Graph and download economic data for Number of New Private Nonfarm Housing Units Started, One-Family for United States (A0201AUSA176NNBR) from 1900 to 1959 about housing starts, 1-unit structures, nonfarm, family, new, private, housing, and USA.

The leading causes of death in the United States have changed significantly from the year 1900 to the present. Leading causes of death in 1900, such as tuberculosis, gastrointestinal infections, and diphtheria have seen huge decreases in death rates and are no longer among the leading causes of death in the United States. However, other diseases such as heart disease and cancer have seen increased death rates. Vaccinations One major factor contributing to the decrease in death rates for many diseases since the year 1900 is the introduction of vaccinations. The decrease seen in the rates of death due to pneumonia and influenza is a prime example of this. In 1900, pneumonia and influenza were the leading causes of death, with around *** deaths per 100,000 population. However, in 2023 pneumonia and influenza were not even among the ten leading causes of death. Cancer One disease that has seen a large increase in death rates since 1900 is cancer. Cancer currently accounts for almost ** percent of all deaths in the United States, with death rates among men higher than those for women. The deadliest form of cancer for both men and women is cancer of the lung and bronchus. Some of the most common avoidable risk factors for cancer include smoking, drinking alcohol, sun exposure, and obesity.

When converted to the value of one US dollar in 2020, goods and services that cost one dollar in 1700 would cost just over 63 dollars in 2020, this means that one dollar in 1700 was worth approximately 63 times more than it is today. This data can be used to calculate how much goods and services from the years shown would cost today, by multiplying the price from then by the number shown in the graph. For example, an item that cost 50 dollars in 1970 would theoretically cost 335.5 US dollars in 2020 (50 x 6.71 = 335.5), although it is important to remember that the prices of individual goods and services inflate at different rates than currency, therefore this graph must only be used as a guide.

This dataset is about books. It has 2 rows and is filtered where the book is No one was turned away : the role of public hospitals in New York City since 1900. It features 7 columns including author, publication date, language, and book publisher.

Digitized (vectorized) representation of the inner city 1899-1913 of the Archaeological Information System Berlin (AISBer).

This table presents a wide variety of historical data in the field of health, lifestyle and health care. Figures on births and mortality, causes of death and the occurrence of certain infectious diseases are available from 1900, other series from later dates. In addition to self-perceived health, the table contains figures on infectious diseases, hospitalisations per diagnosis, life expectancy, lifestyle factors such as smoking, alcohol consumption and obesity, and causes of death. The table also gives information on several aspects of health care, such as the number of practising professionals, the number of available hospital beds, nursing day averages and the expenditures on care. Many subjects are also covered in more detail by data in other tables, although sometimes with a shorter history. Data on notifiable infectious diseases and HIV/AIDS are not included in other tables.

Data available from: 1900

Status of the figures:

2025: The available figures are definite.

2024: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, hiv, aids; - causes of death.

2023: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, HIV/AIDS; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - perinatal and infant mortality. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, HIV/AIDS; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, HIV/AIDS; Figures are revised provisional for: - expenditures on health and welfare.

2020 and earlier: Most available figures are definite. Due to 'dynamic' registrations, figures for notifiable infectious diseases, HIV/AIDS remain provisional.

Changes as of 4 July 2025: The most recent available figures have been added for: - population on January 1; - live born children, deaths; - persons in (very) good health; - notifiable infectious diseases, HIV/AIDS; - diagnoses at hospital admissions; - use of medication; - sickness absence; - lifestyle; - use of health care services; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - expenditures on health and welfare; - healthy life expectancy; - causes of death.

Changes as of 18 december 2024: - Due to a revision of the statistics Health and welfare expenditure 2021, figures for expenditure on health and welfare have been replaced from 2021 onwards. - Revised figures on the volume index of healthcare costs are not yet available, these figures have been deleted from 2021 onwards.

When will new figures be published? December 2025.

