sources

{
    "link": null,
    "retrievedDate": null,
    "additionalInfo": "\nThe homicide rate data shown here is taken from Table 4 on pages 80-81 in Eisner (2015). In the table homicide rate estimates are given for a range of years, we present data for the mid-point year here. For example, if data is given for 1200-1299 we show this as 1250.\n\nDataset notes:\n\n* For 1775 and 1862 the data for Switzerland are for the canton of Zurich only.\n* From 1825 onwards the estimates for Corsica and Sardinia are for Sardinia only.",
    "dataPublishedBy": "OWID based on Eisner (2014); United Nations Office of Drugs and Crime (2022); WHO Mortality Database (2024)"
}

{
    "link": null,
    "retrievedDate": null,
    "additionalInfo": "The homicide rate data shown here is taken from Table 4 on pages 80-81 in Eisner (2015). In the table homicide rate estimates are given for a range of years, we present data for the mid-point year here. For example, if data is given for 1200-1299 we show this as 1250.\n\nDataset notes:\n\n* For 1775 and 1862 the data for Switzerland are for the canton of Zurich only.\n* From 1825 onwards the estimates for Corsica and Sardinia are for Sardinia only.",
    "dataPublishedBy": "OWID based on Eisner (2014); United Nations Office of Drugs and Crime (2022); WHO Mortality Database (2024)"
}

{
    "link": null,
    "retrievedDate": "2024-07-31",
    "additionalInfo": "\nData on COVID-19 vaccinations. We only rely on figures that are verifiable based on public official sources.\n\nYou can download the full dataset here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations",
    "dataPublishedBy": "For source details see https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/locations.csv"
}

{
    "link": null,
    "retrievedDate": "2024-07-31",
    "additionalInfo": "Data on COVID-19 vaccinations. We only rely on figures that are verifiable based on public official sources.\n\nYou can download the full dataset here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations",
    "dataPublishedBy": "For source details see https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/locations.csv"
}

{
    "link": "https://github.com/owid/covid-19-data/tree/master/public/data/hospitalizations",
    "retrievedDate": "2024-07-31",
    "additionalInfo": "\nOur hospital & ICU data is collected from official sources and collated by Our World in Data. The complete list of country-by-country sources is available [on GitHub](https://github.com/owid/covid-19-data/blob/master/public/data/hospitalizations/locations.csv).\n\nOur complete COVID-19 dataset is a collection of the COVID-19 data maintained by *Our World in Data*. **It is updated daily** and includes data on confirmed cases, deaths, and testing.\n\nWe have created a new description of all our data sources. You find it at our GitHub repository **[here](https://github.com/owid/covid-19-data/tree/master/public/data/)**. There you can download all of our data.",
    "dataPublishedBy": "Official data collated by Our World in Data"
}

{
    "link": "https://github.com/owid/covid-19-data/tree/master/public/data/hospitalizations",
    "retrievedDate": "2024-07-31",
    "additionalInfo": "Our hospital & ICU data is collected from official sources and collated by Our World in Data. The complete list of country-by-country sources is available [on GitHub](https://github.com/owid/covid-19-data/blob/master/public/data/hospitalizations/locations.csv).\n\nOur complete COVID-19 dataset is a collection of the COVID-19 data maintained by *Our World in Data*. **It is updated daily** and includes data on confirmed cases, deaths, and testing.\n\nWe have created a new description of all our data sources. You find it at our GitHub repository **[here](https://github.com/owid/covid-19-data/tree/master/public/data/)**. There you can download all of our data.",
    "dataPublishedBy": "Official data collated by Our World in Data"
}

{
    "link": "https://coronavirus.data.gov.uk/details/download",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "",
    "dataPublishedBy": "Government of the United Kingdom"
}

{
    "link": "https://coronavirus.data.gov.uk/details/download",
    "retrievedDate": "2024-07-30",
    "additionalInfo": null,
    "dataPublishedBy": "Government of the United Kingdom"
}

{
    "link": "https://ourworldindata.org/coronavirus-testing#testing-for-covid-19-background-the-our-world-in-data-covid-19-testing-dataset",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nData on COVID-19 testing. Comparisons between countries are compromised for several reasons.\n\nYou can download the full dataset, alongside detailed source descriptions here: https://github.com/owid/covid-19-data/tree/master/public/data/",
    "dataPublishedBy": "For source details see ourworldindata.org/coronavirus-testing#source-information-country-by-country"
}

{
    "link": "https://ourworldindata.org/coronavirus-testing#testing-for-covid-19-background-the-our-world-in-data-covid-19-testing-dataset",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Data on COVID-19 testing. Comparisons between countries are compromised for several reasons.\n\nYou can download the full dataset, alongside detailed source descriptions here: https://github.com/owid/covid-19-data/tree/master/public/data/",
    "dataPublishedBy": "For source details see ourworldindata.org/coronavirus-testing#source-information-country-by-country"
}

{
    "link": "https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nOxCGRT collects publicly available information on indicators of government response. These indicators take policies such as school closures, travel bans, etc. and record them on an ordinal scale. The remainder is financial indicators, such as fiscal or monetary measures.\n\nOxCGRT measures the variation in governments\u2019 responses using its COVID-19 Government Response Stringency Index. This composite measure is a simple additive score of nine indicators measured on an ordinal scale, rescaled to vary from 0 to 100. Please note that this measure is for comparative purposes only, and should not be interpreted as a rating of the appropriateness or effectiveness of a country's response.\n\nIt also includes a 'COVID-19 Containment and Health Response' index which is based on the metrics used in the 'Stringency Index' plus testing policy, contact tracing, face coverings and vaccine policy.\n\nThe specific policy and response categories are coded as follows:\n\nSchool closures:\n0 - No measures\n1 - recommend closing\n2 - Require closing (only some levels or categories,\ne.g. just high school, or just public schools)\n3 - Require closing all levels\nNo data - blank\n\nWorkplace closures:\n0 - No measures\n1 - recommend closing (or work from home)\n2 - require closing (or work from home) for some\nsectors or categories of workers\n3 - require closing (or work from home) all but essential workplaces (e.g. grocery stores, doctors)\nNo data - blank\n\nCancel public events:\n0- No measures\n1 - Recommend cancelling\n2 - Require cancelling\nNo data - blank\n\nRestrictions on gatherings:\n0 - No restrictions\n1 - Restrictions on very large gatherings (the limit is above 1,000 people)\n2 - Restrictions on gatherings between 100-1,000 people\n3 - Restrictions on gatherings between 10-100 people\n4 - Restrictions on gatherings of less than 10 people\nNo data - blank\n\nClose public transport:\n0 - No measures\n1 - Recommend closing (or significantly reduce volume/route/means of transport available)\n2 - Require closing (or prohibit most citizens from using it)\n\nPublic information campaigns:\n0 -No COVID-19 public information campaign\n1 - public officials urging caution about COVID-19\n2 - coordinated public information campaign (e.g. across traditional and social media)\nNo data - blank\n\nStay at home:\n0 - No measures\n1 - recommend not leaving house\n2 - require not leaving house with exceptions for daily exercise, grocery shopping, and \u2018essential\u2019 trips\n3 - Require not leaving house with minimal exceptions (e.g. allowed to leave only once every few days, or only one person can leave at a time, etc.)\nNo data - blank\n\nRestrictions on internal movement:\n0 - No measures\n1 - Recommend movement restriction\n2 - Restrict movement\n\nInternational travel controls:\n0 - No measures\n1 - Screening\n2 - Quarantine arrivals from high-risk regions\n3 - Ban on high-risk regions\n4 - Total border closure\nNo data - blank\n\nTesting policy\n0 \u2013 No testing policy\n1 \u2013 Only those who both (a) have symptoms AND (b) meet specific criteria (eg key workers, admitted to hospital, came into contact with a known case, returned from overseas)\n2 \u2013 testing of anyone showing COVID-19 symptoms\n3 \u2013 open public testing (e.g. \u201cdrive through\u201d testing available to asymptomatic people)\nNo data\n\nContract tracing\n0 - No contact tracing\n1 - Limited contact tracing - not done for all cases\n2 - Comprehensive contact tracing - done for all cases\nNo data\n\nFace coverings\n0- No policy\n1- Recommended\n2- Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible\n3- Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible\n4- Required outside the home at all times, regardless of location or presence of other people\n\nVaccination policy\n0 - No availability\n1 - Availability for ONE of the following: key workers/ clinically vulnerable groups / elderly groups\n2 - Availability for TWO of the following: key workers/ clinically vulnerable groups / elderly groups\n3 - Availability for ALL the following: key workers/ clinically vulnerable groups / elderly groups\n4 - Availability for all three, plus partial additional availability (select broad groups/ages)\n5 - Universal availability",
    "dataPublishedBy": "Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. (2021). \u201cA global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).\u201d Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01079-8"
}

