The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This study was conducted under the auspices of the Center for Studies in Demography and Ecology at the University of Washington. It is a nationally representative sample of the population of the United States in 1900, drawn from the manuscript returns of individuals enumerated in the 1900 United States Census. Household variables include region, state and county of household, size of household, and type and ownership of dwelling. Individual variables for each household member include relationship to head of household, race, sex, age, marital status, number of children, and birthplace. Immigration variables include parents' birthplace, year of immigration and number of years in the United States. Occupation variables include occupation, coded by both the 1900 and 1950 systems, and number of months unemployed. Education variables include number of months in school, whether respondents could read or write a language, and whether they spoke English. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07825.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The clinical records of the Boston Lying-in inpatient and outpatient services, and those of the New England Hospital maternity unit, are housed in the Rare Book Room, Francis A. Countway Library of Medicine, Harvard University, Boston, Massachusetts. While the information found in these records varied somewhat from one hospital to the next, each set of records was consistent throughout the period under review. Four data bases were established, one consisting exclusively of white patients for each of the three clinics and one composed of all black patients from both services of the Boston Lying-in. The four sample populations were constituted in the following ways. The clinical records of the New England Hospital’s maternity clinic exist in continuous series from 1872 to 1900. All births were recorded because there were fewer than 200 deliveries annually. The patient registers of the Boston Lying-in inpatient service span the years 1886-1900, with a gap in 1893 and 1894. A random sample of 200 cases was chosen for each year. The same procedure was followed at the outpatient clinic, whose case files extend from 1884 to 1900, excepting those years in which all were recorded because fewer births occurred, and a short period when all cases were noted even though they totaled more than 200. Because the number of black patients was small, and because the birth weight experience of blacks was distinctive in some important respects, a fourth file was created consisting of all blacks in the Lying-in inpatient and outpatient records. The preliminary data bases consisted of 3480, 2503, 3654, and 373 cases, respectively. The birth weight means in the Lying-in inpatient sample are accurate to 79 grams, and those of the outpatient clinic sample to 65 grams, at the 95 percent confidence level.

This point shapefile contains the locations of state capitals in Brazil in 1900. These data were created using a custom World Polyconic Projection. This layer is part of the Evolução da divisão territorial do Brasil 1872 - 2010 dataset, a collection of data representing the evolution of Brazilian states, municipalities and cities.This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.Read More

This data collection contains three files of county-level electoral returns for Ohio, Michigan, Nebraska, and New York in the period 1912, and 1920-1940. The data files were prepared for instructional use in the ICPSR Training Program and for graduate-level social science courses at the University of Michigan and other university campuses. They contain social, demographic, electoral, and economic data for various areas of the United States, usually for an extended period of time. Part 1, Ohio Referenda Counties as Units, and Part 2, Ohio Referenda as Units, consist of county-level returns for 42 referenda in the 1912 general election in Ohio. Data are provided for the names of counties, votes in the affirmative, total number of votes, and percentage of the "yes" votes for referenda on issues such as civil juries, capital punishment, governor's veto, workmen's compensation, 8-hour day, removal of elected officials, prison labor, women's suffrage, and taxes. The referenda included many questions considered important in the Progressive Movement. Part 3, Data Sets for Three States (Michigan, Nebraska, and New York), consists of electoral returns for the offices of president, governor, and United States representative, as well as ecological and population characteristics data in the period 1920-1940. Data are provided for the raw votes and percentage of the total votes received by the Democratic, Republican, Progressive, and other parties. Items also provide information on population characteristics, such as the total number of population, voting age population, urban population, and persons of other races, and school attendance and religion. Economic variables provide information on local government expenditures and revenues, agriculture and manufacturing, employment and unemployment, and the total number of banks and bank deposits.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

This data set contains reconstructed monthly North American snow extent values for November through March, 1900-1993. Investigators used a combination of satellite and station observations and based the reconstruction on linear regressions between the two types of observations. The data also includes standard errors of estimates as well as the observed values upon which the regressions were based.Station-based snow observations are available for dates since the early twentieth century but lack comprehensive spatial coverage. A remotely sensed product based on visible-band imagery provides more complete spatial coverage but has only been available since the early 1970s. Since the reconstructed values are based on regression, variability is underestimated and therefore the magnitude of some extreme values is underestimated.Data are available via FTP.

