The National Prisoner Statistics (NPS) data collection began in 1926 in response to a congressional mandate to gather information on persons incarcerated in state and federal prisons. Originally under the auspices of the U.S. Census Bureau, the collection moved to the Bureau of Prisons in 1950, and then in 1971 to the National Criminal Justice Information and Statistics Service, the precursor to the Bureau of Justice Statistics (BJS) which was established in 1979. From 1979 to 2013, the Census Bureau was the NPS data collection agent. In 2014, the collection was competitively bid in conjunction with the National Corrections Reporting Program (NCRP), since many of the respondents for NPS and NCRP are the same. The contract was awarded to Abt Associates, Inc. The NPS is administered to 51 respondents. Before 2001, the District of Columbia was also a respondent, but responsibility for housing the District of Columbia's sentenced prisoners was transferred to the Federal Bureau of Prisons, and by yearend 2001 the District of Columbia no longer operated a prison system. The NPS provides an enumeration of persons in state and federal prisons and collects data on key characteristics of the nation's prison population. NPS has been adapted over time to keep pace with the changing information needs of the public, researchers, and federal, state, and local governments.

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

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How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind: •State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

• Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.
• Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.
• Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - 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.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------------------------| | STATE/UT | Name of the state or union territory where the jail is located. (String) | | YEAR | Year when the inmate population data was collected. (Integer) ...

Overview

The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

Methodology and Caveats

The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

About the Data

There are four tables in this data:

• covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

• prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

• staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

• covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

Queries

The Associated Press and The Marshall Project have created several queries to help you use this data:

Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

Rank all systems' most recent data by cases per 100,000 prisoners here

Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

Attribution

In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

Contributors

Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

Questions

If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

This dataset includes people released to Philadelphia from the Philadelphia Department of Prisons (PDP) and the Pennsylvania Department of Correction (PA DOC). Individual-level data for releases from Federal (BOP) incarceration was not available, and makes up less than 2% of people released to Philadelphia in the year analyzed. The dataset also only includes people released to Philadelphia who have been charged with a criminal non-summary type offense in the Philadelphia adult criminal justice system.

Adult correctional services, custodial and community supervision, average counts of offenders in federal programs, Canada and regions, five years of data.

The 1922 Prison System Enquiry Committee Report said that: 'In order to judge our Prison system rightly it is necessary to know what kind of people become prisoners... How many go to prison? For what length of sentence?' These questions persist, and are especially relevant for today's prison crisis. This project will assess nearly 100 years of historical data to explore, for the first time, how prison numbers were largely dictated by the repeat incarceration of recidivist's offenders with short sentences. It questions how the prison authorities attempted to manage increasing numbers of offenders by using early release schemes (licenses) in the nineteenth and twentieth centuries (licenses have only recently become available, generous access granted by The National Archives). This project will explore whether short sentences contributed to repeat offending, and whether early release schemes accelerated or inhibited recidivism. It investigates the financial costs of imprisonment to the country (and the human costs to those imprisoned) and does this over a significant period of time (allowing an examination of how repeated incarcerations affected the whole of an offender's criminal career). It concludes by asking what lessons can be learnt for today's debates about sentencing offenders and managing the prison population? Data was derived from the following sources: PCOM 3 (1853-1887, 1902-08, 1912-42) – these files contained 45,000 licenses and also the registers of license holders. They listed the prisoner’s name, sentence, where/when convicted, dates and conditions of the current license; previous convictions, age, previous occupation, when and from where the prisoner was released; and most had photographs of the prisoner. The National Archives granted us access to these records pre public release (they are now available on Find-My-Past and Ancestry). Criminal Registers 1853-1892 (contained offenders tried for indictable crimes, whether they were found guilty, details of the offence, and sentence imposed). Where possible we traced these offences in the Quarter Sessions Calendars in order to trace the antecedent criminal history. From these main sources, we were then able to trace prisoners released on license using a wide variety of other extant sources. These sources provided us with a considerable amount of additional information on offenders who were released on license: Census returns from 1841-1911 censuses (which gave details of the residence, family status, and occupation, of each person we will be searching for). Online Birth, Marriage and Death indices (which detailed if and when our offender was married, and had children; and, of course, when they died). Military records (mainly referring to World War One; these included service records - which in turn included disciplinary breaches - medal indices and pensions details. Metropolitan Police records including Habitual Criminal Registers (MEPO 6) which contain details of criminals as defined by sections 5-8 of the Prevention of Crimes Act 1871. From the sources above we constructed approximately 650 life grids. These were divided into an early (1853-55 n=62), middle (1871-73 n=201), and late (1885-1887 n=184) tranche, for 356 men and 288 women. Each life-grid charted offending/life histories for each offender. Studies funded by Leverhulme Trust (F/00130/H)) and ESRC (RES-062-23-0416) used life grids and `whole-life’ research methods and the method is now well-tested. The life-grid data was then entered into excel and SPSS in order to produce quantifiable data on - the progress of their criminal careers, their periods of incarceration, their employment careers, life events such as marriage, death of parents, and other significant life events. We had over two hundred thousand fields of data at the conclusion of our data collection/analysis. By analysing each of the life grids we were able to see the relationships and connections between life events and offending post-imprisonment (both short and long periods of custody, whilst on licence, and after license had expired.

