The purpose of this project was to examine possible defendant and victim race effects in capital decisions in the federal system. Per the terms of their grant, the researchers selected cases that were handled under the revised Death Penalty Protocol of 1995 and were processed during Attorney General Janet Reno's term in office. The researchers began the project by examining a sample of Department of Justice Capital Case Unit (CCU) case files. These files contained documents submitted by the United States Attorney's Office (USAO), a copy of the indictment, a copy of the Attorney General's Review Committee on Capital Cases (AGRC's) draft and final memorandum to the Attorney General (AG), and a copy of the AG's decision letter. Next, they created a list of the types of data that would be feasible and desirable to collect and constructed a case abstraction form and coding rules for recording data on victims, defendants, and case characteristics from the CCU's hard-copy case files. The record abstractors did not have access to information about defendant or victim gender or race. Victim and defendant race and gender data were obtained from the CCU's electronic files. Five specially trained coders used the case abstraction forms to record and enter salient information in the CCU hard-copy files into a database. Coders worked on only one case at a time. The resulting database contains 312 cases for which defendant- and victim-race data were available for the 94 federal judicial districts. These cases were received by the CCU between January 1, 1995 and July 31, 2000, and for which the AG at the time had made a decision about whether to seek the death penalty prior to December 31, 2000. The 312 cases includes a total of 652 defendants (see SAMPLING for cases not included). The AG made a seek/not-seek decision for 600 of the defendants, with the difference between the counts stemming mainly from defendants pleading guilty prior to the AG making a charging decision. The database was structured to allow researchers to examine two stages in the federal prosecution process, namely the USAO recommendation to seek or not to seek the death penalty and the final AG charging decision. Finally, dispositions (e.g., sentence imposed) were obtained for all but 12 of the defendants in the database. Variables include data about the defendants and victims such as age, gender, race/ethnicity, employment, education, marital status, and the relationship between the defendant and victim. Data are provided on the defendant's citizenship (United States citizen, not United States citizen), place of birth (United States born, foreign born), offense dates, statute code, counts for the ten most serious offenses committed, defendant histories of alcohol abuse, drug abuse, mental illness, physical or sexual abuse as a child, serious head injury, intelligence (IQ), or other claims made in the case. Information is included for up to 13 USAO assessments and 13 AGRC assessments of statutory and non-statutory aggravating factors and mitigating factors. Victim characteristics included living situation and other reported factors, such as being a good citizen, attending school, past abuse by the defendant, gross size difference between the victim and defendant, if the victim was pregnant, if the victim had a physical handicap, mental or emotional problems or developmental disability, and the victim's present or former status (e.g., police informant, prison inmate, law enforment officer). Data are also provided for up to 13 factors each regarding the place and nature of the killing, defendant motive, coperpetrators, weapons, injuries, witnesses, and forensic and other evidence.

Abstract (en): This data collection provides annual data on prisoners under a sentence of death and on those whose offense sentences were commuted or vacated. Information is available on basic sociodemographic characteristics such as age, sex, race and 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 provided on those inmates removed from death row by yearend 1988 and those inmates who were executed. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Checked for undocumented or out-of-range codes.. Inmates in state prisons under the sentence of death. 2008-11-12 Minor changes have been made to the metadata.2008-10-30 All parts have been moved to restricted access and are available only using the restricted access procedures.2006-01-12 All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.1997-05-30 SAS data definition statements are now available for this collection, and the SPSS data definition statements were updated. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Information collected prior to 1972 is in many cases incomplete and reflects vestiges in the reporting process. (2) The inmate identification numbers were assigned by the Bureau of Census and have no purpose outside this dataset.

--- ### 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). ---

Programming Languages Infrastructure as Code (PL-IaC) enables IaC programs written in general-purpose programming languages like Python and TypeScript. The currently available PL-IaC solutions are Pulumi and the Cloud Development Kits (CDKs) of Amazon Web Services (AWS) and Terraform. This dataset provides metadata and initial analyses of all public GitHub repositories in August 2022 with an IaC program, including their programming languages, applied testing techniques, and licenses. Further, we provide a shallow copy of the head state of those 7104 repositories whose licenses permit redistribution. The dataset is available under the Open Data Commons Attribution License (ODC-By) v1.0. Contents:

metadata.zip: The dataset metadata and analysis results as CSV files. scripts-and-logs.zip: Scripts and logs of the dataset creation. LICENSE: The Open Data Commons Attribution License (ODC-By) v1.0 text. README.md: This document. redistributable-repositiories.zip: Shallow copies of the head state of all redistributable repositories with an IaC program. This artifact is part of the ProTI Infrastructure as Code testing project: https://proti-iac.github.io. Metadata The dataset's metadata comprises three tabular CSV files containing metadata about all analyzed repositories, IaC programs, and testing source code files. repositories.csv:

