CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2018 provides annual data on prisoners under a sentence of death, as well as those who had their sentences commuted or vacated and prisoners who were executed. This study examines basic sociodemographic classifications including age, sex, race and ethnicity, marital status at time of imprisonment, level of education, and state and region of incarceration. Criminal history information includes prior felony convictions and prior convictions for criminal homicide and the legal status at the time of the capital offense. Additional information is provided on those inmates removed from death row by yearend 2018. The dataset consists of one part which contains 9,583 cases. The file provides information on inmates whose death sentences were removed in addition to information on those inmates who were executed. The file also gives information about inmates who received a second death sentence by yearend 2018 as well as inmates who were already on death row.

Investigator(s): Bureau of Justice Statistics These data collections provide annual data on prisoners under a sentence of death and on those whose offense sentences were commuted or vacated during the years indicated. Information is supplied for basic sociodemographic characteristics such as age, sex, race, ethnicity, marital status at time of imprisonment, level of education, and state of incarceration. Criminal history data include prior felony convictions for criminal homicide and legal status at the time of the capital offense. Additional information is available for inmates removed from death row by yearend of the last year indicated and for inmates who were executed. The universe is all inmates on death row since 1972 in the United States. The inmate identification numbers were assigned by the Bureau of the Census and have no purpose outside these data collections.Years Produced: Annually (latest release contains all years)NACJD has produced a resource guide on the Capital Punishment in the United States Series.

Dataset from Singapore Prison Service. For more information, visit https://data.gov.sg/datasets/d_f4081559b7db4f792a395138a540db1d/view

Our publication covers movements in the death row population in India as well as political and legal developments in the administration of the death penalty and the criminal justice system. The statistics are compiled through a combination of processes such as data mining of court websites, media monitoring and Right to Information applications.

This collection furnishes data on executions performed under civil authority in the United States between 1608 and 2002. The dataset describes each individual executed and the circumstances surrounding the crime for which the person was convicted. Variables include age, race, name, sex, and occupation of the offender, place, jurisdiction, date, and method of execution, and the crime for which the offender was executed. Also recorded are data on whether the only evidence for the execution was official records indicating that an individual (executioner or slave owner) was compensated for an execution.

We provide a yearly categorization of death penalty status as well as changes of the status in the world. The database covers the period 1800-2022 for all currently independent countries in the world.

These instructional materials were prepared for use with EXECUTIONS IN THE UNITED STATES, 1608-1991: THE ESPY FILE (ICPSR 8451), compiled by M. Watt Espy and John Ortiz Smykla. The data file (an SPSS portable file) and accompanying documentation are provided to assist educators in instructing students about the history of capital punishment in the United States. An instructor's handout is also included. This handout contains the following sections, among others: (1) general goals for student analysis of quantitative datasets, (2) specific goals in studying this dataset, (3) suggested appropriate courses for use of the dataset, (4) tips for using the dataset, and (5) related secondary source readings. This dataset furnishes data on executions performed under civil authority in the United States between 1608 and April 24, 1991, and describes each individual executed and the circumstances surrounding the crime for which the person was convicted. Variables include age, race, name, sex, and occupation of the offender, place, jurisdiction, date, and method of execution, and the crime for which the offender was executed. Also recorded are data on whether the only evidence for the execution was official records indicating that an individual (executioner or slave owner) was compensated for an execution.

