Search results

Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries.

Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study examined arrestee DNA laws (laws that allowed testing of arrestees DNA pre-adjudication), their implementation in the field and their subsequent effects on agency operations as well as their success in aiding investigations in the 28 states that have these laws. The study investigated five specific questions:

1. What states have passed legislation authorizing the collection of DNA from arrestees?
2. How do the laws and policies regarding collecting DNA from arrestees differ by state?
3. How have the courts ruled on these new laws?
4. How have arrestee DNA laws been implemented in each state?
5. What has been the impact of requiring DNA collection from arrestees on state crime laboratories and other involved agencies?
6. What evidence is available to determine the effects of collecting DNA from arrestees on public safety or other criminal justice outcomes?

To answer these questions, researchers used a mixed methods data collection plan, including reviewing relevant statutes and case law, interviewing state and federal Combined DNA Index System (CODIS) laboratory staff and other forensic experts, and collecting descriptive data from state laboratories.

The database of Genotypes and Phenotypes (dbGaP) was developed to archive and distribute the results of studies that have investigated the interaction of genotype and phenotype. Such studies include genome-wide association studies, medical sequencing, molecular diagnostic assays, as well as association between genotype and non-clinical traits. The advent of high-throughput, cost-effective methods for genotyping and sequencing has provided powerful tools that allow for the generation of the massive amount of genotypic data required to make these analyses possible.

dbGaP provides two levels of access - open and controlled - in order to allow broad release of non-sensitive data, while providing oversight and investigator accountability for sensitive data sets involving personal health information. Summaries of studies and the contents of measured variables as well as original study document text are generally available to the public, while access to individual-level data including phenotypic data tables and genotypes require varying levels of authorization.

Systematic reviews combine the results of many studies. In health research, these reviews can help determine which treatments or types of care work best. As part of a systematic review, researchers find and record important study information, such as design and results, from published journal articles. This process, called abstraction, takes time. If researchers make errors during this process, the systematic review may come to incorrect conclusions, which can affect healthcare decisions. Researchers abstract information in different ways. In single abstraction and verification, one person abstracts information and a second person reviews it for accuracy. In dual abstraction, two people abstract information on their own and compare the results.

In this study, the research team created and tested a new software program to help with abstraction. In the new software, researchers place flags within a journal article, displayed next to a data collection form on a computer screen, to easily find abstracted information. The team compared three approaches for abstracting information:

• Single abstraction and verification with the new software
• Single abstraction and verification without the new software
• Dual abstraction without the new software

The research team looked at how accurate the abstractions were and how much time it took to do them.

To access the software and methods, please visit the DAA Bitbucket.

The detection of gunshot residue (GSR) provides valuable information in violent crimes, accidental shootings, and terrorism. Despite the scientific validity of this discipline, there are persistent challenges regarding the speed of analysis, preservation of the evidence, and interpretation of results. Consequently, there is a critical need to improve the discipline's turnaround times and reliability. This study's overall purpose was to develop a comprehensive approach to overcome these significant concerns and enhance the criminal justice system capabilities. This project developed and validated fast and reliable tests, using laser-induced breakdown spectroscopy (LIBS) and electrochemical (EC) sensors for GSR detection. Also, statistical models were applied for the quantitative interpretation of the evidence. The combination of LIBS and EC data permitted the accurate identification of organic and inorganic residues (OGSR and IGSR, accuracy ranging from 92-99% depending on the subpopulation and classification models). This research focused on developing SMARTER (Simpler, Modern, Affordable, Rapid, Transformative, Effective, and Reliable) solutions for GSR examinations.

The main objectives were:

1. Application of universal and expanded collection methods
2. Development of novel, ultrafast methods for dual detection of IGSR and OGSR
3. Development of modern 3D chemical imaging for crime scene reconstruction
4. Development of novel micro-particle GSR standards
5. Creation of a large population study and probabilistic interpretation framework

The Global Jukebox (GJB) is an ever-expanding audio database of traditional folk, indigenous, and popular songs from cultures around the world. It is dedicated to the world's peoples and cultures and to their models (ideals) of beauty and forms of communicating and bonding. The Global Jukebox includes visualization tools to reveal relationships between expressive systems and the pathways of song and movement styles across the globe.

All regions and areas of world culture are represented on the GJB. With over 6000 songs from 1200 cultures, there are many examples for every region, every people. Researchers can discover the rich variety and inventiveness, as well as the unifying similarities, of singing and music making within a cultural or geographic region or people.

