cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr38393
title: Capital Punishment in the United States, 1973-2020
title: Archival Version

description: CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2020 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 2020. The dataset consists of one part which contains 9,636 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 2020 as well as inmates who were already on death row.
description: Datasets: DS1: Capital Punishment in the United States, 1973-2020
description: All inmates on death row since 1973 in the United States. Smallest Geographic Unit: state
description: mail questionnaire; web-based surveyThe inmate identification numbers were assigned by the Bureau of the Census and have no purpose outside this data collection.To see the BJS report on this data collection, please go to Capital Punishment, 2020 - Statistical Tables.
DOI: 10.3886/icpsr38215.v1
title: Federal Justice Statistics Program: Offenders Released From Prison, 2017
title: Version 1

description: The data contain records of sentenced offenders released from the custody of the Bureau of Prisons (BOP) during fiscal year 2017. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. Records of offenders who exit federal prison temporarily, such as for transit to another location, to serve a weekend sentence, or for health care, are not included in the exiting cohort. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, D.C.) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute through 2012. Data from 2013 and on were prepared by Abt Associates.
description: Datasets: DS1: Federal Justice Statistics Program: Offenders Released From Prison, 2017
description: Offenders released from the custody of the United States Bureau of Prisons during fiscal year 2017.
DOI: 10.3886/icpsr38215
title: Federal Justice Statistics Program: Offenders Released From Prison, 2017
title: Archival Version

description: The data contain records of sentenced offenders released from the custody of the Bureau of Prisons (BOP) during fiscal year 2017. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. Records of offenders who exit federal prison temporarily, such as for transit to another location, to serve a weekend sentence, or for health care, are not included in the exiting cohort. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, D.C.) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute through 2012. Data from 2013 and on were prepared by Abt Associates.
description: Datasets: DS1: Federal Justice Statistics Program: Offenders Released From Prison, 2017
description: Offenders released from the custody of the United States Bureau of Prisons during fiscal year 2017.
DOI: 10.3886/icpsr37390.v1
title: Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court - Terminated, 2015
title: Version 1

description: The data contain records of charges filed against defendants whose cases were terminated by United States attorneys in United States district court during fiscal year 2015. The data are charge-level records, and more than one charge may be filed against a single defendant. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central Charge file. The charge-level data may be linked to defendant-level data (extracted from the EOUSA Central System file) through the CS_SEQ variable, and it should be noted that some defendants may not have any charges other than the lead charge appearing on the defendant-level record. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt and the Bureau of Justice Statistics. Data and documentation were prepared by Abt.
description: Datasets: DS1: 2015 Charges Filed Against Defendants in Criminal Cases in District Court - Terminated Data
description: Charges filed against defendants whose cases were terminated in United States district court during fiscal year 2015.
DOI: 10.3886/icpsr37390
title: Federal Justice Statistics Program: Charges Filed Against Defendants in Criminal Cases in District Court - Terminated, 2015
title: Archival Version

description: The data contain records of charges filed against defendants whose cases were terminated by United States attorneys in United States district court during fiscal year 2015. The data are charge-level records, and more than one charge may be filed against a single defendant. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central Charge file. The charge-level data may be linked to defendant-level data (extracted from the EOUSA Central System file) through the CS_SEQ variable, and it should be noted that some defendants may not have any charges other than the lead charge appearing on the defendant-level record. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt and the Bureau of Justice Statistics. Data and documentation were prepared by Abt.
description: Datasets: DS1: 2015 Charges Filed Against Defendants in Criminal Cases in District Court - Terminated Data
description: Charges filed against defendants whose cases were terminated in United States district court during fiscal year 2015.
DOI: 10.3886/icpsr37998.v2
title: Capital Punishment in the United States, 1973-2019
title: Version 2

description: CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2019 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 2019. The dataset consists of one part which contains 9,617 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 2019 as well as inmates who were already on death row.
description: Datasets: DS1: Capital Punishment in the United States, 1973-2019
description: All inmates on death row since 1973 in the United States. Smallest Geographic Unit: state
description: mail questionnaire; web-based surveyThe inmate identification numbers were assigned by the Bureau of the Census and have no purpose outside this data collection.
DOI: 10.3886/icpsr38510.v1
title: Federal Justice Statistics Program: Defendants in Federal Criminal Cases in District Court - Pending, 2019
title: Version 1

description: The data contain records of defendants in criminal cases filed in United States District Court before or during fiscal year 2019 and still pending as of year-end. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt Associates and the Bureau of Justice Statistics. Data and documentation were prepared by Abt Associates.
description: Datasets: DS1: Federal Justice Statistics Program: Defendants in Federal Criminal Cases in District Court - Pending, 2019
description: Criminal cases filed in United States District Court during fiscal year 2019 and still pending as of year-end.
DOI: 10.3886/icpsr38510
title: Federal Justice Statistics Program: Defendants in Federal Criminal Cases in District Court - Pending, 2019
title: Archival Version

