THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

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.

TBI Hospitalizations/Deaths

A 2001-2010 Study of U.S. Rates

By Health [source]

About this dataset

This dataset examines the troubling national rise in traumatic brain injury (TBI)-related emergency department (ED) visits, hospitalizations and deaths over the past decade. While TBI-related ED visits make up a large share of this increase, rates of hospitalizations related to TBI remain relatively stable. The total combined rate of all three categories steadily increased from 521.0 per 100,000 people in 2001 to 823.7 per 100,000 people in 2010 – an alarming 57% rise that demands our attention and rapid solutions in order to reverse this trend. Not only is the sudden spike concerning but so too is the slightly decreasing rates for TBI-related deaths which dropped from 18.5 per 100,000 to 17.1 per 100,000 over this time period despite overall numbers continuing to climb upwards with no sign of slowing down soon. Have a look at this dataset and explore what we can do together to work towards a healthier future free of needless fatalities caused by preventable injuries such as those related to TBIs

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How to use the dataset

• Take a look at the Total column – it combines all 3 types of hospitalization numbers (Emergency Department Visits, Hospitalizations and Deaths) together into one figure per year. This makes it easy to see what the overall rate over time has been.

• The Emergency Department Visits, Hospitalizations and Deaths columns can be used individually as well – view them separately on their own scales so you can better compare them against each other year by year.

• Use filtering tools or visualizations tools if you’d like to dive deeper into each figure separately in order to pinpoint trends or changes in any particular subcategory more closely.

• The data is displayed historically; however, use math operations such as averaging or percentage increases/decreases across different years if you’d like analyze trends over time more broadly

Research Ideas

• To compare the rate of TBI-related hospitalizations, ED visits and deaths between states/countries/age groups.
• To create a visual representation (i.e., an infographic) to track TBI-related hospitalization, ED visit and death rates over the past decade in order to inform public health initiatives.
• To study the effect of investments made in prevention programs on the rate of TBI-related hospitalizations, ED visits and deaths in different regions or cities over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Rates_of_TBI-related_Emergency_Department_Visits_Hospitalizations_and_Deaths_United_States_2001_2010.csv | Column name | Description | |:--------------------------------|:------------------------------------------------------------------------------------------------| | Year | Year of the data point. (Integer) | | Emergency Department Visits | Number of TBI-related emergency department visits per 100,000 people. (Float) | | Hospitalizations | Number of TBI-related hospitalizations per 100,000 people. (Float) | | Deaths | Number of TBI-related deaths per 100,000 people. (Float) | | Total | Total number of TBI-related ED visits, hospitalizations, and deaths per 100,000 people. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

The dataset shows death claims accepted by the CNESST from January 1 to December 31. The CNESST administers the occupational health and safety regime. The Law on Industrial Accidents and Occupational Diseases (LATMP) aims to compensate for occupational injuries and the consequences they cause for beneficiaries. The death claims presented in the dataset meet the following criteria: * They are the consequence of a work accident or an occupational disease within the meaning of the LATMP. * These claims represent people who were covered by the occupational health and safety insurance plan administered by the CNESST. * The registration date for the acceptance of the death claim is between January 1 and December 31 of the reference year. Note that the death may have occurred during a year prior to the reference year.

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

As of 2024, annual fatality data (including previous years) is reported by year of death.

The dataset contains structured information on the life, work, and death of more than 1 million deceased famous people.

Paper abstract (ICWSM proceedings)

We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project. The dataset is the largest on notable deceased people and includes individuals from a variety of social groups, including but not limited to 107k females, 124 non-binary people, and 90k researchers, who are spread across more than 300 contemporary or historical regions. The final product provides new insights into the demographics of mortality in relation to gender and profession in history. The technical method demonstrates the usability of the latest text mining approaches to accurately clean historical data and reduce the missing values.

