This dataset is being used by the repository https://github.com/islamAndAi/QURAN-NLP

Dataset Structure

• data
  • quran
    • corpus (190,655)
      • dictionary (53,924)
      • morphology (128,219)
      • verbs (1,475)
      • lemmas (3,680)
      • lemmas (grouped) (3,357)
    • quran.csv (6,236)
  • hadith (700,000+)
    • arabichadith (62,169)
    • Sanadset (650,615 hadith)
    • thaqalayn (26,975)
    • kaggle_hadith_clean.csv (34,410)
    • kaggle_rawis.csv (24,028)
  • namesofallah (99)
  • surah (114)
  • tafseer (4 * 6,236)
  • translation (9 * 6,236)
  • main_df.csv (6,236)

Motivation

I thought about using my knowledge of ML & NLP on the Quran to make something out of it. I have tried to get a summary of the Verses and Tafasir, getting the sentiment analysis, I have made a Search Engine so that any query can be searched as easily as a person does on Google

This is an open source project and I am trying to host it somewhere so people can use it and make the most out of it.

Collaborations are HIGHLY welcome! If anyone can help with the code or help fact check the search results or summaries that would be a HUGE help!

Looking forward to do something great with Quran & NLP

مَنْ أَنصَارِىٓ إِلَى ٱللَّهِ

Work till now

1. Notebook to scrape data from website: https://www.altafsir.com/
2. Provided English translation and Tafseer of Quran in easy to use CSV format
3. Used NLP to get top 1000 words used in Quran
4. Used sentiment analysis for Quran each surah
5. Text Summarization for Quran & each Surah
6. Search Engine for Quran using Google USE (Universal Sentence Encoder)
7. Similarity Index of Translation & Tafseer
8. Notebook to scrape data from https://thaqalayn.net/ which is a Comprehensive Shia Hadith Library
9. Notebook to scrape https://corpus.quran.com/ which contains corpus of Quran, including dictionary, verbs, lemmas, morphology

Future Goals

1. Add more Data!
2. Add more Tafaseer and translation to better train the NLP model for Search Engine & Analysis
3. Make a end-to-end application so that everyone can benefit from the newly trained models
4. Find insightful things from the Quran
5. Make an Arabic NLP model capable of understanding Quran & Hadees
6. Make a single graph database encompassing Islamic knowledge
7. Making an AI tool to authenticate Hadith

Important Note

If you find any type of error or mistake in the work please correct me. If you find the work interesting feel free to build more on it!

How To Contribute

Feel free to make notebooks on the current data, add more data (authentic and with sources) and have a look at the current data to make sure it is authentic and up-to-date!

This dataset was created by web scraping from the IslamQA website.

Islam Q&A is an academic, educational, da‘wah website which aims to offer advice and academic answers based on evidence from religious texts in an adequate and easy-to-understand manner. These answers are supervised by Shaykh Muhammad Saalih al-Munajjid (may Allah preserve him). The website welcomes questions from everyone, Muslims and otherwise, about Islamic, psychological and social matters.

"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...

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

Context

Hadith (an Arabic word) refers to the words and actions of Prophet Mohammed. Those collections of Hadiths have been transmitted through generations of Muslim scholars until they have been collected and written in big collections. The chain of narrators is a main area of study in Islamic scholarship because a single hadith may have multiple chains of narrators (that may or may not overlap). However, it has mainly remained a qualitative field where scholars of Hadith try to determine the authenticity of Hadiths by investigating and validating the chains of narrators who transmitted a given hadith. Further, the raw texts of Hadiths have not yet been used in qualitative approaches in data analysis. I hope this dataset makes it easier to further progress in this direction.

Content

Hadith dataset contains the set of all Hadiths from the six primary hadith collections. The data is scraped from http://qaalarasulallah.com/. Note that the chain_indx column refers to scholar_indx column in Hadith Narrators Dataset.

