This dataset is made up of images containing handwritten 3-digit occupation codes from the Norwegian population census of 1950. The occupation codes were added to the census sheets by Statistics Norway after the census was concluded for the purpose of creating aggregated occupational statistics for the entire population. The coding standard used in the 1950 census is, according to Statistics Norway’s official publications (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1950, booklet 4, page 81), very similar to the standards used in the census for 1920. Cf. the 13th booklet published for the 1920 census (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1920, note that this booklet is only available in Norwegian). In short, an occupation code is a 3-digit number that corresponds to a given occupation or type of occupation. According to the official list of occupation codes provided by Statistics Norway there are 339 unique codes. These are not all necessarily sequential or hierarchical in general, but some subgroupings are. This list can be found under Files. It is also worth noting that these images were extracted from the original census sheet images algorithmically. This process was not flawless and lead to additional images being extracted, these can contain written occupation titles or be left entirely blank. The dataset consists of 90,000 unique images, and 9,000 images that were randomly selected and copied from the unique images. These were all used for a research project (link to preprint article: https://doi.org/10.48550/arXiv.2306.16126) where we (author list can be found in preprint) tried to find a more efficient way of reviewing and correcting classification results from a Machine Learning model, where the results did not pass a pre-set confidence threshold. This was a follow-up to our previous article where we describe the initial project and creating of our model in more detail, if it is of interest (“Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes”, https://doi.org/10.51964/hlcs11331).

This dataset is made up of images containing handwritten 3-digit occupation codes from the Norwegian population census of 1950. The occupation codes were added to the census sheets by Statistics Norway after the census was concluded for the purpose of creating aggregated occupational statistics for the entire population. The coding standard used in the 1950 census is, according to Statistics Norway’s official publications (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1950, booklet 4, page 81), very similar to the standards used in the census for 1920. Cf. the 13th booklet published for the 1920 census (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1920, note that this booklet is only available in Norwegian). In short, an occupation code is a 3-digit number that corresponds to a given occupation or type of occupation. According to the official list of occupation codes provided by Statistics Norway there are 339 unique codes. These are not all necessarily sequential or hierarchical in general, but some subgroupings are. This list can be found under Files. It is also worth noting that these images were extracted from the original census sheet images algorithmically. This process was not flawless and lead to additional images being extracted, these can contain written occupation titles or be left entirely blank. The dataset consists of 90,000 unique images, and 9,000 images that were randomly selected and copied from the unique images. These were all used for a research project (link to preprint article: https://doi.org/10.48550/arXiv.2306.16126) where we (author list can be found in preprint) tried to find a more efficient way of reviewing and correcting classification results from a Machine Learning model, where the results did not pass a pre-set confidence threshold. This was a follow-up to our previous article where we describe the initial project and creating of our model in more detail, if it is of interest (“Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes”, https://doi.org/10.51964/hlcs11331).

This product provides tabular data from the U.S. Department of Agriculture (USDA) Census of Agriculture for selected items for the period 1950-2017 for counties in the conterminous United States. Data from 1950-2012 are taken from LaMotte (2015) and 2017 data are retrieved from the USDA QuickStats online tool. Data which are withheld in the Census of Agriculture are filled with estimates. The data include crop production values for 12 commodities (for example, corn in bushels), land use values for 7 land use types (for example, acres of total cropland), and 9 values for livestock types (for example, number of hogs and pigs). The data are largely intended as a 2017 update to the LaMotte dataset for items of research interest. LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016.

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left ...

