An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Florida Site Files" data publication.

Database of the specimens from the Galathea II, Danish Deep-Sea Expedition Round the World 1950-52, published in Galathea Report 1-20, Copenhagen 1956-2005. The published specimens are deposited in the collections of ZMUC, Copenhagen. A full metadata description is available at http://pythonprovider.danbif.dk/repository/collection049424/resource785542/details_html

This dataset provides groundwater ages estimates that were used in an assessment of the penetration depth of modern groundwater in the Central Valley aquifer system (CVAL). Groundwater ages were estimated by calibration of environmental tracers (tritium, tritiogenic helium-3, chlorofluorocarbons, sulfur hexafluoride, carbon-14 and radiogenic helium-4) to lumped parameter models (LPMs) for samples from 650 sample locations. Groundwater samples were collected from wells (mainly drinking-water) in the CVAL between 2004 and 2017 as part of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Priority Basin Project (GAMA-PBP) and the National Water-Quality Assessment (NAWQA) Project. Table 1 reports the primary results of this assessment including mean groundwater age and the input for calculation of mean groundwater age: results of the tritium age classification, condensed results from dissolved gas modeling, and calculated environmental tracer concentrations. Calibrated lumped parameter models provide the optimal mean age and mixing parameter(s) used to compute the distribution of ages that explain the measured tracer concentrations in a sample. Tables 2, 3, and 4 provide results in support of Table 1. Table 2 reports detailed results for the calibration of dissolved gas models to neon, argon, krypton, xenon, and nitrogen. Calibrated dissolved gas models provide the optimal water temperature, excess air, entrapped air, fractionation of gases, and excess nitrogen gas (mainly from denitrification) that explain the measured dissolved gases in a sample. Table 3 reports measured concentrations and the detailed calculations of environmental tracer concentrations derived from the dissolved gas modeling results in Table 2. Calculated concentrations of environmental tracers that can be used in groundwater age calculations are the dry air mixing ratio of sulfur hexafluoride or chlorofluorocarbons, tritiogenic helium-3, which is the concentration of helium-3 from the decay of tritium, and radiogenic helium-4. Table 4 reports information used to calculate the partial exponential model ratios used in the groundwater age modeling. In addition to these four tables, two ancillary tables are included to provide more detailed information about the fields and the abbreviations used in tables 1-4.

This study consists of an aggregate attributes data file and a perception-interaction data file on the Soviet Union and China in the period 1950-1967. The study was part of a project concerned with the Sino-Soviet conflict and its implications for United States' strategic planning in the 1970s. The aggregate attributes data file (Part 1) contains two subsets: (1) measures of economic, demographic, military, and diplomatic national attributes of the Soviet Union and China in the period 1950-1967, and (2) trade of and visits by leaders of the Soviet Union and China with 17 less developed nations in the period 1959-1967. The perception-interaction data file (Part 2) also consists of two subsets: (1) a perception dataset, which contains information obtained by computer content analyses of selected official statements and newspaper contents utilizing Inquirer II, and (2) an interaction dataset that measures Sino-Soviet diplomatic, communications, and trade interactions with each other in the period 1950-1967. Items in the perception subset dataset include the perceptions of the Soviet Union and China of each other and of the United States coded as strong, weak, active, passive, negative, and threatening. Each of these perceptions is presented in the original and weighted forms.

Inflation Rate in Germany remained unchanged at 2 percent in July. This dataset provides the latest reported value for - Germany Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This file contains two sets of raw data. One dataset contains information used to construct aggregate measures of fractionalization and concentration, the other contains data used to construct measures of inequality. The fractionalization and concentration data are recorded for each city, political party, etc., for these variables: city populations, ethnic groups, language groups, export commodities, export receiving countries, distribution of votes by political party, and distribution of seats in the lower legislative house. The inequality data are recorded as distributions of farms, acreage, labor forces and gross domestic product. Data were collected by the World Data Analysis Program at Yale University.

Between 1939 and 1950, the Soviet Union's fertility rate underwent the most drastic change of all the major Allied Powers; falling from 4.9 births per woman in 1939 to just 1.7 births in 1943. In Russia alone, this decline was even greater, falling from 4.9 to 1.3 births in the same time period. After the war's conclusion in 1945, there was an observable increase in fertility in all the given countries, and this marked beginning of the global baby boom of the mid-twentieth century.

Between 1939 and 1950, Japan's annual fertility rate fluctuated between 3.1 and 4.4 births per woman, before spiking to 4.6 in 1947. This was the highest of the Axis powers listed, as European figures were generally much lower at this point in history. The increase in fertility that followed the Second World War was part of the global baby boom of the mid-twentieth century.