Notably, this is a very draft version of the dataset as it is not validated. For example, the number of Hadiths in this dataset is much higher than the real number of Hadiths contained in those sources. This may be due to a bug in my script. Further actions will be taken to further clean up this dataset. However, as it is right now, it can be used to prototype certain analyses in those areas.

Disclaimer: I scraped the data and I hold no responsibility for its accuracy or validation. Use at your own risk!

Acknowledgements

This dataset wouldn't have been possible without the great people who have already transcribed this dataset from primary sources and bibliographies to muslimscholars.info & qaalarasulallah.com database. I only scraped this database with a Python script plus very minimal cleanup.

Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset on bias against Asians, Blacks, Jews, Latines, and Muslims

The present data from 194 customers of small and medium enterprises (SMEs) tell us about their acceptance of Islamic microfinance in Kano State, Nigeria. The dataset includes variables such as gender, age, marital status, duration as customer, account operate, annual income, type of business, service quality, perceived value, corporate image and religiosity of customers in Kano State. We fielded a survey from March to June 2019, self administered questionnaires were used for data collection. This data may help scholars to understand how people of Kano State accept Islamic microfinance interacted with service quality, customer perceived value, corporate image and religiosity.

This dataset contains the Arab-West Report special reports that were published in 2004.This dataset mainly contains the writings of Cornelis Hulsman ,Drs., among other authors on topics related to Muslim- Christian relations and interfaith dialogue between the West and Islamic world. Additionally this dataset contains reports pertaining to certain Muslim –Christian incidents and reports about allegations of forced conversions of Coptic girls. Some of the articles addressed the issue of missionaries.Further reports address monastic life and recommendations of Arab-West Report's work by other social figures.Furthermore, the dataset included commentary on published material from other sources (reviews/critique of articles from other media).Some of the themes that characterized this dataset:-A description of the history of the conflicts around the development of the convent of Patmos on the Cairo-Suez road.-An overview of a book titled “Christians versus Muslims in Modern Egypt: The Century-Long Struggle for Coptic Equality” by S. S. Hasan.- Rumors of forced conversions Of Coptic girls: A report by Hulsman stated that the US Copts Association published a press release on March 25, 2004 with the title “Coptic Pope Denounces Forced Conversion of Coptic Girls.” He criticized that the US Copts Association for not making much of an effort, if any, to check the veracity of the rumors.- A Glimpse into Monastic Life in Egypt: A Visit to St. Maqarius Monastery:- Another report covered the incident in which a priest and two members of the church board of Taha al-ʿAmeda died after an accident with a speeding car driven by a police officer.- A critique of Al-Usbuʿa newspapers: the author accused the newspaper of cherry-picking statements by Coptic extremists, who are much disliked in the US Coptic community and who have no following. He considered that quoting statements from such isolated radicals gives readers the impression that they represent much more than a few individuals. It has all appearance that al-Usbuʿa has highlighted these radicals to create fear and harm the reputation of US Copts in Egypt.- A number of reports highlighted a visit and the speech delivered by the Archbishop of Canterbury, Dr George Carey (Lord Carey) at the Azhar entitled “Muslims/Christian Relationships: A New Age Of Hope?”- A report covered the first visit made by Archbishop Rowan Williams to the Diocese of Egypt since he became the Archbishop of Canterbury. The archbishop met with President Mubarak, Dr. Muhammad Sayyed Tantawi, the Grand Imam of the Azhar, Pope Shenouda and also laid the foundation stone of Harpur Community Health Centre in Sadat City.- Updates on the developments of AWR’s work to create an electronic archive of information pertaining to relations between Muslims and Christians in the Arab-World in general and Egypt in particular.Additionally, this dataset also provides updates of the then-under construction - Center for Arab-West Understanding (CAWU) web-based Electronic Documentation Center (EDC) for contemporary information covering Arab-West and Muslim-Christian relations.- A report discussed the misconceptions of Christians in Islam.- An editorial commenting on the assassination of Theo van Gogh resulted in a debate in Dutch media about the limits of the freedom of expression.- An article calling on the western readers to be careful with Christian persecution stories from Egypt, they may be true but also may be rumours.-The Muslim World And The West; What Can Be Done To Reduce Tensions?-Text of a lecture for students and professors of different faculties at the University of Copenhagen, , about plans to establish the Center for Arab-West Understanding in Cairo, Egypt.- Escalations following the alleged conversion of A priest’s wife to IslamThe list of authors’ featurd in this dataset goes as follows:Cornelis Hulsman, Drs. , Wolfram Reiss, Rev. Dr. , John H. Watson, Kim Kwang-Chan, Dr. , Kamal Abu al-Majd, Fiona McCallum, Mary Picard , Jeff Adams, Dr., Rev., Jennie Marshall , Marcos Emil Mikhael, Usamah W. al-Ahwani, Sawsan Jabrah and Nirmin Fawzi, Hānī Labīb, George Carey (Lord), Rowan Williams, Lambeth Palace Press Office, H.G. Bishop Munir Hanna Anis Armanius, Eildert Mulder, Rīhām Saʿīd, Tharwat al-Kharabāwī, Geir Valle, Janique Blattman, Iqbal Barakah , Munā ʿUmar, Dieter Tewes, ʿAmr Asʿad Khalīl, Dr., Janique Blattmann, Vera Milackova, Tamir Shukri, and Christiane Paulus All reports are written in English, though some reports feature Arabic text or cite Arabic sources.