An ESRI Shapfile containing spatially generalized built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the data processing.

Additionally, there is a CSV file (HISDAC-US_patch_statistics.csv) containing the counts of built-up property records (BUPR), and -locations (BUPL), as well as total building indoor area (BUI) and built-up area (BUA) per CBSA, year, and patch, extraced from the HISDAC-US data (Uhl & Leyk 2018, Uhl et al. 2021). This CSV can be joined to the shapefile (column uid2) by concatenating the columns msaid_year_Id.

Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175 Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States. Earth system science data, 13(1), 119-153. DOI: https://doi.org/10.5194/essd-13-119-2021

Context

The Correlates of State Policy Project aims to compile, disseminate, and encourage the use of data relevant to U.S. state policy research, tracking policy differences across and change over time in the 50 states. We have gathered more than nine-hundred variables from various sources and assembled them into one large, useful dataset. We hope this Project will become a “one-stop shop” for academics, policy analysts, students, and researchers looking for variables germane to the study of state policies and politics.

Content

The Correlates of State Policy Project includes more than nine-hundred variables, with observations across the U.S. 50 states and time (1900 – 2016). These variables represent policy outputs or political, social, or economic factors that may influence policy differences across the states. The codebook includes the variable name, a short description of the variable, the variable time frame, a longer description of the variable, and the variable source(s) and notes.

Take a look at the codebook PDF to get more information about each column

Acknowledgements

This aggregated data set is only possible because many scholars and students have spent tireless hours creating, collecting, cleaning, and making data publicly available. Thus if you use the dataset, please cite the original data sources.

Jordan, Marty P. and Matt Grossmann. 2016. The Correlates of State Policy Project v.1.10. East Lansing, MI: Institute for Public Policy and Social Research (IPPSR).

This dataset was originally downloaded from

http://ippsr.msu.edu/public-policy/correlates-state-policy

United States Immigrants Admitted: All Countries data was reported at 1,127,167.000 Person in 2017. This records a decrease from the previous number of 1,183,505.000 Person for 2016. United States Immigrants Admitted: All Countries data is updated yearly, averaging 451,510.000 Person from Sep 1900 (Median) to 2017, with 118 observations. The data reached an all-time high of 1,827,167.000 Person in 1991 and a record low of 23,068.000 Person in 1933. United States Immigrants Admitted: All Countries data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s United States – Table US.G087: Immigration.

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option; the data can also be downloaded directly from the FSGeodata Clearinghouse. To summarize this dataset by U.S. Forest Service Lands, see the Drought Summary Tool. You can also explore cumulative drought and moisture changes from this StoryMap; additional drought products from the Office of Sustainability and Climate are available in our Climate Gallery and the OSC Drought page.The Moisture Deficit and Surplus map uses moisture difference z-score datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station. A z-score is a statistical method for assessing how different a value is from the mean (average). Mean moisture values were derived from historical data on precipitation and potential evapotranspiration, from 1900 to 2023. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits or surpluses. Thus, the dark red areas on this map indicate a one-year period with extremely dry conditions, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361