This data collection effort was undertaken to analyze the outcomes of capital appeals in the United States between 1973 and 1995 and as a means of assessing the reliability of death penalty verdicts (also referred to herein as "capital judgments" or "death penalty judgments") imposed under modern death-sentencing procedures. Those procedures have been adopted since the decision in Furman v. Georgia in 1972. The United States Supreme Court's ruling in that case invalidated all then-existing death penalty laws, determining that the death penalty was applied in an "arbitrary and capricious" manner and violated Eighth Amendment protections against cruel and unusual punishment. Data provided in this collection include state characteristics and the outcomes of review of death verdicts by state and year at the state direct appeal, state post-conviction, federal habeas corpus, and all three stages of review (Part 1). Data were compiled from published and unpublished official and archived sources. Also provided in this collection are state and county characteristics and the outcome of review of death verdicts by county, state, and year at the state direct appeal, state post-conviction, federal habeas corpus, and all three stages of review (Part 2). After designing a systematic method for identifying official court decisions in capital appeals and state and federal post-conviction proceedings (no official or unofficial lists of those decisions existed prior to this study), the authors created three databases original to this study using information reported in those decisions. The first of the three original databases assembled as part of this project was the Direct Appeal Database (DADB) (Part 3). This database contains information on the timing and outcome of decisions on state direct appeals of capital verdicts imposed in all years during the 1973-1995 study period in which the relevant state had a valid post-Furman capital statute. The appeals in this database include all those that were identified as having been finally decided during the 1973 to 1995 period (sometimes called "the study period"). The second original database, State Post-Conviction Database (SPCDB) (Part 4), contains a list of capital verdicts that were imposed during the years between 1973 and 2000 when the relevant state had a valid post-Furman capital statute and that were finally reversed on state post-conviction review between 1973 and April 2000. The third original database, Habeas Corpus Database (HCDB) (Part 5), contains information on all decisions of initial (non-successive) capital federal habeas corpus cases between 1973 and 1995 that finally reviewed capital verdicts imposed during the years 1973 to 1995 when the relevant state had a valid post-Furman capital statute. Part 1 variables include state and state population, population density, death sentence year, year the state enacted a valid post-Furman capital statute, total homicides, number of African-Americans in the state population, number of white and African-American homicide victims, number of prison inmates, number of FBI Index Crimes, number of civil, criminal, and felony court cases awaiting decision, number of death verdicts, number of Black defendants sentenced to death, rate of white victims of homicides for which defendants were sentenced to death per 100 white homicide victims, percentage of death row inmates sentenced to death for offenses against at least one white victim, number of death verdicts reviewed, awaiting review, and granted relief at all three states of review, number of welfare recipients and welfare expenditures, direct expenditures on the court system, party-adjusted judicial ideology index, political pressure index, and several other created variables. Part 2 provides this same state-level information and also provides similar variables at the county level. Court expenditure and welfare data are not provided in Part 2, however. Part 3 provides data on each capital direct appeal decision, including state, FIPS state and county code for trial court county, year of death verdict, year of decision, whether the verdict was affirmed or reversed, and year of first fully valid post-Furman statute. The date and citation for rehearing in the state system and on certiorari to the United States Supreme Court are provided in some cases. For reversals in Part 4 information was collected about state of death verdict, FIPS state and county code for trial court county, year of death verdict, date of relief, basis for reversal, stage of trial and aspect of verdict (guilty of aggravated capital murder, death sentence) affected by reversal, outcome on retrial, and citation. Part 5 variables include state, FIPS state and county codes for trial court county, year of death verdict, defendant's history of alcohol or drug abuse, whether the defendant was intoxicated at the time of the crime, whether the defense attorney was from in-state, whether the defendant was connected to the community where the crime occurred, whether the victim had a high standing in the community, sex of the victim, whether the defendant had a prior record, whether a state evidentiary hearing was held, number of claims for final federal decision, whether a majority of the judges voting to reverse were appointed by Republican presidents, aggravating and mitigating circumstances, whether habeas corpus relief was granted, what claims for habeas corpus relief were presented, and the outcome on each claim that was presented. Part 5 also includes citations to the direct appeal decision, the state post-conviction decision (last state decision on merits), the judicial decision at the pre-penultimate federal stage, the decision at the penultimate federal stage, and the final federal decision.

