The deadliest animals in the world based on the number of human deaths per year is not a creature that humans usually find scary, such as a lion or snake. Mosquitos are by far the deadliest creature in the world when it comes to annual human deaths, causing around one million deaths per year, compared to 100,000 deaths from snakes and 250 from lions. Perhaps surpringly, dogs are the third deadliest animal to humans. Dogs are responsible for around 30,000 human deaths per year, with the vast majority of these deaths resulting from rabies that is transmitted from the dog.

Malaria

Mosquitos are the deadliest creature in the world because they transmit a number of deadly diseases, the worst of which is malaria. Malaria is a mosquito-borne disease caused by a parasite that results in fever, chills, headache, vomiting and, if left untreated, death. Malaria disproportionately affects poorer regions of the world such as Africa and South-East Asia. In 2020, there were around 627,000 deaths from malaria worldwide.

Mosquito-borne diseases in the U.S.

The most common mosquito-borne diseases reported in the United States include West Nile virus, malaria, and dengue viruses. Many of these cases, however, are from travelers who contracted the disease in another country - this is especially true for malaria, Zika, and dengue. In 2018, the states of California, New York, and Texas reported the highest number of mosquito-borne disease cases in the United States.

Forecast: Number of Under 5 Deaths in the World 2022 - 2026 Discover more data with ReportLinker!

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

Update Frequency: Biannual

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

https://cloud.google.com/bigquery/public-data/world-bank-hnp

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Citation: The World Bank: Health Nutrition and Population Statistics

Banner Photo by @till_indeman from Unplash.

Inspiration

What’s the average age of first marriages for females around the world?

As of 2023, India had **** billion internet users, more than any other country in the world. China ranked second, with **** billion Indians accessing the internet via any device. The United States followed with approximately *** million online users.

Algeria DZ: Number of Death: Under-5 data was reported at 20,295.000 Person in 2023. This records a decrease from the previous number of 21,138.000 Person for 2022. Algeria DZ: Number of Death: Under-5 data is updated yearly, averaging 38,838.000 Person from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 148,273.000 Person in 1970 and a record low of 20,295.000 Person in 2023. Algeria DZ: Number of Death: Under-5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Algeria – Table DZ.World Bank.WDI: Social: Health Statistics. Number of children dying before reaching age five.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Sum;Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This statistic shows the population of school aged children aged five to 19 worldwide from 1950 to 2100. In 2100, the population of school aged children ages five to nine globally is expected to reach about 632.44 million.

Venezuela VE: Number of Deaths Ages 20-24 Years data was reported at 6,823.000 Person in 2019. This records a decrease from the previous number of 7,122.000 Person for 2018. Venezuela VE: Number of Deaths Ages 20-24 Years data is updated yearly, averaging 6,283.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 8,204.000 Person in 2015 and a record low of 2,714.000 Person in 1990. Venezuela VE: Number of Deaths Ages 20-24 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Venezuela – Table VE.World Bank.WDI: Health Statistics. Number of deaths of youths ages 20-24 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Central African Republic CF: Number of Deaths Ages 5-14 Years data was reported at 2,048.000 Person in 2018. This records a decrease from the previous number of 2,192.000 Person for 2015. Central African Republic CF: Number of Deaths Ages 5-14 Years data is updated yearly, averaging 2,371.000 Person from Dec 1990 (Median) to 2018, with 5 observations. The data reached an all-time high of 2,452.000 Person in 1990 and a record low of 2,048.000 Person in 2018. Central African Republic CF: Number of Deaths Ages 5-14 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Number of deaths of children ages 5-14 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

VN: Number of Deaths Ages 5-9 Years data was reported at 1,528.000 Person in 2019. This records a decrease from the previous number of 1,585.000 Person for 2018. VN: Number of Deaths Ages 5-9 Years data is updated yearly, averaging 4,154.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 13,910.000 Person in 1990 and a record low of 1,528.000 Person in 2019. VN: Number of Deaths Ages 5-9 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Health Statistics. Number of deaths of children ages 5-9 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Content

World Marriage Data 2012 provides a comparable and up-to-date set of data on the marital status of the population for all countries and areas of the world. Data are presented for the closest date available around five reference dates: the years closest to 1970, 1985, 1995, 2005 and the most recent data available.

