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To reduce respondent burden for the 2013 collection, the Census of Jails was combined with the Deaths in Custody Reporting Program (DCRP). The census provides the sampling frame for the nationwide Survey of Inmates in Local Jails (SILJ) and the Annual Survey of Jails (ASJ). Previous jail enumerations were conducted in 1970 (ICPSR 7641), 1972 (ICPSR 7638), 1978 (ICPSR 7737), 1983 (ICPSR 8203), 1988 (ICPSR 9256), 1993 (ICPSR 6648), 1999 (ICPSR 3318), 2005 (ICPSR 20367), and 2006 (ICPSR 26602). The RTI International collected the data for the Bureau of Justice Statistics in 2013. The United States Census Bureau was the collection agent from 1970-2006.

The 2013 Census of Jails gathered data from all jail detention facilities holding inmates beyond arraignment, a period normally exceeding 72 hours. Jail facilities were operated by cities and counties, by private entities under contract to correctional authorities, and by the Federal Bureau of Prisons (BOP).

Excluded from the census were physically separate temporary holding facilities such as drunk tanks and police lockups that do not hold persons after being formally charged in court. Also excluded were state-operated facilities in Connecticut, Delaware, Hawaii, Rhode Island, Vermont, and Alaska, which have combined jail-prison systems. Fifteen independently operated jails in Alaska were included in the Census.

The 2013 census collected facility-level information on the number of confined and nonconfined inmates, number of inmates participating in weekend programs, number of confined non-U.S. citizens, number of confined inmates by sex and adult or juvenile status, number of juveniles held as adults, conviction and sentencing status, offense type, number of inmates held by race or Hispanic origin, number of inmates held for other jurisdictions or authorities, average daily population, rated capacity, number of admissions and releases, program participation for nonconfined inmates, operating expenditures, and staff by occupational category.

To reduce respondent burden for the 2019 collection, the Census of Jails was combined with the Deaths in Custody Reporting Program (DCRP). The census provides the sampling frame for the nationwide Survey of Inmates in Local Jails (SILJ) and the Annual Survey of Jails (ASJ). Previous jail enumerations were conducted in 1970 (ICPSR 7641), 1972 (ICPSR 7638), 1978 (ICPSR 7737), 1983 (ICPSR 8203), 1988 (ICPSR 9256), 1993 (ICPSR 6648), 1999 (ICPSR 3318), 2005 (ICPSR 20367), 2006 (ICPSR 26602), and 2013 (ICPSR 36128). The RTI International collected the data for the Bureau of Justice Statistics in 2013 and 2019. The United States Census Bureau was the collection agent from 1970-2006.

The 2019 Census of Jails gathered data from all jail detention facilities holding inmates beyond arraignment, a period normally exceeding 72 hours. Jail facilities were operated by cities and counties, by private entities under contract to correctional authorities, and by the Federal Bureau of Prisons (BOP).

Excluded from the census were physically separate temporary holding facilities such as drunk tanks and police lockups that do not hold persons after being formally charged in court. Also excluded were state-operated facilities in Connecticut, Delaware, Hawaii, Rhode Island, Vermont, and Alaska, which have combined jail-prison systems. Fifteen independently operated jails in Alaska were included in the Census.

The 2019 census collected information on the number of confined inmates, number of persons supervised outside jail, number of inmates participating in weekend programs, number of confined non-U.S. citizens, number of inmates by sex and adult or juvenile status, number of juveniles held as adults, number of inmates who were parole or probation violators, number of inmates by conviction status, number of inmates by felony or misdemeanor status, number of inmates held by race or Hispanic origin, number of inmates held for other jurisdictions or authorities, average daily population, rated capacity, admissions and releases, number of staff employed by local jails, facility functions, and number of jails under court orders and consent decrees.

The 2019 census also included a module to collect data on the effects of the opioid epidemic on local jails and jail responses to the epidemic. Items included:

• Jail practices on opioid use disorder testing, screening, and treatment.
• Number of local jail admissions screened during June 2019.
• Number of positive screens.
• Number of admissions treated for opioid use disorder.
• Number of jail inmates treated for opioid withdrawal at midyear 2019.

ZIP Codes are administrative codes generated by the United States Postal Service (USPS) that refer to the geographic area covered by a specific set of mail delivery routes. The U.S. Census Bureau calculates and distributes aggregated social, economic, and demographic information for the population associated with "ZIP Code Tabulation Areas" (ZCTAs), which are roughly analogous to ZIP Codes and serve as identifiers for specific neighborhoods and communities. These aggregated census data, however, are unable to account for changes in ZIP Code boundaries that occur between decennial censuses, leading to measurement error and missing data problems for scholars who attempt to use the aggregated ZCTA data. The purpose of this crosswalk file is to allow researchers to overcome this limitation, enabling them to appropriately link spatial reference information (ZIP Codes) with characteristics of the populations to which they refer.

