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

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...

"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)

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...

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.

This dataset contains the estimated religious composition of 198 countries and territories for 2010 to 2050.
The data is sourced from PEW RESEARCH CENTER . In original dataset the number and the percentage share of followers for some religions as "<10000" and "<10%". Because of technical limitations of the visualization tool, these values had to be changed into "10000".

data/rounded_population.csv

This file contains the number followers by religions and region for 2010 to 2050 | Column Name | Description | |-----------------|-------------------------------------------------------------------------------------------------| | Year | The year for which the data is recorded. | | Region | The region to which the country belongs. | | Country | The name of the country. | | Buddhists | The estimated number of Buddhists in the country for the given year. | | Christians | The estimated number of Christians in the country for the given year. | | Folk Religions | The estimated number of followers of folk religions in the country for the given year. | | Hindus | The estimated number of Hindus in the country for the given year. | | Jews | The estimated number of Jews in the country for the given year. | | Muslims | The estimated number of Muslims in the country for the given year. | | Other Religions | The estimated number of followers of other religions in the country for the given year. | | Unaffiliated | The estimated number of people with no religious affiliation in the country for the given year. |

data/rounded_percentage.csv

This file contains the percentage share of followers by religions and region for 2010 to 2050 | Column Name | Description | |------------------|------------------------------------------------------------------------------------------------| | Year | The year for which the data is recorded. | | Region | The region to which the country belongs. | | Country | The name of the country. | | Christians | The percentage share of Christians in the country's population for the given year. | | Muslims | The percentage share of Muslims in the country's population for the given year. | | Unaffiliated | The percentage share of people with no religious affiliation in the country's population. | | Hindus | The percentage share of Hindus in the country's population for the given year. | | Buddhists | The percentage share of Buddhists in the country's population for the given year. | | Folk Religions | The percentage share of followers of folk religions in the country's population. | | Other Religions | The percentage share of followers of other religions in the country's population. | | Jews | The percentage share of Jews in the country's population for the given year. | | All Religions | The percentage share of all religious groups combined in the country's population for the year. |

This dataset is extracted from https://en.wikipedia.org/wiki/Islam_by_country. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

The problem involves scrapping two websites (Life Quality and Crime Rate) for collecting life quality and crime rate data, merging them by country name and conducting EDA on Tabealu for finging insights. For details find : https://github.com/NifulIslam/Life-Quality-and-Crime-Rate-Scrapping-and-EDA

In 2020, around 28.8 percent of the global population were identified as Christian. Around 25.6 percent of the global population identify as Muslims, followed by 14.9 percent of global populations as Hindu. The number of Muslims increased by 347 million, when compared to 2010 data, more than all other religions combined.

Context

The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content

This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset

Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Structure

This dataset (world_population_data.csv) covering from 1970 up to 2023 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the World Population Review. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Pew Research Center conducted random, probability-based surveys among 13,122 adults (ages 18 and older) across six South and Southeast Asian countries: Cambodia, Indonesia, Malaysia, Singapore, Sri Lanka and Thailand. Interviewing was carried out under the direction of Langer Research Associates. In Malaysia and Singapore, interviews were conducted via computer-assisted telephone interviewing (CATI) using mobile phones. In Cambodia, Indonesia, Sri Lanka and Thailand, interviews were administered face-to-face using tablet devices, also known as computer-assisted personal interviewing (CAPI). All surveys were conducted between June 1 and Sept. 4, 2022.

This project was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation (grant 61640). This publication does not necessarily reflect the views of the John Templeton Foundation.

As of July 2024, one report has been published that focuses on the findings from this data: Buddhism, Islam and Religious Pluralism in South and Southeast Asia: https://www.pewresearch.org/religion/2023/09/12/buddhism-islam-and-religious-pluralism-in-south-and-southeast-asia/

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.

Rising global food insecurity driven by population growth needs urgent measure for universal access to food. This research employs Comparative Performance Analysis (CPA) to evaluate the Global Food Security Index (GFSI), its components [Affordability (AF), Availability (AV), Quality & Safety (Q&S) and Sustainability & Adaptation (S&A)] in tandem with Annual Population Change (APC) for world’s five most populous countries (India, China, USA, Indonesia and Pakistan) using dataset spanning from 2012 to 2022. CPA is applied using descriptive analysis, correlation analysis, Rule of Thumb (RoT) and testing of hypothesis etc. RoT is used with a new analytical approach by applying the significance measures for correlation coefficients. The study suggests that India should enhance its GFSI rank by addressing AF and mitigating the adverse effects of APC on GFSI with a particular focus on Q&S and S&A. China needs to reduce the impact of APC on GFSI by prioritizing AV and S&A. The USA is managing its GFSI well, but focused efforts are still required to reduce APC’s impact on Q&S and S&A. Indonesia should improve across all sectors with a particular focus on APC reduction and mitigating its adverse effects on AF, AV, and S&A. Pakistan should intensify efforts to boost its rank and enhance all sectors with reducing APC. There is statistically significant and negative relation between GFSI and APC for China, Indonesia and found insignificant for others countries. This study holds promise for providing crucial policy recommendations to enhance food security by tackling its underlying factors.