Context

This is believed to be an unbiased fact-based dataset to get a better understanding of how much damage that the Islamic extremists are doing to the world.

Content

These are not incidents of ordinary crime involving nominal Muslims killing for money or vendetta. Incidents of deadly violence that are reasonably determined to have been committed out of religious duty - as interpreted by the perpetrator - are only included. Islam needs to be a motive, but it need not be the only factor.

For example, the Munich mall shooting in July, 2016 was by a Muslim, but it is not on the list, because it was not inspired by a sense of religious duty.

The incidents were collected each day from public news sources. There is no rumor or word-of-mouth involved. Although every attempt is made to be accurate and consistent, we are not making the claim that this is a scientific product.

Acknowledgements

This dataset is available here on Kaggle, thanks to TheReligionofPeace.com

Inspiration

The point of this dataset is not to convince anyone that they are in mortal danger or that Muslims are innately dangerous people (they are not, of course). Rather it is to point out the sort of terrorism that some of "Religion of Peace" believers produce. It should be acceptable to question and critique the teachings and phrases interpretation particularly those that are supremacist in nature.

Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset on bias against Asians, Blacks, Jews, Latines, and Muslims

The ISCA project compiled this dataset using an annotation portal, which was used to label tweets as either biased or non-biased, among other labels. Note that the annotation was done on live data, including images and context, such as threads. The original data comes from annotationportal.com. They include representative samples of live tweets from the years 2020 and 2021 with the keywords "Asians, Blacks, Jews, Latinos, and Muslims". A random sample of 600 tweets per year was drawn for each of the keywords. This includes retweets. Due to a sampling error, the sample for the year 2021 for the keyword "Jews" has only 453 tweets from 2021 and 147 from the first eight months of 2022 and it includes some tweets from the query with the keyword "Israel." The tweets were divided into six samples of 100 tweets, which were then annotated by three to seven students in the class "Researching White Supremacism and Antisemitism on Social Media" taught by Gunther Jikeli, Elisha S. Breton, and Seth Moller at Indiana University in the fall of 2022, see this report. Annotators used a scale from 1 to 5 (confident not biased, probably not biased, don't know, probably biased, confident biased). The definitions of bias against each minority group used for annotation are also included in the report. If a tweet called out or denounced bias against the minority in question, it was labeled as "calling out bias." The labels of whether a tweet is biased or calls out bias are based on a 75% majority vote. We considered "probably biased" and "confident biased" as biased and "confident not biased," "probably not biased," and "don't know" as not biased.
The types of stereotypes vary widely across the different categories of prejudice. While about a third of all biased tweets were classified as "hate" against the minority, the stereotypes in the tweets often matched common stereotypes about the minority. Asians were blamed for the Covid pandemic. Blacks were seen as inferior and associated with crime. Jews were seen as powerful and held collectively responsible for the actions of the State of Israel. Some tweets denied the Holocaust. Hispanics/Latines were portrayed as being in the country illegally and as "invaders," in addition to stereotypical accusations of being lazy, stupid, or having too many children. Muslims, on the other hand, were often collectively blamed for terrorism and violence, though often in conversations about Muslims in India.