{
    "link": "https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "OxCGRT collects publicly available information on indicators of government response. These indicators take policies such as school closures, travel bans, etc. and record them on an ordinal scale. The remainder is financial indicators, such as fiscal or monetary measures.\n\nOxCGRT measures the variation in governments\u2019 responses using its COVID-19 Government Response Stringency Index. This composite measure is a simple additive score of nine indicators measured on an ordinal scale, rescaled to vary from 0 to 100. Please note that this measure is for comparative purposes only, and should not be interpreted as a rating of the appropriateness or effectiveness of a country's response.\n\nIt also includes a 'COVID-19 Containment and Health Response' index which is based on the metrics used in the 'Stringency Index' plus testing policy, contact tracing, face coverings and vaccine policy.\n\nThe specific policy and response categories are coded as follows:\n\nSchool closures:\n0 - No measures\n1 - recommend closing\n2 - Require closing (only some levels or categories,\ne.g. just high school, or just public schools)\n3 - Require closing all levels\nNo data - blank\n\nWorkplace closures:\n0 - No measures\n1 - recommend closing (or work from home)\n2 - require closing (or work from home) for some\nsectors or categories of workers\n3 - require closing (or work from home) all but essential workplaces (e.g. grocery stores, doctors)\nNo data - blank\n\nCancel public events:\n0- No measures\n1 - Recommend cancelling\n2 - Require cancelling\nNo data - blank\n\nRestrictions on gatherings:\n0 - No restrictions\n1 - Restrictions on very large gatherings (the limit is above 1,000 people)\n2 - Restrictions on gatherings between 100-1,000 people\n3 - Restrictions on gatherings between 10-100 people\n4 - Restrictions on gatherings of less than 10 people\nNo data - blank\n\nClose public transport:\n0 - No measures\n1 - Recommend closing (or significantly reduce volume/route/means of transport available)\n2 - Require closing (or prohibit most citizens from using it)\n\nPublic information campaigns:\n0 -No COVID-19 public information campaign\n1 - public officials urging caution about COVID-19\n2 - coordinated public information campaign (e.g. across traditional and social media)\nNo data - blank\n\nStay at home:\n0 - No measures\n1 - recommend not leaving house\n2 - require not leaving house with exceptions for daily exercise, grocery shopping, and \u2018essential\u2019 trips\n3 - Require not leaving house with minimal exceptions (e.g. allowed to leave only once every few days, or only one person can leave at a time, etc.)\nNo data - blank\n\nRestrictions on internal movement:\n0 - No measures\n1 - Recommend movement restriction\n2 - Restrict movement\n\nInternational travel controls:\n0 - No measures\n1 - Screening\n2 - Quarantine arrivals from high-risk regions\n3 - Ban on high-risk regions\n4 - Total border closure\nNo data - blank\n\nTesting policy\n0 \u2013 No testing policy\n1 \u2013 Only those who both (a) have symptoms AND (b) meet specific criteria (eg key workers, admitted to hospital, came into contact with a known case, returned from overseas)\n2 \u2013 testing of anyone showing COVID-19 symptoms\n3 \u2013 open public testing (e.g. \u201cdrive through\u201d testing available to asymptomatic people)\nNo data\n\nContract tracing\n0 - No contact tracing\n1 - Limited contact tracing - not done for all cases\n2 - Comprehensive contact tracing - done for all cases\nNo data\n\nFace coverings\n0- No policy\n1- Recommended\n2- Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible\n3- Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible\n4- Required outside the home at all times, regardless of location or presence of other people\n\nVaccination policy\n0 - No availability\n1 - Availability for ONE of the following: key workers/ clinically vulnerable groups / elderly groups\n2 - Availability for TWO of the following: key workers/ clinically vulnerable groups / elderly groups\n3 - Availability for ALL the following: key workers/ clinically vulnerable groups / elderly groups\n4 - Availability for all three, plus partial additional availability (select broad groups/ages)\n5 - Universal availability",
    "dataPublishedBy": "Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. (2021). \u201cA global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).\u201d Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01079-8"
}

{
    "link": "https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nRaw data on confirmed cases and deaths for all countries is sourced from the [European Centre for Disease Prevention and Control (ECDC)](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide).\n\nOur complete COVID-19 dataset is a collection of the COVID-19 data maintained by *Our World in Data*. **It is updated daily** and includes data on confirmed cases, deaths, and testing.\n\nWe have created a new description of all our data sources. You find it at our GitHub repository **[here](https://github.com/owid/covid-19-data/tree/master/public/data/)**. There you can download all of our data.",
    "dataPublishedBy": "European Centre for Disease Prevention and Control (ECDC)"
}

{
    "link": "https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Raw data on confirmed cases and deaths for all countries is sourced from the [European Centre for Disease Prevention and Control (ECDC)](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide).\n\nOur complete COVID-19 dataset is a collection of the COVID-19 data maintained by *Our World in Data*. **It is updated daily** and includes data on confirmed cases, deaths, and testing.\n\nWe have created a new description of all our data sources. You find it at our GitHub repository **[here](https://github.com/owid/covid-19-data/tree/master/public/data/)**. There you can download all of our data.",
    "dataPublishedBy": "European Centre for Disease Prevention and Control (ECDC)"
}

{
    "link": "https://covid.cdc.gov/covid-data-tracker/#vaccinations",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nData on vaccinations against COVID-19, collected from the data updated daily by the United States Centers for Disease Control and Prevention.\n\nYou can download the full dataset here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations",
    "dataPublishedBy": "Centers for Disease Control and Prevention \u2013 Last updated 2 November 2022 (Eastern Time)"
}

{
    "link": "https://covid.cdc.gov/covid-data-tracker/#vaccinations",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Data on vaccinations against COVID-19, collected from the data updated daily by the United States Centers for Disease Control and Prevention.\n\nYou can download the full dataset here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations",
    "dataPublishedBy": "Centers for Disease Control and Prevention \u2013 Last updated 2 November 2022 (Eastern Time)"
}

{
    "link": null,
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nData constructed from:\n\n(1) Survey data collected by YouGov in partnership with the Institute of Global Health Innovation (IGHI) at Imperial College London. YouGov has partnered with the Institute of Global Health Innovation (IGHI) at Imperial College London to gather global insights on people\u2019s behaviors in response to COVID-19. The research will cover 29 countries, interviewing around 21,000 people each week.\n\nIt is designed to provide behavioral analysis on how different populations are responding to the pandemic, helping public health bodies in their efforts to limit the impact of the disease. Anonymized respondent level data will be available for all public health and academic institutions globally.\n\nThe questions in the survey, led by IGHI, cover data on testing, symptoms, self-isolating in response to symptoms and the ability and willingness to self-isolate if needed. It also looks at behaviors, including going outdoors, working outside the home, contact with others, handwashing and the extent of compliance with 20 common preventative measures.\n\n(2) COVID-19 vaccination figures collated by Our World in Data from public official sources. You can download the full dataset here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations.",
    "dataPublishedBy": "Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub \u2013 Last updated 15 March 2022, 09:00 (London time)"
}

{
    "link": null,
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Data constructed from:\n\n(1) Survey data collected by YouGov in partnership with the Institute of Global Health Innovation (IGHI) at Imperial College London. YouGov has partnered with the Institute of Global Health Innovation (IGHI) at Imperial College London to gather global insights on people\u2019s behaviors in response to COVID-19. The research will cover 29 countries, interviewing around 21,000 people each week.\n\nIt is designed to provide behavioral analysis on how different populations are responding to the pandemic, helping public health bodies in their efforts to limit the impact of the disease. Anonymized respondent level data will be available for all public health and academic institutions globally.\n\nThe questions in the survey, led by IGHI, cover data on testing, symptoms, self-isolating in response to symptoms and the ability and willingness to self-isolate if needed. It also looks at behaviors, including going outdoors, working outside the home, contact with others, handwashing and the extent of compliance with 20 common preventative measures.\n\n(2) COVID-19 vaccination figures collated by Our World in Data from public official sources. You can download the full dataset here: https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations.",
    "dataPublishedBy": "Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub \u2013 Last updated 15 March 2022, 09:00 (London time)"
}

{
    "link": "https://www.gisaid.org/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nEnabled by data from [![](https://www.gisaid.org/fileadmin/gisaid/img/schild.png)](https://gisaid.org)\n\nOur data on SARS-CoV-2 sequencing and variants is sourced from [GISAID](https://gisaid.org), a global science initiative that provides open-access to genomic data of SARS-CoV-2. We recognize the work of the authors and laboratories responsible for producing this data and sharing it via the GISAID initiative.\n\nKhare, S., et al (2021) GISAID\u2019s Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406\n\nElbe, S. and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID\u2019s innovative contribution to global health. Global Challenges, 1:33-46. doi:10.1002/gch2.1018 PMCID: 31565258\n\nShu, Y. and McCauley, J. (2017) GISAID: from vision to reality. EuroSurveillance, 22(13) doi:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101\n\nWe download aggregate-level data via [CoVariants.org](https://covariants.org).",
    "dataPublishedBy": "GISAID, via CoVariants.org \u2013 Last updated 2 November 2022"
}

{
    "link": "https://www.gisaid.org/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Enabled by data from [![](https://www.gisaid.org/fileadmin/gisaid/img/schild.png)](https://gisaid.org)\n\nOur data on SARS-CoV-2 sequencing and variants is sourced from [GISAID](https://gisaid.org), a global science initiative that provides open-access to genomic data of SARS-CoV-2. We recognize the work of the authors and laboratories responsible for producing this data and sharing it via the GISAID initiative.\n\nKhare, S., et al (2021) GISAID\u2019s Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406\n\nElbe, S. and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID\u2019s innovative contribution to global health. Global Challenges, 1:33-46. doi:10.1002/gch2.1018 PMCID: 31565258\n\nShu, Y. and McCauley, J. (2017) GISAID: from vision to reality. EuroSurveillance, 22(13) doi:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101\n\nWe download aggregate-level data via [CoVariants.org](https://covariants.org).",
    "dataPublishedBy": "GISAID, via CoVariants.org \u2013 Last updated 2 November 2022"
}

{
    "link": "https://www.google.com/covid19/mobility/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nGoogle provide an overview of what its mobility trends represent and how it's measured here: https://support.google.com/covid19-mobility/answer/9824897?hl=en&ref_topic=9822927\n\nAs it describes:\n\"The data shows how visitors to (or time spent in) categorized places change compared to our baseline days. A baseline day represents a normal value for that day of the week. The baseline day is the median value from the 5week period Jan 3 - Feb 6, 2020.\n\nFor each region-category, the baseline isn't a single value-it's 7 individual values. The same number of visitors on 2 different days of the week, result in different percentage changes. So, we recommend the following:\n\n- Don't infer that larger changes mean more visitors or smaller changes mean less visitors.\n- Avoid comparing day-to-day changes. Especially weekends with weekdays.\"\n\nMobility trends are measured across six broad categories:\n(1) Residential: places of residence.\n(2) Grocery & Pharmacy stores: places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.\n(3) Workplaces: places of work.\n(4) Parks: places like local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens.\n(5) Transit stations: places like public transport hubs such as subway, bus, and train stations.\n(6) Retail & Recreation: places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.\n\nThe 'Residential' category shows a change in duration-the other categories measure a change in total visitors.\n\nThis index is smoothed to the rolling 7-day average.",
    "dataPublishedBy": "Google LLC \"Google COVID-19 Community Mobility Reports\"."
}

{
    "link": "https://www.google.com/covid19/mobility/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Google provide an overview of what its mobility trends represent and how it's measured here: https://support.google.com/covid19-mobility/answer/9824897?hl=en&ref_topic=9822927\n\nAs it describes:\n\"The data shows how visitors to (or time spent in) categorized places change compared to our baseline days. A baseline day represents a normal value for that day of the week. The baseline day is the median value from the 5week period Jan 3 - Feb 6, 2020.\n\nFor each region-category, the baseline isn't a single value-it's 7 individual values. The same number of visitors on 2 different days of the week, result in different percentage changes. So, we recommend the following:\n\n- Don't infer that larger changes mean more visitors or smaller changes mean less visitors.\n- Avoid comparing day-to-day changes. Especially weekends with weekdays.\"\n\nMobility trends are measured across six broad categories:\n(1) Residential: places of residence.\n(2) Grocery & Pharmacy stores: places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.\n(3) Workplaces: places of work.\n(4) Parks: places like local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens.\n(5) Transit stations: places like public transport hubs such as subway, bus, and train stations.\n(6) Retail & Recreation: places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.\n\nThe 'Residential' category shows a change in duration-the other categories measure a change in total visitors.\n\nThis index is smoothed to the rolling 7-day average.",
    "dataPublishedBy": "Google LLC \"Google COVID-19 Community Mobility Reports\"."
}

