Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The IPUMS microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1940 census data was collected in April 1940. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.
• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT40, reconstructed using the variable SERIAL40, and the original count is found in the variable NUMPREC40.
• Some variables are missing from this data set for specific enumeration districts. The enumeration districts with missing data can be identified using the variable EDMISS. These variables will be added in a future release.
• Coded variables derived from string variables are still in progress. These variables include: occupation, industry and migration status.
• Missing observations have been allocated and some inconsistencies have been edited for the following variables: Missing observations have been allocated and some inconsistencies have been edited for the following variables: SURSIM, SEX, SCHOOL, RELATE, RACE, OCC1950, MTONGUE, MBPL, FBPL, BPL, MARST, EMPSTAT, CITIZEN, OWNERSHP. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.
• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for many variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next r

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1910 census data was collected in April 1910. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Section 2

This dataset was created on 2020-01-10 23:47:27.924 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1910 households: The Integrated Public Use Microdata Series (IPUMS) Complete Count Data are historic individual and household census records and are a unique source for research on social and economic change.

IPUMS 1910 persons: This dataset includes all individuals from the 1910 US census.

The Cape Shirreff Phocid Census (CS-PHOC) dataset is part of long-term monitoring efforts at Cape Shirreff, Livingston Island. The National Oceanic and Atmospheric Administration (NOAA) United States Antarctic Marine Living Resources Program (U.S. AMLR) and the Chilean Antarctic Institute (INACH) have conducted synoptic, weekly counts of Southern Ocean phocids hauled out on Cape Shirreff during most austral summers since 1997-98. These census data, which will continue to be collected by the U.S. AMLR program and thus updated yearly, provide a rare and valuable source of information about changes in population trends and area use by Southern Ocean phocids in a climate change hot spot.

CS-PHOC is a sampling event type dataset published as open data with technical support provided by SCAR Antarctic Biodiversity Portal (biodiversity.aq) (BELSPO project RT/23/ADVANCE). This dataset is described in the paper “CS-PHOC: weekly census counts of Southern Ocean phocids at Cape Shirreff, Livingston Island” (Woodman et al., 2024).

This dataset contains records of Hydrurga leptonyx, Leptonychotes weddellii, Lobodon carcinophagus, and Mirounga leonina census counts at Cape Shirreff, Livingston Island (62.47° S, 60.77° W). All census records were collected by field biologists using binoculars during field expeditions at Cape Shirreff in the austral summers from December 1997 to February 2023.

The data is published as a standardized Darwin Core Archive, which contains presence, absence, sex and life stage of Southern Ocean phocids observed in each survey. This dataset is published under the license CC0 1.0. Please follow the guidelines from the SCAR Data Policy (SCAR, 2023) when using the data. A manuscript describing the CS-PHOC dataset is currently in review; if you are interested in the project or have any questions regarding this dataset, please contact us via the contact information provided in the metadata or via data-biodiversity-aq@naturalsciences.be. Issues with dataset can be reported at https://github.com/us-amlr/cs-phoc

This dataset is part of the U.S. Antarctic Marine Living Resources program funded by NOAA.

Environmental Analysis Data: These data were compiled to investigate the complex interactions between environmental gradients and geographic distance across the Intermountain West of the western United States. Due to complex topography, physiographic heterogeneity, and complicated relationships with large bodies of water, spatial autocorrelation of environmental similarity may be expected. We provide an R script (VarioAnalysis.R) that uses four associated data files (annualprecip.csv, annualSWA.csv, annualtemp.csv, key.csv) to reproduce Figure 3 in Massatti et al. 2020 (see Larger Work Citation). The data files contain information on yearly soil water availability, temperature, and precipitation, which are summed or averaged and used to test autocorrelations using semi variograms. There is also a shapefile (see Source Data) and raster (RasterbySiteID.tif) that ties all of the site-specific information together and places data into a spatial context. The script and data were developed, extracted, and/or compiled by R.K. Shriver. Genetic Analysis Data: These data were compiled to assess the relationship between genetic differentiation and geographic distance in the Intermountain West of the western United States. Included are 14 files: 13 tab-delimited text files that detail species-specific data and one R script (czi.R) that uses data within the 13 files to reproduce Figures 1 and 2 in Massatti et al. 2020 (see Larger Work Citation). Species-specific files include site names, location information (latitude/longitude), and information on which genetic population each site belongs to according to the original publication document (see Table 1 in the Larger Work Citation). The R script is annotated to provide important information regarding how the analyses work and how they can be modified if users want to tailor analyses to other geographic regions. The script and data were developed, extracted, and/or compiled by R. Massatti.