Content:

This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias. 1180 out of 5880 tweets (20.1 %) contain the keyword "Asians," 590 were posted in 2020 and 590 in 2021. 39 tweets (3.3 %) are biased against Asian people. 370 tweets (31,4 %) call out bias against Asians. 1160 out of 5880 tweets (19.7%) contain the keyword "Blacks," 578 were posted in 2020 and 582 in 2021. 101 tweets (8.7 %) are biased against Black people. 334 tweets (28.8 %) call out bias against Blacks. 1189 out of 5880 tweets (20.2 %) contain the keyword "Jews," 592 were posted in 2020, 451 in 2021, and ––as mentioned above––146 tweets from 2022. 83 tweets (7 %) are biased against Jewish people. 220 tweets (18.5 %) call out bias against Jews. 1169 out of 5880 tweets (19.9 %) contain the keyword "Latinos," 584 were posted in 2020 and 585 in 2021. 29 tweets (2.5 %) are biased against Latines. 181 tweets (15.5 %) call out bias against Latines. 1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims.

File Description:

The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns:
'TweetID': Represents the tweet ID.
'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet.
'Text': Represents the full text of the tweet (not pre-processed). 'CreateDate': Represents the date the tweet was created.
'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0). 'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0). 'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.

Licences

Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)

Acknowledgements

We are grateful for the technical collaboration with Indiana University's Observatory on Social Media (OSoMe). We thank all class participants for the annotations and contributions, including Kate Baba, Eleni Ballis, Garrett Banuelos, Savannah Benjamin, Luke Bianco, Zoe Bogan, Elisha S. Breton, Aidan Calderaro, Anaye Caldron, Olivia Cozzi, Daj Crisler, Jenna Eidson, Ella Fanning, Victoria Ford, Jess Gruettner, Ronan Hancock, Isabel Hawes, Brennan Hensler, Kyra Horton, Maxwell Idczak, Sanjana Iyer, Jacob Joffe, Katie Johnson, Allison Jones, Kassidy Keltner, Sophia Knoll, Jillian Kolesky, Emily Lowrey, Rachael Morara, Benjamin Nadolne, Rachel Neglia, Seungmin Oh, Kirsten Pecsenye, Sophia Perkovich, Joey Philpott, Katelin Ray, Kaleb Samuels, Chloe Sherman, Rachel Weber, Molly Winkeljohn, Ally Wolfgang, Rowan Wolke, Michael Wong, Jane Woods, Kaleb Woodworth, and Aurora Young. This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

Census 2021 data on religion by highest qualification level, by sex, by age, England and Wales combined. This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

The religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it.
This question was voluntary and the variable includes people who answered the question, including “No religion”, alongside those who chose not to answer this question.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

This dataset shows population counts for usual residents aged 16 years and over. Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

Quality notes can be found here

Quality information about Education can be found here

Religion

The 8 ‘tickbox’ religious groups are as follows:

• Buddhist
• Christian
• Hindu
• Jewish
• Muslim
• No religion
• Sikh
• Other religion

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)

