26 datasets found
  1. Graduate labour market statistics - Time Series for Salaries by Gender and...

    • explore-education-statistics.service.gov.uk
    Updated Jun 29, 2023
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    Department for Education (2023). Graduate labour market statistics - Time Series for Salaries by Gender and Graduate Type [Dataset]. https://explore-education-statistics.service.gov.uk/data-catalogue/data-set/24207b43-86ab-4cf9-9d92-9fcdc5a8b0e2
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    Dataset updated
    Jun 29, 2023
    Dataset authored and provided by
    Department for Educationhttps://gov.uk/dfe
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Time period covered
    2007 - 2022
    Description

    Median nominal and real salaries by different demographics time series 2007 - 2022(By gender, age group, and graduate type)

  2. LEO Graduate and Postgraduate Outcomes - Real terms earnings data

    • explore-education-statistics.service.gov.uk
    Updated Jun 26, 2025
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    Department for Education (2025). LEO Graduate and Postgraduate Outcomes - Real terms earnings data [Dataset]. https://explore-education-statistics.service.gov.uk/data-catalogue/data-set/881180a5-faf0-4c32-b454-5bbe492da362
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    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Department for Educationhttps://gov.uk/dfe
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Nominal earnings and real terms earnings, by subject studied for UK domiciled first degree and level 7 graduates of English Higher Education providers.

  3. Tonal languages from mozilla common voice 10

    • kaggle.com
    Updated Feb 1, 2023
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    Enrique Díaz-Ocampo (2023). Tonal languages from mozilla common voice 10 [Dataset]. https://www.kaggle.com/datasets/enriquedazocampo/tonal-languages-mozilla-common-voice/versions/1
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 1, 2023
    Dataset provided by
    Kaggle
    Authors
    Enrique Díaz-Ocampo
    License

    Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
    License information was derived automatically

    Description

    The following dataset is intended to be used for gender recognition using audio files in uncontrolled environments from the Mozilla Common Voice Dataset 10.0. It consists of a table of descriptive statistical characteristics of the fundamental frequency of six tonal languages Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Thai, Vietnamese, and Punjabi. In addition, the estimation of the vocal tract of each of the speakers.

    This dataset contains 18 columns: 'client_id': id speaker from Mozilla Common Voice 'path': Name of the mp3 file 'sentence': The sentence spoken by the speaker 'age': Age in decades (teens, twenties, etc.) 'gender': Binary gender (male or female) 'duration': Duration of mp3 in seconds 'vocal_tract_length': Vocal tract length in cm. 'mean_F4': Mean of the fourth formant in Hz. 'min_pitch': Minimal pitch of the whole pitch contour in Hz. 'mean_pitch': Mean pitch of the whole pitch contour in Hz. 'q1_pitch': : First quartile of the whole pitch contour in Hz. 'median_pitch': : Median pitch of the whole pitch contour Hz. 'q3_pitch': : Third quartile of the whole pitch contour in Hz. 'max_pitch': : Max pitch of the whole pitch contour in Hz. 'stddev_pitch' : Standard deviation of the whole pitch contour in Hz. 'estimated_age': Nominal value (adult or teen) 'estimated_age_gender: Nominal value (adult-male, adult-female, teen-male and teen-female). 'language': Nominal value (Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Thai, Vietnamese, and Punjabi).

    The methodology for the extraction of these characteristics was the following:

    Only the audios from the valid.tsv file of the respective language were analyzed (this file is contained in the Mozilla Common Voice Dataset https://commonvoice.mozilla.org/en/datasets ) the voiced-speech was extracted using Praat's algorithm Vocal ToolKit (https://www.praatvocaltoolkit.com/extract-voiced-and-unvoiced.html)

    2) The vocal tract length was calculated with the Vocal Tool Kit algorithm ( https://www.praatvocaltoolkit.com/calculate-vocal-tract-length.html ) as follows: If the audio came from a teen, then the maximum formant was established at 8000, otherwise it was adjusted to 5000 Hz for men and 5500 for women. Finally, the mean of the fourth formant was calculated for the windows with voiced speech only.

