Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

Dataset

This dataset was created by Ifeanyichukwu Nwobodo

Contents

"Between October 2011 and November 2012, Pew Research Center, with generous funding from The Pew Charitable Trusts and the John Templeton Foundation, conducted a public opinion survey involving more than 30,000 face-to-face interviews in 26 countries in Africa, Asia, the Middle East and Europe. The survey asked people to describe their religious beliefs and practices, and sought to gauge respondents; knowledge of and attitudes toward other faiths. It aimed to assess levels of political and economic satisfaction, concerns about crime, corruption and extremism, positions on issues such as abortion and polygamy, and views of democracy, religious law and the place of women in society.

"Although the surveys were nationally representative in most countries, the primary goal of the survey was to gauge and compare beliefs and attitudes of Muslims. The findings for Muslim respondents are summarized in the Religion & Public Life Project's reports The World's Muslims: Unity and Diversity and The World's Muslims: Religion, Politics and Society, which are available at www.pewresearch.org. [...] This dataset only contains data for Muslim respondents in the countries surveyed. Please note that this codebook is meant as a guide to the dataset, and is not the survey questionnaire." (2012 Pew Religion Worlds Muslims Codebook)

Religious Populations Worldwide

Religious Populations Worldwide by Year and Category

By Throwback Thursday [source]

About this dataset

The dataset includes data on Christianity, Islam, Judaism, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism and various other syncretic and animist religions. For each religion or denomination category, it provides both the total population count and the percentage representation in relation to the overall population.

Additionally, - Columns labeled with Population provide numeric values representing the total number of individuals belonging to a particular religion or denomination. - Columns labeled with Percent represent numerical values indicating the percentage of individuals belonging to a specific religion or denomination within a given population. - Columns that begin with ** indicate primary categories (e.g., Christianity), while columns that do not have this prefix refer to subcategories (e.g., Christianity - Roman Catholics).

In addition to providing precise data about specific religions or denominations globally throughout multiple years,this dataset also records information about geographical locations by including state or country names under StateNme.

This comprehensive dataset is valuable for researchers seeking information on global religious trends and can be used for analysis in fields such as sociology, anthropology studies cultural studies among others

How to use the dataset

Introduction:

• Understanding the Columns:

• Year: Represents the year in which the data was recorded.

• StateNme: Represents the name of the state or country for which data is recorded.

• Population: Represents the total population of individuals.

• Total Religious: Represents the total percentage and population of individuals who identify as religious, regardless of specific religion.

• Non Religious: Represents the percentage and population of individuals who identify as non-religious or atheists.

• Identifying Specific Religions: The dataset includes columns for different religions such as Christianity, Judaism, Islam, Buddhism, Hinduism, etc. Each religion is further categorized into specific denominations or types within that religion (e.g., Roman Catholics within Christianity). You can find relevant information about these religions by focusing on specific columns related to each one.

• Analyzing Percentages vs. Population: Some columns provide percentages while others provide actual population numbers for each category. Depending on your analysis requirement, you can choose either column type for your calculations and comparisons.

• Accessing Historical Data: The dataset includes records from multiple years allowing you to analyze trends in religious populations over time. You can filter data based on specific years using Excel filters or programming languages like Python.

• Filtering Data by State/Country: If you are interested in understanding religious populations in a particular state or country, use filters to focus on that region's data only.

Example - Extracting Information:

Let's say you want to analyze Hinduism's growth globally from 2000 onwards:

• Identify Relevant Columns:
• Year: to filter data from 2000 onwards.

• Hindu - Total (Percent): to analyze the percentage of individuals identifying as Hindus globally.

• Filter Data:

• Set a filter on the Year column and select values greater than or equal to 2000.

• Look for rows where Hindu - Total (Percent) has values.

• Analyze Results: You can now visualize and calculate the growth of Hinduism worldwide after filtering out irrelevant data. Use statistical methods or graphical representations like line charts to understand trends over time.

Conclusion: This guide has provided you with an overview of how to use the Rel

Research Ideas

• Comparing religious populations across different countries: With data available for different states and countries, this dataset allows for comparisons of religious populations across regions. Researchers can analyze how different religions are distributed geographically and compare their percentages or total populations across various locations.
• Studying the impact of historical events on religious demographics: Since the dataset includes records categorized by year, it can be used to study how historical events such as wars, migration, or political changes have influenced religious demographics over time. By comparing population numbers before and after specific events, resea...

Pew Research Center’s “World’s Muslims” dataset is based on a survey conducted between October 2011 and November 2012. The study explores the religious beliefs, practices, social attitudes, and political views of Muslims across multiple countries, providing insights into diversity within the global Muslim population.