Global Jukebox data is open access and is updated and expanded periodically on:

• The project's Github repository.
• D-PLACE, the Database of Places, Language, Culture, and Environment, where it can be viewed alongside other cross-cultural datasets. D-PLACE is an expandable, open-access database that brings together information on the geography, language, culture, and environment of over 1400 human societies. It enables researchers to investigate the extent to which patterns in cultural diversity are shaped by different forces, including history, demographics, migration/diffusion, cultural innovations, and environmental and ecological conditions.

Researchers often combine data from different sources, such as insurance claims and health records, to get a better picture of patients' health and use of health care. Researchers use unique identifiers, like Social Security numbers, to connect patient records and make them more complete. But sometimes this approach doesn't work well, especially when records don't have much personal information. Having limited personal data can lead to errors when linking records.

In this study, the research team created new methods to link data sets with limited personal information. Then they compared the new methods with existing ones. They also applied the new methods with real patient data.

Researchers often combine patient health data from different sources, such as claims and health records. These data contain personal information, such as names and social security numbers.

In this study, the research team wanted to learn patients' views on sharing and combining health data for research. The team surveyed patients about their views on

• Sharing health and personal data, such as social security numbers
• Benefits and risks of data sharing
• Ways to help patients feel comfortable sharing health data

Researchers often have trouble collecting complete information on patient health, as patients may receive care at different places. Linking patient records from different places may help researchers get a more complete picture.

One way to link records is through personal information, such as names and birth dates. But this method increases risks to patient privacy. Another way, known as privacy-preserving record linkage, or PPRL, masks personal information. But current PPRL methods only work when linking entire sets of patient data, including data that have already been shared and linked. Linking entire data sets takes a long time. Also, sharing the same records multiple times increases data privacy risks.

In this study, the research team developed and tested a new PPRL method called incremental PPRL. This method links only new or updated data rather than re-linking entire data sets.

This study collected historical data on state DNA database size and the timing of state DNA database expansions in order to examine any impacts on crime rates.

The Ithaka S+R Library Survey 2022 examines strategy and leadership issues from the perspective of library deans and directors at not-for-profit four-year academic institutions across the United States. Respondents were asked about their strategies related to services, collections, diversity, equity, inclusion, and accessibility (DEIA), budget, and personnel, their leadership roles within and outside of the library, and their vision for the role of the library. Demographic variables include respondents' number of years in their position, respondents' age, race/ethnicity, and Carnegie Classification.

Data from healthcare systems, patients and communities, and health plans can support health research. Two types of data sources are

• Patient-powered research networks, or PPRNs. In PPRNs, patients, families, caregivers, and community members share health data with the network. They work closely with researchers to plan and conduct research.
• Health plan research networks, or HPRNs. In HPRNs, networks of health plans have access to health claims data from members for research.

By linking patient records across PPRNs and HPRNs, researchers may be able to do more robust research. To link records, researchers use computer programs to connect the records of people in a PPRN with their claims data in an HPRN. Current methods to link records require use of personal information, such as names and dates of birth. But patients may not want to share this information.

In this project, the research team developed methods for linking data from PPRNs and HPRNs without using patients' personal information.

The National Spinal Cord Injury Statistical Center (NSCISC) is operated by the University of Alabama at Birmingham Department of Physical Medicine and Rehabilitation through funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). NSCISC supports and directs the collection, management, and analysis of the world's largest and longest spinal cord injury (SCI) research database. Organizationally, NSCISC is currently at the hub of a network of 14 NIDILRR-sponsored and 5 subcontract-funded Spinal Cord Injury Model Systems located at major medical centers throughout the United States. In addition to maintaining the national SCI database, NSCISC personnel conduct ongoing, database-oriented research. NSCISC produces annuals reports and "Facts and Figures at a Glance" which can be accessed here.

The National Spinal Cord Injury Database has been in existence since 1973 and captures data from an estimated 6% of new SCI cases in the U.S. Since its inception, 28 federally funded SCI Model Systems have contributed data to the National SCI Database. As of March 2016, the database contained information on 31,645 persons who sustained traumatic spinal cord injuries. To assure comparability of data acquired by personnel in various centers, rigid scientific criteria have been established for the collection, management, and analysis of information entered into the database. National Spinal Cord Injury Statistical Center staff has also developed quality control procedures that further enhance the reliability and validity of the database.

Within the scope of the Spinal Cord Injury Model System program, the purposes of the National SCI Database are as follows:

1. to study the longitudinal course of traumatic SCI and factors that affect that course;
2. to identify and evaluate trends over time in etiology, demographic, and injury severity characteristics of persons who incur a SCI;
3. to identify and evaluate trends over time in health services delivery and treatment outcomes for persons with SCI;
4. to establish expected rehabilitation treatment outcomes for persons with SCI; and
5. to facilitate other research such as the identification of potential persons for enrollment in appropriate SCI clinical trials and research projects or as a springboard to population-based studies.