description: The data contain records of defendants in criminal cases filed in United States District Court before or during fiscal year 2019 and still pending as of year-end. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt Associates and the Bureau of Justice Statistics. Data and documentation were prepared by Abt Associates.
description: Datasets: DS1: Federal Justice Statistics Program: Defendants in Federal Criminal Cases in District Court - Pending, 2019
description: Criminal cases filed in United States District Court during fiscal year 2019 and still pending as of year-end.
DOI: 10.5281/zenodo.7410142
title: Processed Synthetic Real-World Data for binary modelling

description: This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted. This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question. The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).
description: {"references": ["Pnevmatikakis, Aristodemos, Stathis Kanavos, George Matikas, Konstantina Kostopoulou, Alfredo Cesario, and Sofoklis Kyriazakos. 2021. \"Risk Assessment for Personalized Health Insurance Based on Real-World Data\" Risks 9, no. 3: 46. https://doi.org/10.3390/risks9030046"]}
DOI: 10.3886/icpsr36165.v2
title: Capital Punishment in the United States, 1973-2011
title: Version 2

description: CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2011 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 2011. The dataset consists of one part which contains 9,058 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 2011 as well as inmates who were already on death row.
description: Datasets: DS1: Capital Punishment in the United States, 1973-2011
description: All inmates on death row since 1973 in the United States. Smallest Geographic Unit: state
description: mail questionnaire; web-based surveyThe inmate identification numbers were assigned by the Bureau of the Census and have no purpose outside this data collection.
DOI: 10.5281/zenodo.7410141
title: Processed Synthetic Real-World Data for binary modelling

description: This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted. This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question. The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).
description: {"references": ["Pnevmatikakis, Aristodemos, Stathis Kanavos, George Matikas, Konstantina Kostopoulou, Alfredo Cesario, and Sofoklis Kyriazakos. 2021. \"Risk Assessment for Personalized Health Insurance Based on Real-World Data\" Risks 9, no. 3: 46. https://doi.org/10.3390/risks9030046"]}
DOI: 10.3886/icpsr36165
title: Capital Punishment in the United States, 1973-2011
title: Archival Version

description: CAPITAL PUNISHMENT IN THE UNITED STATES, 1973-2011 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 2011. The dataset consists of one part which contains 9,058 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 2011 as well as inmates who were already on death row.
description: Datasets: DS1: Capital Punishment in the United States, 1973-2011
description: All inmates on death row since 1973 in the United States. Smallest Geographic Unit: state
description: mail questionnaire; web-based surveyThe inmate identification numbers were assigned by the Bureau of the Census and have no purpose outside this data collection.
DOI: 10.5281/zenodo.7410183
title: Processed Synthetic Real-World Data for tristate modelling

description: This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted. This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question. The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).
description: {"references": ["Pnevmatikakis, Aristodemos, Stathis Kanavos, George Matikas, Konstantina Kostopoulou, Alfredo Cesario, and Sofoklis Kyriazakos. 2021. \"Risk Assessment for Personalized Health Insurance Based on Real-World Data\" Risks 9, no. 3: 46. https://doi.org/10.3390/risks9030046"]}
DOI: 10.5281/zenodo.7410184
title: Processed Synthetic Real-World Data for tristate modelling

description: This model learning dataset is created out of the Raw Synthetic RWD raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted. This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question. The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).
description: {"references": ["Pnevmatikakis, Aristodemos, Stathis Kanavos, George Matikas, Konstantina Kostopoulou, Alfredo Cesario, and Sofoklis Kyriazakos. 2021. \"Risk Assessment for Personalized Health Insurance Based on Real-World Data\" Risks 9, no. 3: 46. https://doi.org/10.3390/risks9030046"]}
DOI: 10.5281/zenodo.7409762
title: Raw Synthetic Real-World Data

description: Real World Data from a simulator for 1,000 people belonging to any of four behavioural groups: athletic, normal, unfit and feeble, simulated over 2 years and 3 months. The dataset is organised into 4 CSV files: aggregations.csv contains the daily physiological data, including their short- & long-term averages and their trends. Every row corresponds to a day for a person. answers.csv contains the answers to questions. Each row corresponds to a question answered. activity_summaries.csv contains summaries of the detected activities. Each row corresponds to an activity someone performed. participants_features.csv contains the demographic info of each simulated person. This is static info, every row corresponding to one person. An ML engineer can combine information from any of these files to setup model learning datasets. This is done in the “Processed Synthetic RWD for binary modelling” and the “Processed Synthetic RWD for tristate modelling” datasets that are also shared here.
description: {"references": ["Pnevmatikakis, Aristodemos, Stathis Kanavos, George Matikas, Konstantina Kostopoulou, Alfredo Cesario, and Sofoklis Kyriazakos. 2021. \"Risk Assessment for Personalized Health Insurance Based on Real-World Data\" Risks 9, no. 3: 46. https://doi.org/10.3390/risks9030046"]}