Global Religious Demographics

Global religious demographics over time

By Throwback Thursday [source]

About this dataset

The dataset contains information on a wide range of religions, including Christianity, Judaism, Islam, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism, Zoroastrianism, Syncretic Religions (religious practices that blend elements from multiple faiths), Animism (belief in spiritual beings in nature), Non-Religious individuals or those without any religious affiliation.

For each religion and region/country combination recorded in the dataset we have the following information:

• Total population: The total population of the region or country.
• Religious affiliation percentages: The percentages of the population that identify with specific religious affiliations.
• Subgroup populations/percentages: The populations or percentages within specific denominations or sects of each religion.

The dataset also provides additional variables like Year and State Name (for regional data) for further analysis.

How to use the dataset

• Understanding the Columns

  The dataset contains several columns with different categories of information. Here's a brief explanation of some important columns:

  • Year: The year in which the data was recorded.
  • Total Population: The total population of a country or region.
  • State Name (StateNme): The name of the state or region.

  Each religion has specific columns associated with it, such as Christianity, Buddhism, Islam, Hinduism, Judaism, Taoism, Shintoism etc., representing its percentage and population for each category/denomination within that religion.

• Selecting Specific Data

  If you are interested in exploring data related to a particular religion or geographic location:

  • To filter data by Religion: Identify relevant columns associated with that religion such as 'Christianity', 'Buddhism', 'Islam', etc., and extract their respective percentage and population values for analysis.

    Example: If you want to analyze Christianity specifically, extract columns related to Christianity like 'Christianity (Percent)', 'Christianity (Population)', etc.

    Note: There might be multiple columns related to a specific religion indicating different categories or denominations within that religion.

  • To filter data by Geographic Location: Utilize the 'State Name' column ('StateNme') to segregate data corresponding to different states/regions.

    Example: If you want to analyze religious demographics for a particular state/region like California or India:

    i) Filter out rows where State Name is equal to California or India.

    ii) Extract relevant columns associated with your selected religion as mentioned above.

• Finding Trends and Insights

  Once you have selected the specific data you are interested in, examine patterns and trends over time or across different regions.

  • Plotting data using visualizations: Use graphical tools such as line charts, bar charts, or pie charts to visualize how religious demographics have changed over the years or vary across different regions.

  • Analyzing population proportions: By comparing the percentage values of different religions for a given region or over time, you can gather insights into changes in religious diversity.

• Comparing Religions

  If you wish to compare multiple religions:

Research Ideas

• Comparing religious affiliations across different countries or regions: With data on various religions such as Christianity, Islam, Buddhism, Judaism, Hinduism, etc., researchers can compare the religious affiliations of different countries or regions. This can help in understanding the cultural and religious diversity within different parts of the world.
• Exploring the growth or decline of specific religions: By examining population numbers for specific religions such as Jainism, Taoism, Zoroastrianism, etc., this dataset can be used to investigate the growth or decline of these religious groups over time. Researchers can analyze factors contributing to their popularity or decline in particular regions or countries

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday 201912 - Religion.csv | Column name...

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

This dataset contains Food Prices data for Iran (Islamic Republic of), sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Islam and Christianity form the two dominant religions in Nigeria. The basis of traditional religions was systematically exterminated in the religio-cultural life of the Nigerian people after their contact with colonialism. Approximately 90 percent of the Nigerian people have since preferred to be identified with either Islam or Christianity.Nigeria’s contact with Islam predated that of Christianity and European colonialism; its spread was facilitated into Sub-Saharan Africa through trade and commerce. The northern part of Nigeria is symbolic to the history of Islam, as it penetrated the area through the Kanem-Borno Empire in the 11th century before spreading to the other predominately Hausa states. Islam was then introduced into the traditional societies of the Yoruba-speaking people of south-west Nigeria through their established commercial relationship with people of the North, particularly the Nupe and Fulani.Christianity reached Nigeria in the 15th century with the visitation of the Roman and Catholic missionaries to the coastal areas of the Niger-Delta region, although there were few recorded converts and churches built during this period. Christianity soon recorded a boost in the southern region given its opposition to the slave trade and its promotion of Western education. In contrast to the smooth process Christian evangelization underwent in the South, its process in the North was difficult because Islam had already become well-established.Given the philosophy of Islam as a complete way of life for a Muslim, Islam has always been closely attached to politics in Nigeria. The emergence of particular Islamic groups was significantly influenced by international events, particularly the 1979 Iranian revolution and the corresponding disenchantment from the West. These developments shaped Nigerian national politics of the period as Muslims radically redefined their political interests in line with religion and began to clamor for the incorporation of the Sharia legal system into the country’s judicial system. Nigeria then tried to harness opportunities accruable from other Muslim countries by becoming a registered member with the Organization of Islamic Conference (OIC) in 1985. This inflamed Christians and nurtured the fear of domination by their Muslim counterparts and the possibility of a gradual extinction of their religio-political strength in the national political structure. The distinct religious separation has also instigated violence in present-day Nigeria, including the Sharia riot in Kaduna in 2000, ongoing ethno-religious violence in Jos since 2001, and the 2011 post-election violence that erupted in some northern states. Nigerians’ continued loyalty to religion compared to that of the country continues to sustain major political debate, conflict, and violent outbreaks between populations of the two faiths.