{
    "link": "https://www.gisaid.org/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nEnabled by data from [![](https://www.gisaid.org/fileadmin/gisaid/img/schild.png)](https://gisaid.org)\n\nOur data on SARS-CoV-2 sequencing and variants is sourced from [GISAID](https://gisaid.org), a global science initiative that provides open-access to genomic data of SARS-CoV-2. We recognize the work of the authors and laboratories responsible for producing this data and sharing it via the GISAID initiative.\n\nKhare, S., et al (2021) GISAID\u2019s Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406\n\nElbe, S. and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID\u2019s innovative contribution to global health. Global Challenges, 1:33-46. doi:10.1002/gch2.1018 PMCID: 31565258\n\nShu, Y. and McCauley, J. (2017) GISAID: from vision to reality. EuroSurveillance, 22(13) doi:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101\n\nWe download aggregate-level data via [CoVariants.org](https://covariants.org).",
    "dataPublishedBy": "GISAID, via CoVariants.org \u2013 Last updated 2 November 2022"
}

{
    "link": "https://www.gisaid.org/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Enabled by data from [![](https://www.gisaid.org/fileadmin/gisaid/img/schild.png)](https://gisaid.org)\n\nOur data on SARS-CoV-2 sequencing and variants is sourced from [GISAID](https://gisaid.org), a global science initiative that provides open-access to genomic data of SARS-CoV-2. We recognize the work of the authors and laboratories responsible for producing this data and sharing it via the GISAID initiative.\n\nKhare, S., et al (2021) GISAID\u2019s Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406\n\nElbe, S. and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID\u2019s innovative contribution to global health. Global Challenges, 1:33-46. doi:10.1002/gch2.1018 PMCID: 31565258\n\nShu, Y. and McCauley, J. (2017) GISAID: from vision to reality. EuroSurveillance, 22(13) doi:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101\n\nWe download aggregate-level data via [CoVariants.org](https://covariants.org).",
    "dataPublishedBy": "GISAID, via CoVariants.org \u2013 Last updated 2 November 2022"
}

{
    "link": "https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/bekraftade-fall-i-sverige/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nThis data on confirmed COVID-19 deaths is imported directly from the dataset published by the Swedish Public Health Agency (Folkh\u00e4lsomyndigheten).\n\nMore information on this dataset and the reporting method is available in [our dedicated blog post](https://ourworldindata.org/covid-sweden-death-reporting).",
    "dataPublishedBy": "Swedish Public Health Agency \u2013 Last updated 28 October 2022"
}

{
    "link": "https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/bekraftade-fall-i-sverige/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "This data on confirmed COVID-19 deaths is imported directly from the dataset published by the Swedish Public Health Agency (Folkh\u00e4lsomyndigheten).\n\nMore information on this dataset and the reporting method is available in [our dedicated blog post](https://ourworldindata.org/covid-sweden-death-reporting).",
    "dataPublishedBy": "Swedish Public Health Agency \u2013 Last updated 28 October 2022"
}

{
    "link": "https://github.com/open-numbers/ddf--gapminder--systema_globalis ; https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators ; https://population.un.org/wpp/Download/Standard/Population/ ; https://docs.google.com/spreadsheets/d/14_suWY8fCPEXV0MH7ZQMZ-KndzMVsSsA5HdR-7WqAC0/edit#gid=501532268 ; https://dataportaal.pbl.nl/downloads/HYDE/",
    "retrievedDate": "December 12, 2022",
    "additionalInfo": "The Internet dataset by Our World in Data contains different Internet-related metrics.",
    "dataPublishedBy": "Gapminder (Systema Globalis); World Development Indicators - World Bank (2022.05.26); UN, World Population Prospects (2022); Gapminder (v6); HYDE (v3.2)"
}

{
    "link": "https://github.com/open-numbers/ddf--gapminder--systema_globalis ; https://population.un.org/wpp/Download/Standard/Population/ ; http://data.worldbank.org/data-catalog/world-development-indicators ; https://docs.google.com/spreadsheets/d/14_suWY8fCPEXV0MH7ZQMZ-KndzMVsSsA5HdR-7WqAC0/edit#gid=501532268 ; https://dataportaal.pbl.nl/downloads/HYDE/",
    "retrievedDate": "December 12, 2022",
    "additionalInfo": "The most important handful of indicators for use directly and in transforming other statistics.",
    "dataPublishedBy": "Gapminder (Systema Globalis); UN, World Population Prospects (2022); World Bank; Gapminder (v6); HYDE (v3.2)"
}

{
    "link": "https://web.archive.org/web/20211024081702/https://apps.who.int/dracunculiasis/dradata/html/report_Countries_t0.html ; https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal",
    "retrievedDate": "2023-06-29",
    "additionalInfo": "",
    "dataPublishedBy": "World Health Organization; Dracunculiasis Eradication Portal, World Health Organization"
}

{
    "link": "https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal",
    "retrievedDate": "2024-06-17",
    "additionalInfo": "Reported cases of guinea worm disease (dracunculiasis) as recorded by WHO.\n\nFor Cameroon, Central African Republic, Cote d'Ivoire, Mauritania, Senegal and Yemen data is gathered from:\n\n1986-2017: https://web.archive.org/web/20220208133814/https://apps.who.int/dracunculiasis/dradata/html/report_Countries_i2.html\n2018: Table 1a: https://web.archive.org/web/20230629130727/https://apps.who.int/iris/bitstream/handle/10665/324786/WER9420-233-251.pdf?sequence=1&isAllowed=y\n2019: Table 1a: https://web.archive.org/web/20230629130619/https://apps.who.int/iris/bitstream/handle/10665/332086/WER9520-209-227-eng-fre.pdf?sequence=1&isAllowed=y\n2020: Table 1a: https://web.archive.org/web/20230226162934/https://apps.who.int/iris/bitstream/handle/10665/341529/WER9621-173-194-eng-fre.pdf?sequence=1&isAllowed=y\n2021: Table 1a: https://web.archive.org/web/20230226163027/https://apps.who.int/iris/bitstream/handle/10665/354576/WER9721-22-225-247-eng-fre.pdf?sequence=1&isAllowed=y\n2022: Table 1a: https://web.archive.org/web/20230629124651/https://apps.who.int/iris/bitstream/handle/10665/367924/WER9820-205-224.pdf?sequence=1&isAllowed=y\n\nFor all other countries data is gathered from the following sources:\n\n1980-2020: https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal\n2021: Table 1a: https://web.archive.org/web/20230226163027/https://apps.who.int/iris/bitstream/handle/10665/354576/WER9721-22-225-247-eng-fre.pdf?sequence=1&isAllowed=y\n2022: Table 1a: https://web.archive.org/web/20230629124651/https://apps.who.int/iris/bitstream/handle/10665/367924/WER9820-205-224.pdf?sequence=1&isAllowed=y\n2023: Table 1a: https://iris.who.int/bitstream/handle/10665/376790/WER9920-249-269.pdf?sequence=1\n\nGlobal totals are calculated yearly as the sum of the number of reported cases in each country.",
    "dataPublishedBy": "Dracunculiasis Eradication Portal"
}

{
    "link": "https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal",
    "retrievedDate": "2024-06-17",
    "additionalInfo": "Reported cases of guinea worm disease (dracunculiasis) as recorded by WHO.\n\nFor Cameroon, Central African Republic, Cote d'Ivoire, Mauritania, Senegal and Yemen data is gathered from:\n\n1986-2017: https://web.archive.org/web/20220208133814/https://apps.who.int/dracunculiasis/dradata/html/report_Countries_i2.html\n2018: Table 1a: https://web.archive.org/web/20230629130727/https://apps.who.int/iris/bitstream/handle/10665/324786/WER9420-233-251.pdf?sequence=1&isAllowed=y\n2019: Table 1a: https://web.archive.org/web/20230629130619/https://apps.who.int/iris/bitstream/handle/10665/332086/WER9520-209-227-eng-fre.pdf?sequence=1&isAllowed=y\n2020: Table 1a: https://web.archive.org/web/20230226162934/https://apps.who.int/iris/bitstream/handle/10665/341529/WER9621-173-194-eng-fre.pdf?sequence=1&isAllowed=y\n2021: Table 1a: https://web.archive.org/web/20230226163027/https://apps.who.int/iris/bitstream/handle/10665/354576/WER9721-22-225-247-eng-fre.pdf?sequence=1&isAllowed=y\n2022: Table 1a: https://web.archive.org/web/20230629124651/https://apps.who.int/iris/bitstream/handle/10665/367924/WER9820-205-224.pdf?sequence=1&isAllowed=y\n\nFor all other countries data is gathered from the following sources:\n\n1980-2020: https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal\n2021: Table 1a: https://web.archive.org/web/20230226163027/https://apps.who.int/iris/bitstream/handle/10665/354576/WER9721-22-225-247-eng-fre.pdf?sequence=1&isAllowed=y\n2022: Table 1a: https://web.archive.org/web/20230629124651/https://apps.who.int/iris/bitstream/handle/10665/367924/WER9820-205-224.pdf?sequence=1&isAllowed=y\n2023: Table 1a: https://web.archive.org/web/20240617154248/https://iris.who.int/bitstream/handle/10665/376790/WER9920-249-269.pdf?sequence=1\n\nGlobal totals are calculated yearly as the sum of the number of reported cases in each country.",
    "dataPublishedBy": "Dracunculiasis Eradication Portal"
}