The Iziko SA Museum’s marine invertebrate collection is one of the largest and most comprehensive in Africa, and as such is an important collection for marine invertebrate systematists. The collection comprises ~130,000 lots of specimens (including un-accessioned material). Eleven hand-written catalogues exist for marine invertebrates dating back to 1871 and includes 76,184 entries.

This layer 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.

This shapefile represents sovereign states of Europe for the year 1900. Sovereign states are considered as sovereign or independent states all entities fulfilling the following conditions: a) a territory covering a geographic area, b) an own population, c) an authority ruling the territory and the population, d) this authority is sovereign, i.e. not subject to any other power or state. This layer is part of the Euratlas Georeferenced Vector Data collection that is composed of 21 maps, one for each century from year 1 to year 2000. These maps depict the detailed political situation of Europe at the first day of each centennial year C.E. from year 1 to 2000. Each map is composed of two kinds of layers: physical features layers, such as seas and rivers, and political features layers, such as states and cities. Some layers also cover adjacent portions of North Africa and the Middle East.

Data Set Overview The Mars Express (MEX) Radio Science (MaRS) Data Archive is a timeordered collection of raw and partially processed data collected during the MEX Mission to Mars. For more information on the investigations proposed see the MaRS User Manual MARSUSERMANUAL2004 in the MaRS DOCUMENT/MRS_DOC folder. This is a Global Gravity measurement covering the time 20090413T23:41:44.500 to 20090414T02:45:08.500. This data set was collected during the MEX Extended Mission Phase 2 (EXT2) 2007 to tbd. This is a measurement of the Global Gravity field of Mars. Global gravity measurements were typically done when Mars Express was around Apocenter. There were three types of scientific measurements conducted during Extended Mission: Occultation, Bistatic Radar and Gravity where one has to distinguish between global gravity measurements which were conducted around apocenter and target gravity measurements which were conducted around pericenter over interesting geophysical structures. For more information see INST.CAT or the MaRS User Manual MARSUSERMANUAL2004. For all measurements if not indicated otherwise Transponder 1 onboard the s/c was used. Transponder 2 is designed to be a backup. Mission Phase Definition It should be noted that the Mars Express (MEX) Radio Science (MaRS) group uses mission phases which deviate from the ones defined in the MISSION.CAT files given by ESA in order to keep the keywords and abbreviations consistent for Mars Express, and Rosetta. For Venus Express other definitions are used. Those mission phase abbreviations are also used in the data description field of the dataset_id. MaRS mission name | abbreviation | time span Near Earth Verification | NEV | 20030602 20030731 Cruise 1 | CR1 | 20030801 20031225 truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

This layer 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.

In 2024, one troy ounce of gold had an annual average price of ******** U.S. dollars. Gold pricing determinants Gold is a metal that is considered malleable, ductile, and is known for its bright lustrous yellow color. This transition metal is highly valued as a precious metal for its use in coins, jewelry, and in investments. Gold was also once used as a standard for monetary policies between different countries. The price of gold is determined by daily fixings where participants agree to buy or sell at a set price or to maintain the price through supply and demand control. For gold, companies like Barclays Capital, Scotia-Mocatta, Sociétè Générale, HSBC, and Deutsche Bank are members in gold fixing at the London Bullion Market Association.

This dataset provides global mean sea level and regional sea level reconstructions derived from tide gauge records and satellite altimetry observations. It represents an improved version of the reconstruction by Church and White (2011), utilizing a reduced-space optimal interpolation technique. Enhancements include the incorporation of sea level fingerprints, sterodynamic sea level change patterns, and more comprehensive estimates of local vertical land motion.

### Data supplement for "Improved Sea Level Reconstruction from 1900 to 2019" by Jinping Wang, John A. Church, Xuebin Zhang, and Xianyao Chen.

Please email wangjinping@ouc.edu for questions.

### PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA ###
Please cite “Improved Sea Level Reconstruction from 1900 to 2019” when using this data set.
Our results rely on previous work, and please acknowledge the previous work by citing the original sources of the data.