--- ### Context This dataset contains the complete detail about the Prison and various characteristics of inmates. This will help to understand better about prison system in India. ### Content 1. Details of Jail wise population of prison inmates 1. Details about the list of jails in India at the end of year 2015. 1. Jail category wise population of inmates. 1. Capacity of jails by inmate population. 1. Age group, nationality and gender wise population of inmates. 1. Religion and gender wise population of inmates. 1. Caste and gender wise population of inmates. 1. Education standards of inmates. 1. Domicile of inmates. 1. Incidence of recidivism. 1. Rehabilitation of prisoners. 1. Distribution of sentence periods of convicts in various jails by sex and age-groups. 1. Details of under trial prisoners by the type of IPC (Indian Penal Code) offences. 1. Details of convicts by the type of IPC (Indian Penal Code) offences. 1. Details of SLL (special & local law) Crime headwise distribution of inmates who convicted 1. Details of SLL (special & local law) Crime head wise distribution of inmates under trial 1. Details of educational facilities provided to prisoners. 1. Details of Jail breaks, group clashes and firing in jail (Tranquility). 1. Details of wages per day to convicts. 1. Details of Prison inmates trained under different vocational training. 1. Details of capital punishment (death sentence) and life imprisonment. 1. Details of prison inmates escaped. 1. Details of prison inmates released. 1. Details of Strength of officials 1. Details of Total Budget and Actual Expenditure during the year 2015-16. 1. Details of Budget 1. Details of Expenditure 1. Details of Expenditure on inmates 1. Details of Inmates suffering from mental ilness 1. Details of Period of detention of undertrials 1. Details of Number of women prisoners with children 1. Details of Details of inmates parole during the year 1. Details of Value of goods produced by inmates 1. Details of Number of vehicles available 1. Details of Training of Jail Officers 1. Details of Movements outside jail premises 1. Details of Details of electronic equipment used in prison ### Inspiration There are many questions about Indian prison with this dataset. Some of the interesting questions are 1. Percentage of jails over crowded. Is there any change in percentage over time? 1. How many percentage of inmates re-arrested? 1. Which state/u.t pay more wages to the inmates? 1. Which state/u.t has more capital punishment/life imprisonment inmates? 1. Inmates gender ratio per state ### Acknowledgements National Crime Records Bureau (NCRB), Govt of India has shared this [dataset](https://data.gov.in/dataset-group-name/prison-statistics) under [Govt. Open Data License - India](https://data.gov.in/government-open-data-license-india). NCRB has also shared prison data on their [website](http://ncrb.nic.in/StatPublications/PSI/PrevPublications.htm). ---

There are no exact statistics of victims of the communist regime in the USSR. Firstly, there is a lack of reliable documentary materials. Secondly, it is difficult to define even this very concept – "victim of the regime".

It can be understood narrowly: victims are persons arrested by the political police (security agencies) and convicted on political charges by various judicial and quasi–judicial instances. Then, with small errors, the number of repressed in the period from 1921 to 1953 will be about 5.5 million people.

https://bessmertnybarak.ru/img/article_img/1524392960_stalin_spisok_004.jpg" alt="">

• Attracted means people who have been brought to criminal responsibility (most, but not all of them were arrested before that). The figures in that column reflect, rather, the number of cases conducted by the state security agencies in a given year, rather than the number of people actually affected (for example, this number includes all those released during the investigation).
• Convicted information about people who have been sentenced to various punishments by various tribunals or administrative commissions ("troika", "two", "special meeting", etc.) is reflected here. It should be borne in mind that the "convicts" are not necessarily among the "involved" of the same year – often the conviction takes place in the next calendar year.
• Execution (capital punishment) this column provides information about the number of people who have received death sentences.