The total population in Germany was estimated at 83.6 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - Germany Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The study of social class and corresponding measurement schemes has evolved separately in Europe and the US. On both continents a standardized occupational coding system exists that can be transferred into a wide scala of measures of socioeconomic status. This dataset contains a crosswalk between the two standardized historical occupational coding schemes: HISCO and Occ1950.The Historical International Standardized Classification of Occupations (HISCO) is the European standard for occupational coding and can be used to generate social class schemes, such as HISCLASS, SOCPO, and HISCAM. The U.S. Bureau of the Census' 1950 standard (Occ1950) is the U.S. standard for occupational coding and can be used to generate social class schemes, like NPBOSS, OCCSCORE, PRESGL, and SEI. With the crosswalk, HISCO can be converted to the American class coding schemes and Occ1950 into the European class coding schemes.Occupational categories were linked between HISCO and Occ1950 on the underlying occupations. Both HISCO and Occ1950 consist of multiple layers of occupational groups. HISCO is divided in 7 major, 76 minor, 296 unit, and 1,675 micro groups, which roughly correspond with: social classes, sectors, occupational groups, and occupational subgroups. Occ1950 on the other hand is divided in 10 social classes and 269 occupational groups. HISCO’s micro groups and Occ1950’s occupational subgroups are based on a well-documented number of occupations, which can easily be compared and matched between both occupational coding schemes.In the translation from HISCO to Occ1950 1,675 occupational categories were collapsed into 229 Occ1950 unique occupational groups. Although 40 occupational groups in Occ1950 could not be retrieved from HISCO, all occupations were successfully attributed to the right social class. Vice versa, 269 occupational groups in Occ1950 were recoded into 227 HISCO micro groups. Together these 227 unique codes are well-spread over the different branches of the HISCO tree, as they cover most of the unit groups.#Please note that this is not the crosswalk from Occ1950 to the intermediate HISCO used by the NAPP project, also known as OCCHISCO or NAPPHISCO. This crosswalk can be retrieved from: https://github.com/rlzijdeman/o-clack/tree/master/crosswalks/occhisco_to_hisco#HISCO is the European standard for occupational coding and can be used to generate HISCLASS, SOCPO and HISCAM classifications. The necessary conversion table has been made available by Mandemakers et al. and is available on: https://socialhistory.org/en/hsn/hsn-standardized-hisco-coded-and-classified-occupational-titles-release-201301?language=en#Occ1950 is the US standard for occupational coding. The occupational coding system is based on the US Census of 1950 and can be transferred into OCCSCORE, PRESGL, SEI, and Nam-Powers-Boyd. Crosswalks are available on request: https://usa.ipums.org/usa/vols_4_5_index.shtml

The total population in the United Kingdom was estimated at 69.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - United Kingdom Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This product provides tabular estimates of kilograms of nitrogen and phosphorus from a) fertilizer, and b) manure, for counties in the conterminous United States for the period 1950-2017. Data are generated for approximate five-year periods over the time, coinciding with U.S. Department of Agriculture Census of Agriculture census years. This data release also includes a model archive suitable for recreating the 2017 fertilizer estimates.

This dataset contains four tables of estimates of the yearly sum of nitrogen and phosphorus inputs from fertilizer applications and from manure applications in selected watersheds in Chesapeake Bay drainage basin from 1950 to 2012. For the fertilizer data, county-level loads were used from three published sources. Data from 1950 to 1985 are from Alexander and Smith (1990). Data for the year 1986 are from Battaglin and Goolsby (1995). Data from 1987 through 2012 are from Brakebill, Gronberg, and Spahr (2016). These data were used in conjunction with 30-meter resolution land cover data from the National Land Cover Dataset for the year 2001 to distribute county-level fertilizer loads to cropland and summed by NHDPlus Version 1 catchments. Manure nutrient mass data for the Census of Agriculture years from 1950 to 2012 are from Gronberg and Arnold (2017). Formulae for calculating nitrogen and phosphorus mass per animal unit were also used from the Brakebill and Gronberg report. These data were used in conjunction with 30-meter resolution land cover data from the National Land Cover Dataset for the year 2001 to distribute county-level manure loads to pasture land and summed by NHDPlus Version 1 catchments. Eight digit hydrologic unit code identifiers are also included.

The National Prisoner Statistics (NPS) data collection began in 1926 in response to a congressional mandate to gather information on persons incarcerated in state and federal prisons. Originally under the auspices of the U.S. Census Bureau, the collection moved to the Bureau of Prisons in 1950, and then in 1971 to the National Criminal Justice Information and Statistics Service, the precursor to the Bureau of Justice Statistics (BJS) which was established in 1979. From 1979 to 2013, the Census Bureau was the NPS data collection agent. In 2014, the collection was competitively bid in conjunction with the National Corrections Reporting Program (NCRP), since many of the respondents for NPS and NCRP are the same. The contract was awarded to Abt Associates, Inc. The NPS is administered to 51 respondents. Before 2001, the District of Columbia was also a respondent, but responsibility for housing the District of Columbia's sentenced prisoners was transferred to the Federal Bureau of Prisons, and by yearend 2001 the District of Columbia no longer operated a prison system. The NPS provides an enumeration of persons in state and federal prisons and collects data on key characteristics of the nation's prison population. NPS has been adapted over time to keep pace with the changing information needs of the public, researchers, and federal, state, and local governments.

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.