    3) The fundamental frequency was calculated using the PRAAT Software in the To Pitch (ac) option and a) Time step (s) 0.0 (=auto) b) Pitch floor (Hz) 75.0 c) Max. number of candidates 15 d) Vey accurate=True e) Silence Threshold= 0.03 f) Voicing threshold= 0.45 g) Octave Cost= 0.01 h) Octave jump cost = 0.35 i) Voiced/ Unvoiced cost= 0.14 j) Pitch ceiling (Hz) = 350

    4) The statistical characteristics of the fundamental frequency were calculated only in the windows that were detected as voiced speech.

  4. ACTIVATE Falcon Aircraft Merge Data Files - Dataset - NASA Open Data Portal

    • data.nasa.gov
    Updated Apr 1, 2025
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    nasa.gov (2025). ACTIVATE Falcon Aircraft Merge Data Files - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/activate-falcon-aircraft-merge-data-files-69f5c
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    ACTIVATE_Merge_Data is the pre-generated merge data files created from data collected onboard the HU-25 Falcon aircraft during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.

  5. Changes In Average Monthly Nominal Earnings Per Employee, (Compared To The...

    • data.gov.sg
    Updated Sep 12, 2025
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    Singapore Department of Statistics (2025). Changes In Average Monthly Nominal Earnings Per Employee, (Compared To The Same Period A Year Ago), Annual [Dataset]. https://data.gov.sg/datasets?sort=updatedAt&page=1&resultId=d_64f98475cef1e94300362cb400a50012
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    Dataset updated
    Sep 12, 2025
    Dataset authored and provided by
    Singapore Department of Statistics
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Time period covered
    Dec 2000 - Dec 2024
    Description

    Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_64f98475cef1e94300362cb400a50012/view

  6. ACTIVATE Supplementary Model Data - Dataset - NASA Open Data Portal

    • data.nasa.gov
    Updated Apr 1, 2025
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    nasa.gov (2025). ACTIVATE Supplementary Model Data - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/activate-supplementary-model-data-bd1b3
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    ACTIVATE_Model_Data is the MERRA-2 variables sampled along the HU-25 flight tracks during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.

  7. Average Monthly Nominal Earnings Per Employee, Annual

    • data.gov.sg
    Updated Sep 12, 2025
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    Singapore Department of Statistics (2025). Average Monthly Nominal Earnings Per Employee, Annual [Dataset]. https://data.gov.sg/datasets/d_5d2a513a20f58239f8c449ea6c9b6ecd/view
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    Dataset updated
    Sep 12, 2025
    Dataset authored and provided by
    Singapore Department of Statistics
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Time period covered
    Jan 2001 - Dec 2024
    Description

    Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5d2a513a20f58239f8c449ea6c9b6ecd/view

  8. ACTIVATE Falcon In Situ Cloud Data - Dataset - NASA Open Data Portal

    • data.nasa.gov
    Updated Apr 1, 2025
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    nasa.gov (2025). ACTIVATE Falcon In Situ Cloud Data - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/activate-falcon-in-situ-cloud-data-1fb15
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    ACTIVATE_Cloud_AircraftInSitu_Falcon_Data is the cloud data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.

  9. u

    nominal eQTL statistics for all tested SNP-gene pairs

    • figshare.unimelb.edu.au
    • datasetcatalog.nlm.nih.gov
    application/gzip
    Updated Oct 17, 2023
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    Heini M. Natri; Georgi Hudjashov; Guy S. Jacobs; Pradiptajati Kusuma; Lauri Saag; Chelzie Crenna Darusallam; Mait Metspalu; Herawati Sudoyo; Murray P. Cox; IRENE GALLEGO ROMERO; Nicholas E. Banovich (2023). nominal eQTL statistics for all tested SNP-gene pairs [Dataset]. http://doi.org/10.26188/12871007.v1
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    application/gzipAvailable download formats
    Dataset updated
    Oct 17, 2023
    Dataset provided by
    The University of Melbourne
    Authors
    Heini M. Natri; Georgi Hudjashov; Guy S. Jacobs; Pradiptajati Kusuma; Lauri Saag; Chelzie Crenna Darusallam; Mait Metspalu; Herawati Sudoyo; Murray P. Cox; IRENE GALLEGO ROMERO; Nicholas E. Banovich
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    Supplementary file 3 from Natri et al, "Genetic architecture of gene regulation in Indonesian populations identifies QTLs associated with local ancestry and archaic introgression", 2020 (biorxiv)Contains nominal eQTL statistics for all tested SNP-gene pairs.

