places or where property damage has occurred. The map shows only accidents involving personal injury. Accidents in which only material damage occurred are not shown. The accident atlas contains information from road accident statistics based on reports from police departments. Accidents to which the police have not been called are not included in the statistics. Before the accident coordinates recorded by the police are summarised on the basis of road sections and presented as points in the accident atlas, they must undergo a multi-stage plausibility process. During this process, individual accidents that do not meet plausibility requirements can be sorted out. These accidents are not depicted in the accident atlas. Killed: People who died of the accident within 30 days Severely injured: Persons admitted directly to a hospital for inpatient treatment (at least 24 hours) Lightly injured: all other injured Accident with goods vehicle (GKFZ): Accident involving at least one truck with a normal body and a total weight exceeding 3.5 tonnes, a tanker or special body truck, a tractor unit or other tractor unit (this category is included in “accident with others” in 2016 and 2017) Further information: https://unfallatlas.statistikportal.de/_opendata2021.html Link to the interactive application: https://unfallatlas.statistikportal.de

Participants Twenty people with borderline personality disorder were recruited from outpatient and support services from around Edinburgh, Scotland. Diagnoses were confirmed using the Structured Clinical Interview for DSM-IV (SCID-II). Current symptoms were assessed using the Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD [1]). Adverse childhood events were assessed using the Childhood Trauma Questionnaire (CTQ [2]). Fifteen BPD participants were receiving antidepressant medication and twelve were taking antipsychotic medication. Twenty age- and sex-matched controls were recruited from the community, however four were excluded due to technical issues during scanning, leaving sixteen controls. Exclusion criteria for all participants included pregnancy, MRI contraindications, diagnosis of a psychotic disorder, previous head injury or current illicit substance dependence. Controls met the additional criteria of no personal or familial history of major mental illness. Ethical approval was obtained from the Lothian National Health Service Research Ethics Committee, and all participants provided written informed consent before taking part.

Experimental task Participants performed the Cyberball social exclusion task [3] during functional magnetic resonance imaging (fMRI), adapted from a previous implementation by Kumar et al 2009 [4]. The task involves playing “catch” with two computer-controlled players, during which the participant can be systematically included or excluded from the game. We used this task as it assesses neural responses to social exclusion, is known to activate a range of social brain regions [5] and is amenable to reinforcement learning modelling [4]. The task was modified such that inclusion was varied parametrically over four levels: 0%, 33%, 66% and 100%, achieved by arranging the task into blocks of nine throws, respectively involving zero, one, two or three throws to the participant. Here, 100% inclusion means the degree to which the participant was included was equal to that of the other two players, with each receiving three throws per nine-throw block. Participants were asked to imagine that the other players were real, as exclusion by both human or simulated players has been previously reported to be similarly distressing [6-8]. When the participant received the ball, they indicated which computer player they wished to throw the ball to with a button press. There were four repetitions of each inclusion level, providing 16 experimental blocks in total, with the first block being 100% inclusion, and all subsequent blocks being randomised. Each throwing event had a mean duration of 2700ms, with each being preceded by randomised jitter that was in part adjusted to accommodate the participant’s reaction time from the previous trial, when applicable. This was achieved by comparing the total duration of the previous trial, including reaction time, with the ideal trial time of 2700ms: if this value was exceeded, a random jitter between 0 and 1000ms was subtracted from the mean jitter time of 1500ms; otherwise, the random jitter was added to 1500ms. Jitter therefore varied between 500ms and 2500ms. Mean block duration was 24s, with onsets denoted by the appearance of the cartoon figures following rest, and offsets by the conclusion of the final throw animation. Blocks were randomized, and interleaved with 13s rest blocks. Within blocks, throwing events were jittered to permit event disambiguation for reinforcement learning analysis.

Neuroimaging Scanning took place at the Clinical Research Imaging Centre in Edinburgh, using a 3T Siemens Magnetom Verio scanner. Echo Planar Blood Oxygen Level Dependent images were acquired axially with a TR 1560ms, TE 26ms, flip angle 66’, field of view 220 x 220mm, in-plane resolution 64 x 64, 26 interleaved slices, 347 volumes, resolution 3.4 x 3.4 x 5mm. A high resolution T1 MPRAGE structural image was acquired with TR 2300ms, TE 2.98ms, flip angle 90’, field of view 256 x 256mm, in-plane resolution 256 x 256, 160 interleaved slices, resolution 1 x 1 x 1mm.

References 1 Zanarini MC, Vujanovic AA, Parachini EA, Boulanger JL, Frankenburg FR, Hennen J. Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD): a continuous measure of DSM-IV borderline psychopathology. J Pers Disord 2003; 17: 233–242 2 Bernstein DP, Fink L. Childhood trauma questionnaire: A retrospective self-report: Manual. Psychological Corporation, 1998. 3 Williams KD, Cheung CK, Choi W. Cyberostracism: effects of being ignored over the Internet. J Pers Soc Psychol 2000; 79: 748–762. 4 Kumar P, Waiter G, Ahearn TS, Milders M, Reid I, Steele JD. Frontal operculum temporal difference signals and social motor response learning. Hum Brain Mapp 2009; 30: 1421–1430. 5 Eisenberger NI, Lieberman MD, Williams KD. Does rejection hurt? An FMRI study of social exclusion. Science 2003; 302: 290–292. 6 Zadro L, Williams KD, Richardson R. How low can you go? Ostracism by a computer is sufficient to lower self-reported levels of belonging, control, self-esteem, and meaningful existence. Journal of Experimental Social Psychology 2004; 40: 560–567. 7 Sebastian CL, Tan GCY, Roiser JP, Viding E, Dumontheil I, Blakemore S-J. Developmental influences on the neural bases of responses to social rejection: implications of social neuroscience for education. NeuroImage 2011; 57: 686–694. 8 Gradin VB, Waiter G, Kumar P, Stickle C, Milders M, Matthews K et al. Abnormal neural responses to social exclusion in schizophrenia. PLoS ONE 2012; 7: e42608.