The Jails and Prisons sub-layer is part of the Emergency Law Enforcement Sector and the Critical Infrastructure Category. A Jail or Prison consists of any facility or location where individuals are regularly and lawfully detained against their will. This includes Federal and State prisons, local jails, and juvenile detention facilities, as well as law enforcement temporary holding facilities. Work camps, including camps operated seasonally, are included if they otherwise meet the definition. A Federal Prison is a facility operated by the Federal Bureau of Prisons for the incarceration of individuals. A State Prison is a facility operated by a state, commonwealth, or territory of the US for the incarceration of individuals for a term usually longer than 1 year. A Juvenile Detention Facility is a facility for the incarceration of those who have not yet reached the age of majority (usually 18 years). A Local Jail is a locally administered facility that holds inmates beyond arraignment (usually 72 hours) and is staffed by municipal or county employees. A temporary holding facility, sometimes referred to as a "police lock up" or "drunk tank", is a facility used to detain people prior to arraignment. Locations that are administrative offices only are excluded from the dataset. This definition of Jails is consistent with that used by the Department of Justice (DOJ) in their "National Jail Census", with the exception of "temporary holding facilities", which the DOJ excludes. Locations which function primarily as law enforcement offices are included in this dataset if they have holding cells. If the facility is enclosed with a fence, wall, or structure with a gate around the buildings only, the locations were depicted as "on entity" at the center of the facility. If the facility's buildings are not enclosed, the locations were depicted as "on entity" on the main building or "block face" on the correct street segment. Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes of constables are included due to the fact that many constables work out of their homes. TGS has made a concerted effort to include all correctional institutions. This dataset includes non license restricted data from the following federal agencies: Bureau of Indian Affairs; Bureau of Reclamation; U.S. Park Police; Federal Bureau of Prisons; Bureau of Alcohol, Tobacco, Firearms and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection. The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. With the merge of the Law Enforcement and the Correctional Institutions datasets, NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer). Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 05/03/2006 and the newest record dates from 10/19/2009.

Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. A threat to cause the mass release of prisoners by an outside terrorist group has been identified. Steps need to be taken to provide extra security at the targeted prisons. 2. Massive civil unrest has resulted in a large number of arrests. Appropriate space is needed outside of the immediate area to house the arrested individuals. 3. Massive civil unrest has resulted in a large number of arrests. A "holding camp" has been established to hold those arrested. Trained security guards are needed to staff the holding camp. 4. A disaster has caused the need for an emergency labor force (e.g., sandbagging during a flood) and prisoners may fill that need. 5. Inmates may need to be evacuated, or appropriate steps may need to be taken at a prison to protect the inmates and to ensure that a disaster does not present an opportunity for escape.