Most ZIP Codes do not change boundaries in a decade, but a large enough percentage do as to create a problem with missing or mis-specified data. Boundary changes typically involve one or more of the following three processes, although a small number of cases do not conform to these typologies: (1) two or more existing ZIP Codes are combined to create a single surviving ZIP Code, (2) an existing ZIP Code is divided into multiple resulting ZIP Codes, and (3) boundaries between two or more existing ZIP Codes are altered.

Each of these types of changes alters the geographic area that a ZIP Code refers to, and as such, the spatial unit identified by the ZIP Code includes a different population, with a different array of characteristics. By linking the spatial units associated with ZIP Codes as these boundary changes are enacted, the research team can both prevent the loss of observations due to missing data, and more accurately measure social, demographic, and economic characteristics associated with each ZIP Code.

This data set identifies changes in ZIP Code boundaries between 1990 and 2020, and provides numeric codes that cluster the ZIP Codes into the smallest geographic unit, or group of ZIP Codes, that are consistent across a decade: 1990 - 2000, 2000 - 2010, and 2010 - 2020. This "crosswalk" covers the contiguous United States, Alaska, Hawaii, and the District of Columbia. Since much administrative data is available with ZIP Code as the smallest identifiable geography, ZIP Codes are often used to embed observations from administrative data (patients, businesses, survey respondents, etc.) within their social, demographic, and economic contexts. However, ZIP Code boundaries change over time, resulting in measurement error (matching observations to the wrong contextual unit) or missing data (due to an observation reporting a ZIP Code that did not exist at the beginning of the observational period). These data were collected, and the crosswalk created, in an attempt to resolve these data quality issues.

This collection comprises a single data file which was produced as part of the data harmonization efforts of the Robert Wood Johnson Foundation and the United States Centers for Disease Control and Prevention. The file contains merged data from five sources:

1. 2010 National Profile of Local Health Departments, a survey of local health departments conducted by the National Association of County and City Health Officials (NACCHO).

2. 2011 National Profile Survey of Local Boards of Health, a survey of local boards of health conducted by the National Association of Local Boards of Health (NALBOH).

3. 2010 State and Territorial Public Health Survey, a survey of state and United States territory health departments conducted by the Association of State and Territorial Health Officials (ASTHO).

4. 2011 County Health Rankings, a compilation of county-level health measures and within-state county health rankings produced by the University of Wisconsin Population Health Institute.

5. 2010 Census Demographic Profile Summary File, a series of tables with housing and population data from the 2010 Census.

Produced by matching data from the last four sources to the NACCHO data, the data file contains one case for each of the 2,107 local health departments (LHD) that responded to the NACCHO survey. Each LHD's record in the file includes the ASTHO data for its state health department and the NALBOH data for its local board of health (LBH), if it had a LBH and the LBH responded to the NALBOH survey. (If a LHD had multiple LBHs, then the first one in the NALBOH data was matched to the LHD). In addition, county (or county equivalent)-level data from the County Health Rankings and Census Demographic Profile Summary File were matched to the records of the 1,535 LHDs represented in the data file with a jurisdiction covering a single county or county equivalent.

This data collection represents a set of United States county net migration estimates by age and sex for the 1980-1990 decade, and is part of a series of estimates done for each decade since 1950 (1950-1970: see NET MIGRATION OF THE POPULATION BY AGE, SEX, AND RACE, 1950-1970 [ICPSR 8493]; 1970-1980: see NET MIGRATION OF THE POPULATION OF THE UNITED STATES BY AGE, RACE, AND SEX, 1970-1980 [ICPSR 8697]; 1990-2000: see COUNTY-SPECIFIC NET MIGRATION BY FIVE-YEAR AGE GROUPS, HISPANIC ORIGIN, RACE, AND SEX, 1990-2000 [ICPSR 4171]).

Net migration, the difference between the number of people moving into an area and the number moving out over a period, is measured here, and in all the other sets of estimates in the series, by the residual method. That is, net migration is equal to the population change over the period minus the natural increase (births -- deaths). Full details on how natural increase is estimated for each county, as well as other details of the data collection, are described in the codebook.