{
    "link": "https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal",
    "retrievedDate": "2024-06-14",
    "additionalInfo": "Reported cases of guinea worm disease (dracunculiasis) as recorded by WHO.\n\nFor Cameroon, Central African Republic, Cote d'Ivoire, Mauritania, Senegal and Yemen data is gathered from:\n\n1986-2017: https://web.archive.org/web/20220208133814/https://apps.who.int/dracunculiasis/dradata/html/report_Countries_i2.html\n2018: Table 1a: https://web.archive.org/web/20230629130727/https://apps.who.int/iris/bitstream/handle/10665/324786/WER9420-233-251.pdf?sequence=1&isAllowed=y\n2019: Table 1a: https://web.archive.org/web/20230629130619/https://apps.who.int/iris/bitstream/handle/10665/332086/WER9520-209-227-eng-fre.pdf?sequence=1&isAllowed=y\n2020: Table 1a: https://web.archive.org/web/20230226162934/https://apps.who.int/iris/bitstream/handle/10665/341529/WER9621-173-194-eng-fre.pdf?sequence=1&isAllowed=y\n2021: Table 1a: https://web.archive.org/web/20230226163027/https://apps.who.int/iris/bitstream/handle/10665/354576/WER9721-22-225-247-eng-fre.pdf?sequence=1&isAllowed=y\n2022: Table 1a: https://web.archive.org/web/20230629124651/https://apps.who.int/iris/bitstream/handle/10665/367924/WER9820-205-224.pdf?sequence=1&isAllowed=y\n\nFor all other countries data is gathered from the following sources:\n\n1980-2020: https://www.who.int/teams/control-of-neglected-tropical-diseases/dracunculiasis/dracunculiasis-eradication-portal\n2021: Table 1a: https://web.archive.org/web/20230226163027/https://apps.who.int/iris/bitstream/handle/10665/354576/WER9721-22-225-247-eng-fre.pdf?sequence=1&isAllowed=y\n2022: Table 1a: https://web.archive.org/web/20230629124651/https://apps.who.int/iris/bitstream/handle/10665/367924/WER9820-205-224.pdf?sequence=1&isAllowed=y\n2023: Table 1a: https://iris.who.int/bitstream/handle/10665/376790/WER9920-249-269.pdf?sequence=1\n\nGlobal totals are calculated yearly as the sum of the number of reported cases in each country.",
    "dataPublishedBy": "Dracunculiasis Eradication Portal"
}

{
    "link": "",
    "retrievedDate": "2024-04-26",
    "additionalInfo": "",
    "dataPublishedBy": "Unknown"
}

{
    "link": "",
    "retrievedDate": "2024-04-25",
    "additionalInfo": "",
    "dataPublishedBy": "Unknown"
}

{
    "link": "",
    "retrievedDate": "2024-04-29",
    "additionalInfo": "",
    "dataPublishedBy": "Unknown"
}

{
    "link": "https://www.who.int/data/global-health-estimates",
    "retrievedDate": "2021-09-08",
    "additionalInfo": "GHE estimated burden of disease",
    "dataPublishedBy": "World Health Organisation"
}

{
    "link": "https://population.un.org/wpp/Download/",
    "retrievedDate": "2022-09-09",
    "additionalInfo": "\nWorld Population Prospects 2022 is the 27th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. More details at https://population.un.org/wpp/Publications/.",
    "dataPublishedBy": "United Nations, Department of Economic and Social Affairs, Population Division (2022)"
}

{
    "link": "https://population.un.org/wpp/Download/",
    "retrievedDate": "2022-09-09",
    "additionalInfo": "World Population Prospects 2022 is the 27th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. More details at https://population.un.org/wpp/Publications/.",
    "dataPublishedBy": "United Nations, Department of Economic and Social Affairs, Population Division (2022)"
}

{
    "link": null,
    "retrievedDate": null,
    "additionalInfo": "",
    "dataPublishedBy": "World Mortality Dataset (2024); Human Mortality Database (2024)"
}

{
    "link": null,
    "retrievedDate": null,
    "additionalInfo": "",
    "dataPublishedBy": "Human Mortality Database (2024); World Mortality Dataset (2024)"
}

{
    "link": null,
    "retrievedDate": null,
    "additionalInfo": "",
    "dataPublishedBy": "Human Mortality Database (2024); World Mortality Dataset (2024); Karlinsky and Kobak (2021)"
}

{
    "link": "https://www.mortality.org/Data/STMF ; https://github.com/akarlinsky/world_mortality/ ; https://github.com/dkobak/excess-mortality",
    "retrievedDate": "2024-07-31",
    "additionalInfo": "All-cause mortality data is from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project and the World Mortality Dataset (WMD). Both sources are updated weekly.\n\nWe do not use the data from some countries in WMD because they fail to meet the following data quality criteria: 1) at least three years of historical data; and 2) data published either weekly or monthly. The full list of excluded countries and reasons for exclusion can be found in this spreadsheet: https://docs.google.com/spreadsheets/d/1JPMtzsx-smO3_K4ReK_HMeuVLEzVZ71qHghSuAfG788/edit?usp=sharing.\n\nFor a full list of source information (i.e., HMD or WMD) country by country, see: https://ourworldindata.org/excess-mortality-covid#source-information-country-by-country.\n\nWe calculate P-scores using the reported deaths data from HMD and WMD and the projected deaths since 2020 from WMD (which we use for all countries and regions, including for deaths broken down by age group). The P-score is the percentage difference between the reported number of weekly or monthly deaths since 2020 and the projected number of deaths for the same period based on previous years (years available from 2015 until 2019).\n\nWe calculate the number of weekly deaths for the United Kingdom by summing the weekly deaths from England & Wales, Scotland, and Northern Ireland.\n\nFor important issues and caveats to understand when interpreting excess mortality data, see our excess mortality page at https://ourworldindata.org/excess-mortality-covid.\n\nFor a more detailed description of the HMD data, including week date definitions, the coverage (of individuals, locations, and time), whether dates are for death occurrence or registration, the original national source information, and important caveats, see the HMD metadata file at https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf.\n\nFor a more detailed description of the WMD data, including original source information, see their GitHub page at https://github.com/akarlinsky/world_mortality.\nIn response to the COVID-19 pandemic, the HMD team decided to establish a new data resource: Short-term Mortality Fluctuations (STMF) data series. Objective and internationally comparable data are crucial to determine the effectiveness of different strategies used to address epidemics. Weekly death counts provide the most objective and comparable way of assessing the scale of short-term mortality elevations across countries and time. More details about this data project can be found in the recently published paper (https://www.nature.com/articles/s41597-021-01019-1).\n\nBefore using the data, please consult the STMF Methodological Note (https://www.mortality.org/File/GetDocument/Public/STMF_DOC/STMFNote.pdf), which provides a more comprehensive description of this data project, including important aspects related to data collection and data processing. We also recommend that you read the STMF Metadata (https://www.mortality.org/File/GetDocument/Public/STMF_DOC/STMFmetadata.pdf). This document includes country-specific information about data availability, completeness, data sources, as well as specific features of included data.\n\nData will be frequently updated and new countries will be added. Data are published under CC BY 4.0 license.\n\nFor citing STMF data, please follow the HMD data citation guidelines (https://www.mortality.org/Research/CitationGuidelines).\n\nHMD provides an online STMF visualization toolkit (https://mpidr.shinyapps.io/stmortality).\nWorld Mortality Dataset: international data on all-cause mortality.\n\nThis dataset contains country-level data on all-cause mortality in 2015\u20132024 collected from various sources. They are currently providing data for 122 countries and territories.\n\nFor a complete and up-to-date list of notes on the dataset, please refer to their GitHub page at https://github.com/akarlinsky/world_mortality/.\n\nFor the list of sources that they use, please go to https://github.com/akarlinsky/world_mortality/#sou rces.\n\nPublished paper available at https://elifesciences.org/articles/69336.\nThe data are sourced from the World Mortality Dataset (https://github.com/akarlinsky/world_mortality). Excess mortality is computed relative to the baseline obtained using linear extrapolation of the 2015\u201319 trend (different baselines for 2020, 2021, and 2022). In each subplot in the figure below, gray lines are 2015\u201319, black line is baseline for 2020, red line is 2020, blue line is 2021, orange line is 2022. Countries are sorted by the total excess mortality as % of the 2020 baseline.\n\nFor more details, refer to https://github.com/dkobak/excess-mortality#excess-mortality-during-the-covid-19-pandemic.",
    "dataPublishedBy": "HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.; Karlinsky & Kobak 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset, eLife https://doi.org/10.7554/eLife.69336; Karlinsky & Kobak, 2021, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset. eLife 10:e69336. https://elifesciences.org/articles/69336"
}

{
    "link": "https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001305",
    "retrievedDate": "2023-07-25",
    "additionalInfo": "\nThis dataset estimates the usage of antimicrobials in livestock (cattle, sheep, chicken, and pigs) by country. Data on antimicrobials comes from government reports, surveillance systems and national surveys. In addition, the authors estimate the biomass of livestock in the country, to adjust for differences in antimicrobial usage by animal size. Biomass data comes from the Food and Agriculture Organization (FAO). 'The PCU represents the total number of animals in a country (alive or slaughtered), multiplied by the average weight of the animal at the time of treatment. Therefore, the PCU is a standardization metric that accounts for differences in animal weight, and number of production cycles per year between countries.' Therefore, mg/PCU refers to the usage of antimicrobials per animal population-corrected unit.",
    "dataPublishedBy": "Mulchandani, R., Wang, Y., Gilbert, M., & Van Boeckel, T. P. (2023). Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLOS Global Public Health, 3(2), e0001305. https://doi.org/10.1371/journal.pgph.0001305"
}

{
    "link": "https://www.heartrhythmjournal.com/article/S1547-5271(23)00027-9/fulltext",
    "retrievedDate": "2023-07-13",
    "additionalInfo": "",
    "dataPublishedBy": "Heart Rhythm Journal"
}

{
    "link": null,
    "retrievedDate": "2023-05-31",
    "additionalInfo": "",
    "dataPublishedBy": "World Health Organization"
}

{
    "link": "https://pubmed.ncbi.nlm.nih.gov/29356216/",
    "retrievedDate": "2023-05-11",
    "additionalInfo": "\nThis dataset comes from the World Mental Health surveys, which conducted national studies in 21 countries, using validated structured interviews to survey members of the general population about symptoms of mental illnesses they had in the past 12 months and their lifetime so far. The source describes the dataset: \"Data came from 24 community epidemiological surveys administered in 21 countries as part of the WMH surveys (Kessler & Ustun, 2004). These included 12 surveys carried out in high-income countries, 6 surveys in upper-middle-income countries and 6 in low or lower-middle income countries (see table 1). The majority of surveys were based on nationally representative household samples. Three were representative of urban areas in their countries (Colombia, Mexico, and Peru). Three were representative of selected regions in their countries (Japan, Nigeria, and Murcia, Spain). Four were representative of selected Metropolitan Areas (Sao Paulo, Brazil; Medellin, Colombia; and Beijing-Shanghai and Shenzhen in the People\u2019s Republic of China (PRC)). Trained lay interviewers conducted face-to-face interviews with respondents, aged 18 years and over. The interviews took place within the households of the respondents. To reduce respondent burden, the interview was divided into two parts. Part I assessed core mental disorders and was administered to all respondents. Part II, which assessed additional disorders and correlates, was administered to all Part I respondents who met lifetime criteria for any disorder plus a probability subsample of other Part I respondents. Part II data, the focus of this report, were weighted by the inverse of their probabilities of selection into Part II and additionally weighted to adjust samples to match population distributions on the cross-classification of key socio-demographic and geographic variables. Further details about WMH sampling and weighting are available elsewhere(Heeringa et al., 2008). Response rates ranged between 45.9% and 97.2% and had a weighted average of 70.1% across all surveys.\"\nData comes from Community surveys of the general population",
    "dataPublishedBy": "Alonso et al. (2017)"
}