How to cite:

Annamoradnejad, Issa; Annamoradnejad, Rahimberdi (2022), “Age dataset: A structured general-purpose dataset on life, work, and death of 1.22 million distinguished people”, In Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media (ICWSM), doi: 10.36190/2022.82

Source: http://workshop-proceedings.icwsm.org/abstract?id=2022_82

This dataset is about countries per year in Sudan. It has 64 rows. It features 4 columns: country, death rate, and individuals using the Internet.

The indicator measures the number of fatal accidents that occur during the course of work and lead to the death of the victim within one year of the accident. The incidence rate refers to the number of fatal accidents per 100 000 persons in employment. An accident at work is 'a discrete occurrence in the course of work which leads to physical or mental harm'. This includes all accidents in the course of work, whether they happen inside or outside the premises of the employer, accidents in public places or different means of transport during a journey in the course of the work (commuting accidents are excluded) and at home (such as during teleworking). It also includes cases of acute poisoning and wilful acts of other persons, if these happened during the course of the work.

Notable Deaths on Wikipedia

Pageviews and Patterns of Visitation

By data.world's Admin [source]

About this dataset

We’re asking how stories live on in digital spaces—and what that reveals about our broader cultural values. We invite you to consider this data from a socially critical perspective to explore which people get their stories honored long term via massive online attention… is it limited to celebrities or do more ordinary people have a place? What can we learn when we look closely at those who are memorialized through vast networks? Join us in attempting this open-ended conversation through exploring Wiki Deaths!

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How to use the dataset

This dataset contains information about notable deaths from 2015-2018, and can be used to analyze the public impact of these people after their deaths on Wikipedia.

Here are a few steps you can use to work with this dataset:

• Explore the available variables - The dataset includes fields such as name, year of birth and death, pageviews and other related information which can be used to compare the impact of different individuals after their death.

• Investigate differences between years - Use this data set to compare how public interest changes across years by looking at variables such as median pageviews after death.

• Identify outliers - Take a look at maximum pageviews compared to median pageviews before death in order to identify individual cases that had particularly high increases in traffic or particularly dramatic falls in traffic following the person's death.

• Analyze trends and patterns - Look through extracted HTML fields for specific patterns related to notable deaths from 2015-2018 in order to gain better understanding of what topics were popular during that time frame and where interest has been growing or declining since then

Research Ideas

• Investigating the effects of trends on notable deaths and related pageview activity. For example, analyzing how the peak popularity of a celebrity within a certain year may impact their Wikipedia pageviews posthumously.
• Exploring the impact of social media campaigns surrounding a notable death and the potential increase in web traffic that follows during this period.
• Examining how display practices (e.g., smaller thumbnails, fewer clickable links) can influence user engagement with certain Wikipedia pages as well as collective memorialization behaviors post-death of notables on the platform

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: people.csv | Column name | Description | |:-----------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------| | link | The link to the Wikipedia page of the notable person. (String) | | name | The name of the notable person. (String) | | year_of_birth | The year the notable person was born. (Integer) | | year_of_death | The year the notable person died. (Integer) | | date_of_death | The date the notable person died. (Date) | | timestamp_of_death | The timestamp of the notable person's death. (Timestamp) | | median_views | The median pageviews of the notable person's Wikipedia page. (Integer) | | median_views_before | The median pageviews of the notable person's Wikiped...

What is The Counted? The Counted is a project by the Guardian – and you – working to count the number of people killed by police and other law enforcement agencies in the United States throughout 2015, to monitor their demographics and to tell the stories of how they died. The database will combine Guardian reporting with verified crowdsourced information to build a more comprehensive record of such fatalities. The Counted is the most thorough public accounting for deadly use of force in the US, but it will operate as an imperfect work in progress – and will be updated by Guardian reporters and interactive journalists as frequently and as promptly as possible.

This dataset is about countries per year in Egypt. It has 64 rows. It features 4 columns: country, death rate, and individuals using the Internet.

This dataset is about countries per year in Qatar. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, death rate, and individuals using the Internet.

This dataset is about countries per year in Moldova. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, death rate, and individuals using the Internet.