ID (integer): GitHub repository ID url (string): GitHub repository URL downloaded (boolean): Whether cloning the repository succeeded name (string): Repository name description (string): Repository description licenses (string, list of strings): Repository licenses redistributable (boolean): Whether the repository's licenses permit redistribution created (string, date & time): Time of the repository's creation updated (string, date & time): Time of the last update to the repository pushed (string, date & time): Time of the last push to the repository fork (boolean): Whether the repository is a fork forks (integer): Number of forks archive (boolean): Whether the repository is archived programs (string, list of strings): Project file path of each IaC program in the repository programs.csv:

ID (string): Project file path of the IaC program repository (integer): GitHub repository ID of the repository containing the IaC program directory (string): Path of the directory containing the IaC program's project file solution (string, enum): PL-IaC solution of the IaC program ("AWS CDK", "CDKTF", "Pulumi") language (string, enum): Programming language of the IaC program (enum values: "csharp", "go", "haskell", "java", "javascript", "python", "typescript", "yaml") name (string): IaC program name description (string): IaC program description runtime (string): Runtime string of the IaC program testing (string, list of enum): Testing techniques of the IaC program (enum values: "awscdk", "awscdk_assert", "awscdk_snapshot", "cdktf", "cdktf_snapshot", "cdktf_tf", "pulumi_crossguard", "pulumi_integration", "pulumi_unit", "pulumi_unit_mocking") tests (string, list of strings): File paths of IaC program's tests testing-files.csv:

file (string): Testing file path language (string, enum): Programming language of the testing file (enum values: "csharp", "go", "java", "javascript", "python", "typescript") techniques (string, list of enum): Testing techniques used in the testing file (enum values: "awscdk", "awscdk_assert", "awscdk_snapshot", "cdktf", "cdktf_snapshot", "cdktf_tf", "pulumi_crossguard", "pulumi_integration", "pulumi_unit", "pulumi_unit_mocking") keywords (string, list of enum): Keywords found in the testing file (enum values: "/go/auto", "/testing/integration", "@AfterAll", "@BeforeAll", "@Test", "@aws-cdk", "@aws-cdk/assert", "@pulumi.runtime.test", "@pulumi/", "@pulumi/policy", "@pulumi/pulumi/automation", "Amazon.CDK", "Amazon.CDK.Assertions", "Assertions_", "HashiCorp.Cdktf", "IMocks", "Moq", "NUnit", "PolicyPack(", "ProgramTest", "Pulumi", "Pulumi.Automation", "PulumiTest", "ResourceValidationArgs", "ResourceValidationPolicy", "SnapshotTest()", "StackValidationPolicy", "Testing", "Testing_ToBeValidTerraform(", "ToBeValidTerraform(", "Verifier.Verify(", "WithMocks(", "[Fact]", "[TestClass]", "[TestFixture]", "[TestMethod]", "[Test]", "afterAll(", "assertions", "automation", "aws-cdk-lib", "aws-cdk-lib/assert", "aws_cdk", "aws_cdk.assertions", "awscdk", "beforeAll(", "cdktf", "com.pulumi", "def test_", "describe(", "github.com/aws/aws-cdk-go/awscdk", "github.com/hashicorp/terraform-cdk-go/cdktf", "github.com/pulumi/pulumi", "integration", "junit", "pulumi", "pulumi.runtime.setMocks(", "pulumi.runtime.set_mocks(", "pulumi_policy", "pytest", "setMocks(", "set_mocks(", "snapshot", "software.amazon.awscdk.assertions", "stretchr", "test(", "testing", "toBeValidTerraform(", "toMatchInlineSnapshot(", "toMatchSnapshot(", "to_be_valid_terraform(", "unittest", "withMocks(") program (string): Project file path of the testing file's IaC program Dataset Creation scripts-and-logs.zip contains all scripts and logs of the creation of this dataset. In it, executions/executions.log documents the commands that generated this dataset in detail. On a high level, the dataset was created as follows:

A list of all repositories with a PL-IaC program configuration file was created using search-repositories.py (documented below). The execution took two weeks due to the non-deterministic nature of GitHub's REST API, causing excessive retries. A shallow copy of the head of all repositories was downloaded using download-repositories.py (documented below). Using analysis.ipynb, the repositories were analyzed for the programs' metadata, including the used programming languages and licenses. Based on the analysis, all repositories with at least one IaC program and a redistributable license were packaged into redistributable-repositiories.zip, excluding any node_modules and .git directories. Searching Repositories The repositories are searched through search-repositories.py and saved in a CSV file. The script takes these arguments in the following order:

Github access token. Name of the CSV output file. Filename to search for. File extensions to search for, separated by commas. Min file size for the search (for all files: 0). Max file size for the search or * for unlimited (for all files: *). Pulumi projects have a Pulumi.yaml or Pulumi.yml (case-sensitive file name) file in their root folder, i.e., (3) is Pulumi and (4) is yml,yaml. https://www.pulumi.com/docs/intro/concepts/project/ AWS CDK projects have a cdk.json (case-sensitive file name) file in their root folder, i.e., (3) is cdk and (4) is json. https://docs.aws.amazon.com/cdk/v2/guide/cli.html CDK for Terraform (CDKTF) projects have a cdktf.json (case-sensitive file name) file in their root folder, i.e., (3) is cdktf and (4) is json. https://www.terraform.io/cdktf/create-and-deploy/project-setup Limitations The script uses the GitHub code search API and inherits its limitations:

Only forks with more stars than the parent repository are included. Only the repositories' default branches are considered. Only files smaller than 384 KB are searchable. Only repositories with fewer than 500,000 files are considered. Only repositories that have had activity or have been returned in search results in the last year are considered. More details: https://docs.github.com/en/search-github/searching-on-github/searching-code The results of the GitHub code search API are not stable. However, the generally more robust GraphQL API does not support searching for files in repositories: https://stackoverflow.com/questions/45382069/search-for-code-in-github-using-graphql-v4-api Downloading Repositories download-repositories.py downloads all repositories in CSV files generated through search-respositories.py and generates an overview CSV file of the downloads. The script takes these arguments in the following order:

Name of the repositories CSV files generated through search-repositories.py, separated by commas. Output directory to download the repositories to. Name of the CSV output file. The script only downloads a shallow recursive copy of the HEAD of the repo, i.e., only the main branch's most recent state, including submodules, without the rest of the git history. Each repository is downloaded to a subfolder named by the repository's ID.

Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),

test selection: SDC-Scissor and related tool

test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test..json).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.

The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.

The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.

The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.

The speed limit. The maximum speed at which the driving agent under test can travel.

Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.

Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test..json contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)

The test ID. The unique identifier of the test in the experiment.

Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] }

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] }

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    DEFAULT
    200 × 200
    120
    5 (real time)
    0.95
    BeamNG.AI - 0.7


    SBST
    200 × 200
    70
    2 (real time)
    0.5
    BeamNG.AI - 0.7

Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.

SDC Scissor

With SDC-Scissor we collected data based on the Frenetic test generator. The data is stored inside data/sdc-scissor.tar.gz. The following table summarizes the used parameters.

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    SDC-SCISSOR
    200 × 200
    120
    16 (real time)
    0.5
    BeamNG.AI - 1.5

The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its repository.

Dataset Statistics

Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).

Generating new Data

Generating new data, i.e., test cases, can be done using the SBST CPS Tool Competition pipeline and the driving simulator BeamNG.tech.

Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline Documentation;

The purpose of the Habeas Corpus Litigation in United States District Courts: An Empirical Study, 2007 is to provide empirical information about habeas corpus cases filed by state prisoners in United States District Courts under the Antiterrorism and Effective Death Penalty Act of 1996 (AEDPA). The writ of habeas corpus is a remedy regulated by statute and available in federal court to persons "in custody in violation of the Constitution..." When a federal court grants a writ of habeas corpus, it orders the state court to release the prisoner, or to repeat the trial, sentencing, or other proceeding that led to the prisoner's custody. Each year, state prisoners file between 16,000 and 18,000 cases seeking habeas corpus relief. The study was the first to collect empirical information about this litigation, a decade after AEDPA was passed. It sought to shed light upon an otherwise unexplored area of habeas corpus law by looking at samples of capital and non-capital cases and describing the court processing and general demographic information of these cases in detail. AEDPA changed habeas law by: Establishing a 1-year statute of limitation for filing a federal habeas petition, which begins when appeal of the state judgment is complete, and is tolled during "properly filed" state post-conviction proceedings; Authorizing federal judges to deny on the merits any claim that a petitioner failed to exhaust in state court; Prohibiting a federal court from holding an evidentiary hearing when the petitioner failed to develop the facts in state court, except in limited circumstances; Barring successive petitions, except in limited circumstances; and Mandating a new standard of review for evaluating state court determinations of fact and applications of constitutional law. The information found within this study is for policymakers who design or assess changes in habeas law, for litigants and courts who address the scope and meaning of the habeas statutes, and for researchers who seek information concerning the processing of habeas petitions in federal courts. Descriptive findings are provided detailing petitioner demographics, state proceedings, representation of petitioner in federal court, petitions, type of proceeding challenged, claims raised, intermediate orders, litigation steps, processing time, non-merits dispositions and merits disposition for both capital and non-capital cases which lead into the comparative and explanatory findings that provide information on current and past habeas litigation and how it has been effected by the Antiterrorism and Effective Death Penalty Act of 1996.