{
    "link": "https://pubmed.ncbi.nlm.nih.gov/29356216/",
    "retrievedDate": "2023-05-11",
    "additionalInfo": "\nThis dataset comes from the World Mental Health surveys, which conducted national studies in 21 countries, using validated structured interviews to survey members of the general population about symptoms of mental illnesses they had in the past 12 months and their lifetime so far. The source describes the dataset: \"Data came from 24 community epidemiological surveys administered in 21 countries as part of the WMH surveys (Kessler & Ustun, 2004). These included 12 surveys carried out in high-income countries, 6 surveys in upper-middle-income countries and 6 in low or lower-middle income countries (see table 1). The majority of surveys were based on nationally representative household samples. Three were representative of urban areas in their countries (Colombia, Mexico, and Peru). Three were representative of selected regions in their countries (Japan, Nigeria, and Murcia, Spain). Four were representative of selected Metropolitan Areas (Sao Paulo, Brazil; Medellin, Colombia; and Beijing-Shanghai and Shenzhen in the People\u2019s Republic of China (PRC)). Trained lay interviewers conducted face-to-face interviews with respondents, aged 18 years and over. The interviews took place within the households of the respondents. To reduce respondent burden, the interview was divided into two parts. Part I assessed core mental disorders and was administered to all respondents. Part II, which assessed additional disorders and correlates, was administered to all Part I respondents who met lifetime criteria for any disorder plus a probability subsample of other Part I respondents. Part II data, the focus of this report, were weighted by the inverse of their probabilities of selection into Part II and additionally weighted to adjust samples to match population distributions on the cross-classification of key socio-demographic and geographic variables. Further details about WMH sampling and weighting are available elsewhere(Heeringa et al., 2008). Response rates ranged between 45.9% and 97.2% and had a weighted average of 70.1% across all surveys.\"\nData comes from Community surveys of the general population",
    "dataPublishedBy": "Alonso et al. (2017)"
}

{
    "link": "https://www.who.int/data/gho/data/themes/topics/indicator-groups/legally-binding-controls-on-lead-paint",
    "retrievedDate": null,
    "additionalInfo": "",
    "dataPublishedBy": "World Health Organization (WHO)"
}

{
    "link": "https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30925-9/fulltext",
    "retrievedDate": "2023-05-05",
    "additionalInfo": "\nDataset showing the number of countries with primary data on the prevalence of mental illnesses. These were found after a systematic review, grey literature search and expert consultation, to identify studies with data on the prevalence of each mental illness.\n\n'The GBD inclusion criteria stipulated that: (1) the diagnostic criteria must be from 1980 onward; (2) \u201ccaseness\u201d must be based on clinical threshold as established by the DSM, ICD, Chinese Classification of Mental Disorders (CCMD), or diagnosed by a clinician using established tools; (3) sufficient information must be provided on study method and sample characteristics to assess the quality of the study; and (4) study samples must be representative of the general population (i.e., case studies, veterans, or refugee samples were excluded). No limitation was set on the language of publication.'\nCountry-level prevalence data",
    "dataPublishedBy": "Vos, T., Lim, S. S., Abbafati, C., Abbas, K. M., Abbasi, M., Abbasifard, M., Abbasi-Kangevari, M., Abbastabar, H., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abolhassani, H., Aboyans, V., Abrams, E. M., Abreu, L. G., Abrigo, M. R. M., Abu-Raddad, L. J., Abushouk, A. I., \u2026 Murray, C. J. L. (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1204\u20131222."
}

{
    "link": "https://www.gov.uk/government/statistics/adult-psychiatric-morbidity-survey-mental-health-and-wellbeing-england-2014",
    "retrievedDate": "2022-12-01",
    "additionalInfo": "\nThis is a dataset of the prevalence of current depression in the general population in England, living in private households. Households were sampled randomly and individuals were interviewed using the revised Clinical Interview Schedule (CIS-R), which is a diagnostic structured interview format to determine whether people had common mental disorders in the past week. In this dataset, presence of a current episode of major depression was determined.\nSurveys of individuals in randomly-selected private households in England",
    "dataPublishedBy": "\"McManus S, Bebbington P, Jenkins R, Brugha T. (eds.) (2016) Mental health and wellbeing in England: Adult Psychiatric Morbidity Survey 2014. Leeds: NHS Digital\""
}

{
    "link": null,
    "retrievedDate": null,
    "additionalInfo": "\nAmong others these are the original source:\n\nMcEvedy, Colin and Richard Jones, 1978, \u201cAtlas of World Population History,\u201d Facts on File, New York, pp. 342-351.\n\nBiraben, Jean-Noel, 1980, An Essay Concerning Mankind\u2019s Evolution, Population, Selected Papers, December, table 2.\n\nDurand, John D., 1974, \u201cHistorical Estimates of World Population: An Evaluation,\u201d University of Pennsylvania, Population Center, Analytical and Technical Reports, Number 10, table 2.\n\nHaub, Carl, 1995, \u201cHow Many People Have Ever Lived on Earth?\u201d Population Today, February, p. 5.\n\nThomlinson, Ralph, 1975, \u201cDemographic Problems, Controversy Over Population Control,\u201d Second Edition, Table 1.\n\nUnited Nations, 1999, The World at Six Billion, Table 1, \u201cWorld Population From\u201d Year 0 to Stabilization, p. 5,\nU.S. Census Bureau (USCB), 2012, Total Midyear Population for the World: 1950-2050.\n\nMichael Kremer (1993) \u201cPopulation Growth and Technological Change: One Million B.C. to 1990\u201d, Quarterly Journal of Economics., August 1993, pp.681-716.",
    "dataPublishedBy": "Multiple sources compiled by Our World in Data (2019)"
}

{
    "link": "https://www.hoplofobia.info/wp-content/uploads/2015/08/From-Swords-to-Words_Eisner2014.pdf",
    "retrievedDate": null,
    "additionalInfo": "\nThe homicide rate data shown here is taken from Table 4 on pages 80-81 in Eisner (2015). In the table homicide rate estimates are given for a range of years, we present data for the mid-point year here. For example, if data is given for 1200-1299 we show this as 1250.\n\nDataset notes:\n\n* For 1775 and 1862 the data for Switzerland are for the canton of Zurich only.\n* From 1825 onwards the estimates for Corsica and Sardinia are for Sardinia only.",
    "dataPublishedBy": "Eisner, M. 2014. \u201cFrom Swords to Words: Does Macro-Level Change in Self-Control Predict Long-Term Variation in Levels of Homicide?\u201d In Why Crime Rates Fall and Why They Don\u2019t, edited by Michael Tonry. Vol. 43 of Crime and Justice: A Review of Research, edited by Michael Tonry. Chicago: University of Chicago Press."
}

{
    "link": "",
    "retrievedDate": "2024-02-29",
    "additionalInfo": "",
    "dataPublishedBy": "Unknown"
}

{
    "link": "https://en.wikipedia.org/wiki/List_of_quantum_processors",
    "retrievedDate": "2024-02-19",
    "additionalInfo": "",
    "dataPublishedBy": "Wikipedia, List of quantum processors, Circuit-based quantum processors"
}

{
    "link": "https://unstats.un.org/sdgs/dataportal/database",
    "retrievedDate": "2023-08-16",
    "additionalInfo": "\nThe United Nations Sustainable Development Goal (SDG) dataset is the primary collection of data tracking progress towards the SDG indicators,\ncompiled from officially-recognized international sources.",
    "dataPublishedBy": "SDG Indicators Database, United Nations, Department of Economic and Social Affairs (2023)"
}

{
    "link": "https://unstats.un.org/sdgs/dataportal/database",
    "retrievedDate": "2023-08-16",
    "additionalInfo": "The United Nations Sustainable Development Goal (SDG) dataset is the primary collection of data tracking progress towards the SDG indicators,\ncompiled from officially-recognized international sources.",
    "dataPublishedBy": "SDG Indicators Database, United Nations, Department of Economic and Social Affairs (2023)"
}

{
    "link": "http://www.fao.org/faostat/en/#data/",
    "retrievedDate": "2023-06-12",
    "additionalInfo": "\nAdditional variables created using data from different FAOSTAT datasets.\n",
    "dataPublishedBy": "Food and Agriculture Organization of the United Nations"
}

{
    "link": "http://www.fao.org/faostat/en/#data/",
    "retrievedDate": "2023-06-12",
    "additionalInfo": "Additional variables created using data from different FAOSTAT datasets.\n",
    "dataPublishedBy": "Food and Agriculture Organization of the United Nations"
}

{
    "link": "https://today.yougov.com/topics/politics/trackers/worry-about-automation",
    "retrievedDate": "2023-06-08",
    "additionalInfo": "\nBiannual tracking of Americans' level of concern regarding job automation replacing their employment. The provided results are weighted to ensure they are representative of the United States population across various demographic factors.\n\nThe weighting is based on factors such as age, gender, race, education, region, political party affiliation, and income level. This weighting process helps to account for the diversity within the population and ensures that the results reflect a broader representation of the United States as a whole. Next update in January. Each wave comprises responses from 460 to 495 working US adults.\n",
    "dataPublishedBy": "YouGov (2023)"
}

{
    "link": "https://today.yougov.com/topics/politics/trackers/robot-intelligence",
    "retrievedDate": "2023-06-07",
    "additionalInfo": "\nBiannual tracker of Americans' concerns regarding robotic intelligence. Next update is in January. The provided results are weighted to ensure they are representative of the United States population across various demographic factors. The weighting is based on factors such as age, gender, race, education, region, political party affiliation, and income level. This weighting process helps to account for the diversity within the population and ensures that the results reflect a broader representation of the United States as a whole. Each wave comprises responses from 1004 - 1085 US Adults.\n\nRespondents were asked, \"Which ONE, if any, of the following statements do you MOST agree with\n\n\n- Most robots have already developed higher levels of intelligence than humans\n\n- None of these\n\n- Robots will be able to develop higher levels of intelligence than humans in the future\n\n- Robots will never be able to develop higher levels of intelligence than humans\n\n- Don't know\n",
    "dataPublishedBy": "YouGov (2023)"
}

{
    "link": "https://today.yougov.com/topics/politics/trackers/worry-about-automation",
    "retrievedDate": "2023-06-08",
    "additionalInfo": "Biannual tracking of Americans' level of concern regarding job automation replacing their employment. The provided results are weighted to ensure they are representative of the United States population across various demographic factors.\n\nThe weighting is based on factors such as age, gender, race, education, region, political party affiliation, and income level. This weighting process helps to account for the diversity within the population and ensures that the results reflect a broader representation of the United States as a whole. Next update in January. Each wave comprises responses from 460 to 495 working US adults.\n",
    "dataPublishedBy": "YouGov (2023)"
}