Adult correctional services, custodial and community supervision, average counts of adults in provincial and territorial programs, five years of data.

This data collection, conducted in a federal penitentiary and prison camp in Terre Haute, Indiana, between September 1986 and July 1988, was undertaken to examine the reliability and validity of psychological classification systems for adult male inmates. The classification systems tested were Warren's Interpersonal Maturity Level (I-level), Quay Adult Internal Management Systems (AIMS), Jesness Inventory, Megargee's MMPI-Based Prison Typology, and Hunt's Conceptual Level. The study sought to answer the following questions: (a) Which psychological classification systems or combination of systems could be used most effectively with adult populations? (b) What procedures (e.g., interview, paper-and-pencil test, staff assessment, or combination) would assure maximum efficiency without compromising psychometric precision? (c) What could the commonalities and differences among the systems reveal about the specific systems and about general classification issues pertinent to this population? and (d) How could the systems better portray the prison experience? The penitentiary was a low-maximum-security facility and the prison camp was a minimum-security one. A total of 179 penitentiary inmates and 190 camp inmates participated. The study employed both a pre-post and a correlational design. At intake, project staff members interviewed inmates, obtained social, demographic, and criminal history background data from administrative records and test scores, and then classified the inmates by means of an I-level diagnosis. Social and demographic data collected at intake included date of entry into the prison, age, race, marital status, number of dependents, education, recorded psychological diagnoses, occupation and social economic status, military service, evidence of problems in the military, ability to hold a job, and residential stability. Criminal history data provided include age at first nontraffic arrest, arrests and convictions, prison or jail sentences, alcohol or drug use, total number and kinds of charges for current offense, types of weapon and victims involved, co-offender involvement, victim-offender relationship, if the criminal activity required complex skills, type of conviction, and sentence length. T-scores for social maladjustment, immaturity, autism, alienation, manifest aggression, withdrawal, social anxiety, repression, and denial were also gathered via the Jesness Inventory and the MMPI. Interview data cover the inmates' interactions within the prison, their concerns about prison life, their primary difficulties and strategies for coping with them, evidence of guilt or empathy, orientation to the criminal label, relationships with family and friends, handling problems and affectivity, use of alcohol and drugs, and experiences with work and school. For the follow-up, the various types of assessment activities were periodically conducted for six months or until the inmate's release date, if the inmate was required to serve less than six months. Data collected at follow-up came from surveys of inmates, official reports of disciplinary infractions or victimizations, and prison staff assessments of inmates' prison adjustment and work performance. The follow-up surveys collected information on inmates' participation in treatment and educational programs, work absenteeism, health, victimization experiences and threats, awards, participation in aggressive, threatening, or other illegal activities, contact with family and friends, communication strategies, stress, sources of stress, and attitudes and beliefs about crime and imprisonment. Follow-up ratings by prison staff characterized the inmates on several clinical scales, according to each rater's global assessment of the interviewee. These characteristics included concern for others, role-taking abilities, assertiveness, inmate's relations with other inmates, authorities, and staff, verbal and physical aggressiveness, emotional control under stress, cooperativeness, need for supervision, response to supervision, maturity, behavior toward other inmates, and behavior toward staff.