Time series data for the statistic Labor_Force_Participation_Rate and country Iran, Islamic Rep.. Indicator Definition:Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.The statistic "Labor Force Participation Rate" stands at 44.05 percent as of 12/31/2024, the lowest value since 12/31/2015. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.183 percentage points compared to the value the year prior.The 1 year change in percentage points is -0.183.The 3 year change in percentage points is -0.436.The 5 year change in percentage points is -3.32.The 10 year change in percentage points is 0.706.The Serie's long term average value is 45.64 percent. It's latest available value, on 12/31/2024, is 1.59 percentage points lower, compared to it's long term average value.The Serie's change in percentage points from it's minimum value, on 12/31/2011, to it's latest available value, on 12/31/2024, is +1.34.The Serie's change in percentage points from it's maximum value, on 12/31/1990, to it's latest available value, on 12/31/2024, is -4.00.

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...

This dataset contains the Arab West Report special reports published in the year 2003. The majority of the material in this dataset focuses on in depth analysis of Muslim-Christian relations in Egypt, however, Judaism is also the subject of a great deal of analysis in these reports. A number of the reports address relations between religious minorities such as 'dhimmi' status, and the complex relationship between national identity and religious identity. A number of reports are also media critique, a staple of AWR’s work.The AWR reports in this dataset also describe the early work of AWR, and introduce several of its early board members and affiliates. Authors include:- Cornelis Hulsman, Drs.- Sunni M. Khalid- Jeff Adams (Dr. Rev.)- Larry F. Levine (Dr.)- Victor M. Ordonez- Michael Reimer (Dr.)- Wolfram Reiss, (Rev. Dr.)- Johanna Pink (Dr.)- Nirmīn Fawzī- Hedda Klip- Munīr Hannā Anīs Armanius (Bishop)- Cassandra Chambliss- Adam Hannestad- David Weaver- Konrad Knolle (Rev.)- Usamah Wadi‘ al-Ahwani- Marjam Van Oort- Nawal al-Sa‘dawi- M.E. van Gent- Subhi ‘Uwaydah, (Rev. Dr.)- Andreas Van Agt, (Dr.)Institutional authors include AWR Editorial Board, AWR Board of Advisors, Center for the Study of Christianity in Islamic Lands (CSCIL), and EKD Presservice. All reports are written in English, though some reports feature Arabic text or cite Arabic sources.Team including job titles:Sparks, MA M.R. (Center for Intercultural Dialogue and Translation (CIDT))Adams, Rev.Dr. J. (Religious News Service from the Arab-World (RNSAW))Levine, Dr. L.Khalid, S.Reimer, Dr. M. (American University in Cairo)Ordonez, Dr. V.Reiss, Rev. Dr. W.Pink, Dr. J.Fawzi, N. (Religious News Service from the Arab World (RNSAW))Klip, Rev. H. (Swiss Reformed Church)Hannā Anīs Armanius, Bishop M. (Episcopal Church)Chambliss, C. (Intern-Center for Arab-West Understanding (CAWU))Hannestad, A.Weaver, D. (Church World Service, USA)Knolle, Rev. K. (German Reformed Church in Cairo)Al-Ahwani, U. (Religious News Service from the Arab-World (RNSAW))Oort, M. Van (Roos Foundation)Al-Sa'adawi, N.Gent, M.E. VanUwaydah, Rev. Dr. S. (Coptic Evangelical Church Ismailia, Egypt)van Agt, Dr. A.EKD Press ServiceCenter for the Study of Christianity in Islamic Lands (CSCIL)AWR Editorial BoardAWR Board of AdvisorsHulsman, Drs. C. Mr. (Center for Intercultural Dialogue and Translation