The Corporate Tax Rate in the United States stands at 21 percent. This dataset provides - United States Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset, termed "GAGES II", an acronym for Geospatial Attributes of Gages for Evaluating Streamflow, version II, provides geospatial data and classifications for 9,322 stream gages maintained by the U.S. Geological Survey (USGS). It is an update to the original GAGES, which was published as a Data Paper on the journal Ecology's website (Falcone and others, 2010b) in 2010. The GAGES II dataset consists of gages which have had either 20+ complete years (not necessarily continuous) of discharge record since 1950, or are currently active, as of water year 2009, and whose watersheds lie within the United States, including Alaska, Hawaii, and Puerto Rico. Reference gages were identified based on indicators that they were the least-disturbed watersheds within the framework of broad regions, based on 12 major ecoregions across the United States. Of the 9,322 total sites, 2,057 are classified as reference, and 7,265 as non-reference. Of the 2,057 reference sites, 1,633 have (through 2009) 20+ years of record since 1950. Some sites have very long flow records: a number of gages have been in continuous service since 1900 (at least), and have 110 years of complete record (1900-2009) to date. The geospatial data include several hundred watershed characteristics compiled from national data sources, including environmental features (e.g. climate – including historical precipitation, geology, soils, topography) and anthropogenic influences (e.g. land use, road density, presence of dams, canals, or power plants). The dataset also includes comments from local USGS Water Science Centers, based on Annual Data Reports, pertinent to hydrologic modifications and influences. The data posted also include watershed boundaries in GIS format. This overall dataset is different in nature to the USGS Hydro-Climatic Data Network (HCDN; Slack and Landwehr 1992), whose data evaluation ended with water year 1988. The HCDN identifies stream gages which at some point in their history had periods which represented natural flow, and the years in which those natural flows occurred were identified (i.e. not all HCDN sites were in reference condition even in 1988, for example, 02353500). The HCDN remains a valuable indication of historic natural streamflow data. However, the goal of this dataset was to identify watersheds which currently have near-natural flow conditions, and the 2,057 reference sites identified here were derived independently of the HCDN. A subset, however, noted in the BasinID worksheet as “HCDN-2009”, has been identified as an updated list of 743 sites for potential hydro-climatic study. The HCDN-2009 sites fulfill all of the following criteria: (a) have 20 years of complete and continuous flow record in the last 20 years (water years 1990-2009), and were thus also currently active as of 2009, (b) are identified as being in current reference condition according to the GAGES-II classification, (c) have less than 5 percent imperviousness as measured from the NLCD 2006, and (d) were not eliminated by a review from participating state Water Science Center evaluators. The data posted here consist of the following items:- This point shapefile, with summary data for the 9,322 gages.- A zip file containing basin characteristics, variable definitions, and a more detailed report.- A zip file containing shapefiles of basin boundaries, organized by classification and aggregated ecoregion.- A zip file containing mainstem stream lines (Arc line coverages) for each gage.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition. The BILO gridded data set contains daily fields of selected meteorological variables at 0.05 degrees resolution for the whole Australian continent, including Tasmania. It was obtained by CSIRO for use in the Australian Water Availability Project. In addition to daily data fields, some aggregates at monthly and annual intervals have been created. The variable is daily rainfall. Current data is updated daily by automatic download from the BoM website. Periodic updates (approximately every 6 months) of the dataset include new data and reprocessed data in immediately preceding years. These different revisions are distinguished by an element in the file names "bYYMM" which gives the last two digits of the year and the two digit month corresponding to the revision delivery date. These data represent the snapshot of current data as at 14/10/2013. This dataset has been provided to the BA Programme for use within the programme only. For copyright information go to http://www.bom.gov.au/other/copyright.shtml. Information on how to request a copy of data can be found at www.bom.gov.au/climate/data. Dataset History The data are a snapshot of the climate dataset known as BILO which represents the data as at 14/10/2013. CSIRO maintain a copy of the data as licenced though the Australian Water Availability Project. The BoM version is constantly updated and revised when new data are obtained, when errors in data are identified and when interpolation routines are revised. Therefore there may be difference in the values of some grid cells in the current BoM data compared to this snapshot held by CSIRO. The current BoM archive for these data are listed in the URLs below. Data provided by BoM on disk or directly downloaded from BoM website. http://www.bom.gov.au/cgi-bin/silo/reg/brs/rarchives_awa .cgi?state=nat&period=daily&data_type=totals&format_type=grid http://www.bom.gov.au/cgi-bin/silo/reg/brs/tarchives_awa .cgi?state=nat&period=daily&data_type=maxave&format_type=grid http://www.bom.gov.au/cgi-bin/silo/reg/brs/tarchives_awa .cgi?state=nat&period=daily&data_type=minave&format_type=grid http://www.bom.gov.au/cgi-bin/silo/reg/brs/sarchives_awa. cgi?state=nat&period=daily&data_type=solarave&format_type=grid Processing Steps Data provided by BoM in Arc/Info ASCII raster format. Reformatted to binary flt and NetCDF. Dataset Citation Bureau of Meteorology (2013) BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/7aaf0621-a0e5-4b01-9333-53ebcb1f1c14.