{
    "link": "https://today.yougov.com/topics/politics/trackers/robot-intelligence",
    "retrievedDate": "2023-06-07",
    "additionalInfo": "Biannual tracker of Americans' concerns regarding robotic intelligence. Next update is in January. The provided results are weighted to ensure they are representative of the United States population across various demographic factors. The weighting is based on factors such as age, gender, race, education, region, political party affiliation, and income level. This weighting process helps to account for the diversity within the population and ensures that the results reflect a broader representation of the United States as a whole. Each wave comprises responses from 1004 - 1085 US Adults.\n\nRespondents were asked, \"Which ONE, if any, of the following statements do you MOST agree with\n\n\n- Most robots have already developed higher levels of intelligence than humans\n\n- None of these\n\n- Robots will be able to develop higher levels of intelligence than humans in the future\n\n- Robots will never be able to develop higher levels of intelligence than humans\n\n- Don't know\n",
    "dataPublishedBy": "YouGov (2023)"
}

{
    "link": "https://unstats.un.org/sdgs/dataportal/database",
    "retrievedDate": "2023-08-16",
    "additionalInfo": "\nThe United Nations Sustainable Development Goal (SDG) dataset is the primary collection of data tracking progress towards the SDG indicators,\ncompiled from officially-recognized international sources.",
    "dataPublishedBy": "SDG Indicators Database, United Nations, Department of Economic and Social Affairs (2023)"
}

{
    "link": "https://drive.google.com/drive/folders/1ma9WZJzKreS8f2It1rMy_KkkbX6XwDOK",
    "retrievedDate": "2023-06-19",
    "additionalInfo": "\nTo evaluate the impact of Copilot on productivity, well-being, and workflow, GitHub conducted a survey in 2022. Over 2,000 developers who were using Copilot participated in the survey. Additionally, GitHub recruited 95 developers and randomly split them into two groups, one of which used Copilot as part of a coding task and the other which did not.\n\nThe AI Index is an independent initiative at the Stanford University Institute for Human-Centered Artificial Intelligence. The mission of the AI Index is \u201cto provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI.\u201d Their flagship output is the annual AI Index Report, which has been published since 2017.",
    "dataPublishedBy": "GitHub Survey (2022) AI Index 2023 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023"
}

{
    "link": "https://drive.google.com/drive/folders/1ma9WZJzKreS8f2It1rMy_KkkbX6XwDOK",
    "retrievedDate": "2023-06-19",
    "additionalInfo": "To evaluate the impact of Copilot on productivity, well-being, and workflow, GitHub conducted a survey in 2022. Over 2,000 developers who were using Copilot participated in the survey. Additionally, GitHub recruited 95 developers and randomly split them into two groups, one of which used Copilot as part of a coding task and the other which did not.\n\nThe AI Index is an independent initiative at the Stanford University Institute for Human-Centered Artificial Intelligence. The mission of the AI Index is \u201cto provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI.\u201d Their flagship output is the annual AI Index Report, which has been published since 2017.",
    "dataPublishedBy": "GitHub Survey (2022) AI Index 2023 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023"
}

{
    "link": "https://data.who.int/dashboards/covid19/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "\nRaw data on confirmed cases and deaths for all countries is sourced from the [WHO COVID-19 Dashboard](https://data.who.int/dashboards/covid19/data).\n\nOur complete COVID-19 dataset is a collection of the COVID-19 data maintained by *Our World in Data*. **It is updated daily** and includes data on confirmed cases, deaths, hospitalizations, and testing.\n\nWe have created a new description of all our data sources. You find it at our GitHub repository **[here](https://github.com/owid/covid-19-data/tree/master/public/data/)**. There you can download all of our data.\n\nThe WHO licenses this data under CC BY-NC-SA 3.0 IGO. You can read more [here](https://www.who.int/about/policies/publishing/copyright).\n\nAttribute the data as the \"WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://data.who.int/dashboards/covid19/\".",
    "dataPublishedBy": "WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020."
}

{
    "link": "https://data.who.int/dashboards/covid19/",
    "retrievedDate": "2024-07-30",
    "additionalInfo": "Raw data on confirmed cases and deaths for all countries is sourced from the [WHO COVID-19 Dashboard](https://data.who.int/dashboards/covid19/data).\n\nOur complete COVID-19 dataset is a collection of the COVID-19 data maintained by *Our World in Data*. **It is updated daily** and includes data on confirmed cases, deaths, hospitalizations, and testing.\n\nWe have created a new description of all our data sources. You find it at our GitHub repository **[here](https://github.com/owid/covid-19-data/tree/master/public/data/)**. There you can download all of our data.\n\nThe WHO licenses this data under CC BY-NC-SA 3.0 IGO. You can read more [here](https://www.who.int/about/policies/publishing/copyright).\n\nAttribute the data as the \"WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://data.who.int/dashboards/covid19/\".",
    "dataPublishedBy": "WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020."
}

{
    "link": "https://paperswithcode.com/",
    "retrievedDate": "2023-06-14",
    "additionalInfo": "\nThe goal of Papers With Code website is to compile a comprehensive collection of ML papers, code implementations, datasets, methods, and evaluation tables, all made freely available.\n\nThe comparisons to human performance are very approximate and based on small samples of people \u2014 they are only meant to give a rough comparison. You can read more details in the papers that describe the benchmarks:\n\n-Hendrycks et al (2021) Measuring Massive Multitask Language Understanding (MMLU) (page 3): https://arxiv.org/pdf/2009.03300.pdf\n\n-Hendrycks et al (2021) Measuring Mathematical Problem Solving With the MATH Dataset (page 5): https://arxiv.org/pdf/2103.03874v2.pdf\n",
    "dataPublishedBy": "Papers With Code"
}

{
    "link": "https://wrp.lrfoundation.org.uk/data-resources/",
    "retrievedDate": "2023-06-26",
    "additionalInfo": "\nSpread across four themed reports, the 2021 World Risk Poll covers the biggest risks facing people and communities globally, ranging from road crashes, severe weather, climate change and disaster resilience, to work-related harm, violence and harassment at work, and use of personal data. It will be repeated at least two more times, in 2023 and 2025.\n\nThis data includes aggregates of survey responses to the following two questions:\n\n1) Will artificial intelligence help or harm people in the next 20 years?\n\n2) Would you feel safe in a car driven by computer without a human driver?",
    "dataPublishedBy": "World Risk Poll 2021, Lloyd's Register Foundation (2022)"
}

{
    "link": "https://chipexplorer.eto.tech/",
    "retrievedDate": "2023-07-07",
    "additionalInfo": "\nThe Advanced Semiconductor Supply Chain Dataset includes manually compiled, high-level information about the tools, materials, processes, countries, and firms involved in the production of advanced logic chips. The current version of the dataset reflects how researchers understood this supply chain in early 2021. It uses a wide variety of sources, such as corporate websites and disclosures, specialized market research, and industry group publications.\n\nMost of the data is taken from the CSET report The Semiconductor Supply Chain: Assessing National Competitiveness, published in 2021. In 2022, ETO researchers augmented the company profiles in the data with information manually collected from producer websites and the open internet.\n\nThe data can be accessed here: https://eto.tech/dataset-docs/chipexplorer/",
    "dataPublishedBy": "\"Emerging Technology Observatory Advanced Semiconductor Supply Chain Dataset (2022 release)\"\n"
}

{
    "link": "https://today.yougov.com/topics/technology/articles-reports/2023/04/14/ai-nuclear-weapons-world-war-humanity-poll",
    "retrievedDate": "2023-06-08",
    "additionalInfo": "\nThis poll was conducted online on April 7 - 11, 2023 among 1,000 U.S. adult citizens. Respondents were selected from YouGov\u2019s opt-in panel using sample matching.",
    "dataPublishedBy": "YouGov"
}

{
    "link": "https://docs.cdn.yougov.com/0jv1wfqlmo/results_AI%20Effects%20on%20Job%20Market.pdf",
    "retrievedDate": "2023-06-08",
    "additionalInfo": "\nSurvey conducted on 2000 U.S. adults between March 9 - 16, 2023 on what people think the effects of AI will be on job markets.",
    "dataPublishedBy": "AI Effects on Job Market (YouGov Survey, 2023)"
}

{
    "link": "https://www.monmouth.edu/polling-institute/reports/monmouthpoll_us_021523",
    "retrievedDate": "2023-06-07",
    "additionalInfo": "\nThe Monmouth University Poll was sponsored and conducted by the Monmouth University Polling Institute from January 26 to 30, 2023 with a probability-based national random sample of 805 adults age 18 and older. This includes 281 contacted by a live interviewer on a landline telephone and 524 contacted by a live interviewer on a cell phone, in English. Telephone numbers were selected through a mix of random digit dialing and list-based sampling. Landline respondents were selected with a modified Troldahl-Carter youngest adult household screen. Interviewing services were provided by Braun Research, with sample obtained from Dynata (RDD, n= 569), Aristotle (list, n= 152) and a panel of prior Monmouth poll participants (n= 84). Monmouth is responsible for all aspects of the survey design, data weighting and analysis. The full sample is weighted for region, age, education, gender and race based on US Census information (ACS 2021 one-year survey). For results based on this sample, one can say with 95% confidence that the error attributable to sampling has a maximum margin of plus or minus 5.7 percentage points (adjusted for sample design effects). Sampling error can be larger for sub-groups (see table below). In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.\n",
    "dataPublishedBy": "Monmouth (\u201cMon-muth\u201d) University Poll, 2023"
}

{
    "link": "https://drive.google.com/drive/folders/1ma9WZJzKreS8f2It1rMy_KkkbX6XwDOK",
    "retrievedDate": "2023-06-19",
    "additionalInfo": "\nAnnually, the IFR publishes the World Robotics Report, which provides comprehensive insights into global trends concerning robot installations.\n\nBy monitoring the installation of industrial robots, which often incorporate AI-based software technologies, it becomes feasible to gather valuable information about the implementation of AI-ready infrastructure in real-world scenarios. This data is sourced from the International Federation of Robotics (IFR), a nonprofit organization dedicated to the advancement and safeguarding of the robotics industry.\n\nThe AI Index is an independent initiative at the Stanford University Institute for Human-Centered Artificial Intelligence. The mission of the AI Index is \u201cto provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI.\u201d Their flagship output is the annual AI Index Report, which has been published since 2017.\n",
    "dataPublishedBy": "International Federation of Robotics (IFR) via the AI Index 2023 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023"
}