The Database, however, is not intended to study the effectiveness of model systems care as compared to other systems of health care delivery. It is also not by itself intended to gather and maintain population-based data on spinal cord injuries.

The NIDA Genetics Consortium was created in 1999 and has several overarching missions: (1) identify human chromosomal regions containing genes and/or specific genes that confer susceptibility to drug addiction; (2) create a repository for data (i.e., clinical information and biospecimens containing DNA; (3) generate a database on molecular genetics of drug use disorders and related phenotypes to provide controlled access to collaborative studies with the broader scientific community; and (4) establish a consortium of scientists who meet regularly and collaborate on projects.

Along with the description of the NIDA Genetics Consortium, the Web site outlines policies for access and distribution of DNA and clinical data from NIDA-funded studies on the genetics of addiction vulnerability.

The Public Libraries Survey (PLS) provides statistics on the status of public libraries in the United States. It is a voluntary survey conducted annually by the Institute of Museum and Library Services (IMLS). The Fiscal Year (FY) 2013 survey is the 25th in the series.

The data files include all public libraries identified by state library administrative agencies in the 50 States, the District of Columbia, and the outlying areas of American Samoa, Guam, the Northern Mariana Islands, Puerto Rico and the U.S. Virgin Islands. The reporting unit for the survey is the administrative entity, defined as the agency that is legally established under local or state law to provide public library service to the population of a local jurisdiction.

For Fiscal Year 2013, IMLS collected the following information via a web-based survey for the PLS:

• Library Data - Data on each public library, such as its name and address, population of legal service area, service outlets, collections, full-time-equivalent staff, and operating revenue and expenditures State characteristics data, including the reporting period starting and ending dates, the state total population estimate, and the total unduplicated population of legal service areas for the state. These data are contained in dataset 1 and include 9,309 records; 9,290 were public libraries and 19 were administrative entities that closed in or temporarily closed for FY 2012.
• State Data - Each state library agency reported these data on the "State Characteristics" record because they are not library-level data. These data are in dataset 2 and include 56 records, one for each state and outlying area.
• Outlet Data - Data on each public library service outlet, such as its name and address, type, county location, metropolitan status, square footage, public service hours per year, and number of weeks a library outlet is open. These data are in dataset 3 and include 17,554 total records, 17,463 are public library service outlets (central, branch, bookmobile, and books-by-mail-only outlets). The remaining 91 records are outlets that closed or temporarily closed for FY 2012.

This data collection is useful to researchers, journalists, the public, and policymakers at the federal, state, and local levels. These data are used by federal, state and local officials, professional associations, and local practitioners for planning, evaluation, and policy making.

The Public Libraries Survey (PLS) provides statistics on the status of public libraries in the United States. It is a voluntary survey conducted annually by the Institute of Museum and Library Services (IMLS). The Fiscal Year (FY) 2014 survey is the 26th in the series.

The data files include all public libraries identified by state library administrative agencies in the 50 States, the District of Columbia, and the outlying areas of American Samoa, Guam, the Northern Mariana Islands, Puerto Rico and the U.S. Virgin Islands. The reporting unit for the survey is the administrative entity, defined as the agency that is legally established under local or state law to provide public library service to the population of a local jurisdiction. In this survey, the term public library means an administrative entity.

For Fiscal Year 2014, IMLS collected the following information via a web-based survey for the PLS:

• Library Data - Data on each public library, such as its name and address, population of legal service area, service outlets, collections, full-time-equivalent staff, and operating revenue and expenditures. State characteristics data, including the reporting period starting and ending dates, the state total population estimate, and the total unduplicated population of legal service areas for the state. These data are contained in dataset 1 and include 9,305 records; 9,295 were public libraries and 10 were administrative entities that closed or temporarily closed for FY 2014.
• State Data - Each state library agency reported these data on the "State Characteristics" record because they are not library-level data. These data are in dataset 2 and include 56 records, one for each state and outlying area.
• Outlet Data - Data on each public library service outlet, such as its name and address, type, county location, metropolitan status, square footage, public service hours per year, and number of weeks a library outlet is open. These data are in dataset 3 and include 17,566 total records, 17,492 are public library service outlets (central, branch, bookmobile, and books-by-mail-only outlets). The remaining 74 records are outlets that closed or temporarily closed for FY 2014.