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The National Child Development Deaths Dataset, 1958-2014: Special Licence Access contains data on known deaths among members of the NCDS birth cohort from 1958 to 2013. Information on deaths has been taken from the records maintained by the organisations responsible for the study over the life time of the study: the National Birthday Trust Fund, the National Children’s Bureau (NCB), the Social Statistics Research Unit (SSRU) and the CLS. The information has been gleaned from a variety of sources, including death certificates and other information from the National Health Service Central Register (NHSCR), and from relatives and friends during survey activities and cohort maintenance work by telephone, letter and e-mail. It includes all deaths up to 31st December 2013. In only 6 cases are the date of death unknown. By the end of December 8.7 per cent of the cohort were known to have died.

The National Child Development Study Response and Outcomes Dataset, 1958-2013 (SN 5560) covers other responses and outcomes of the cohort members and should be used alongside this dataset.

For the 3rd edition (July 2018) an updated version of the data was deposited. The new edition includes data on known deaths among members of the National Child Development Study (NCDS) birth cohort up to 2016. The user guide has also been updated.

This dataset is about countries per year in Burkina Faso. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, death rate, and individuals using the Internet.

The dataset shows death claims accepted by the CNESST from January 1 to December 31. The CNESST administers the occupational health and safety regime. The Law on Industrial Accidents and Occupational Diseases (LATMP) aims to compensate for occupational injuries and the consequences they cause for beneficiaries. The death claims presented in the data set meet the following criteria: * They are the consequence of a work accident or an occupational disease within the meaning of the LATMP. * These claims represent people who were covered by the occupational health and safety insurance plan administered by the CNESST. * The date of registration of the acceptance of the death claim is between January 1 and December 31 of the reference year. Note that the death may have occurred during a year prior to the reference year.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

The 1970 British Cohort Study (BCS70) is a longitudinal birth cohort study, following a nationally representative sample of over 17,000 people born in England, Scotland and Wales in a single week of 1970. Cohort members have been surveyed throughout their childhood and adult lives, mapping their individual trajectories and creating a unique resource for researchers. It is one of very few longitudinal studies following people of this generation anywhere in the world.Since 1970, cohort members have been surveyed at ages 5, 10, 16, 26, 30, 34, 38, 42, 46, and 51. Featuring a range of objective measures and rich self-reported data, BCS70 covers an incredible amount of ground and can be used in research on many topics. Evidence from BCS70 has illuminated important issues for our society across five decades. Key findings include how reading for pleasure matters for children's cognitive development, why grammar schools have not reduced social inequalities, and how childhood experiences can impact on mental health in mid-life. Every day researchers from across the scientific community are using this important study to make new connections and discoveries.BCS70 is run by the Centre for Longitudinal Studies (CLS), a research centre in the UCL Institute of Education, which is part of University College London. The content of BCS70 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from BCS70 that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Secure Access datasetsSecure Access versions of BCS70 have more restrictive access conditions than versions available under the standard End User Licence (EUL). The 1970 British Cohort Study Deaths Dataset, 1970-2014: Special Licence Access contains data on known deaths among members of the BCS70 birth cohort from 1970 to 2014. Information on deaths has been taken from the records maintained by the organisations responsible for the study over the lifetime of the study - the National Birthday Trust Fund, the National Children’s Bureau (NCB), the SSRU and the CLS. The information has been gleaned from a variety of sources, including death certificates and other information from the National Health Service Central Register (NHSCR), and from relatives and friends during survey activities and cohort maintenance work by telephone, letter and e-mail. It includes all deaths up to 31st December 2014. In only 40 cases are the date of death unknown. By the end of December 8.7 per cent of the cohort were known to have died.

The 1970 British Cohort Study Response Dataset, 1970-2012 (SN 5641) covers other responses and outcomes of the cohort members and should be used alongside this dataset. In April 2025, the user guide for SN 5641 was added to this study to replace the previous one, at the depositor's request.

The study includes the following four variables:BCS70 serial numbermonth of deathyear of deathsource of death information