{
    "link": "https://paperswithcode.com/",
    "retrievedDate": "2023-06-14",
    "additionalInfo": "\nThe goal of Papers With Code website is to compile a comprehensive collection of ML papers, code implementations, datasets, methods, and evaluation tables, all made freely available.\n\nThe comparisons to human performance are very approximate and based on small samples of people \u2014 they are only meant to give a rough comparison. You can read more details in the papers that describe the benchmarks:\n\n-Hendrycks et al (2021) Measuring Massive Multitask Language Understanding (MMLU) (page 3): https://arxiv.org/pdf/2009.03300.pdf\n\n-Hendrycks et al (2021) Measuring Mathematical Problem Solving With the MATH Dataset (page 5): https://arxiv.org/pdf/2103.03874v2.pdf\n",
    "dataPublishedBy": "Papers With Code"
}

{
    "link": "https://wrp.lrfoundation.org.uk/data-resources/",
    "retrievedDate": "2023-06-26",
    "additionalInfo": "\nSpread across four themed reports, the 2021 World Risk Poll covers the biggest risks facing people and communities globally, ranging from road crashes, severe weather, climate change and disaster resilience, to work-related harm, violence and harassment at work, and use of personal data. It will be repeated at least two more times, in 2023 and 2025.\n\nThis data includes aggregates of survey responses to the following two questions:\n\n1) Will artificial intelligence help or harm people in the next 20 years?\n\n2) Would you feel safe in a car driven by computer without a human driver?",
    "dataPublishedBy": "World Risk Poll 2021, Lloyd's Register Foundation (2022)"
}

{
    "link": "https://www.space-track.org/",
    "retrievedDate": "2023-06-09",
    "additionalInfo": "\nThis dataset is extracted from Space-Track.org, a website maintained by the 18th Space Defense Squadron of the United States Space Force.\n\nThe original data includes information on more than 50,000 space objects tracked over time, including their launch date and decay date (if they have reentered the Earth's atmosphere).\n\nIn our charts, low Earth orbit is defined by a periapsis altitude below 2,000 kilometers; medium Earth orbit between 2,000 and 35,586 kilometers; geostationary orbit between 35,586 and 35,986 kilometers; high Earth orbit above 35,986 kilometers.\n",
    "dataPublishedBy": "18th Space Defense Squadron, United States Space Force"
}

{
    "link": "https://exoplanetarchive.ipac.caltech.edu/index.html",
    "retrievedDate": "2023-06-09",
    "additionalInfo": "\nThis dataset makes use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.\n\nTransit method: If a planet crosses (or transits) in front of its parent star's disk, then the star's observed brightness drops by a small amount. The amount by which the star dims depends on its size and the planet's size, among other factors.\n\nRadial velocity (or Doppler method): As a planet orbits a star, the star also moves in its small orbit around the system's center of mass. Variations in the star's radial velocity\u2014the speed with which it moves towards or away from Earth\u2014can be detected from displacements in the star's spectral lines due to the Doppler effect.\n\nMicrolensing: Microlensing occurs when the gravitational field of a star acts like a lens, magnifying the light of a distant background star. Planets orbiting the lensing star can cause detectable anomalies in the magnification as it varies over time.\n\nDetails on other methods of discovery are available on Wikipedia: https://en.wikipedia.org/wiki/Exoplanet#Detection_methods\n",
    "dataPublishedBy": "NASA Exoplanet Archive, operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program"
}

{
    "link": "https://www.gallup.com/analytics/468179/global-wellbeing-initiative-dataset.aspx",
    "retrievedDate": "2023-05-04",
    "additionalInfo": "\nGallup and the Wellbeing for Planet Earth Foundation joined forces in 2019 for a groundbreaking partnership known as the Global Wellbeing Initiative (GWI, https://www.globalwellbeinginitiative.org/) to develop a more nuanced and globally inclusive measurement and understanding of wellbeing.\n\nThe GWI has worked to develop new items for the World Poll that will lay the foundation for future research projects and transform our perception of wellbeing.\n\n\nSURVEY METHODS:\n\nThe survey involved more than 122,000 people in 116 countries in 2020 and was run as part of the Gallup World Poll. Typically, 1,000 individuals were surveyed in each country. In China, India and Russia, the sample sizes were 2,000 or greater. In rare instances, the sample size fell between 500 and 1,000.\n\nThe dataset includes the following variables:\n\n\u2022 Responses to the Global Wellbeing Initiative questionnaire\n\u2022 Gender and age\n\u2022 Educational level, employment status and per capita income quintiles\n\u2022 Country income level (World Bank)\n\u2022 Urban/Rural location\n",
    "dataPublishedBy": "Gallup World Poll, Global Wellbeing Initiative dataset, 2020"
}

{
    "link": "https://stats.oecd.org/",
    "retrievedDate": "2023-05-01",
    "additionalInfo": "\nThe OECD Health Database offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool to carry out comparative analyses and draw lessons from international comparisons of diverse health systems.\n\nThis dataset is a subset of all the metrics in the database. More details at https://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=HEALTH_PHMC&Lang=en\n",
    "dataPublishedBy": "OECD, Health: Pharmaceutical Market. 2022"
}

{
    "link": "https://vizhub.healthdata.org/gbd-results/",
    "retrievedDate": "2023-05-15",
    "additionalInfo": "\nThe Global Burden of Disease (GBD) provides a comprehensive picture of mortality and disability across countries, time, age, and sex. It quantifies health loss from hundreds of diseases, injuries, and risk factors, so that health systems can be improved and disparities eliminated.\n\nGBD research incorporates both the prevalence of a given disease or risk factor and the relative harm it causes. With these tools, decision-makers can compare different health issues and their effects.\n\nThis dataset only contains Prevalence rate metrics for mental health-related causes.\n",
    "dataPublishedBy": "Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. Available from https://vizhub.healthdata.org/gbd-results/"
}

{
    "link": "https://www.who.int/data/gho",
    "retrievedDate": "2023-03-09",
    "additionalInfo": "",
    "dataPublishedBy": "WHO, Global Health Observatory (2022)"
}

{
    "link": "www.wittgensteincentre.org/dataexplorer",
    "retrievedDate": "2023-04-07",
    "additionalInfo": "\nThis dataset presents a set of different scenarios of future population and human capital trends in 201 countries of the world to 2100. The result of the population projections by levels of educational attainment were published in 2018 in Lutz, Goujon, KC, Stonawski, and Stilianakis (Eds.) (2018) (https://ec.europa.eu/jrc/en/publication/demographic-and-human-capital-scenarios-21st-century-2018-assessment-201-countries). They provide an update of the projections (scope, coverage and quality) presented in Lutz, Butz and KC in 2014 (https://global.oup.com/academic/product/world-population-and-human-capital-in-the-twenty-first-century-9780198703167?cc=at&lang=en&), based on the work of a large team of researchers at the Wittgenstein Centre for Demography and Global Human Capital and at other institutions.\n\nThe present version (2.0) benefited from the partnership with the Joint Research Centre in the Centre of Expertise on Population and Migration (CEPAM). On top of the assumptions about future trends in fertility, mortality, and education, the projections study the effect of several migration assumptions applied to the context of the set of Shared Socioeconomic Pathways (SSP) scenarios related to the Intergovernmental Panel on Climate Change (IPCC).\n\nThe new version also includes the reconstruction of population by levels of educational attainment from 1950 to 2015 for 185 countries. More information in Speringer et al. 2019 (https://www.oeaw.ac.at/fileadmin/subsites/Institute/VID/IMG/Publications/Working_Papers/WP2019_02.pdf).\n\nData for 1950 to 2015: Based on UN Population Division, World Population Prospects 2017 (http://esa.un.org/wpp/documentation/WPP%202010%20publications.htm).\n\nOnly Medium (SSP2) scenario: This middle-of-the-road scenario can also be seen as the most likely path for each country. It combines all countries with medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario.\n",
    "dataPublishedBy": "Wittgenstein Centre for Demography and Global Human Capital (WIC) Wittgenstein Centre Data Explorer. Version 2.0 2018"
}

{
    "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217148",
    "retrievedDate": "2023-05-26",
    "additionalInfo": "\nRay et al. (2019) modelled the impact of observed climate change on yields of the top ten global crops. These ten crops account for ~83% of global kilocalorie production.\n\nThe impact of climate change on crop yields is calculated as the difference between actual observed yields under current climate conditions and the yields that would have been achieved until historical climate conditions.\n\nHistorical climate is defined as the 30-year average weather prior to 1974. Current climate is defined as the historical climate plus the addition of the linear trend of the weather for the 35 years ending in 2008, from the year 1974.\n",
    "dataPublishedBy": "Ray DK, West PC, Clark M, Gerber JS, Prishchepov AV, et al. (2019) Climate change has likely already affected global food production. PLOS ONE 14(5): e0217148. https://doi.org/10.1371/journal.pone.0217148"
}

{
    "link": "https://www.nature.com/articles/s43016-020-00185-6",
    "retrievedDate": "2023-05-26",
    "additionalInfo": "\nThe dataset for this paper can be accessed at:\nhttps://www.research-collection.ethz.ch/handle/20.500.11850/430354\nVia the ETH Z\u00fcrich Research Collection. However, the data is in Stata format.\nTherefore, the data uploaded here is an xlsx file kindly provided by the authors.\n",
    "dataPublishedBy": "Wuepper, D., Le Clech, S., Zilberman, D. et al. Countries influence the trade-off between crop yields and nitrogen pollution. Nat Food 1, 713\u2013719 (2020). https://doi.org/10.1038/s43016-020-00185-6"
}

{
    "link": "https://www.unep.org/resources/report/global-trends-renewable-energy-investment-2019",
    "retrievedDate": "2023-01-03",
    "additionalInfo": "\nRenewable energy investments, extracted from Fig. 21 of the Global Trends in Renewable Energy Investment 2019 report, by UN Environment, Frankfurt School-UNEP Centre, BNEF.\n\nInvestment adjusts for re-invested equity, and includes estimates for undisclosed deals. Figures excludes investments in large hydropower schemes.\n",
    "dataPublishedBy": "UN Environment, Frankfurt School-UNEP Centre, BNEF. Copyright \u00a9 Frankfurt School of Finance & Management gGmbH 2019\n"
}

{
    "link": "https://www.uspto.gov/web/offices/ac/ido/oeip/taf/reports.htm",
    "retrievedDate": "2023-05-24",
    "additionalInfo": "\nThe U.S. Patent and Trademark Office (USPTO)'s Patent Technology Monitoring Team periodically issues general statistics and reports that profile patenting activity. The data imported by Our World in Data focuses on annual, U.S. patent application and grant activity from 1790 to the present. Annual activity is determined based on the calendar year.",
    "dataPublishedBy": "U.S. Patent and Trademark Office, U.S. Patent Activity Calendar Years 1790 to the Present"
}

