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 consists of a series of rasters covering the conterminous United States. Each raster is a one kilometer (km) grid for 18 selected Census of Agriculture statistics mapped to land use pixels for the time period 1950 to 2012. A supplemental set of 9 statistics mapped at the entire county level are also provided as 1-km rasters. The rasters are posted as ArcGIS grids. The statistics represent values for crops, livestock, irrigation, fertilizer, and manure usage. Most statistics are mapped for all 14 Census of Agriculture reporting years in that time frame: 1950, 1954, 1959, 1964, 1969, 1974, 1978, 1982, 1987, 1992, 1997, 2002, 2007, and 2012.

Between 1944–1950, almost eight million expellees arrived in West Germany. We introduce a rich county-level database on the expellees’ socio-economic situation in post-war Germany. The database contains regionally disaggregated information on the number, origin, age, gender, religious denomination and labour force status of expellees. It also records corresponding information on the West German population as a whole, on the pre-war economic and religious structure of host and origin regions, and on war destructions in West Germany. The main data sources are the West German censuses of 1939, 1946, 1950 and 1961. Altogether, the database consists of 18 data tables (in xsls format). We have digitized the data as printed in the statistical sources, adding only an English translation of the table head (along with the original table head in German). Each data table has two tabs: The first tab (named “source”) lists the reference(s) of the printed source, the second (“data”) contains the actual data. Please consult the readme file for an overview of each data table’s content and the paper for additional information.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

Historical population as enumerated and corrected from 1790 through 2020. North Carolina was one of the 13 original States and by the time of the 1790 census had essentially its current boundaries. The Census is mandated by the United States Constitution and was first completed for 1790. The population has been counted every ten years hence, with some limitations. In 1790 census coverage included most of the State, except for areas in the west, parts of which were not enumerated until 1840. The population for 1810 includes Walton County, enumerated as part of Georgia although actually within North Carolina. Historical populations shown here reflect the population of the respective named county and not necessarily the population of the area of the county as it was defined for a particular census. County boundaries shown in maps reflect boundaries as defined in 2020. Historic boundaries for some counties may include additional geographic areas or may be smaller than the current geographic boundaries. Notes below list the county or counties with which the population of a currently defined county were enumerated historically (Current County: Population counted in). The current 100 counties have been in place since the 1920 Census, although some modifications to the county boundaries have occurred since that time. For historical county boundaries see: Atlas of Historical County Boundaries Project (newberry.org)County Notes: Note 1: Total for 1810 includes population (1,026) of Walton County, reported as a Georgia county but later determined to be situated in western North Carolina. Total for 1890 includes 2 Indians in prison, not reported by county. Note 2: Alexander: *Iredell, Burke, Wilkes. Note 3: Avery: *Caldwell, Mitchell, Watauga. Note 4: Buncombe: *Burke, Rutherford; see also note 22. Note 5: Caldwell: *Burke, Wilkes, Yancey. Note 6: Cleveland: *Rutherford, Lincoln. Note 7: Columbus: *Bladen, Brunswick. Note 8: Dare: *Tyrrell, Currituck, Hyde. Note 9: Hoke: *Cumberland, Robeson. Note 10: Jackson: *Macon, Haywood. Note 11: Lee: *Moore, Chatham. Note 12: Lenoir: *Dobbs (Greene); Craven. Note 13: McDowell: *Burke, Rutherford. Note 14: Madison: *Buncombe, Yancey. Note 15: Mitchell: *Yancey, Watauga. Note 16: Pamlico: *Craven, Beaufort. Note 17: Polk: *Rutherford, Henderson. Note 18: Swain: *Jackson, Macon. Note 19: Transylvania: *Henderson, Jackson. Note 20: Union: *Mecklenburg, Anson. Note 21: Vance: *Granville, Warren, Franklin. Note 22: Walton: Created in 1803 as a Georgia county and reported in 1810 as part of Georgia; abolished after a review of the State boundary determined that its area was located in North Carolina. By 1820 it was part of Buncombe County. Note 23: Watauga: *Ashe, Yancey, Wilkes; Burke. Note 24: Wilson: *Edgecombe, Nash, Wayne, Johnston. Note 25: Yancey: *Burke, Buncombe. Note 26: Alleghany: *Ashe. Note 27: Haywood: *Buncombe. Note 28: Henderson: *Buncombe. Note 29: Person: Caswell. Note 30: Clay: Cherokee. Note 31: Graham: Cherokee. Note 32: Harnett: Cumberland. Note 33: Macon: Haywood.

Note 34: Catawba: Lincoln. Note 35: Gaston: Lincoln. Note 36: Cabarrus: Mecklenburg.
Note 37: Stanly: Montgomery. Note 38: Pender: New Hanover. Note 39: Alamance: Orange.
Note 40: Durham: Orange, Wake. Note 41: Scotland: Richmond. Note 42: Davidson: Rowan. Note 43: Davie: Rowan.Note 44: Forsyth: Stokes. Note 45: Yadkin: Surry.
Note 46: Washington: Tyrrell.Note 47: Ashe: Wilkes. Part III. Population of Counties, Earliest Census to 1990The 1840 population of Person County, NC should be 9,790. The 1840 population of Perquimans County, NC should be 7,346.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

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.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Chart and table of Pakistan population from 1950 to 2025. United Nations projections are also included through the year 2100.

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.