The Places of Worship dataset is composed of any type of building or portion of a building that is used, constructed, designed, or adapted to be used as a place for religious and spiritual activities. These facilities include, but are not limited to, the following types: chapels, churches, mosques, shrines, synagogues, and temples. The license free Large Protestant Churches, Mosques, Jewish Synagogues, and Roman Catholic Churches in Large Cities datasets were merged together to create the initial data for the Places of Worship dataset. Additional entities have been added from TGS research. This dataset contains Buddhist, Christian, Hindu, Islamic, Judaic, and Sikh places of worship. Unitarian places of worship have been included when a congregation from one of these religions meets at a church owned by a Unitarian congregation. Some Protestant denominations are not currently represented in this dataset. The Places of Worship dataset is not intended to include homes of religious leaders (unless they also serve as a place of organized worship), religious schools (unless they also serve as a place of organized worship for people other than those enrolled in the school), Jewish Mikvahs or Hillel facilities, and buildings that serve a purely administrative purpose. If a building's primary purpose is something other than worship (e.g., a community center, a public school), but a religious group uses the building for worship on a regular basis, it was included in this dataset if it otherwise met the criteria for inclusion. Convents and monasteries are included in this dataset, regardless of whether or not the facilities are open to the public, because religious services are regularly held at these locations. On 08/07/2007, TGS ceased making phone calls to verify information about religious locations. Therefore, most entities in this dataset were verified using alternative reference sources such as topographic maps, parcel maps, various sources of imagery, and internet research.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Iran, Islamic Rep. is 1005.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

EyeFi Dataset

This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.

Data Collection Setup

In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.

The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers.

To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.

List of Files
Here is a list of files included in the dataset:

|- 1_person
  |- 1_person_1.h5
  |- 1_person_2.h5
|- 2_people
  |- 2_people_1.h5
  |- 2_people_2.h5
  |- 2_people_3.h5
|- 3_people
  |- 3_people_1.h5
  |- 3_people_2.h5
  |- 3_people_3.h5
|- 5_people
  |- 5_people_1.h5
  |- 5_people_2.h5
  |- 5_people_3.h5
  |- 5_people_4.h5
|- 10_people
  |- 10_people_1.h5
  |- 10_people_2.h5
  |- 10_people_3.h5
|- Kitchen
  |- 1_person
    |- kitchen_1_person_1.h5
    |- kitchen_1_person_2.h5
    |- kitchen_1_person_3.h5
  |- 3_people
    |- kitchen_3_people_1.h5
|- training
  |- shuffuled_train.h5
  |- shuffuled_valid.h5
  |- shuffuled_test.h5
View-Dataset-Example.ipynb
README.md

In this dataset, folder `1_person/` , `2_people/` , `3_people/` , `5_people/`, and `10_people/` contains data collected from the lab area whereas `Kitchen/` folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section Access the data.

The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from `1_person/` folder collected in the lab area (`1_person_1.h5` and `1_person_2.h5`).

Why multiple files in one folder?

Each folder contains multiple files. For example, `1_person` folder has two files: `1_person_1.h5` and `1_person_2.h5`. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like `1_person_1.h5`, `1_person_2.h5`) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system.

Special note:

For `1_person_1.h5`, this file is generated by the same person who is holding the phone, and `1_person_2.h5` contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.

Access the data
To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: https://www.hdfgroup.org/downloads/hdfview/. We also provide an example Python code View-Dataset-Example.ipynb to demonstrate how to access the data.

Each file is structured as (except the files under *"training/"* folder):

|- csi_imag
|- csi_real
|- nPaths_1
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- nPaths_2
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- nPaths_3
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- nPaths_4
  |- offset_00
    |- spotfi_aoa
  |- offset_11
    |- spotfi_aoa
  |- offset_12
    |- spotfi_aoa
  |- offset_21
    |- spotfi_aoa
  |- offset_22
    |- spotfi_aoa
|- num_obj
|- obj_0
  |- cam_aoa
  |- coordinates
|- obj_1
  |- cam_aoa
  |- coordinates
...
|- timestamp

The `csi_real` and `csi_imag` are the real and imagenary part of the CSI measurements. The order of antennas and subcarriers are as follows for the 90 `csi_real` and `csi_imag` values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. `nPaths_x` group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with `x` number of multiple paths specified during calculation. Under the `nPath_x` group are `offset_xx` subgroup where `xx` stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:

|Antennas | Offset 1 (rad) | Offset 2 (rad) |
|:-------:|:---------------:|:-------------:|
| 1 & 2 |   1.1899   |   -2.0071
| 1 & 3 |   1.3883   |   -1.8129

The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the `offset_xx` naming. For example, `offset_12` is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.