This data collection is useful to researchers, journalists, the public, and policymakers at the federal, state, and local levels. These data are used by federal, state and local officials, professional associations, and local practitioners for planning, evaluation, and policy making.

The Public Libraries Survey (PLS) provides statistics on the status of public libraries in the United States. It is a voluntary survey conducted annually by the Institute of Museum and Library Services (IMLS). The Fiscal Year (FY) 2015 survey is the 27th in the series.

The data files include all public libraries identified by state library administrative agencies in the 50 States, the District of Columbia, and the outlying areas of American Samoa and Guam. The reporting unit for the survey is the administrative entity, defined as the agency that is legally established under local or state law to provide public library service to the population of a local jurisdiction. In this survey, the term public library means an administrative entity.

For Fiscal Year 2015, IMLS collected the following information via a web-based survey for the PLS:

• Library Data - Data on each public library, such as its name and address, population of legal service area, service outlets, collections, full-time-equivalent staff, and operating revenue and expenditures. State characteristics data, including the reporting period starting and ending dates, the state total population estimate, and the total unduplicated population of legal service areas for the state. These data are contained in dataset 1 and include 9,251 records; 9,231 were public libraries and 20 were administrative entities that closed or temporarily closed for FY 2015.
• State Data - Each state library agency reported these data on the "State Characteristics" record because they are not library-level data. These data are in dataset 2 and include 53 records, one for each state and outlying area.
• Outlet Data - Data on each public library service outlet, such as its name and address, type, county location, metropolitan status, square footage, public service hours per year, and number of weeks a library outlet is open. These data are in dataset 3 and include 17,408 total records. The file includes identifying information and a few basic data items for public library service outlets (central, branch, bookmobile, and books-by-mail-only outlets). The file includes 17,328 outlets in the 50 states and the District of Columbia, 8 outlets in the outlying areas, and 72 records for outlets that were reported as closed or were temporarily closed for FY 2015.

This data collection is useful to researchers, journalists, the public, and policymakers at the federal, state, and local levels. These data are used by federal, state and local officials, professional associations, and local practitioners for planning, evaluation, and policy making.

The Public Libraries Survey (PLS) is a voluntary census of public libraries conducted annually by the Institute of Museum and Library Services (IMLS). The Fiscal Year (FY) 2016, 2017, and 2018 surveys are the 28th, 29th, and 30th in the series, respectively. The American Institutes for Research (AIR) served as the data collection agent for all three surveys.

The PLS data files include all public libraries identified by state library administrative agencies in the 50 states, the District of Columbia and the outlying territories of American Samoa and Guam. The Northern Mariana Islands participated in FY 2017 and FY 2018, and the U.S. Virgin Islands participated in FY 2018. The reporting unit in each state or territory for the survey is the administrative entity (AE), defined as the agency that is legally established under local or state law to provide public library service to the population of a local jurisdiction. In this survey, the terms public library and public library system mean an AE. The AE may have a single outlet or multiple outlets. The term "outlet" refers to a library point of service, which may be a physical building, bookmobile, or a books-by-mail provider. Each PLS collected the following information:

• Data from each public library, such as its name and address, population of legal service area, service outlets, collections, full-time-equivalent (FTE) staff, and operating revenue and expenditures (see Appendix F). These data were reported in the AE record.
• State characteristics data, including the state total population estimate, the total unduplicated population of legal service areas for the state, and the state's reporting period start and end dates (see the survey questionnaire in Appendix F, items 100-103). Each state library administrative agency reported these data in the state characteristics record because they are not library-level data.
• Data from each public library service outlet, such as its name and address, type, county location, square footage, public service hours per year, and number of weeks it is open (see Appendix F). These data were reported in the outlet record.

This data collection is useful to researchers, journalists, the public, and policymakers at the federal, state, and local levels. These data are used by federal, state and local officials, professional associations, and local practitioners for planning, evaluation, and policy making.

To diagnose rare genetic conditions, doctors look at patients' genetic data and a phenotypic profile. A phenotypic profile is a record of all the physical traits of a condition. It uses a list of standard terms called Human Phenotype Ontology, or HPO. Doctors and clinic staff do a thorough exam with the patient to create the profile. The exam takes a long time and often more than one visit.

Patients may be able to create phenotypic profiles themselves using surveys. These surveys may take less time than clinic visits. But it is unclear whether patient surveys can provide enough details to correctly identify conditions.

In this project, the research team tested two surveys:

• Phenotypr. This survey asks patients to describe their symptoms and then matches the descriptions to plain language HPO or clinical HPO terms.
• GenomeConnect. This survey uses multiple choice questions to asks patients about their health and symptoms.