  10. ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories -...

    • data.nasa.gov
    Updated Apr 1, 2025
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    nasa.gov (2025). ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/activate-flexible-particle-flexpart-dispersion-model-back-trajectories-2d342
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    ACTIVATE-FLEXPART is the FLEXible PARTicle dispersion model back-trajectories ending at the HU-25 Falcon locations. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.

  11. Share Of Nominal Gross Value Added, By Industry (SSIC 2020), Annual

    • data.gov.sg
    Updated Sep 2, 2025
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    Singapore Department of Statistics (2025). Share Of Nominal Gross Value Added, By Industry (SSIC 2020), Annual [Dataset]. https://data.gov.sg/datasets/d_23b79a9ab5cb28aa1b75fce6e53cd38d/view
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    Dataset updated
    Sep 2, 2025
    Dataset authored and provided by
    Singapore Department of Statistics
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Time period covered
    Jan 1960 - Dec 2024
    Description

    Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_23b79a9ab5cb28aa1b75fce6e53cd38d/view

  12. f

    Historical nominal catches 1950–2010

    • ices-library.figshare.com
    txt
    Updated Jul 10, 2024
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    ICES (2024). Historical nominal catches 1950–2010 [Dataset]. http://doi.org/10.17895/ices.data.25817698.v1
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    txtAvailable download formats
    Dataset updated
    Jul 10, 2024
    Dataset provided by
    Data Outputs
    Authors
    ICES
    License

    https://www.ices.dk/Pages/library_policies.aspxhttps://www.ices.dk/Pages/library_policies.aspx

    Description

    This dataset contains figures for nominal catches in FAO area 27 by country, species, area and year, for the period 1950–2010. Source: Eurostat/ICES database on catch statistics - ICES 2011, CopenhagenFishery statistics for the International Council for the Exploration of the Sea (ICES) area were published from 1904 to 1989 under the title "Bulletin Statistique des Pêches Maritimes". The series was renamed "ICES Fisheries Statistics' and continued until 2001. For the period 1999-2004 the data was published on a CD-ROM. Since 2004, Eurostat and ICES have worked together on the processing and publishing of these data: https://ec.europa.eu/eurostat/web/fisheries/database

  13. Z

    Open-source traffic and CO2 emission dataset for commercial aviation

    • data.niaid.nih.gov
    • zenodo.org
    Updated Nov 29, 2023
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    Planès, Thomas (2023). Open-source traffic and CO2 emission dataset for commercial aviation [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10125898
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    Dataset updated
    Nov 29, 2023
    Dataset provided by
    Delbecq, Scott
    Planès, Thomas
    Salgas, Antoine
    Lafforgue, Gilles
    Sun, Junzi
    License

    https://www.gnu.org/licenses/gpl-3.0-standalone.htmlhttps://www.gnu.org/licenses/gpl-3.0-standalone.html

    Description

    This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data. 1- DISCLAIMER The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. 2- DESCRIPTION Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns:

    "First column" : index airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found. acft_icao : ICAO code of the aircraft type acft_class : Aircraft class identifier, own classification.

    WB: Wide Body NB: Narrow Body RJ: Regional Jet PJ: Private Jet TP: Turbo Propeller PP: Piston Propeller HE: Helicopter OTHER seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model. source: Original data source for the record, before compilation and enrichment.