ISO3 - International Organization for Standardization 3-digit country code

AREA_AFF - Geographic area affected by disease

DT_START - Date health event started

DT_END - Date health event ended

TYPE - Type of disease group

DISEASE - Name of disease

NUM_DTH - Number of people reported dead from disease

NUM_AFF - Number of people affected from disease

SOURCE_DT - Source creation date

SOURCE - Primary source

Collection

This HGIS was created using information collected from several websites. EM-DAT, the World Health Organization, and news reports provided information about the outbreaks.

The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe Analytics is not responsible for the accuracy and completeness of data compiled from outside sources.

Sources (HGIS)

Egunganga, Vincent, Ami Sadiq, and Hir Joseph. All AfricaHIR JOSEPH, "Nigeria: Lassa Fever Returns Vicio." Last modified March 09, 2013. Accessed April 16, 2013. http://allafrica.com/.

EM DAT, "Country Database; Nigeria." Last modified March 2013. Accessed April 16, 2013. http://www.emdat.be/.

World Health Organization, "Global Health Observatory; Nigeria." Last modified 2012. Accessed April 16, 2013. http://www.who.int/en/.

Sources (Metadata)

Encyclopedia of the Nations, "Nigeria Country Specific Information." Last modified 2013. Accessed March 28, 2013. http://www.nationsencyclopedia.com.

Kates, Jennifer, and Alyssa Wilson Leggoe. The Henry J. Kaiser Family Foundation, "HIV/AIDS; The HIV/AIDS Epidemic in Nigeria." Last modified October 2005. Accessed April 16, 2013. http://www.kff.org/.

United States Embassy in Nigeria, "Nigeria Malaria Fact Sheet." Last modified December 2011. Accessed April 16, 2013. http://nigeria.usembassy.gov.

World Health Organization, "Global Task Force on Cholera Control." Last modified January 18, 2012. Accessed April 16, 2013. http://www.who.int/.

World Health Organization, "Meningococcal disease: situation in the African Meningitis Belt." Last modified 2012. Accessed March 14, 2013. http://www.who.int/csr/don/2012_05_24/en/index.html.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

This map highlights 8962 stations with monthly discharge data, including data derived daily up to 20 December 2013. The GRDB (Global Runoff DataBase) is built on an initial dataset collected in the early 1980s from the responses to WMO (World Meteorological Organization request to its member countries to provide a global hydrological data set to complement a specific set of atmospheric data in the framework of the First Global GARP Experiment (FCGE). The initial dataset of monthly river discharge data over a period of several years around 1980 was supplemented with the UNESCO monthly river discharge data collection 1965-85. Today the database comprises discharge data of nearly 9.000 gauging stations from all over the world. Since 1993 the total number of station-years has increased by a factor of around 10.Credits and partnerships:OSU - College of Earth, Ocean and Atmospheric SciencesCarniege Corporation of New YGloabl orkNASCE - Northwest Alliance for Computational Science & EngineeringInternational Water Management InstituteUNESCO - United Nations Educational, Scientific and Cultural OrganisationUSGS - United States Geological Survey

