BJS FY 2026 Criminal History Record Assessment and Research Program (CHRARP)

BJS uses criminal history data collected from the Federal Bureau of Investigation (FBI) and state record repositories to study recidivism patterns and criminal careers of various offenders, including persons on probation or discharged from prison. Each state has a central repository that maintains criminal history records within their jurisdiction and operates a system for disseminating those records. The FBI also maintains a repository from the federal justice system and U.S. territories. The repository also contains records from state, local, and tribal agencies that the FBI is responsible for disseminating. The content of these administrative records varies among FBI and state repositories and the recipient of funds will assist BJS with converting these data into datasets for statistical analysis. The recipient will also assist with multiple recidivism statistics and research projects.

Prior CHRARP programs developed programs, codes, and crosswalks tables. The crosswalk tables hold BJS’s national recode of state- and federal-specific data fields found in the rap sheet datasets which provide consistent data coding across studies and reduce the time and effort to standardize a subsequent parsed dataset when the text fields (e.g., offense and court descriptions) in the criminal history data match the same text fields encountered in datasets from previous studies. The 2026 CHRARP will continue building upon the work of the prior CHRARP programs (CCHRRD, and the 2015 and 2020 CHRARPs) by refining state crosswalks, increasing accuracy of codes to process the criminal history records, and data analysis. Information about these prior programs is available in the full funding opportunity description. The 2026 CHRARP will thoroughly examine the content and attributes of the criminal history data, identifying and documenting differences in the composition of the arrest, prosecution, court, and supervision information within and across jurisdictions. The recipient will work with BJS to identify potential data quality issues and obtain feedback from federal, state, and tribal agencies as needed about these issues. The recipient will develop methods to successfully transform 100% of the data fields found in the parsed rap sheet datasets into BJS’s standardized values and work with BJS to identify existing offense classification tables from state and federal agencies and prior CHRARP projects that could assist with the standardization tasks for this project. Applicants are encouraged to propose cost-efficient methods and innovative solutions to convert state- and federal-specific data fields into a uniform coding structure and account for variations in administrative records when analyzing multistate data.

BJS uses criminal history data collected from the Federal Bureau of Investigation (FBI) and state record repositories to study recidivism patterns and criminal careers of various offenders, including persons on probation or discharged from prison. Each state has a central repository that maintains criminal history records within their jurisdiction and operates a system for disseminating those records. The FBI also maintains a repository from the federal justice system and U.S. territories. The repository also contains records from state, local, and tribal agencies that the FBI is responsible for disseminating. The content of these administrative records varies among FBI and state repositories and the recipient of funds will assist BJS with converting these data into datasets for statistical analysis. The recipient will also assist with multiple recidivism statistics and research projects.

Prior CHRARP programs developed programs, codes, and crosswalks tables. The crosswalk tables hold BJS’s national recode of state- and federal-specific data fields found in the rap sheet datasets which provide consistent data coding across studies and reduce the time and effort to standardize a subsequent parsed dataset when the text fields (e.g., offense and court descriptions) in the criminal history data match the same text fields encountered in datasets from previous studies. The 2026 CHRARP will continue building upon the work of the prior CHRARP programs (CCHRRD, and the 2015 and 2020 CHRARPs) by refining state crosswalks, increasing accuracy of codes to process the criminal history records, and data analysis. Information about these prior programs is available in the full funding opportunity description. The 2026 CHRARP will thoroughly examine the content and attributes of the criminal history data, identifying and documenting differences in the composition of the arrest, prosecution, court, and supervision information within and across jurisdictions. The recipient will work with BJS to identify potential data quality issues and obtain feedback from federal, state, and tribal agencies as needed about these issues. The recipient will develop methods to successfully transform 100% of the data fields found in the parsed rap sheet datasets into BJS’s standardized values and work with BJS to identify existing offense classification tables from state and federal agencies and prior CHRARP projects that could assist with the standardization tasks for this project. Applicants are encouraged to propose cost-efficient methods and innovative solutions to convert state- and federal-specific data fields into a uniform coding structure and account for variations in administrative records when analyzing multistate data.

BJS uses criminal history data collected from the Federal Bureau of Investigation (FBI) and state record repositories to study recidivism patterns and criminal careers of various offenders, including persons on probation or discharged from prison. Each state has a central repository that maintains criminal history records within their jurisdiction and operates a system for disseminating those records. The FBI also maintains a repository from the federal justice system and U.S. territories. The repository also contains records from state, local, and tribal agencies that the FBI is responsible for disseminating. The content of these administrative records varies among FBI and state repositories and the recipient of funds will assist BJS with converting these data into datasets for statistical analysis. The recipient will also assist with multiple recidivism statistics and research projects.

Prior CHRARP programs developed programs, codes, and crosswalks tables. The crosswalk tables hold BJS’s national recode of state- and federal-specific data fields found in the rap sheet datasets which provide consistent data coding across studies and reduce the time and effort to standardize a subsequent parsed dataset when the text fields (e.g., offense and court descriptions) in the criminal history data match the same text fields encountered in datasets from previous studies. The 2026 CHRARP will continue building upon the work of the prior CHRARP programs (CCHRRD, and the 2015 and 2020 CHRARPs) by refining state crosswalks, increasing accuracy of codes to process the criminal history records, and data analysis. Information about these prior programs is available in the full funding opportunity description. The 2026 CHRARP will thoroughly examine the content and attributes of the criminal history data, identifying and documenting differences in the composition of the arrest, prosecution, court, and supervision information within and across jurisdictions. The recipient will work with BJS to identify potential data quality issues and obtain feedback from federal, state, and tribal agencies as needed about these issues. The recipient will develop methods to successfully transform 100% of the data fields found in the parsed rap sheet datasets into BJS’s standardized values and work with BJS to identify existing offense classification tables from state and federal agencies and prior CHRARP projects that could assist with the standardization tasks for this project. Applicants are encouraged to propose cost-efficient methods and innovative solutions to convert state- and federal-specific data fields into a uniform coding structure and account for variations in administrative records when analyzing multistate data.