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

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.

Context

Global Environmental Indicators serve as vital measures to understand our planet's health. These indicators, simplified measures, offer an efficient way to monitor the environment, signaling potential issues before they escalate. Compiled by UNSD, these indicators cover themes like water, waste, and more, drawing from diverse international data sources. While some data gaps exist, these indicators provide valuable insights, aiding policymakers in making informed decisions for a sustainable future.

Content

UNSD Environmental Indicators disseminate global environment statistics on ten indicator themes compiled from a wide range of data sources. The themes and indicator tables were selected based on the current demands for international environmental statistics and the availability of internationally comparable data. Indicator tables, charts, and maps with relatively good quality and coverage across countries, as well as links to other international sources, are provided under each theme.

Dataset

This dataset covers 10 thematic areas of environmental indicators as follows: - Air and Climate - Biodiversity - Energy and Minerals - Forest - Governance - Inland Water Resources - Land and Agriculture - Marine and Coastal Areas - Natural Disasters - Waste

Acknowledgment:

The primary dataset was retrieved from the United Nations Statistics Division (UNSD). I would like to sincerely thank the UNSD team for providing the core data used in this dataset.

© Image credit: Freepik

In the aftermath of the attacks on September 11, 2001, and subsequent terrorist attacks elsewhere around the world, a key counterterrorism concern was the possible radicalization of Muslims living in the United States. The purpose of the study was to examine and identify characteristics and practices of four American Muslim communities that have experienced varying levels of radicalization. The communities were selected because they were home to Muslim-Americans that had experienced isolated instances of radicalization. They were located in four distinct regions of the United States, and they each had distinctive histories and patterns of ethnic diversity. This objective was mainly pursued through interviews of over 120 Muslims located within four different Muslim-American communities across the country (Buffalo, New York; Houston, Texas; Seattle, Washington; and Raleigh-Durham, North Carolina), a comprehensive review of studies an literature on Muslim-American communities, a review of websites and publications of Muslim-American organizations and a compilation of data on prosecutions of Muslim-Americans on violent terrorism-related offenses.

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...

Context

Understanding global economic dynamics, specifically the trends in inflation rates, is paramount for policymakers, economists, and researchers. This dataset, covering the years 1980 to 2024, offers a comprehensive perspective on inflation across various countries. The primary focus is on dissecting the data based on country-specific indicators, providing valuable insights into the multifaceted factors influencing economic environments on a global scale.

Content

The dataset comprises crucial columns including country name, indicator type, and annual average inflation rates from 1980 to 2024. This extensive collection of information facilitates detailed analysis and correlation studies, enabling researchers to uncover patterns and trends. By examining the nuanced relationships between country-specific indicators and inflation rates, valuable conclusions can be drawn about the complexities of global economic dynamics over the years. This dataset serves as a valuable resource for anyone seeking to delve into the intricacies of inflation trends and their implications across diverse nations.

Dataset Structure:

This dataset (global_inflation_data.csv) covering from 1980 to 2024 consists of the following columns:

Acknowledgment

The primary dataset was retrieved from the World Bank. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Context

HealthStats compiles an extensive array of health, nutrition, and population statistics gleaned from a diverse array of global sources. Encompassing themes ranging from population dynamics to health financing and disease prevalence, this repository covers a broad spectrum of indicators, including immunization rates, infectious diseases, HIV/AIDS, and population projections. Additionally, HealthStats presents nuanced statistics categorized by wealth quintiles, offering a comprehensive view of societal disparities.

Content

Within this dataset, a compendium of 470 indicators sheds light on critical metrics such as immunization rates, malnutrition prevalence, and vitamin A supplementation across 266 countries worldwide. Spanning a timeframe from 1960 to 2022, this data collection encapsulates yearly statistics, providing a comprehensive historical perspective on health, nutrition, and population dynamics.

Dataset Structure

This dataset (health_nutrition_population_statistics.csv) covering from 1960 up to 2022 includes the following columns:

Questions for Exploration

• Are there unexpected correlations or shifts in indicators that warrant inclusion for a more holistic understanding?
• Is there a discernible relationship between condom use and HIV transmission rates among newborns, and how have these trends evolved over time?
• Identifying countries with the highest iodized salt consumption and tracking changes over time could reveal intriguing patterns. Are there other indicators that correlate significantly with this consumption trend?

Acknowledgment

The primary dataset was retrieved from the World Bank's Data Catalog. I would like to express our sincere appreciation to the World Bank team for providing the core data used in this dataset.

© Image credit: Freepik