The death penalty is like no other punishment. Its continued existence in many countries of the world creates political tensions within these countries and between governments of retentionist and abolitionist countries. After the Second World War, more and more countries have abolished the death penalty. This article argues that the major determinants of this global trend towards abolition are political, a claim which receives support in a quantitative cross-national analysis from 1950 to 2002. Democracy, democratisation, international political pressure on retentionist countries and peer group effects in relatively abolitionist regions all raise the likelihood of abolition. There is also a partisan effect, as abolition becomes more likely if the chief executive’s party is left wing-oriented. Cultural, social and economic determinants receive only limited support. The global trend towards abolition will go on if democracy continues to spread around the world and abolitionist countries stand by their commitment to press for abolition all over the world.

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What the general public thinks about crime and punishment is a vexed question. In an effort to bring systematic data to bear on this question, I have assembled the largest compilation of aggregated survey data on attitudes to crime and punishment in England and Wales to date. The dataset contains 1,190 question-year pairs, which track popular attitudes across four areas: (i) Crime concern 1965-2023, (ii) Punitiveness 1981-2023, (iii) Support for the death penalty 1962-2023, and (iv) Prioritisation of crime/law-and-order as a social issue 1973-2023.

For example, in 2014, 58% of respondents to the British Election Studies Internet Panel thought that the level of crime was increasing. By 2019, this number had increased to 83%, and by 2023 it had fallen back to 77%. For 16-24 year olds, the numbers are 38%, 69% and 65%.

Harmonised latent trends for each area can be derived from the aggregated survey data using Stimson’s (2018) Dyad Ratio Algorithm for different demographic groups using the R script below.

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The dataset consists in many runs of the same quantum circuit on different IBM quantum machines. We used 9 different machines and for each one of them, we run 2000 executions of the circuit. The circuit has 9 differents measurement steps along it. To obtain the 9 outcome distributions, for each execution, parts of the circuit are appended 9 times (in the same call to the IBM API, thus, in the shortest possible time) measuring a new step each time. The calls to the IBM API followed two different strategies. One was adopted to maximize the number of calls to the interface, parallelizing the code with as many possible runs and even running 8000 shots per run but considering for 8 times 1000 out of the memory to get the probabilities. The other strategy was slower, without parallelization and with a minimum waiting time between subsequent executions. The latter was adopted to get more uniformly distributed executions in time.

This dataset contains models submitted by students in the Alloy4Fun platform to solve the challenge models from various editions of formal methods courses in the University of Minho (UM) and the University of Porto (UP) between the fall of 2019 and the spring of 2023, totalling about 100.000 entries. Participants include those enrolled in the optional MSc course "Specification and Modelling" (EM) and the mandatory MSc course "Formal Methods in Software Engineering" (MFES) in UM, and the optional MSc course "Formal Methods for Critical Systems" (MFS) in UP. Note that since the challenges' permalinks are publicly available, the dataset may contain submissions from other participants outside the classroom context.

The analysis of the 2021 dataset is reported in the Science of Computer Programming paper "Experiences on Teaching Alloy with an Automated Assessment Platform" (extending the ABZ'20 conference version analysing the 2020 dataset).

    Name
    Permalink
    Courses (Students)
    Entries




    Trash FOL
    sDLK7uBCbgZon3znd
    EM 19/20 (~20) and 20/21 (~20), MFS 21/22 (~10) and 22/23 (~10)
    4092


    Classroom FOL
    YH3ANm7Y5Qe5dSYem
    EM 19/20 (~20) and 20/21 (~20), MFS 21/22 (~10) and 22/23 (~10)
    5893


    Trash RL
    PQAJE67kz8w5NWJuM
    EM 19/20 (~20) and 20/21 (~20)
    4361


    Classroom RL
    zRAn69AocpkmxXZnW
    EM 19/20 (~20) and 20/21 (~20)
    6341


    Graphs
    gAeD3MTGCCv8YNTaK
    EM 19/20 (~20) and 20/21 (~20)
    3211


    LTS
    zoEADeCW2b2suJB2k
    EM 19/20 (~20) and 20/21 (~20)
    3382


    Production line

jyS8Bmceejj9pLbTW

bNCCf9FMRZoxqobfX (v2)

aTwuoJgesSd8hXXEP (v3)