    ANAC: Brasilian Civil Aviation Authorities AUS Stats: Australian Civil Aviation Authorities BTS: US Bureau of Transportation Statistics T100 Estimation: Own model, estimation on Wikipedia-parsed route database Eurocontrol: Aggregation and enrichment of R&D database OpenSky World Bank seats: Number of seats available for the data entry, AFTER airport residual scaling n_flights: Number of flights of the data entry, when available iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal. departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling distance_km: Flight distance (km) ask: Available Seat Kilometres rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor) fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available fuel_burn: Total fuel burn of the data entry (kg) co2: Total CO2 emissions of the data entry (kg) domestic: Domestic/international boolean (Domestic=1, International=0)

    3- Citation Please cite the support paper instead of the dataset itself.

    Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

  14. Graduate labour market statistics - Graduate salary breakdowns

    • explore-education-statistics.service.gov.uk
    Updated Jun 27, 2024
    + more versions
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    Department for Education (2024). Graduate labour market statistics - Graduate salary breakdowns [Dataset]. https://explore-education-statistics.service.gov.uk/data-catalogue/data-set/cb060ad9-c303-4ac3-803e-ad86a6ff4d01
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    Dataset updated
    Jun 27, 2024
    Dataset authored and provided by
    Department for Educationhttps://gov.uk/dfe
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Time period covered
    2023
    Description

    Graduate nominal salaries for those of working age and the young population by gender and industry in 2023.

  15. Nominal Change in Average Monthly Household Employment Income (Including...

    • data.gov.sg
    Updated Sep 26, 2025
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    Singapore Department of Statistics (2025). Nominal Change in Average Monthly Household Employment Income (Including Employer CPF Contributions) Among Resident Employed Households by Type of Dwelling (Household Employment Income, Annual 2000-2024) [Dataset]. https://data.gov.sg/datasets?sort=updatedAt&page=1&resultId=d_b2c4b6540304c1dbef8fe621e5726750
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    Dataset updated
    Sep 26, 2025
    Dataset authored and provided by
    Singapore Department of Statistics
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Description

    Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_b2c4b6540304c1dbef8fe621e5726750/view

  16. Nominal Change in Average Monthly Household Employment Income Per Household...

    • data.gov.sg
    Updated Aug 23, 2025
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    Singapore Department of Statistics (2025). Nominal Change in Average Monthly Household Employment Income Per Household Member (Including Employer CPF Contributions) Among Resident Employed Households by Deciles (Household Employment Income, Annual 2000-2024) [Dataset]. https://data.gov.sg/datasets/d_c2edb22238bea80e5726a803d50ce39f/view
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    Dataset updated
    Aug 23, 2025
    Dataset authored and provided by
    Singapore Department of Statistics
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Description

    Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_c2edb22238bea80e5726a803d50ce39f/view

  17. North American Roads

    • catalog.data.gov
    Updated Jul 17, 2025
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    Bureau of Transportation Statistics (BTS) (Point of Contact) (2025). North American Roads [Dataset]. https://catalog.data.gov/dataset/north-american-roads1
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    Dataset updated
    Jul 17, 2025
    Dataset provided by
    Bureau of Transportation Statisticshttp://www.rita.dot.gov/bts
    Description

    The North American Roads dataset was compiled on October 27, 2020 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). On March 31, 2025, the errant records with a value of 2 in the "NHS" field were corrected to have a value of 7 (Other NHS). This dataset contains geospatial information regarding major roadways in North America. The data set covers the 48 contiguous United States plus the District of Columbia, Alaska, Hawaii, Canada and Mexico. The nominal scale of the data set is 1:100,000. The data within the North American Roads layer is a compilation of data from Natural Resources Canada, USDOT’s Federal Highway Administration, and the Mexican Transportation Institute. North American Roads is a digital single-line representation of major roads and highways for Canada, the United States, and Mexico with consistent definitions by road class, jurisdiction, lane counts, speed limits and surface type. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529071