The `num_obj` field is used to store the number of human subjects present in the scene. The `obj_0` is always the subject who is holding the phone. In each file, there are `num_obj` of `obj_x`. For each `obj_x1`, we have the `coordinates` reported from the camera and `cam_aoa`, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the `training` folder) . It reflects the way the person carried the phone moved in the space (for `obj_0`) and everyone else walked (for other `obj_y`, where `y` > 0).

The `timestamp` is provided here for time reference for each WiFi packets.

To access the data (Python):

import h5py

data = h5py.File('3_people_3.h5','r')

csi_real = data['csi_real'][()]
csi_imag = data['csi_imag'][()]

cam_aoa = data['obj_0/cam_aoa'][()] 
cam_loc = data['obj_0/coordinates'][()] 

For file inside `training/` folder:

Files inside training folder has a different data structure:

|- nPath-1
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi
|- nPath-2
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi
|- nPath-3
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi
|- nPath-4
  |- aoa
  |- csi_imag
  |- csi_real
  |- spotfi

The group `nPath-x` is the number of multiple path specified during the SpotFi calculation. `aoa` is the camera generated angle of arrival (AoA) (can be considered as ground truth), `csi_image` and `csi_real` is the imaginary and real component of the CSI value. `spotfi` is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across `1_person_1.h5` and `1_person_2.h5`. All the rows under the same `nPath-x` group are aligned (i.e., first row of `aoa` corresponds to the first row of `csi_imag`, `csi_real`, and `spotfi`. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the `1_person_1.h5` and `1_person_2.h5` files.

Citation
If you use the dataset, please cite our paper:

@inproceedings{eyefi2020,
 title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching},
 author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar},
 booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)},
 year={2020},