The data in the Historical Demographic Data of Southeastern Europe series derive primarily from the ethnographic and archival research of Joel M. Halpern, Professor Emeritus of Anthropology at the University of Massachusetts at Amherst, in southeastern Europe from 1953 to 2006. The series is comprised of historical demographic data from several towns and villages in the countries of Bosnia, Croatia, Macedonia, Montenegro, Serbia, and Slovenia, all of which are former constituent republics of the Socialist Federal Republic of Yugoslavia. The data provide insight into the shift from agricultural to industrial production, as well as the more general processes of urbanization occurring in the last days of the Yugoslav state. With an expansive timeframe ranging from 1818 to 2006, the series also contains a wide cross-section of demographic data types. These include, but are not limited to, population censuses, tax records, agricultural and landholding data, birth records, death records, marriage and engagement records, and migration information.

This component of the series focuses exclusively on the Serbian village of Orasac and is composed of 64 datasets. These data record a variety of demographic and economic information between the years of 1824 and 1975. General population information at the individual level is available in official census records from 1863, 1884, 1948, 1953, and 1961, and from population register records for the years of 1928, 1966, and 1975. Census data at the household level is also available for the years of 1863, 1928, 1948, 1953, and 1961. These data are followed by detailed records of engagement and marriage. Many of these data were obtained through the courtesy of village and county officials. Priest book records from 1851 through 1966, as well as death records from 1863 to 1976 and tombstone records from 1975, are also available. Information regarding migrants and emigrants was obtained from the village council for the years of 1946 through 1975. Lastly, the data provide economic and financial information, including records of individual landholdings (for the years of 1863, 1952, 1966, and 1975), records of government taxation at the individual or household level (for 1813 through 1840, as well as for 1952), and livestock censuses (at both the individual and household level for the years of 1824 and 1825, and only at the individual level for the years of 1833 and 1834).

This study, known as the Los Angeles Neighborhood Services and Characteristics data (L.A.NSC) includes data from governmental and business data sources on the characteristics of census tracts in Los Angeles County from 1980 to 2010. The unit of analysis is 1990 census tract (census tracts using 1990 census tract boundaries). The data also include crosswalks for census boundaries across census years. It is public use data.

The L.A.NSC data were assembled by the L.A.FANS projects to be used with L.A.FANS survey data restricted data versions 2, 2.5, and 3 all of which contain 1990 census tract numbers which can be linked with data in this file. However, the L.A.NSC files includes ALL census tracts in Los Angeles County, not just the 65 tracts sampled by L.A.FANS. Thus, the L.A.NSC data can be analyzed on their own without linkage to L.A.FANS survey data and provide longitudinal data on the characteristics of Los Angeles census tracts over a 30 year period.

Additional information on the data is available at the RAND website. See also:

• The L.A.NSC Database Users' Guides (The Los Angeles Neighborhood Services and Characteristics Database: Codebook and 2010 Neighborhood Services and Characteristics Database: NSC2).

These data come from surveillance activities conducted by the Guttmacher Institute over several decades, collecting or compiling data for the period 1994 through 2016. These activities track the numbers of women who have a potential demand for contraceptive care (because they are of reproductive age, sexually active and not seeking to become pregnant), the subset of these women who likely need public support for care (because of their family income level or their age), the numbers of women who receive contraceptive services from publicly funded clinics, and the numbers of clinics providing publicly supported contraceptive services. These efforts have been conducted periodically, typically about every five years, but at times the intervals between efforts were shorter or longer than five years. The most recent data were collected or compiled for 2015 (women served) and 2016 (women with potential demand for services).

This release includes two separate datasets. Dataset 1, "Need for contraceptive services," provides county-level aggregate data for 6 different years (1995, 2000, 2002, 2006, 2010, and 2016). For each county, the data represent estimates of the number of women who have a potential demand for contraceptive services and the number who likely need public support for care, both in total, and according to key socio-demographic characteristics. Dataset 2, "Clinics providing contraceptive services and women served," provides county-level aggregate data for six different years (1994, 1997, 2001, 2006, 2010, and 2015). For each county, the data represent the number of publicly funded clinics according to clinic type and funding status and the number of female contraceptive patients served at those clinics.

Tsogolo la Thanzi (TLT) is a longitudinal study in Balaka, Malwai designed to examine how young people navigate reproduction in an AIDS epidemic. Tsogolo la Thanzi means "Healthy Futures" in Chichewa, which is Malawi's most widely spoken language.

The 2019 Household Listing Data are the result of a new census of the original catchment area, 10 years after the initial study began. These data are supplemental to the main TLT series, providing insights into how the Balaka area developed over the past 10 years. It includes data from all persons living within 7 kilometers of the TLT research center.