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  • 9500+ Downloads
    Updated 9 November 2021 | Dataset date: September 01, 2016-April 01, 2018
    This dataset updates: As needed
    More than 200 million businesses use Facebook. Facebook partners with various academic and international organizations to survey these businesses throughout the year to learn about their perspectives, challenges and opportunities. With more businesses leveraging online tools each day, these surveys provide a lens into a new mobilized and digital economy. The Future of Business Survey (FoBS) is a collaboration between Facebook, the OECD and the World Bank to provide timely insights on the perceptions, challenges, and outlook of online Small and Medium Enterprises or Businesses (SMEs/SMBs). The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. The Future of Business Survey was first launched as a monthly survey in 17 countries in February 2016 and expanded to 42 countries in 2018. In 2019, the Future of Business survey increased coverage to 97 countries and moved to a bi-annual cadence. In 2020, the Future of Business Survey shifted to do additional monthly waves with questions focused on business challenges related to COVID-19. Facebook also shares information from other SMB surveys, such as those that feed into the Global State of Small Business Reports, which are fielded to active Facebook Page Administrators and the general population of Facebook users. These surveys are conducted in approximately 30 countries across the globe and are also conducted on a roughly bi-annual basis. Survey questions for all surveys cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, gender inequity, access to finance, digital technologies, reduction in revenues, business closures, reduction of employees and challenges/needs of the business. Aggregated country level data is available to the public here for each wave and controlled access microdata is available to Data for Good partners. To request access to survey microdata or to see published Facebook reports, please visit: futureofbusinesssurvey.org Update for data users 10/4/21: Aggregate data in files [gsosb_2021julyaugust_data_aggregate_weighted.csv] and [gsosb_2021february_data_aggregate_weighted.csv] have been updated to provide weighted results and to correct an error found in those two files. These files were discovered to present results for large businesses rather than small and medium businesses and have been updated to provide the information for the latter group. Downloads of these two files that took place between April 1 and September 30 2021 were affected and analysis using downloads from this period may need to be amended. No analyses or reports published by Facebook were affected. Over the course of the next month, we are transitioning all small business survey datasets to have survey weights applied. Please contact dataforgood@fb.com for any questions or if you need access to unweighted data for your work.
  • 20000+ Downloads
    Updated Live | Dataset date: January 18, 2020-November 25, 2021
    This dataset updates: Live
    'Our World in Data' is compiling COVID-19 testing data over time for many countries around the world. They are adding further data in the coming days as more details become available for other countries. In some cases figures refer to the number of tests, in other cases to the number of individuals who have been tested. Refer to documentation provided here.
  • Updated Live | Dataset date: January 01, 2019-November 27, 2021
    This dataset updates: Live
    Live list of active aid activities for China, Hong Kong Special Administrative Region shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org
  • 14000+ Downloads
    Updated 19 October 2021 | Dataset date: October 13, 2021-October 13, 2021
    This dataset updates: As needed
    We use an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook. Details on the underlying data and the construction of the index are provided in the “Facebook Social Connectedness Index - Data Notes.pdf” file. Please also see https://dataforgood.fb.com/ as well as the associated research paper “Social Connectedness: Measurement, Determinants and Effects,” published in the Journal of Economic Perspectives (https://www.aeaweb.org/articles?id=10.1257/jep.32.3.259). Region identifiers are taken from GADM v2.8 https://gadm.org/download_country_v2.html. Future versions will update IDs to be compatible with the newest GADM version.
  • 500+ Downloads
    Updated 4 August 2021 | Dataset date: January 01, 1970-December 31, 2019
    This dataset updates: Every three months
    Education indicators for China, Hong Kong Special Administrative Region. Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: National Monitoring (made 2021 March), SDG 4 Global and Thematic (made 2021 March), Demographic and Socio-economic (made 2021 March)
  • 200+ Downloads
    Updated 18 July 2021 | Dataset date: January 01, 2000-December 31, 2020
    This dataset updates: Every year
    Food Security Indicators for China, Hong Kong Special Administrative Region. Contains data from the FAOSTAT bulk data service.