EM 19/20 (~20) and 20/21 (~20)

MFES 21/22 (~200), MFS 21/22 (~10) and 22/23 (~10)

MFES 22/23 (~200)

898

4903

3175

    CV

JC8Tij8o8GZb99gEJ

WGdhwKZnCu7aKhXq9 (v2)

EM 19/20 (~20)

EM 20/21 (~20)

1199

393

    Trash LTL
    9jPK8KBWzjFmBx4Hb
    EM 19/20 (~20) and 20/21 (~20)
    5279


    Train Station

FwCGymHmbqcziisH5

QxGnrFQnXPGh2Lh8C (v2)

EM 20/21 (~20)

MFES 21/22 (~200) and 22/23 (~200), MFS 21/22 (~10) and 22/23 (~10)

1264

8158

    Courses

PSqwzYAfW9dFAa9im

JDKw8yJZF5fiP3jv3 (v2)

MFES 21/22 (~200), MFS 21/22 (~10) and 22/23 (~10)

MFES 22/23 (~200)

14884

7632

    Social network
    dkZH6HJNQNLLDX6Aj
    MFES 21/22 (~200) and 22/23 (~200), MFS 21/22 (~10) and 22/23 (~10)
    22690

Each entry of the dataset registers either an execution (which may have returned a result or an error) or the creation of a permalink for sharing, and contains:

_id: the id of the interaction

time: the timestamp of its creation

derivationOf: the parent entry

original: the first ancestor with secrets (always the same within an exercise)

code: the complete code of the model (excluding the secrets defined in the original entry) (with student comments removed)

sat: whether the command was satisfiable (counter-example found for checks), or -1 when error thrown [only for executions]

cmd_i: the index of the executed command [only for executions]

cmd_n: the name of the executed command [only for successful executions, i.e. no error thrown]

cmd_c: whether the command was a check [only for successful executions, i.e. no error thrown]

msg: the error or warning message [only for successful executions with warnings or when error thrown]

theme: the visualisation theme [only for sharing entries]

User comments were removed from the code to guarantee anonymization.

Overview

This repository contains a modified version of the existing, recently published dataset, Westermo. The initial dataset was gathered at Westermo Network Technologies AB, located in Västerås, Sweden. It encompasses over 1 Million verdicts obtained from testing embedded systems, collected over a span of more than 500 consecutive days of nightly testing. The dataset has been transformed and tailored specifically to cater to the research community, particularly for addressing challenges such as regression test selection, identification of flaky tests, and visualization of test results. The original dataset can be accessed through the reference provided in [1].

The Westermo dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different Test case Selection and Prioritization (TSP) techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.

This dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different test case prioritization and selection techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.

Table 1: Dataset Overview


    Test Cases
    1855


    CI Cycles
    15,197


    Verdict
    1,036,818


    Failed
    5.03%

However, the diversity and multitude of the features in the dataset can be irrelevant to some TSP approaches. This led us to perform a dataset conversion, where we customized Westermo to have the same features from Paint Control and IOF/ROL, two widely used datasets in Reinforcement Learning based TSP approaches.

This conversion required the combination of multiple variables and generating the target ones. When it comes to generating the “LastResults” and “Cycle” values, further analysis was required and the data handling needed an in-depth understanding of how the nightly testing was conducted. This led us to investigate what a CI cycle is in their context, and we followed their definition of a session, stating that “a session is when we run a suite of tests on one test system with a certain software version and testware version”. When splitting the data according to the 9 different systems used, we were able to generate 9 different sub-sets that fit the CI context.

File Format

The compressed .zip file contains 9 files, each one corresponding to each of the 9 systems. The datasets are available in CSV format, with the semicolon (;) serving as the delimiter. The columns included are represented in the table below along with their descriptions.