{
    "link": "https://wbl.worldbank.org/en/wbl-data",
    "retrievedDate": "2023-05-12",
    "additionalInfo": "\nBy developing a time series and further researching the interaction between inequality of opportunity for women and labor market dynamics, Women, Business and the Law strengthens insights into how women\u2019s employment and entrepreneurship are affected by legal gender discrimination, and in turn how this affects economic outcomes.\n\nThis specific dataset is an additional file provided by the World Bank, which contains a set of 14 non-scored questions (years 2017, 2019, and 2020), including data on child marriage, customary law, citizenship, sexual harassment, and access to ID cards.\n",
    "dataPublishedBy": "World Bank. 2023. Women, Business and the Law 2023. Washington, DC: World Bank."
}

{
    "link": "https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups",
    "retrievedDate": "2023-04-30",
    "additionalInfo": "\nFor the current 2023 fiscal year, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method (https://datahelpdesk.worldbank.org/knowledgebase/articles/378832-what-is-the-world-bank-atlas-method), of $1,085 or less in 2021; lower middle-income economies are those with a GNI per capita between $1,086 and $4,255; upper middle-income economies are those with a GNI per capita between $4,256 and $13,205; high-income economies are those with a GNI per capita of $13,205 or more. For details on past years, please refer to file: https://datacatalogfiles.worldbank.org/ddh-published/0037712/DR0090754/OGHIST.xlsx\n\nNote 1: Income classifications are set each year on July 1 for all World Bank member economies, and all other economies with populations of more than 30,000. These official analytical classifications are fixed during the World Bank's fiscal year (ending on June 30), thus economies remain in the categories in which they are classified irrespective of any revisions to their per capita income data. The historical classifications shown are as published on July 1 of each fiscal year.\n\nNote 2: Regions in this table include economies at all income levels. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics. For more information about how the World Bank classifies countries please read https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries\n\nFind more details in World Development Indicators website (https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html).",
    "dataPublishedBy": "World Bank, Income classifications (2022)"
}

{
    "link": "https://www.unwto.org/tourism-statistics/economic-contribution-SDG",
    "retrievedDate": "2023-05-09",
    "additionalInfo": "\nUNWTO Department of Statistics data on the Sustainable Development Goals indicators 8.9.1 and 12.b.1, included in the Global Indicator Framework. Data collection started in 2019 and provides data from 2008 onwards, the latest update took place on 15 March 2023. Indicator 8.9.1 on Tourism Direct GDP helps to monitor Target 8.9 which calls on countries \u201cto promote sustainable tourism\u201d under Goal 8 on decent Work and Economic Growth. The data collection is done in cooperation with the OECD.\n\nThe United Nations World Tourism Organization (UNWTO) collects data from countries through annual questionnaires that follow the International Recommendations for Tourism Statistics (IRTS 2008) standard, a framework approved by the United Nations.\n",
    "dataPublishedBy": "World Tourism Organization, 2023"
}

{
    "link": "https://www.unwto.org/tourism-statistics/economic-contribution-SDG",
    "retrievedDate": "2023-05-09",
    "additionalInfo": "\nImplementation of standards accounting tools to monitor the economic and environmental aspects of tourism sustainability (indicator 12.b.1). Indicator 12.b.1 shows the preparedness of countries to \u201cdevelop and implement tools to monitor sustainable development impacts for sustainable tourism\u201d called for in target 12.b under Goal 12 on Sustainable Consumption and Production. More specifically, it tracks the implementation of the most relevant Tourism Satellite Account (TSA) and System of Environmental Economic Accounting (SEEA) tables.\n",
    "dataPublishedBy": "World Tourism Organization, 2023"
}

{
    "link": "https://www.itopf.org/knowledge-resources/data-statistics/statistics/",
    "retrievedDate": "2023-05-18",
    "additionalInfo": "\nITOPF maintains a database of oil spills from tank vessels, including combined carriers, FPSOs and barges. This contains information on accidental spillages of persistent and non-persistent hydrocarbon oil since 1970, except those resulting from acts of war.\n",
    "dataPublishedBy": "International Tanker Owners Pollution Federation (ITOPF)"
}

{
    "link": "https://www.nber.org/research/data/historical-cross-country-technology-adoption-hccta-dataset",
    "retrievedDate": "2023-03-16",
    "additionalInfo": "\nThe Historical Cross Country Technology Adoption Dataset (HCCTAD) is a dataset collected by Diego Comin (NYU) and Bart Hobijn (Federal Reserve Bank of New York), to allow for the analysis of the adoption patterns of some of the major technologies introduced in the past 250 years across the World\u2019s leading industrialized economies.\n",
    "dataPublishedBy": "Comin, D. and Hohijn B., \"Cross-Country Technological Adoption: Making the Theories Face the Facts\". Journal of Monetary Economics, January 2004, pp. 39-83."
}

{
    "link": "https://www.bls.gov/data/tools.htm",
    "retrievedDate": "2023-03-09",
    "additionalInfo": "\nThe Bureau of Labor Statistics report on the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state level.\n\nCPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.\n",
    "dataPublishedBy": "U.S. Bureau of Labor Statistics"
}

{
    "link": "https://github.com/karlrupp/microprocessor-trend-data",
    "retrievedDate": "2023-03-08",
    "additionalInfo": "\nThis data comes from a repository of microprocessor trend data maintained by Karl Rupp on GitHub.\n\nWhere data for several microprocessors is given for a single year, we have shown the highest transistor count per chip of that year.\n",
    "dataPublishedBy": "Karl Rupp, Microprocessor Trend Data (2022)"
}

{
    "link": "https://www.gsma.com/mobilemoneymetrics/#global",
    "retrievedDate": "2023-03-06",
    "additionalInfo": "\nThe Global Mobile Money Dataset is a set of global metrics for the mobile money industry based on data collected and analyzed by the GSMA Mobile Money program.\n\nThe GSMA Mobile Money program considers services that meet the following definitions:\n\n\u2022 The service must be available to the unbanked, e.g., people who do not have access to a formal account at a financial institution.\n\u2022 The service must offer at least one of the following services: Storage of value; Domestic or international transfer; mobile payment, including bill payment, bulk disbursement, and merchant payment.\n\u2022 The service must offer a network of physical transactional points outside bank branches and ATMs, making the service widely accessible to everyone.\n\u2022 The service must offer an interface for initiating transactions for agents or customers that is available on mobile devices.\n\u2022 Mobile banking services that offer the mobile phone as just another channel to access a traditional banking product are not included.\n\u2022 Payment services linked to a traditional banking product or credit cards, such as Apple Pay and Google Wallet, are not included.",
    "dataPublishedBy": "GSMA"
}

{
    "link": "https://api.healthdata.org/sdg/v1/docs & https://api.healthdata.org/sdg/v1/docs",
    "retrievedDate": "2023-05-05",
    "additionalInfo": "\nIHME produced estimates and forecasts for 13 of the SDG indicators included in the Goalkeepers Report.\n\nIHME provides estimated data for the years 1990-2021 and projections for three different scenarios for the years 2021-2023.\n\nThe scenarios provided are: a reference case, best case and worst case.\n\nThe full description of the methods used to produce the data can be found here: https://www.gatesfoundation.org/goalkeepers/report/2022-report/data-sources/#ExploretheIndicatorPages\n",
    "dataPublishedBy": "Institute for Health Metrics and Evaluation (IHME). Health-related SDGs. Seattle, WA: IHME, University of Washington, 2022. Available from https://api.healthdata.org/sdg/v1/docs"
}

{
    "link": "https://dataunodc.un.org/dp-intentional-homicide-victims",
    "retrievedDate": "2023-07-04",
    "additionalInfo": "\nThe United Nations Office on Drugs and Crime Intentional Homicide data are sourced from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual.\n\nThe criminal justice data was collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources.\n\nData on homicide from public health sources were primarily obtained from the WHO Mortality Database.10 This dataset is a comprehensive collection of mortality data by cause of death, sex, and age group conducted yearly by the WHO with Member States. Deaths coded with Internatioanl Classification of Disease (ICD10) codes X85-Y09 (injuries inflicted by another person with intent to injure or kill), and ICD10 code Y87.1 (sequelae of assault), generally correspond to the definition of intentional homicide\n\nThe population data used to calculate homicide rates is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs.\n\nThe statistical definition contains three elements that characterize the killing of a person as \u201cintentional homicide\u201d:\n\n1. The killing of a person by another person (objective element).\n\n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element).\n\n3. The unlawfulness of the killing (legal element).\n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices.  Killings as a result of terrorist activities are also to be classified as a form of intentional homicide.\n\nIn order to compile consistent time series of total homicides back to 1990, in several cases data from multiple sources were combined to expand the number of available years within a country\u2019s time series. Time series adjustments were performed when a country had two sources coveringdifferent year-ranges, which had very similar trends in an overlapping time period, but where these trends were at different levels.\n\nThe countries for which adjusted series for total homicide counts prior to the year 2000 have been produced were the following: Belgium, Brazil, China, Ecuador, Germany, Netherlands, New Zealand, Portugal, South Korea, Spain, Thailand, and United Kingdom.\n",
    "dataPublishedBy": "UNODC (2022), UNODC Research, Data Portal, Intentional Homicide. https://dataunodc.un.org/dp-intentional-homicide-victims"
}

{
    "link": "https://drive.google.com/drive/folders/1ma9WZJzKreS8f2It1rMy_KkkbX6XwDOK",
    "retrievedDate": "2023-06-28",
    "additionalInfo": "\nThe CRA Taulbee Survey is the principal source of information on the enrollment, production, and employment of Ph.D.s in information, computer science and computer engineering (I, CS & CE) and in providing salary and demographic data for faculty in I, CS & CE in North America. Statistics given include gender and ethnicity breakdowns.",
    "dataPublishedBy": "CRA Taulbee Survey (2022) via AI Index 2023 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023"
}

{
    "link": "https://drive.google.com/drive/folders/1ma9WZJzKreS8f2It1rMy_KkkbX6XwDOK",
    "retrievedDate": "2023-06-28",
    "additionalInfo": "\nThe CRA Taulbee Survey is the principal source of information on the enrollment, production, and employment of Ph.D.s in information, computer science and computer engineering (I, CS & CE) and in providing salary and demographic data for faculty in I, CS & CE in North America. Statistics given include gender and ethnicity breakdowns.\nThe AI Index is an independent initiative at the Stanford University Institute for Human-Centered Artificial Intelligence. The mission of the AI Index is \u201cto provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI.\u201d Their flagship output is the annual AI Index Report, which has been published since 2017.\n",
    "dataPublishedBy": "CRA Taulbee Survey (2022) via AI Index 2023 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023"
}