This dataset lists inmates incarcerated at Cockatoo Island prison in Sydney (Australia) between 1847-1869. It offers insights into how the colonial criminal justice system operated after New South Wales’ transition from a penal colony to a ‘free’ colony when transportation ceased in 1840. It is a useful tool for genealogists tracing the lives of their criminal ancestors and for historians of crime and punishment researching nineteenth-century Australia. The dataset includes prisoners' names and aliases, their ship of arrival, place of origin, details of their colonial conviction(s) (trial place, court, offence, sentence), date(s) admitted to Cockatoo Island, and when and how they were discharged from Cockatoo Island. In some cases, it also includes prisoners' place of origin, occupation, biometric information (height, eye/hair colour, complexion, scars, tattoos), 'condition upon arrival' (convict or free), and (for convicts) details of their original conviction in Britain or Ireland. As a UNESCO World Heritage 'Convict Site' Cockatoo Island is best known as a site of secondary punishment for recidivist convicts, especially those transferred from Norfolk Island. This dataset demonstrates the diversity of the prison population: including nominally free convicts (ticket-of-leave holders), migrants from Britain, China and other Australian colonies drawn in by the gold rush, exiles from Port Phillip, Aboriginal Australians convicted during frontier warfare, colonial-born white Australians (including bushrangers), and black, Indian and American sailors visiting Sydney. Significant attention has been paid to the more than 160,000 British and Irish convicts who were transported Australia as colonists between 1787 and 1868. Much less has been said about those punished within the criminal justice system that arose in the wake of New South Wales' transition from 'penal' to 'free' colony (Finnane, 1997: x-xi). Cockatoo Island prison opened in 1839, a year before convict transportation to New South Wales ceased, and was intended to punish the most recidivist and violent of the transported convicts. This archetype has prevailed in historical discourses, and they have been described as 'criminal lunatics... [and] criminals incapable of reform' (Parker, 1977: 61); 'the most desperate and abandoned characters' (O'Carrigan, 1994: 64); and people of 'doubtful character' (NSW Government Architect's Office, 2009: 29). Yet, this was far from the truth. My analysis of 1666 prisoners arriving between 1839-52 show they were overwhelming non-violent offenders, tried for minor property crimes at lower courts. They were also far more diverse population than commonly recognised, including Indigenous Australian, Chinese and black convicts alongside majority British and Irish men (Harman, 2012). This project will make publicly available extremely detailed records relating to Cockatoo Island's prisoners to show people firsthand exactly who made up the inmate population. The digital version of the original registers will include information on convicts' criminal record, but also their job, whether they were married or had children, and even what they looked like. It will also be a name-searchable database so family historians can search for their ancestors, who may have been incarcerated on the island. As it stands, they will be able find information online about ancestors who were transported as long as they remained in the 'convict system', but they may seem to disappear as soon as they are awarded their ticket-of-leave and become 'free'. However, many former convicts, and free immigrants, to New South Wales were convicted locally, and these records can give us information about their lives within the colony. The type of data included in these registers will also allow researchers to investigate questions including: (1) were convicts more likely to offend again than free immigrants? (2) Were the children of convicts more likely to offend than others? (3) Did the influx of mostly Chinese migrants during the gold rush actually lead to a crime-wave, as reported in the press? (4) Were laws introduced between 1830 and 1853, actually effective at prosecuting bushrangers (highwaymen)? (5) Was the criminal-judicial system in Australia more rehabilitative, despite developing out of a harsher convict transportation system? Alongside the dataset, the website will include 'life-biographies' of individual convicts to show you how this dataset can be used to piece together a life-story. It also to warns against understanding a real-life person only through the records of their conviction. There many of fascinating stories to tell, including those 'John Perry' ('Black Perry') the prizewinning boxer; the love story of the 'Two Fredericks'; and Tan, the Chinese gold-digger who resisted his incarceration. In addition, there will be teaching resources for secondary school children and undergraduate university students who want to engage directly with historical materials, without having to leave their classroom. Overall, this website invites anyone with an interest in the history of crime and punishment, and any visitors to the UNESCO world heritage site 'Cockatoo Island', to try searching for a name in the database or read about a featured convict's life story. It asks them, though, to think about how and why these people's lives intersected with the state, leading to their incarceration, and how history has erased much of their lives outside of it. Data collection involved photographing a Cockatoo Island’s surviving prison registers and returns kept at the State Archives of New South Wales (call numbers: 4/4540, 4/6501, 4/6509, 6571, 4/6572, 4/6573, 4/6574, 4/6575, X819). In these volumes, clerks had listed details of incoming prisoners on the dates they arrived between April 1847 and October 1869. This prison register for the period 1839-46 (call number: 2/8285) had not survived to a good enough quality for accurate transcription and was excluded from data collection. I photographed and then transcribed these records in full into a tabular form, with minor standardisation of abbreviations and irregular spellings. Where multiple records existed for one person I combined information from two separate archival records into one line of the dataset. Where I could not verify that two people with the same name were the same person, I listed them as separate entries. Barring errors in entry at the time of record creation, the studied population represents the entire population of prisoners incarcerated at Cockatoo Island between April 1847 and October 1869 when the prison closed.