During the period covered by this report (July through September 1950), two treating plant runs were made. The first, run No. 7, was made to produce Diesel fuel which was used by the Denver and Rio Grande Railroad on a trial train operation between Salt Lake City and Denver on September 1 and 2, 1950. The primary objective of this run was to determine the acid treatment that would be necessary to substantially reduce the sulfur content of the raw oil. Charge stock for run No. 7 was light gas oil from visbreaking operations (cracking plant run No. 19).

Ziel der Untersuchung ist, über die seit der Vorkriegszeit eingetretenen Veränderungen der Lohn- und Gehaltssituation zu informieren. Unter „Vorkriegszeit“ werden die Jahre der Weimarer Republik (im engeren Sinne die Jahre von 1924 bis 1929), unter „Nachkriegszeit“ die Jahre seit 1959 verstanden (bis 1954). Das Hauptgewicht des Vergleichs liegt auf der Situation der Bundesrepublik, deren Verhältnisse mit denen der Vorkriegszeit verglichen werden. Für beide Perioden wird für sämtliche zu vergleichenden Jahre eine gemeinsame Indexbasis zugrunde gelegt. Diese gemeinsame Basis stellt das Jahr 1928 dar. „Gerade wegen der ‚Vergleichspriorität‘, die der Gegenwart zugemessen wird, war ein Jahr aus der Weimarer Zeit als Basisjahr heranzuziehen. Die Wahl fiel auf das Jahr 1928, weil für Einkommensbetrachtungen, die einen ‚isolierten‘ Vergleich intendieren, Jahre konjunktureller Höhepunkte am geeignetsten sind. Zwar hatte die Konjunkturentwicklung der Weimarer Zeit erst im Verlaufe des Jahres 1929 ihren Höhepunkt erreicht; da sich aber im Jahre 1929 der Einfluss der Beginnenden Krise bereits stark bemerkbar machte, ist das Jahr 1928 als das Jahr mit der gleichsam ‚reinsten‘ Hochkonjunktur der Vorkriegszeit anzusehen und wurde deshalb als Basisjahr genommen. In der Untersuchung“ (Menges/Kolbeck, a. a. O., S. XII). In der Untersuchung werden zwei Typen des Periodenvergleichs unterschieden. (1) „Eliminierter Vergleich“: Vergleich, dem ausschließlich das Gebiet der Bundesrepublik Deutschland (Gebietsstand von 1950) zugrunde liegt; (2) „Uneliminierter Vergleich“: Vergleich, dem für die Jahre bis 1945 das Reichsgebiet (Gebietsstand von 1925) und für die Jahre ab 1945 das Gebiet der Bundesrepublik Deutschland (Stand 1950) zugrunde liegt. Der weitaus größte Teil der präsentierten Daten stellen Schätzungen dar, die „in jedem Falle fundiert sind, aber doch häufig nur als grob gelten können“ (Menges/Kolbeck, a. a. O., S. VII). Datentabellen in HISTAT:Die ‚Datentabellen können grob den folgenden inhaltlichen Schwerpunkten zugeordnet werden:A. Strukturdaten (Erwerbspersonen, Altersgliederung der Arbeitnehmer)B. Die Kaufkraft des GeldesC. Löhne und Gehälter in ihrer Gesamtheit im Vergleich zum VolkseinkommenD. Die Löhne und Gehälter im Einzelnen: ArbeiterE. Die Löhne und Gehälter im Einzelnen: AngestellteF. Die Löhne und Gehälter im Einzelnen: ZusammenfassungG. Ergänzende Beurteilung: Haushaltseinkommen, Arbeitszeit, Sozialversicherung, Arbeitslosigkeit

In 2020, the median age of the Slovak population was around 40.2 years while in 2025 it was expected to reach 42.3 years. In 2100, it is forecasted to be nearly 10 years higher. The median age is the age that divides a population into two numerically equal groups; that is, half the people are younger than this age and half are older. It is a single index that summarizes the age distribution of a population.