  • 200+ Downloads
    Updated 30 May 2021 | Dataset date: January 01, 2008-December 31, 2020
    This dataset updates: Every year
    Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "People Displaced" refers to the number of people living in displacement as of the end of each year. "New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Contains data from IDMC's Global Internal Displacement Database.
  • 100+ Downloads
    Updated 4 May 2021 | Dataset date: January 01, 1990-December 31, 2030
    This dataset updates: Every year
    The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.
  • 10+ Downloads
    Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    This dataset updates: Every year
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator) -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method. -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674
  • 10+ Downloads
    Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    This dataset updates: Every year
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App. The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): - Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019) -Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019). -Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets. -Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020. -Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019). Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
  • 384000+ Downloads
    Updated Live | Dataset date: January 22, 2020-November 25, 2021
    This dataset updates: Live
    Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github. Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths. On 23/03/2020, a new data structure was released. The current resources for the latest time series data are: time_series_covid19_confirmed_global.csv time_series_covid19_deaths_global.csv time_series_covid19_recovered_global.csv ---DEPRECATION WARNING--- The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020: time_series_19-covid-Confirmed.csv time_series_19-covid-Deaths.csv time_series_19-covid-Recovered.csv
  • Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten','school','college','university') OR building IN ('kindergarten','school','college','university') Features may have these attributes: name source operator:type capacity:persons addr:full amenity building addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity = 'ferry_terminal' OR building = 'ferry_terminal' OR port IS NOT NULL Features may have these attributes: name source port operator:type addr:full amenity building addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 10+ Downloads
    Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site' Features may have these attributes: name addr:full capacity:persons source aeroway emergency operator:type emergency:helipad building addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office') Features may have these attributes: name source addr:full amenity operator addr:city network This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay') Features may have these attributes: depth layer name source water natural waterway width tunnel covered blockage This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: railway IN ('rail','station') Features may have these attributes: layer name source ele operator:type addr:full addr:city railway This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 10+ Downloads
    Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy') Features may have these attributes: healthcare:speciality name capacity:persons source healthcare operator:type addr:full amenity building addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 10+ Downloads
    Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: place IN ('isolated_dwelling','town','village','hamlet','city') Features may have these attributes: name source is_in place population This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 10+ Downloads
    Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: highway IS NOT NULL Features may have these attributes: layer name source bridge surface smoothness width oneway lanes highway This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 10+ Downloads
    Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL Features may have these attributes: name source rooms man_made addr:housenumber beds addr:street tourism addr:full amenity opening_hours addr:city shop This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • Updated 3 July 2020 | Dataset date: March 05, 2021-March 05, 2021
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: building IS NOT NULL Features may have these attributes: office building:materials name source building:levels addr:street addr:housenumber addr:full building addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 40+ Downloads
    Updated 29 June 2020 | Dataset date: January 01, 2000-December 31, 2020
    This dataset updates: Every year
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. A description of the modelling methods used for age and sex structures can be found in Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/sex structure proportion datasets here. Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020. The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646
  • Updated 29 June 2020 | Dataset date: January 01, 2018-December 31, 2018
    This dataset updates: Every year
    The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of pregnancies to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age, age-specific fertility rates, still births and abortions to map the estimated distributions of pregnancies for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al and James et al.. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Hong Kong 1km Pregnancies. Version 1.0 2015 estimates of numbers of pregnancies per grid square, with national totals adjusted to match national estimates on numbers of pregnancies made by the Guttmacher Institute (http://www.guttmacher.org) DOI: 10.5258/SOTON/WP00600
  • Updated 29 June 2020 | Dataset date: January 01, 2018-December 31, 2018
    This dataset updates: Every year
    The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al. and James et al.. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Hong Kong 1km Births. Version 1.0 2015 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/). DOI: 10.5258/SOTON/WP00549