The gridded data represent modeled, historical exposure of U.S. offshore, coastal, and international waters to tropical cyclone activity within the North Atlantic and Pacific Ocean basins (1900-2016). BOEM Outer Continental Shelf Lease Blocks and equivalent areas for coastal and international waters were used to construct the grid by which exposure was quantified. Exposure was quantified using intersecting storm tracks, overlapping wind intensity areas, and mathematical return intervals. Symbology is based on the modeled occurrence of tropical storm force (34-knot) or greater winds per grid cell. Due to the way winds were calculated differently over land and over water, the interpretation of wind exposure metrics within coastal areas should be interpreted carefully. Data represent past climatology only and do not suggest predicted future impacts or exposure. The storm segments data represent a unique subset of the International Best Track Archive for Climate Stewardship (IBTrACS) data set. Features represent IBTrACS storm track segments that 1) are attributed to the North Atlantic or Eastern Pacific basins; 2) do not cross the International Date Line; 3) occur in or after 1900 (through 2016); 4) have maximum wind values above 33 knots; and 5) are attributed as extratropical, subtropical, or tropical. Furthermore, those storm segments that were attributed as tropical within the source data were modified to the appropriate storm category based on the maximum wind speed value per segment.

The Correlates of State Policy Project includes more than 3000 variables, with observations across the 50 U.S. states and across time (1900–2019, approximately). These variables represent policy outputs or political, social, or economic factors that may influence policy differences. The codebook includes the variable name, a short description of the variable, the variable time frame, a longer description of the variable, and the variable source(s) and notes.

See the codebook files for detailed column description

Citations

This aggregated dataset is only possible because many scholars and students have spent countless hours creating, collecting, cleaning, and making data publicly available. Thus, if you use the dataset, please cite the original data sources. To quickly generate these citations, see our web application or R package(link is external) - both can export citations for any variable in the dataset. For example, if you use the dataset to examine the relationship between the Policy Innovativeness Score created by Boehmke & Skinner (2012) and the Policy Liberalism Score created by Caughey & Warshaw (2015), you should include the following three citations: - Boehmke, Frederick J., and Paul Skinner. 2012. “State Policy Innovativeness Revisited.” State Politics and Policy Quarterly 12(3):303-29. - Caughey, Devin, and Christopher Warshaw. 2015. “The Dynamics of State Policy Liberalism, 1936–2014.” American Journal of Political Science 60 (4): 899–913. - Grossmann, M., Jordan, M. P. and McCrain, J. (2021) “The Correlates of State Policy and the Structure of State Panel Data,” State Politics & Policy Quarterly. Cambridge University Press, pp. 1–21. doi: 10.1017/spq.2021.17.

Acknowlegement

Foto von Samuel Branch auf Unsplash

This dataset of U.S. mortality trends since 1900 highlights childhood mortality rates by age group for age at death.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Age groups for childhood death rates are based on age at death.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.

4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Government Debt in the United States decreased to 36213557 USD Million in April from 36214310 USD Million in March of 2025. This dataset provides - United States Government Debt- actual values, historical data, forecast, chart, statistics, economic calendar and news.