The population of Europe was estimated to be 745 million in 2024, an increase of around 4 million when compared with 2012. Over 35 years between 1950 and 1985, the population of Europe grew by approximately 157.8 million. But 35 years after 1985 it was estimated to have only increased by around 38.7 million. Since the 1960s, population growth in Europe has fallen quite significantly and was even negative during the mid-1990s. While population growth has increased slightly since the low of -0.07 percent in 1998, the growth rate for 2020 was just 0.04 percent. Which European country has the biggest population? As of 2024, the population of Russia was estimated to be approximately 144.8 million and was by far Europe's largest country in terms of population, with Turkey being the second-largest at over 87 million. While these two countries both have territory in Europe, however, they are both only partially in Europe, with the majority of their landmasses being in Asia. In terms of countries wholly located on the European continent, Germany had the highest population at 84.5 million, and was followed by the United Kingdom and France at 69.1 million and 66.5 million respectively. Characteristics of Europe's population There are approximately 384.6 million females in Europe, compared with 359.5 million males, a difference of around 25 million. In 1950, however, the male population has grown faster than the female one, with the male population growing by 104.7 million, and the female one by 93.6 million. As of 2024, the single year of age with the highest population was 37, at 10.6 million, while in the same year there were estimated to be around 136 thousand people aged 100 or over.

This dataset contains the digitized treatments in Plazi based on the original journal article Stöhr, Sabine, O’Hara, Timothy D. (2021): Deep-sea Ophiuroidea (Echinodermata) from the Danish Galathea II Expedition 1950 - 52, with taxonomic revisions. Zootaxa 4963 (3): 505-529, DOI: 10.11646/zootaxa.4963.3.6

This data set contains Tornadoes that occurred in Tennessee between 1950 and 2017. The data was downloaded from the NWS Storm Prediction Center.Column Names and Definitions from the NWS (pdf)om - Tornado number - A count of tornadoes during the y ear: Prior to 2007, these numbers were assigned to the tornado as the information arrived in the NWS database. Since 2007, the numbers may have been assigned in sequential (temporal) order after event date/times are converted to CST. However, do not use "om" to count the sequence of tornadoes through the year as sometimes new entries have come in late, or corrections are made, and the data are not re-sequenced.NOTE: Tornado segments that cross state borders and/or more than 4 counties will have same OM number. See information about fields 22-24 below.yr - Year, 1950-2017mo - Month, 1-12dy - Day, 1-31date - Date - in format yyyy-mm-dd formattime - Time - in format HH:MM:SStz - Time Zone - All t imes, except for ?=unkown and 9=GMT, were converted to 3=CST. This should be accounted for when building queries for GMT summaries such as 12z- 12z.st - State - Two letter postal abbreviation (PR=Puerto Rico. VI=Virgin Islands)stf - State FIPS Number - Note some Puerto Rico codes are incorrectstn - State Number - number of this tornado, in this state, in this year: May not be sequential in some years. Note: discontinued in 2008. This number can be calculated in a spreadsheet by sorting and after accounting for border crossing tornadoes and 4+ county segments.f - F-Scale - F-scale (EF-scale after Jan. 2007): values -9, 0, 1, 2, 3, 4, 5 (-9=unknown).inj - Injuries - when summing for state totals use sn=1, not sg=1 (see below)fat - Fatalities - when summing for state totals use sn=1, not sg=1 (see below)loss - Estimated property loss information - Prior to 1996 this is a categorization of tornado damage by dollar amount (o or blank-unknown; 1<$50, 2=$50-$500, 3=$500-$5,000, 4=$5,000-$50,000; 5=$50,000-$500,000, 6=$500,000-$5,000,000, 7=$5,000,000-$50,000,000, 8=$50,000,000-$500,000,000; 9=$5,000,000,000) When summing for state total use sn= 1, not Sg=1 (see below). From 1996, this is tornado property damage in millions of dollars. Note: this may change to whole dollar amounts in the future. Entry of 0 does not mean $0.closs - Estimated crop loss in millions of dollars (started in 2007). Entry of 0 does not mean 0$Tornado database file updated to add "fc" field for estimated F-scale rating in 2016. Valid for records altered between 1950-1982. slat - Starting latitude in decimal degreesslong - Starting longitude in decimal degreeselat - Ending latitude in decimal degreeselon - Ending longitude in decimal degreeslen - Length in mileswid - Width in yardsns, sn, sg - Understanding these fields is critical to counting state tornadoes, totaling state fatalities/losses. The tornado segment information can be thought of as follows:ns - Number of States affected by this tornado: 1, 2, or 3.sn - State Number 1 or 0 (1=entire track info in this state)sg - Tornado Segment number: 1, 2, or -9 (1 = entire track info)1,1,1 = Entire record for the track of the tornado (unless all 4 fips codes are non -zero).1,0,-9 = Continuing county fips code information only from 1,1,1 record, above (same om).2,0,1 = A two-state tornado (st=state of touchdown, other fields summarize entire track).2,1,2 = First state segment for a two-state (2,0,1) tornado (state same as above, same om).2,1,2 = Second state segment for two-state (2,0,1) tornado (state tracked into, same om).2,0,-9 = Continuing county fips for a 2,1,2 record that exceeds 4 counties (same om).3,0,1 = A three-state (st=state of touchdown, other fields summarize entire track).3,1,2 = First state segment for a three-state (3,0,1) tornado (state same as 3,0,1, same om).3,1,2 = Second state segment for three-state (3,0,1) tornado (2nd state tracked into, same om as 3,0,1 record).3,1,2 = Third state segment for a three-state (3,0,1) tornado (3rd state tracked into, same om as the initial 3,0,1 record).f1 - 1st county FIPS codef2 - 2nd county FIPS codef3 - 3rd county FIPS codef4 - 4th county FIPS codefc - fc = 0 for unaltered (E)F - scale rating. fc = 1 if previous rating was -9 (unknown)