For 75 parts of town of greater Berlin election results of the city representative elections of 1929, the Reichstag elections of 1930 and 1932 (November), the city representatives elections of 1946 as well as census data on population status, religious denomination and sex of 1933 and 1946. For 40 West Berlin parts of town election results of the city representatives elections of 1948 and the House of Representatives elections of 1950, 1954, 1958 and 1963, as well as census data of 1950 on population status, religious denomination, sex, age, occupation and number of residences. Für 75 Gesamt-Berliner Ortsteile Wahlergebnisse der Stadtverordnetenwahlen von 1929, der Reichstagswahlen von 1930 und 1932 (November), der Stadtverordnetenwahlen von 1946 sowie Volkszählungsdaten zu Bevölkerungsstand, Konfession und Geschlecht von 1933 und 1946. Für 40 West-Berliner Ortsteile Wahlergebnisse der Stadtverordnetenwahlen von 1948 und den Abgeordnetenhauswahlen von 1950, 1954, 1958 und 1963, sowie Volkszählungsdaten von 1950 zu Bevölkerungsstand, Konfession, Geschlecht, Alter, Beruf und Wohnungsbestand. Census Totalerhebung Sources: Publication of the election results of the Berlin city representatives election 1929 as well as the Reichstag elections 1930 and 1932 (November) for the individual SPD departments in ´Vorwaerts´ on the following day after the election (number of votes without information on election turnout and the results of splinter parties); publications of the voting district results of the city representatives election 1946 in the ´Telegraf´ on the day after the election; the election results of 1948, 1950, 1954, 1958 and 1963 as well as the data of the census of 1950 were obtained from the corresponding publications of the Berlin statistics (published by the Berlin state office for statistics). Quellen: Veröffentlichung der Wahlergebnisse der Berliner Stadtverordnetenwahl 1929 sowie der Reichstagswahlen 1930 und 1932 (November) für die einzelnen SPD-Abteilungen im ´Vorwärts´ am jeweils folgenden Tag nach der Wahl (Anzahl der Stimmen ohne Angabe der Wahlbeteiligung und der Ergebnisse von Splitterparteien); Veröffentlichen der Stimmbezirksergebnisse der Stadtverordnetenwahl 1946 im ´Telegraf´ am Tag nach der Wahl; die Wahlergebnisse von 1948, 1950, 1954, 1958 und 1963 sowie die Daten der Volkszählung von 1950 wurden den entsprechenden Veröffentlichungen der Berliner Statistik (hrsg. vom Berliner Landesamt für Statistik) entnommen.

Für 75 Gesamt-Berliner Ortsteile Wahlergebnisse derStadtverordnetenwahlen von 1929, der Reichstagswahlen von 1930 und1932 (November), der Stadtverordnetenwahlen von 1946 sowieVolkszählungsdaten zu Bevölkerungsstand, Konfession und Geschlechtvon 1933 und 1946. Für 40 West-Berliner Ortsteile Wahlergebnisseder Stadtverordnetenwahlen von 1948 und den Abgeordnetenhauswahlenvon 1950, 1954, 1958 und 1963, sowie Volkszählungsdaten von 1950 zuBevölkerungsstand, Konfession, Geschlecht, Alter, Beruf undWohnungsbestand. For 75 parts of town of greater Berlin election results of the cityrepresentative elections of 1929, the Reichstag elections of 1930 and1932 (November), the city representatives elections of 1946 as wellas census data on population status, religious denomination and sexof 1933 and 1946. For 40 West Berlin parts of town election resultsof the city representatives elections of 1948 and the House ofRepresentatives elections of 1950, 1954, 1958 and 1963, as well ascensus data of 1950 on population status, religious denomination,sex, age, occupation and number of residences. Quellen: Veröffentlichung der Wahlergebnisse der Berliner Stadtverordnetenwahl 1929 sowie der Reichstagswahlen 1930 und 1932 (November) für die einzelnen SPD-Abteilungen im ´Vorwärts´ am jeweils folgenden Tag nach der Wahl (Anzahl der Stimmen ohne Angabe der Wahlbeteiligung und der Ergebnisse von Splitterparteien); Veröffentlichen der Stimmbezirksergebnisse der Stadtverordnetenwahl 1946 im ´Telegraf´ am Tag nach der Wahl; die Wahlergebnisse von 1948, 1950, 1954, 1958 und 1963 sowie die Daten der Volkszählung von 1950 wurden den entsprechenden Veröffentlichungen der Berliner Statistik (hrsg. vom Berliner Landesamt für Statistik) entnommen. Sources: Publication of the election results of the Berlin city representatives election 1929 as well as the Reichstag elections 1930 and 1932 (November) for the individual SPD departments in ´Vorwaerts´ on the following day after the election (number of votes without information on election turnout and the results of splinter parties); publications of the voting district results of the city representatives election 1946 in the ´Telegraf´ on the day after the election; the election results of 1948, 1950, 1954, 1958 and 1963 as well as the data of the census of 1950 were obtained from the corresponding publications of the Berlin statistics (published by the Berlin state office for statistics). Bevölkerung Berlins The population of Berlin Auswahlverfahren Kommentar: Totalerhebung