BJS FY 2026 National Criminal History Improvement Program (NCHIP) Training and Technical Assistance

This program focuses on enhancing the technical capacity and efficiency of the NIBRS Estimation System, improving the timeliness of crime and arrest estimates, refining measurement methodologies and supporting state UCR program work.

The ASPP collection is BJS’s most comprehensive source of information on persons under community supervision, which accounts for the largest segment of the adult correctional population in the United States. At yearend 2023, more than two-thirds of persons under correctional supervision were supervised in the community on probation or parole (3,772,000), while almost one-third (1,852,900) were incarcerated in state or federal prisons or local jails. The ASPP collection fills a valuable role by measuring the number of persons under community supervision each year, the change in this number and factors associated with the change (such as movements onto and off community supervision), the characteristics of this population, and outcomes of supervision. Because these surveys measure the vast majority of the U.S. correctional population, the ASPP—and the Annual Probation Survey in particular—are the most complex and difficult of BJS’s annual correctional surveys. The goal of the ASPP is to accurately report on the number of individuals who are involved with correctional supervision in the community. Data are collected as a census from the known universe of probation and parole supervising agencies, using central reporters wherever possible to minimize burden on the public. The Annual Surveys of Probation and Parole collect data through the Annual Survey of Parole (CJ-7) sent to 52 agencies, the Annual Survey of Probation (CJ-8) sent to 229 central reporting agencies and those supervising at least one person on felony probation, and the Annual Survey of Probation-Misdemeanor Supervision (CJ-8M) sent to about 548 agencies supervising people on misdemeanor probation only. BJS collects data from state-level respondents in an effort to reduce burden whenever possible. The nature of the data collection presents a number of challenges, which BJS is continually working to address. These surveys collect information from both large and small agencies, which are located at various levels and branches of government and have highly diverse data record systems and resources. One challenge facing the collection is determining the quality of data when some respondents report the population as a count of supervised individuals and others report the number of cases probation or parole officers handle. BJS continuously seeks to improve the Annual Probation Survey in response to different populations. BJS anticipates making one award to cover the data collection, processing, and analysis activities for five reference years of ASPP data, 2026–2030. It is likely that this will be one award covering all five years. The project will begin October 1, 2026, and end September 30, 2031.

This opportunity seeks to provide support to further the Bureau of Justice Statistics (BJS)’s mission to produce accurate and timely statistics on recidivism and criminal histories of offenders. The focus of the work is to assist BJS with converting state- and federal-specific fields in criminal history records into research datasets with standardized codes that support statistical analysis and to provide support with multiple recidivism statistics and research projects. 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.

The Motor Carrier Safety Assistance Program (MCSAP) is a Federal formula grant program that provides financial assistance to States, and U.S. Territories to reduce the number and severity of crashes, injuries, and fatalities and hazardous material incidents involving commercial motor vehicles.

The opportunity for the Federal Aviation Administration (FAA) Center of Excellence for Alternative Jet Fuel and Environment (AJFE) was awarded in 2013 to the ASCENT cooperative aviation research organization. ASCENT is a coalition of 16 leading US research universities committed to improving the understanding of aviation noise and emissions and the modeling of aviation systems. ASCENT is co-led by Washington State University and the Massachusetts Institute of Technology (https://ascent.aero/). Any new grant opportunities under AJFE are open and available only to the university members of ASCENT.”

The United States Marine Highway Program (USMHP), codified at 46 U.S.C. 55601, was originally established by Section 1121 of the Energy Independence and Security Act of 2007 to reduce landside congestion through the designation of Marine Highway Routes. The USMHP statute authorizes the U.S. Department of Transportation (“Department” or “DOT”) to make grants to implement Projects or components of Projects that 1) provide a coordinated and capable alternative to landside transportation; mitigate or relieve landside congestion; promote Marine Highway Transportation; or use vessels documented under 46 U.S.C. chapter 121; and 2) develop, expand, or promote Marine Highway Transportation or shipper use of Marine Highway Transportation

Expected Impact: The action under this topic is expected to achieve the following impacts: Faster and more informed decision-making through the use of an AI-driven NAM (AI Foundation Toxicology Model) and increased efficiency through rapid processing of vast amounts of data [1] . Increased consistency and standardisation in a NAM-based approach, specifically an AI model, used by industry in the efficient development, testing and production of safe and effective innovative health technologies, improving industrial competitiveness. Regulatory adoption of a NAM-enabled second species waiver model (AI Foundation Toxicology Model) and weight-of-evidence framework, in line with recommendations and more consistent global decision-making on waiving second species testing. Reduction in animal use, accelerated timelines and lower costs, enhancing the competitiveness of the European health industry through economical and ethical benefits. Improved public health as patients will benefit from safe and effective medicines developed faster using validated NAMs. The action is expected to contribute to the EU Directive (2010/63/EU) [2] on the protection of animals used for scientific purposes and…

Expected Impact: The action under this topic is expected to achieve the following impacts: Accelerate EU access to more cost-effective interventions in an increasingly ageing population by identifying personalised treatment approaches for elderly-onset immune diseases. Decrease disease risk later in life by defining specific prevention strategies based on ageing biomarkers and risk factors. Halt age-associated disease exacerbation by the identification of predictive and digital biomarkers that can stratify patients for early intervention. Improve quality of life for healthy individuals and patients by preventing further health decline, avoiding escalating care costs, and properly stratifying individuals earlier in the diagnostic pathway. Accelerate adoption of innovative diagnostic, preventative, and therapeutic strategies, strengthening the EU’s position as a healthcare innovator. Evaluate digital biomarker as potential regulatory endpoints in the ultimate goal to develop medicines for ‘healthy ageing’. Integrate fragmented research efforts by bringing together health industry sectors and stakeholders to develop clinical and multi-omics data integration capabilities. Enable new…

Expected Impact: The action under this topic is expected to achieve the following impacts and contribute to the following EU policies/initiatives: deliver innovative, early technology solutions that contribute to addressing strategic unmet public health needs across multiple therapy areas to improve prevention, early diagnosis, and treatment; leverage the unique network and scale of IHI JU members to create a pipeline to support innovative startups in the health industry, fully integrated into European initiatives in support of start-ups and entrepreneurship; drive early cross-sector health R&D and innovation to strengthen the European healthcare industry’s global competitiveness, contributing to the EU Industrial Strategy and Pharmaceutical Strategy objectives; create a sustainable network of European healthcare incubators to guide and support highly talented and innovative early-stage companies; harness digital health and data-sharing technologies (e.g., AI and big data) to enable interoperable health solutions, contributing to the European Health Data Space (EHDS) and improved evidence based care;- the action should generate a portfolio of early‑stage companies that have…

Expected Outcome: This action will establish a strategic European initiative to develop scalable, modular, and interoperable photonic quantum computing platforms. Proposals for this topic are expected to address and provide credible solutions to at least two major technical roadblocks currently limiting the advancement of photonic quantum computing such as: The lack of deterministic, high-efficiency photonic entanglement and loss-tolerant architectures suitable for fault-tolerant scaling The absence of a standardised, integrated control stack combining photonic hardware, firmware, and system software with reliable benchmarking across platforms Project results are expected to contribute to the following expected outcomes: By 2028, demonstration of a photonic NISQ processor with ≥100 photonic qubits, integrating deterministic single-photon sources, low-loss waveguides, on-chip detectors, and a firmware stack (scheduler, controller, compiler), validated via hardware-agnostic benchmarks and hybrid photonic-HPC applications demonstrating classical-quantum crossover By 2030, delivery of a full-stack, high-connectivity photonic quantum computer, with modular scalability, integrated…