Summary: This is a collection of publicly reported data relevant to the COVID-19 pandemic scraped from state and federal prisons in the United States. Data are collected each night from every state and federal correctional agency’s site that has data available. Data from Massachusetts come directly from the ACLU Massachusetts COVID-19 website (https://data.aclum.org/sjc-12926-tracker/), not the Massachusetts DOC website. Data from a small number of states come from Recidiviz (https://www.recidiviz.org/) whose team manually collects data from these states. Not all dates are available for some states due to websites being down or changes to the website that cause some data to be missed by the scraper.The data primarily cover the number of people incarcerated in these facilities who have tested positive, negative, recovered, and have died from COVID-19. Many - but not all - states also provide this information for staff members. This dataset includes every variable that any state makes available. While there are dozens of variables in the data, most apply to only a small number of states or a single state.The data is primarily at the facility-date unit, meaning that each row represents a single prison facility on a single date. The date is the date we scraped the data (we do so each night between 9pm-3am EST) and not necessarily the date the data was updated. While many states update daily, some do so less frequently. As such, you may see some dates for certain states contain the same values. A small number of states do not provide facility-level data, or do so for only a subset of all the variables they make available. In these cases we have also collected state-level data and made that available separately. Please note: When facility data is available, the state-level file combines the aggregated facility-level data with any state-level data that is available. You should therefore use this file when doing a state-level analysis instead of aggregating the facility-level data, as some states report values only at the state level (these states may still have some data at the facility-level), and some states report cumulative numbers at the state level but do not report them at the facility level. As a result, when we identify this, we typically add the cumulative information to the state level file. The state level file is still undergoing quality checks and will be released soon.These data were scraped from nearly all state and federal prison websites that make their data available each night for several months, and we continue to collect data. Over time some states have changed what variables are available, both adding and removing some variables, as well as the definition of variables. For all states and time periods you are using this data for, please carefully examine the data to detect these kinds of issues. We have spent extensive time doing a careful check of the data to remove any issues we find, primarily ones that could be caused by a scraper not working properly. However, please check all data for issues before using it. Contact us at covidprisondata@gmail.com to let us know if you find any issues, have questions, or if you would like to collaborate on research.

Absolute changes in life expectancy at age 20 among people in prisons, by race & sex across periods, 2000–2014.

In order to develop a better understanding of the factors that influence whether a male prisoner's family stays involved in his life during incarceration, researchers conducted face-to-face interviews with inmates from two New Jersey prisons and their family members between May 2005 and July 2006. A total of 35 (25 from one prison and 10 from the other) inmates and 15 family members were interviewed, comprising 13 inmate and family dyads, 1 inmate and family triad, and an additional 21 inmate interviews. The data include variables that explore the family's relationship with the incarcerated individual in the following areas: the inmate's relationship with the family prior to the incarceration, the strain (emotional, economic, stigma) that the incarceration has placed on the family, the economic resources available to the family to maintain the inmate, the family's social support system, and the inmate's efforts to improve or rehabilitate himself while incarcerated.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study is a secondary analysis of data from ICPSR Study Number 27101, Serious and Violent Offender Reentry Initiative (SVORI) Multi-site Impact Evaluation, 2004-2011 [United States]- specifically the adult male dataset -to examine the associations among child support obligations, employment and reentry outcomes. The study addressed the following research questions: Are the demographic, criminal justice and employment-related characteristics of incarcerated men with child support orders significantly different in any important way from incarcerated males without child support orders? Did SVORI clients receive more support and services related to child support orders and modification of debt after release from prison compared to non-SVORI participants? Does having legal child support obligations decrease the likelihood of employment in later waves, net of key demographic and criminal justice history factors? How does employment influence the relationship between child support debt and recidivism? and Is family instrumental support a significant predictor of reduced recidivism or increased employment in models assessing the relationship between child support obligations, employment and recidivism? The study includes one document (Syntax_ChildSupport_Reentry_forICPSR_2012-IJ-CX-0012.docx) which contains SPSS and Stata syntax used to create research variables.