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

This Thematic Collection contains links to the datasets of the Stichting Arab-West Foundation (AWF), in The Netherlands in close cooperation with the Center for Intercultural Dialogue and Translation (CIDT). These datasets cover the period 1994-2016. The data consists of the reporting of Dutch sociologist Cornelis Hulsman, reporting supervised by him, full-transcript interviews, audio recordings and summaries of these audio recordings.The Arab-West Foundation was established in 2005 to support the work of Cornelis Hulsman and his wife Eng. Sawsan Gabra Ayoub Hulsman-Khalil in Egypt. Cornelis Hulsman left The Netherlands for Egypt in October 1994. Sawsan Hulsman followed suit in 1995. They focused primarily on the study of Muslim-Christian relations and the role of religion in society in Egypt and neighboring countries, while obtaining their income from journalism.The purpose of this work was to foster greater understanding between Muslims and Christians in Egypt and to show non-Egyptians that relations between the two faiths in Egypt cannot be described in reductive black and white terms, rather they are diverse and complicated. Working towards mutual understanding of different cultures and beliefs helps to reduce tensions and conflicts. Too often, parties present themselves as the victim of the other which results in biased reporting. Sometimes this is done deliberately to gain support. What is lacking in cases like this, is an in-depth understanding of the wider context in which narratives of victimization occur. Hulsman found several patterns that are key to understanding Muslim-Christian relations in Egypt such as- the impact of a culture of honor and shame and- aversion in traditional areas for visible changes in public (which includes church buildings and making one’s conversion to another religion public).The datasets also include material on the place of Islamists in society, as well as wider information about Egyptian society since this is the context in which religious numerical minorities in Egypt live (the term minority is widely rejected in Egypt since all Egyptians, regardless of religion, are one. But in terms of numbers Christians are a minority).It was Hulsman’s ambition to obtain a PhD but the challenges of making a living in Egypt prevented him from accomplishing this goal. Up until the year 2001, Cornelis only had an income from traditional media reporting. After 2004 he became largely dependent upon working with Kerk in Actie (Netherlands), Missio and Misereor (Germany).Hulsman was dedicated towards non-partisan Muslim-Christian understanding. This began starting with a large number of recorded interviews, followed by research into why so many Christian girls convert to Islam (1995-1996). This work in turn led to the creation of an electronic newsletter called Religious News Service from the Arab World (RNSAW) and a growing number of investigative reports. In 2003 the RNSAW was renamed Arab-West Report. In 2004 they attempted to establish an Egyptian NGO but since no answer was obtained from authorities, the procedure was taken to the Council of State who ruled in 2006 that the request for NGO status was valid. This in turn resulted in a formal registration of the NGO with the Ministry of Social Solidarity in 2007. Because the outcome of this process was insecure in 2005 the Hulsmans established the Center for Intercultural Dialogue and Translation (CIDT) . CIDT was established as a tawsiya basita (sole proprietorship) on the name of Sawsan Gabra Ayoub Khalil since it was extremely complicated to do this on the name of a non-Egyptian. In the same year friends of the Hulsman family established the Arab-West Foundation (AWF). CIDT tawsiya basita was closed in 2012. A new company was established under the same name but now as limited liability company and again it was not possible for Cornelis Hulsman to become a partner.As a consequence the Hulsmans have been working since 2005 with an Egyptian company and a Dutch support NGO. Since 2007 they have also been working with an Egyptian NGO. This was important, since Egyptian law prohibits companies from receiving donations and carrying out not-for-profit work. NGOs, on the other hand, need to request permissions from the Ministry of Social Solidarity for each donation they receive. Such permissions are hard to obtain.CIDT functions as a thinktank with funding from Kerk in Actie (Netherlands), Missio and Misereor (Germany) and at times projects with other organizations. CIDT produces the electronic newsletter Arab-West Report and has built the Arab West Report Database based on these data. Publication of this data is accomplished through the Arab-West Foundation since it turned out to be extremely hard to register Arab-West Report in Egypt. CAWU became the prime organization hosting student interns from Egypt and countries all over the world, which was possible since CAWU does not charge student interns for its services and neither pays...

Updated for 2025, this powerful dataset presents an in-depth, multi-dimensional view of global military capabilities, combining economic, geographic, and energy-related variables across 57 key attributes. Ideal for analysts, researchers, data scientists, journalists, and students interested in geopolitics, defense economics, and strategic forecasting. The data are extracted from a reliable website called "Global Firepower".

Tags & Keywords that are relevant to this dataset: military, military ranking, geopolitics, defense, army, air force, navy, world military, military strength, military statistics, military equipment, military resources, militaries and weapons, tanks, aircraft, navy ships, military spending, military 2025, global defense, military budget, energy statistics, war and conflict, natural resources, infrastructure, country comparison, geopolitical analysis, military dataset, open data, Kaggle military dataset

India Census: Population: by Religion: Muslim: Urban data was reported at 68,740,419.000 Person in 2011. This records an increase from the previous number of 49,393,496.000 Person for 2001. India Census: Population: by Religion: Muslim: Urban data is updated yearly, averaging 59,066,957.500 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 68,740,419.000 Person in 2011 and a record low of 49,393,496.000 Person in 2001. India Census: Population: by Religion: Muslim: Urban data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE001: Census: Population: by Religion.