Table 2: Parameters of the dataset


    Column Name
    Content




    jid
    job id, together with the system name, the pair (jid, system) forms a unique key for a test session


    System
    Name of the test system


    Name
    Unique numeric identifier of the test case


    Verdict

Test verdict of this test execution (Failed: 1, Passed: 0)

    Duration
    Approximated runtime of the test case


    Cycle
    The number of the CI cycle this test execution belongs to.


    Group
    The group test case belongs to. 


    LastRun
    Previous last execution of the test case as date-time-string (Format: YYYY-MM-DD HH:ii )


    Id
    Unique numeric identifier of the test execution


    CalcPrio
    Priority of the test case, calculated by the prioritization algorithm (output column, initially 0)


    result_array
    List of previous test results (Failed: 1, Passed: 0), ordered by ascending age. Lists are delimited by [ ].

The implications of this conversion are important as it can help the previous works to re-assess their approaches and have more data for training and testing, as well as opening a broader data spectrum for future researchers in this field to find ready-to-use, rich datasets, on which they could evaluate their approaches and contribute to the TSP community. This also addresses the limitations in the field discussed in the systematic literature review [2], stating that future research on TSP techniques should focus on collecting data from more recent subjects in a CI context with varying failure rates and larger execution times, as reproducible studies with appropriate datasets are needed to develop a usable body of knowledge regarding TSP over time. We believe that this conversion of the Westermo dataset is our contribution to alleviating the gap for the RL-based approaches.

The original dataset can be found here.

These data offer objective and subjective information about current death row inmates and the management policies and procedures related to their incarceration. The major objectives of the study were to gather data about the inmate population and current management policies and procedures, to identify issues facing correctional administrators in supervising the growing number of condemned inmates, and to offer options for improved management. Four survey instruments were developed: (1) a form for the Department of Corrections in each of the 37 states that had a capital punishment statute as of March 1986, (2) a form for each warden of an institution that housed death-sentenced inmates, (3) a form for staff members who worked with such inmates, and (4) a form for a sample of the inmates. The surveys included questions about inmate demographics (e.g., date of birth, sex, race, Hispanic origin, level of education, marital status, and number of children), the institutional facilities available to death row inmates, state laws pertaining to them, training for staff who deal with them, the usefulness of various counseling, medical, and recreational programs, whether the inmates expected to be executed, and the challenges in managing the death row population. The surveys did not probe legal, moral, or political arguments about the death penalty itself.

This dataset contains the models submitted to the shared models in the Alloy4Fun platform during the 2019/20 edition of the "Specification and Modelling" graduate course at the University of Minho with 17 enrolled students, as reported in the ABZ'20 paper "Experiences on Teaching Alloy with an Automated Assessment Platform". Trash FOL, sDLK7uBCbgZon3znd Classroom FOL, YH3ANm7Y5Qe5dSYem Trash RL, PQAJE67kz8w5NWJuM Classroom RL, zRAn69AocpkmxXZnW Graphs, gAeD3MTGCCv8YNTaK LTS, zoEADeCW2b2suJB2k Production, jyS8Bmceejj9pLbTW CV, JC8Tij8o8GZb99gEJ Trash LTL, 9jPK8KBWzjFmBx4Hb Each entry of the dataset registers either an execution (which may have returned a result or an error) or the creation of a permalink for sharing, and contains: _id: the id of the interaction time: the timestamp of its creation derivationOf: the parent entry original: the first ancestor with secrets (always the same within an exercise) code: the complete code of the model (excluding the secrets defined in the original entry) (with student comments removed) sat: whether the command was satisfiable (counter-example found for checks), or -1 when error thrown [only for executions] cmd_i: the index of the executed command [only for executions] cmd_n: the name of the executed command [only for successful executions, i.e. no error thrown] cmd_c: whether the command was a check [only for successful executions, i.e. no error thrown] msg: the error or warning message [only for successful executions with warnings or when error thrown] theme: the visualisation theme [only for sharing entries]