This data supplement contains the following files:
GMSL_yr.nc & GMSL_yr.txt
The reconstructed global mean sea level time series from 1900 to 2019, along with the associated one standard deviation uncertainty.

Regional_yr.nc
The regional sea level reconstruction fields from 1900 to 2019 at 1° spatial resolution.

This dataset compiles the values of Precipitation (ppt), Maximum Temperature (tmax) and Minimum Temperature (tmin) for the future (6 GCM average) in the Central Basin, as obtained using Ecoclim spatial climate data at 4km2 resolution.

Each climate variable is provided for all 12 months (January through December) for 5 time slices: a 1900-1979 baseline to establish conditions from which change is measured, and then 2 current time slices of 15 years (1980-1995 and 1995-2010), and 2 future time slices for trend analysis - a decadal average of 6 GCMs for the 2020s and the 2050s.

The layer contains the month of the year (CBR_jan, CBR_feb, etc), and contains: 1) the raw values of each climatic variable (Precipitation, Maximum temperature and Minimum Temperature) for three time slices (1900-1979,2020s and 2050s), 2) The raw values of Standard Deviation (STDV) pixel by pixel, 3) The deltas between the baseline (1900-1979) and the two future time slices, and 4) the pixels that are above or below 1 and 2 STDVs, with its corresponding latitude (x) and longitude (y) coordinates.

• Month_prism: This tab contains, for any given X and Y coordinate, the raw values of three climatic variables (Precipitation = ppt, Maximum Temperature = Tmax, and Minimum temperature = Tmin) of a particular month (January in the case of the tab labeled Jan_prism) for the baseline (1900-1979).
• Stdv_values: This tab shows the STDV value of every pixel for three climatic variables (ppt, Tmax and Tmin) during the 1900-1979 baseline period.
• AVG20: Contains the raw values of Precipitation, Tmax and Tmin of every pixel within the Central Basin Region for the decadal average of the 2020s.
• AVG50: Contains the raw values of Precipitation, Tmax and Tmin of every pixel within the Central Basin Region for the decadal average of the 2050s.
• Difference: This tab shows the value that results from the subtraction of the value associated to the 2020s (Prcp_20, Tmax_20, Tmin20) and the 2050s (Prcp_20, Tmax_20, Tmin20) time slices minus the value, for that same variable, associated to the 1900-1979 baseline. It is the delta between time slices for any given variable in a particular month.
• Stdv+1: The value in each column could be either 0, 1 or -1. Zero (0) indicates that the value of the delta (mentioned above), for a particular pixel, is neither higher nor lower than the standard deviation (STDV) calculated for the baseline (1900-1979). A value of positive one (1) indicates that the delta is higher than the STDV of the baseline (1900-1979), and a value of negative one (-1) indicates that the delta is lower than the STDV of the baseline. The results are presented for each variable (prcp, tmax and tmin) and every future time slice (20 = 2020s and 50 = 2050s).
• Stdv+2: The value in each column could be either 0, 1 or -1. Zero (0) indicates that the value of the delta (mentioned above), for a particular pixel, is neither higher nor lower than two standard deviations (STDV) calculated for the baseline (1900-1979). A value of positive one (1) indicates that the delta is higher than 2 times the STDV of the baseline (1900-1979), and a value of negative one (-1) indicates that the delta is 2 times lower than the STDV of the baseline. The results are presented for each variable (prcp, tmax and tmin) and every future time slice (20 = 2020s and 50 = 2050s).