This dataset contains the digitized treatments in Plazi based on the original journal article Larson, Eric R., Abbott, Cathryn L., Gilmore, Scott R., Helbing, Caren C., Lopez, Mark Louie D., Macintosh, Hugh, Stenhouse, Liane M., Williams, Bronwyn W., Usio, Nisikawa (2025): Genome skimming supports two new crayfish species from the genus Pacifastacus Bott, 1950 (Decapoda: Astacidae). Zootaxa 5632 (3): 501-521, DOI: 10.11646/zootaxa.5632.3.4, URL: https://doi.org/10.11646/zootaxa.5632.3.4

Analysis of ‘NCHS - Births and General Fertility Rates: United States’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/704dd4ab-8519-4c5c-8d00-fbaa83389c06 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset includes crude birth rates and general fertility rates in the United States since 1909.

The number of states in the reporting area differ historically. In 1915 (when the birth registration area was established), 10 states and the District of Columbia reported births; by 1933, 48 states and the District of Columbia were reporting births, with the last two states, Alaska and Hawaii, added to the registration area in 1959 and 1960, when these regions gained statehood. Reporting area information is detailed in references 1 and 2 below. Trend lines for 1909–1958 are based on live births adjusted for under-registration; beginning with 1959, trend lines are based on registered live births.

SOURCES

NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/).

REFERENCES

1. National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf.

2. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf.

3. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf.

4. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.

5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf.

6. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.

--- Original source retains full ownership of the source dataset ---

This dataset contains the digitized treatments in Plazi based on the original journal article Hugel, Sylvain (2019): Panoploscelis scudderi Beier, 1950 and Gnathoclita vorax (Stoll, 1813): two katydids with unusual acoustic, reproductive and defense behaviors (Orthoptera, Pseudophyllinae). Zoosystema 41 (17): 327-340, DOI: 10.5252/zoosystema2019v41a17

Table 2. Average uncorrected p-distance (%) in COI between and within species of Scincella Mittleman, 1950 used in this study. Mean uncorrected interspecific p-distances are given on the diagonal.

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 prolonged development arc in Sub-Saharan Africa.

The HTR model ‘NIOD_WarLet_1935-1950_NoBasemodel’ was trained using 968 ‘Ground Truth’ transcriptions of high-resolution scans of various handwritten letters. These letters are all written in Dutch and originate from the period 1935-1950. The training set contains personal correspondence from a wide variety of letter writers (e.g., children, soldiers, Jewish people in hiding). These personal correspondences are all part of the archival collection known as ‘247 Correspondentie’ held by the NIOD Institute for War, Holocaust, and Genocide Studies in Amsterdam.

This model was created as part of the project ‘First-Hand Accounts of War: War letters (1935-1950) from NIOD digitised’. All documents used for training and validation were scanned and transcribed within this project. This project ran from 2020 to 2023 and was funded by the Mondriaan Fund, the Dutch Ministry of Health, Welfare, and Sport, and the NIOD Institute for War, Holocaust, and Genocide Studies in Amsterdam.

The ‘Ground Truth’ training set is created by project members Annelies van Nispen, Carlijn Keijzer and Milan van Lange. Additional transcription and correction of ‘Ground Truth’ transcriptions was performed under supervision of Muriël Bouman by citizen scientists Hillebrand Verkroost, Bart Cohen, Evelien Bachrach, Marjo Janssens, and Cocky Sietses. The validation set contains a sample of 17 ‘Ground Truth’ transcriptions from various writers and sub-collections. Due to legal restrictions only a limited sample of the training set is published publicly.

The model is trained using PyLaia HTR, max. 500 epochs (321 epochs trained), learning rate 0.0003. No basemodel was used. See also: https://readcoop.eu/model/niod_warlet_1945-1950_nobasemodel/