Acknowledgements

I have pulled this data from the United Nations Data portal, did some simple post-processing to make it more user-friendly.

Inspiration

I primarily feel this data will be useful in conjunction with other datasets related to different disciplines wherein understanding the marriage trends will add value to the analysis.

US: People Practicing Open Defecation: Urban: % of Urban Population data was reported at 0.000 % in 2015. This stayed constant from the previous number of 0.000 % for 2014. US: People Practicing Open Defecation: Urban: % of Urban Population data is updated yearly, averaging 0.000 % from Dec 2000 (Median) to 2015, with 16 observations. US: People Practicing Open Defecation: Urban: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).; Weighted Average;

This dataset, titled "Global COVID-19 Statistics - Jan 2025," contains the latest COVID-19 statistics collected from the Worldometer website on Jan 09, 2025. The data includes crucial metrics such as the total number of cases, deaths, recoveries, and active cases for countries around the world. The information is extracted from the comprehensive table provided by Worldometer, which is widely regarded as a reliable source for real-time coronavirus statistics. Source and Collection Date Source: Worldometer Coronavirus Page Date of Collection: Jan 09, 2024

The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about

Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

Colombia CO: Number of Deaths Ages 5-9 Years data was reported at 814.000 Person in 2019. This records a decrease from the previous number of 823.000 Person for 2018. Colombia CO: Number of Deaths Ages 5-9 Years data is updated yearly, averaging 1,271.000 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 1,716.000 Person in 1990 and a record low of 814.000 Person in 2019. Colombia CO: Number of Deaths Ages 5-9 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Health Statistics. Number of deaths of children ages 5-9 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Dataset uploaded by Jessica Li

Context

Object recognition and image processing has become one of the hottest topics in machine learning due to its vast and creative potential applications in the real world. The ability to process visual information using machine learning algorithms can be very useful, such as measuring the quality of NYC Bike Lanes through street imagery. Within this field, the Street View House Numbers (SVHN) dataset is one of the most popular ones. It has been used in neural networks created by Google to read house numbers and match them to their geolocations. This is a great benchmark dataset to play with, learn and train models that accurately identify street numbers, and incorporate into all sorts of projects.

Content

This dataset contains three .zip files that contain over 600k labelled real-world images of house numbers taken from Google Street View. The sequence of numbers in the images are of bounded length.

• test.zip: 26,032 digits for testing
• train.zip: 73,257 digits for training
• extra.zip: 531,131 additional, somewhat less difficult samples, to use as extra training data

Additional Notes

• There are 10 classes, 1 for each digit. Digit '1' has label 1, '9' has label 9 and '0' has label 10.
• The images are the original, variable-resolution, color house-number images with character level bounding boxes in .png format.
• digitStruct.mat: Contains bounding box information for each respective .zip file are stored as digitStruct.mat, which can be loaded using Matlab. The digitStruct.mat files contain a struct called digitStruct with the same length as the number of original images.
• Each element in digitStruct has the following fields:
  • name: string containing the filename of the corresponding image
  • bbox: struct array that contains the position, size and label of each digit bounding box in the image. Ex. digitStruct(300).bbox(2).height gives height of the 2nd digit bounding box in the 300th image.

Acknowledgements

The SVHN dataset originates from http://ufldl.stanford.edu/housenumbers/. The banner photo was by Annie Spratt on Unsplash.

The original paper that introduces and examines this data:

Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011.

Inspiration & Resources

• Given the testing and training data, can you train a model (try Keras and/or TensorFlow) that accurately identifies house numbers in an image (with difficulties like picture brightness, blurriness)?
• What are some interesting datasets that can be merged with object detection datasets like this to form new applications?
• Additional resources are Getting Started with SVHN Dataset and Ji Yan's project that aims to tackle the SHVN dataset using Convolutional Network in Tensorflow.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Global Number of Scientific Publications Among the World's 10% Top-Cited Publications in Artificial Intelligence Share by Country (Units (Publications)), 2023 Discover more data with ReportLinker!

Population, female (% of total population) in World was reported at 49.72 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.