OLS estimates of the association between state-level criminal justice and socioeconomic conditions on prison mortality rates in states reporting to the NCRP, 2000–2014.

Abstract copyright UK Data Service and data collection copyright owner. This is a qualitative data collection. The project examined individual and collective identities in prisons. In particular, it focused on how ethnic and masculine identities have a bearing on prisoners' social relationships. The study had three main aims:to examine the role of ethnic identities in shaping social relationships in prison, and compare this with relations in prisoners' home communitiesto determine how different racialised masculine identities are expressed in terms of, for example, ethnicity, religion, age, nationality and regionalityto assess the influence of institutional practices on individuals and group identities, the extent and nature of prisoner solidarity, and provide evidence of social hierarchies and gang membership that are influenced by identityThe project comprised two ethnographic studies conducted in Kent, at a male young offenders' institution (Her Majesty's Young Offenders' Institution (HMYOI) Rochester) and an adult male prison (Her Majesty's Prison (HMP) Maidstone). Two main research methods were used: interaction and observation of prison life over an eight-month period; and semi-structured interviews with 60 young adult prisoners and 50 adult prisoners. Activities at the two research sites were as follows: at HMP Maidstone, which is a Category C prison for adult men, the researchers observed prisoner social relations through informal interactions with prisoners on the wings, interacting with them at work, in classes, during association, exercise, worship and visiting times. Fifty prisoners agreed to be interviewed, and five also took part in a focus group interviewAt HMYOI Rochester, a prison for young men aged 18-21 years, the researchers observed prisoner social relations through informal interactions with prisoners on the wings, interacting with them at work, in classes, during associations, exercise, worship, and visiting times. Sixty prisoners at Rochester agreed to be interviewed.This collection comprises 111 interview transcripts. Users should note that access to these data requires express permission of the depositor. Further information may be found on the Ethnicity, Identity, and Social Relations in Prison ESRC project award web page, and on the Identities and Social Action research programme web site. Main Topics: Topics covered in the interviews include background and family circumstances, educational and employment history, social networks and friendships, significant personal relationships, strategies for coping with life while in prison, interaction with other prisoners, and observations on social structures and networks based around ethnicity/religion in prison. One-stage stratified or systematic random sample

Contains national information on prisoners who were in custody on 30 June each year. The statistics are derived from information collected by the ABS from corrective services agencies in each state and territory. Details are provided on the number of people in correctional institutions (including people on remand), imprisonment rates, most serious offence and sentence length. Information is also presented on prisoner characteristics (age, sex, Indigenous status) and on the type of prisoner (all prisoners, sentenced prisoners, and unsentenced prisoners (remandees).

A. SUMMARY To confirm the completeness of this dataset please contact the Sheriff's Office at sheriff.tech.services@sfgov.org

The dataset provides summary information on individuals booked into the San Francisco City and County Jail since 2012, categorized by Male/Female. The table provides a breakdown of the total number of bookings by month and Male/Female. The unit of measure is the jail booking number. The data is collected by the Sheriff's Office and includes self-reported and assigned data.

The booking process refers to the procedure that occurs after an individual has been arrested and is taken into custody. The process begins with the arrest of an individual by law enforcement officers. The arrest can take place on the scene or at a later time if a warrant is issued. Once the individual has been arrested, and statutory law requires incarceration, they would be transported to the jail for booking. The arresting officer will record the reason for the arrest, along with any other relevant information. The sheriff’s deputies will then book the individual into jail, which involves taking their fingerprints, photograph, and recording personal information. The jail will assign a booking number, which is used to identify the individual throughout their time in custody. Once the booking process is complete, the individual will be incarcerated and will remain in custody until they are released per court order.