This dataset consists of the articles and reports of the RNSAW content that were published in the year 2002. As previously noted that at the time the articles were published, Arab-West Report did not exist. Religious News Service from the Arab World, the organization which would ultimately become Arab-West Report, published the following documents.The dataset contains primarily the writings of Cornelis Hulsman, Drs., reporting on Christians situation in Egypt and subjects related to the Copts in the US with a number of editorials serve as a media critique of the Coptic organizations press releases issued abroad and other articles published in the local Egyptian newspapers.The reports covered the following topics:- A report that addressed a conflict between the Coptic Orthodox and Brethren Churches in Al- Ashmonein and described the relations between the two denomination there as “tense”.- A critique of what the authored believed as” Distorted Reporting” About Coptic Christians in Egypt. The three authors criticized a report in the Layman, an American Presbyterian publication, claiming Christians in Egypt are persecuted. They believe the author of this article was ill-informed and provided readers with wrong information.- An overview of the contents of the three books about Coptic Orthodox ecclesiastical law, published by the Monastery of Makarios.- A List of Churches in Assiut Governorate for Which Governorate Decrees For Restoration And Presidential Decrees For Building And Renovated Were Issued.-An overview of the activities of the Coptic Catholic peace movement, Justice and Peace in EgyptMedia critique:Press release of the US Copts Association about the decision of the governor of Assiut instructing to remove an illegally built section of the church which the association does not mention and thereby providing only part of the information needed to form an accurate picture of this issue. The press release is also very aggressive in the last paragraph where it calls the governor an Islamic extremist.- Criticizing the press release of the US Copts Association. Some Copts frequently resort to claims of Islamic extremism if they are dissatisfied with the decisions made.- An Interview with Bishop Marcos about the Succession of Pope Shenouda, Father Matta Al-Meskeen, Ecommunications and Other Subjects. In Addition to that the dataset included an Interview with Father Johanna and Father Basilius of the Monastery of Makarios.-Summary of the Ph.D. thesis of Revd. Dr. Wolfram Reiss about the Sunday School movement in the Coptic Orthodox Church with a focus on the role of Pope Shenouda III and Father Matta el-Meskeen and the place of the church in a Muslim society. Reiss´ study provides an excellent insight into the contemporary history of the church and explains differences between church leaders in the past decades.- A crisis in the Egyptian Church Resulting from an article in the Sunday School magazine requesting the pope to avoid public [political] activities.-A text of the statement of the Anglican/Al-Azhar Dialogue Commission- Egyptian TV Addresses Inter-Religious Dialogue-Egyptian Cultural TV broadcasted on October 27 a live discussion with Patrick Haenni, social researcher at the CEDEJ in Cairo and Cornelis Hulsman, Drs., on inter-religious dialogue.A report about A group of Germans belonging to the YMCA [Young Men´s Christian Association] and Evangelical Church of Saxony, Germany [Evangelische Kirche Deutschland] who discovered a very different Christian Egypt from what they had expected from press reports in their home country. The group had followed the trail of the Holy Family between Beni Suef and Assiut and met with many different people.- A report about a claimed apparition of the Holy Virgin In Giza- An analysis of the Arab and Western Press in terms of the biases of the Western media and the limits imposed on political and freedom press in Egypt.- A report about Dr. Naṣr Ḥāmid Abū Zayd, sympathizer and supporter of the RNSAW, receiving the Franklin Delano Roosevelt prize for his contribution to the Freedom of Worship- Media criticism of ‘Reckless, Anti-Islamic Statement’ of major US Christian leader.- An evaluation of the RNSAW workshop for Egyptian journalists. The report shed light on the objectives and program of a RNSAW workshop for Egyptian journalists in cooperation with the Al-Ahram Institute for Regional Journalism. The workshop was financed by the Dutch Embassy and Friedrich Ebert Stiftung and covered human rights issues, women, Western and Arab media, freedom of expression and reporting about the Israel-Palestinian conflict.- The Dialogue Agreement between the Azhar and the Church of EnglandThe authors of this material include Cornelis Hulsman, Drs., Jos Strengholt, Rudolph Yanni, Peter Zarqah, Dr. Kamal Burayqa‘ ‘Abd al-Salam, Israel Shamir, Holger Jensen, Phil Reeves, Philip Smucker, Paul Perry, Michael Fowler, Michael Sabah, Dr. Naṣr Ḥāmid Abū Zayd, Yusuf Sidhom, Nirmin Fawzi, Chris...

Census 2021 data on religion by general health, by sex, by age; religion by disability, by sex, by age; and, religion by unpaid care, by sex, by age; England and Wales combined. This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

The religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it.
This question was voluntary and the variable includes people who answered the question, including “No religion”, alongside those who chose not to answer this question.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

The population base for unpaid care is usual residents aged 5 years and above. We have used 5-year age bands for the majority of analysis; however, age groups "5 to 17" and "18 to 24" have been used to allow commentary on young carers and young working age carers.

Quality notes can be found here

Religion

The 8 ‘tickbox’ religious groups are as follows:

• Buddhist
• Christian
• Hindu
• Jewish
• Muslim
• No religion
• Sikh
• Other religion

General health

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

Disability

The definition of disability used in the 2021 Census is aligned with the definition of disability under the Equality Act (2010). A person is considered disabled if they self-report having a physical or mental health condition or illness that has lasted or is expected to last 12 months or more, and that this reduces their ability to carry out day-to-day activities.

Unpaid care

An unpaid carer may look after, give help or support to anyone who has long-term physical or mental ill-health conditions, illness or problems related to old age. This does not include any activities as part of paid employment. This help can be within or outside of the carer's household.