Comments added by Openfmri Curators

===========================================

Defacing

Defacing was performed by the submitter.

Quality Control

Mriqc output was not run on this dataset due to issues we are having with the software. It will be included in the next revision.

Where to discuss the dataset

1) www.openfmri.org/dataset/ds******/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and dsXXXXXX accession number. 3) Send an email to submissions@openfmri.org. Please include the dsXXXXXX accession number in your email.

Bids-validator Output

1: This file is not part of the BIDS specification, make sure it isn't included in 

the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. (code: 1 - NOT_INCLUDED) /participants.json Evidence: participants.json

  Summary:         Available Tasks:    Available Modalities:
  116 Files, 2.01GB    Cyberball        T1w
  36 - Subjects                  bold
  1 - Session

Road traffic accidents are accidents in which people are killed or injured or property is damaged as a result of driving on public roads and squares. The accident atlas contains accidents involving personal injury. Accidents that only result in property damage are not shown. The accident atlas contains information from the statistics of road traffic accidents based on reports from the police stations. The published CSV data was treated as follows: * - Only the accidents in the city of Konstanz were taken into account. * - A unique ID number was created for each accident. * - The historical data for the years 2016 to 2019 have been aggregated. * - The variable "IstStreet" was renamed to "FAULT STATE" after 2017. For consistency, both have been labeled "FAULT" for the entire period. * - The measurements are the same (0, 1, 2). * - The "LIGHT" variable was renamed to "ULICHTVERH" after 2017. For consistency, both were labeled "ULICHTVERH" for the entire period. The measurements are the same (0, 1, 2) * - The complete data set was also subdivided into 6 more for the years 2016 to 2019, each containing accidents involving only bicycles, passenger cars, pedestrians, motorcycles, goods vehicles and others. * - Year and month have been combined in an additional column to facilitate time series comparisons. Variables: * - AccidentID: unique number for each accident * - year-month: year and month combined 2016-1 * - UJAHR: accident year * - UMONAT: accident month * - HOURS: accident hour * - UWEEKDAY: Day of the week (1 = Sunday 2 = Monday 3 = Tuesday 4 = Wednesday 5 = Thursday 6 = Friday 7 = Saturday) * - UK CATEGORIES: Accident categories (criterion for allocation is the most serious consequence of the accident) 1 = accident with fatalities 2 = accident with serious injuries 3 = accident with minor injuries UART: Accident type 1 = collision with approaching/stopping/stationary vehicle 2 = collision with preceding/waiting vehicle 3 = collision with sideways vehicle moving in the same direction 4 = collision with oncoming vehicle 5 = Collision with turning / crossing vehicle 6 = Collision between vehicle and pedestrian 7 = Impact with lane obstacle 8 = Lane departure to the right 9 = Lane departure to the left 0 = Other type of accident * - UART: Type of accident 1 = Collision with approaching/stopping /stationary vehicle 2 = collision with vehicle ahead/waiting 3 = collision with vehicle traveling sideways in the same direction 4 = collision with oncoming vehicle 5 = collision with turning / crossing vehicle 6 = collision between vehicle and pedestrian 7 = collision with roadway obstacle 8 = departure from lane to the right 9 = departure from lane to the left 0 = accident of a different kind * - UTYP1: Accident type 1 = driving accident 2 = turning accident 3 = turning / crossing accident 4 = crossing accident 5 = accident caused by stationary traffic 6 = accident in parallel traffic 7 = other accident * - LIGHT CONDITIONS: lighting conditions 0 = daylight 1 = twilight 2 = darkness * - IstRad: accident in which at least one bicycle was involved 0 = accident without bicycle involvement 1 = accident with bicycle involvement * - IstPKW Accident with car: Accident in which at least one passenger car was involved 0 = Accident without car involvement 1 = Accident with car involvement * - IstFuss Accident with pedestrian: Accident in which at least one pedestrian was involved 0 = Accident without Pedestrian participation 1 = accident involving pedestrians * - IstKrad Accident involving a motorcycle: Accident involving at least one motorcycle, e.g. B. moped, motorcycle/scooter was involved 0 = accident without motorcycle participation 1 = accident with motorcycle participation * - IstGkfz: Accident with goods vehicle (GKFZ): Accident involving at least one truck with normal body and a total weight of more than 3.5 t truck with tank support or special body, a tractor unit or another tractor unit was involved (this category is included in "Accident with other" in 2016 and 2017) 0 = accident without goods vehicle involvement 1 = accident with goods vehicle involvement * - ActualOther: Accident with other: accident involving at least one means of transport not mentioned above, e.g. B. a bus or a tram (2016 and 2017 inclusive) accident with goods vehicle (GKFZ), from 2018 without accident with GKFZ) 0 = accident without involving a means of transport not mentioned above 1 = accident involving a means of transport not mentioned above * - LINREFX and LINREFY : Graphical coordinate 1 and graphic coordinate 2LINREFX and LINREFY form the coordinate of the accident location on the road section (UTM coordinate of the reference system ETRS89, zone 32N). XGCSWGS84 and YGCSWGS84: Graphic coordinate 1 and graphic coordinate 2 XGCSWGS84 and YGCSWGS84 form the coordinate of the accident site on the road section (coordinate of the reference system GK 3) For further explanations see destatis.de (Source: Accident Atlas of the Federal and State Statistical Offices - Open Data) ### Data source : Open Data Konstanz under DL-DE/BY 2.0