We introduce a broad yet detailed data framework to assess the physical basis of modern industrial societies based on trends in their commodity demand. Using the example of the United States from 1900 to 2020, we consider both absolute commodity demand (ABS) and demand indexed to economic activity, otherwise known as intensity of use (IOU), for one hundred commodities that constitute the bulk of the material entering the US economy annually. The commodities selected are generally raw materials that enter the economy as industrial inputs that are further processed and incorporated into intermediate and finished products. The data show that ABS for nearly all the 100 commodities rose steadily until 1970 with IOU consistently rising for many as well. After that time, the trends for different groups of commodities start to diverge. Comparing ABS and IOU for the years 1970 and 2020 brings into focus the divergent trends. We find that for many metals and mineral commodities, domestic consumpt..., The database is an Excel workbook with three tabs. Tab 1 - Table S1 (Historical Data)  - Contains the reported data on apparent consumption of 100 commodities in the United States generally denominated in metric tons. These data were not generated, or calculated, but taken directly from were collected online from US government natural resources agencies. No processing of the data took place other than normalization. Table S1 also includes a times series from 1900 to 2020 on US Gross Domestic Product. Tab 2  - Table S2 (Technical notes) - Gives the source of data for each of the commodities listed in Table S1 as well as technical notes regarding the data as necessary. Tab 3 - ABS-IOU – Presents the calculated values for the variables IOU2020/1970 and ASB2020/1970 as shown in Figure 3 of the paper. These calculated values are based directly on data from Table S1., , # Data from: Is America dematerializing? Trends and tradeoffs in historic demand for one hundred commodities in the United States https://doi.org/10.5061/dryad.k6djh9wfq

Reported Consumption Data for 100 commodities in the United States for the years 1900-2020 (coverage subject to data availability)

Description of the data and file structure

Simple excel file. Worksheet 1 includes the historical data by commodity. Worksheet 2 contains the technical notes for each commodity including the data sources. Worksheet 3 contains the calculated values for ABS and IOU, which are shown in Figure 3. of the paper.

Sharing/Access information

Links to other publicly accessible locations of the data:

• https://phe.rockefeller.edu/wp-content/uploads/2024/05/Wernick_100-commodities-Tables-S1-S2-050624.xlsx

Data was derived from the following ...

Prepared by ICPSR under a project to automate major portions of the Statistique Generale de la France, this is a collection of demographic, social, education, economic, population, and vital statistics data for France, 1833-1925. This conversion project is a continuation of one conducted in 1972, for which a similar data collection was created, SOCIAL, DEMOGRAPHIC, AND EDUCATIONAL DATA FOR FRANCE, 1801-1897 (ICPSR 0048). The project to collect and prepare these data was sponsored by two French and two American groups: ICPSR and the Center for Western European Studies at the University of Michigan, and the Fourth and Sixth Sections of the Ecole Pratique des Hautes Etudes and Conseil National de la Recherches Scientifique in France. Both collections include data recorded at the departement, arrondissement, chef-lieu, and ville level. In this collection, materials from the vital statistics series were prepared for selected years rather than for each year in the period from 1900-1925. The years that were chosen clustered around the quinquennial censuses and also included (because of the violent demographic dislocations produced by World War I) each year in the 1914-1919 period. In addition, some vital statistics for the nineteenth century (1836-1850, 1880, and 1892) obtained from fugitive published volumes that could not be located during the course of the 1972 project were prepared. The 136 datasets in this collection contain: (1) French population, economic, and social data obtained from the quenquennial censuses of 1901, 1906, 1911, and 1921, that detail the composition of the population by categories of age, sex, nativity, marital status, religion, place of residence, and occupation, (2) industrial census data for the years 1861-1896, (3) data on primary education in France for 1833, 1901, and 1906, as well as data on secondary and higher education in France for the years 1836-1850, 1880, and 1892, and (4) data from a separate series of annual vital statistics (Mouvement de la Population) that cover the years 1836-1850, 1892, and 1900-1925, citing births, deaths, and marriages in the nation.

This layer 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.

This layer 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.

This dataset contains information on the number of deaths and age-adjusted death rates for the five leading causes of death in 1900, 1950, and 2000. 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). 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 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. 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. 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. 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. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Summary of the input (1,200 points) and output (1,900 points) data shown in Fig. 1.