Disclaimer: The San Francisco Sheriff's Office does not guarantee the accuracy, completeness, or timeliness of the information as the data is subject to change as modifications and updates are completed.

B. HOW THE DATASET IS CREATED When an arrest is presented to the Sheriff’s Office, relevant data is manually entered into the Sheriff Office's jail management system. Data reports are pulled from this system on a semi-regular basis, and added to Open Data.

C. UPDATE PROCESS This dataset is scheduled to update monthly

D. HOW TO USE THIS DATASET This data can be used to identify trends and patterns in the jail population over time. The date in this dataset is based on the date the suspect was booked into county jail for the arresting incident. The unit of measurement for this dataset is the booking number. A jail booking number is a unique identifier assigned to each individual who is booked into a jail facility. It is used to track the individual throughout their time in custody and to link their records to other relevant information, such as court appearances and medical records.

E. RELATED DATASETS • Bookings by Age • Bookings by Race • Bookings by Ethnicity

The data consists of 37 interview transcripts from the first round of Lives Sentenced research. Interviews with thirteen incarcerated men, nine men in the community, twelve incarcerated women and three women in the community are included. The interviews explore the changing meaning of the accumulation of sentences in their lives, and the interactions of these meanings with life outside. Hopes for the future and motivations to desist are also discussed. This data collection includes the first round interviews, which were followed two years later by another round of interviews with 17 of the original participants. These interviews will be deposited in a separate collection. There has been little research examining how those who are punished by the criminal justice system give meaning to their sentences. For many offenders, criminal punishments are not experienced in isolation, but rather are given meaning in the context of wider lives and previous penal experiences. This is especially the case for persistent offenders, who generally have long punishment careers. This research explores how they interpret the accumulation of sentences in their lives. Thirty-seven men and women in Scotland who had been repeatedly sentenced over at least 5-10 years were interviewed, using life history methods. Qualitative life history interviews with men and women who had long punishment careers (spanning around 10 years for the men, around 5 years for the women) and whose most recent sentence was or had been one of short-term imprisonment. Some of the interviews took place in prison (those preceded by P), others in the community. Participants within the prison were recruited through staff, who consulted their systems or knowledge of prisoners to identify those with long punishment careers, and occasionally through other staff. Participants in the community were recruited through third sector organisations or criminal justice social work.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study, with assistance from the National Institute of Justice's Data Resources Program (FY2012), is a reanalysis of data from the national evaluation of the federal Serious and Violent Offender Reentry Initiative (SVORI). SVORI provided funding to 69 agencies across the United States to enhance reentry programs and coordination between corrections and community services. The national evaluation covered 16 of these sites, twelve of which provided services to the 2,054 adult ex-prisoners who are the focus of the present study. The purpose of this study is to understand whether or not offenders receive the services they say they need, and whether the degree of 'fit' between this self-reported criminogenic need and services received is related to recidivism. This study analyzes data from the SVORI multisite evaluation to assess the potential explanations for the mixed effectiveness of reentry programs. The goal is to understand whether or not service-risk/need fit is related to successful reentry outcomes, or whether the needs of returning prisoners are unrelated to their risk of recidivism regardless of how well they are addressed. For the present study researchers obtained the SVORI (ICPSR 27101) outcome evaluation datasets from the National Archive of Criminal Justice Data (NACJD). The archive holds four separate datasets from the evaluation: Adult Males Data (Part 1, N=1,697), Adult Females Data (Part 2, N=357), Juvenile Males Data (Part 3, N=337) and official recidivism and reincarceration data (Part 4, N=35,469), which can be linked on a one-to-many basis with the individual-level data in the other three datasets. To prepare the SVORI data for analysis researchers merged Datasets 1 and 2 (Adult Males and Adult Females) and created seven separate datasets containing Waves 1 through 4 survey data, National Crime Information Center (NCIC) crime data, administrative data, and sampling weights. This deposit to NACJD is intended to complement the existing SVORI dataset (ICPSR 27101). It contains an R syntax file to be used with the datasets contained in the ICPSR 27101 collection.