  18. Statistical Area 3 2023 Clipped (generalised)

    • datafinder.stats.govt.nz
    csv, dwg, geodatabase +6
    Updated Dec 1, 2022
    + more versions
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    Stats NZ (2022). Statistical Area 3 2023 Clipped (generalised) [Dataset]. https://datafinder.stats.govt.nz/layer/111204-statistical-area-3-2023-clipped-generalised/
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    mapinfo tab, pdf, geopackage / sqlite, mapinfo mif, dwg, geodatabase, shapefile, csv, kmlAvailable download formats
    Dataset updated
    Dec 1, 2022
    Dataset provided by
    Statistics New Zealandhttp://www.stats.govt.nz/
    Authors
    Stats NZ
    License

    https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/

    Area covered
    Description

    Statistical area 3 (SA3) is a new output geography, introduced in 2023, that allows aggregations of population data between the SA2 geography and territorial authority geography.

    This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 929 SA3s, including 4 non-digitised SA3s.

    The SA3 geography aims to meet three purposes:

    1. approximate suburbs in major, large, and medium urban areas,
    2. in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities,
    3. minimise data suppression.

    SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations.

    Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s.

    Zero or nominal population SA3s

    To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations.

    Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority.

    Small population island SA2s are included in their adjacent land-based SA3.

    Island SA2s outside territorial authority or region are the same in the SA3 geography.

    Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification.

    Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority.

    Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council.

    The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name):

    70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region.

    SA3 numbering and naming

    Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb,recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District).

    SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2023, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained.

    For more information please refer to the Statistical standard for geographic areas 2023.

    Clipped version

    This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries.

    Macrons

    Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

    Digital data

    Digital boundary data became freely available on 1 July 2007.

    To download geographic classifications in table formats such as CSV please use Ariā

  19. d

    All India and Yearly Indices of Real Effective Exchange Rate (REER) and...

    • dataful.in
    Updated Aug 29, 2025
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    Dataful (Factly) (2025). All India and Yearly Indices of Real Effective Exchange Rate (REER) and Nominal Effective Exchange Rate (NEER) of the Indian Rupee for Fiscal Year [Dataset]. https://dataful.in/datasets/17746
    Explore at:
    csv, xlsx, application/x-parquetAvailable download formats
    Dataset updated
    Aug 29, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    India
    Variables measured
    neer, reer
    Description

    The dataset contains All India and Yearly Indices of Real Effective Exchange Rate (REER) and Nominal Effective Exchange Rate (NEER) of the Indian Rupee for Fiscal Year

    Note: 1. REER figures are based on Consumer Price Index (combined). 2. Data for 2018-19, 2019-20 and 2021-22 to 2022-23 are provisional. 3. The details on methodology used for compilation of NEER/REER indices are available in December 2005, April 2014 and January 2021 issues of the RBI Bulletin. 4. The details on methodology used for compilation of REER (CPI-based) are available in the article Real Effective Exchange Rate based on CPI as Price Index for India’ published in April 2014 issue of RBI Monthly Bulletin.

  20. ACTIVATE Miscellaneous and Ancillary Data - Dataset - NASA Open Data Portal

    • data.nasa.gov
    Updated Apr 1, 2025
    + more versions
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    nasa.gov (2025). ACTIVATE Miscellaneous and Ancillary Data - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/activate-miscellaneous-and-ancillary-data-b416e
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    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    ACTIVATE_Miscellaneous_Data is the supplementary miscellaneous data collected and utilized during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.

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Department for Education (2023). Graduate labour market statistics - Time Series for Salaries by Gender and Graduate Type [Dataset]. https://explore-education-statistics.service.gov.uk/data-catalogue/data-set/24207b43-86ab-4cf9-9d92-9fcdc5a8b0e2
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Graduate labour market statistics - Time Series for Salaries by Gender and Graduate Type

yearly_salaries_by_gender_2007222.csv

Time Series for Salaries by Gender and Graduate Type

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Dataset updated
Jun 29, 2023
Dataset authored and provided by
Department for Educationhttps://gov.uk/dfe
License

Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically

Time period covered
2007 - 2022
Description

Median nominal and real salaries by different demographics time series 2007 - 2022(By gender, age group, and graduate type)

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