The World Religion Project (WRP) aims to provide detailed information about religious adherence worldwide since 1945. It contains data about the number of adherents by religion in each of the states in the international system. These numbers are given for every half-decade period (1945, 1950, etc., through 2010). Percentages of the states' populations that practice a given religion are also provided. (Note: These percentages are expressed as decimals, ranging from 0 to 1, where 0 indicates that 0 percent of the population practices a given religion and 1 indicates that 100 percent of the population practices that religion.) Some of the religions are divided into religious families. To the extent data are available, the breakdown of adherents within a given religion into religious families is also provided.

The project was developed in three stages. The first stage consisted of the formation of a religion tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. To develop the religion tree we prepared a comprehensive literature review, the aim of which was (i) to define a religion, (ii) to find tangible indicators of a given religion of religious families within a major religion, and (iii) to identify existing efforts at classifying world religions. (Please see the original survey instrument to view the structure of the religion tree.) The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. This created a dataset that included multiple records for some states for a given point in time. It also contained multiple missing data for specific states, specific time periods and specific religions. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources and imputing data for the missing cases.

The National Religion Dataset: The observation in this dataset is a state-five-year unit. This dataset provides information regarding the number of adherents by religions, as well as the percentage of the state's population practicing a given religion.

All mosques from around the world by available capacity, that belong to any Islamic school or branch, that can accommodate at least 15,000 worshippers in all available places of prayer such as prayer halls (musala), courtyards (ṣaḥn) and porticoes (riwāq). All the mosques in this list are congregational mosques – a type of mosque that hosts the Friday prayer (ṣalāt al-jumuʿa) in congregation (jamāʿa).

Census: Population: by Religion: Christian: Punjab: Female data was reported at 166,189.000 Person in 03-01-2011. This records an increase from the previous number of 138,127.000 Person for 03-01-2001. Census: Population: by Religion: Christian: Punjab: Female data is updated decadal, averaging 152,158.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 166,189.000 Person in 03-01-2011 and a record low of 138,127.000 Person in 03-01-2001. Census: Population: by Religion: Christian: Punjab: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE004: Census: Population: by Religion: Christian.

Census: Population: by Religion: Christian: Lakshadweep: Male data was reported at 286.000 Person in 03-01-2011. This records a decrease from the previous number of 422.000 Person for 03-01-2001. Census: Population: by Religion: Christian: Lakshadweep: Male data is updated decadal, averaging 354.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 422.000 Person in 03-01-2001 and a record low of 286.000 Person in 03-01-2011. Census: Population: by Religion: Christian: Lakshadweep: Male data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE004: Census: Population: by Religion: Christian.

This repository contains historical data collected in the digital humanities project Dhimmis & Muslims – Analysing Multireligious Spaces in the Medieval Muslim World. The project was funded by the VolkswagenFoundation within the scope of the Mixed Methods initiative. The project was a collaboration between the Institute for Medieval History II of the Goethe University in Frankfurt/Main, Germany, and the Institute for Visualization and Interactive Systems at the University of Stuttgart, and took place there from 2018 to 2021. The objective of this joint project was to develop a novel visualization approach in order to gain new insights on the multi-religious landscapes of the Middle East under Muslim rule during the Middle Ages (7th to 14th century). In particular, information on multi-religious communities were researched and made available in a database accessible through interactive visualization as well as through a pilot web-based geo-temporal multi-view system to analyze and compare information from multiple sources. The code for this visualization system is publicly available on GitHub under the MIT license. The data in this repository is a curated database dump containing data collected from a predetermined set of primary historical sources and literature. The core objective of the data entry was to record historical evidence for religious groups in cities of the Medieval Middle East. In the project, data was collected in a relational PostgreSQL database, the structure of which can be reconstructed from the file schema.sql. An entire database dump including both the database schema and the table contents is located in database.sql. The PDF file database-structure.pdf describes the relationship between tables in a graphical schematic. In the database.json file, the contents of the individual tables are stored in JSON format. At the top level, the JSON file is an object. Each table is stored as a key-value pair, where the key is the database name, and the value is an array of table records. Each table record is itself an object of key-value pairs, where the keys are the table columns, and the values are the corresponding values in the record. The dataset is centered around the evidence, which represents one piece of historical evidence as extracted from one or more sources. An evidence must contain a reference to a place and a religion, and may reference a person and one or more time spans. Instances are used to connect evidences to places, persons, and religions; and additional metadata are stored individually in the instances. Time instances are connected to the evidence via a time group to allow for more than one time span per evidence. An evidence is connected via one or more source instances to one or more sources. Evidences can also be tagged with one or more tags via the tag_evidence table. Places and persons have a type, which are defined in the place type and person type tables. Alternative names for places are stored in the name_var table with a reference to the respective language. For places and persons, references to URIs in other data collections (such as Syriaca.org or the Digital Atlas of the Roman Empire) are also stored, in the external_place_uri and external_person_uri tables. Rules for how to construct the URIs from the fragments stored in the last-mentioned tables are controlled via the uri_namespace and external_database tables. Part of the project was to extract historical evidence from digitized texts, via annotations. Annotations are placed in a document, which is a digital version of a source. An annotation can be one of the four instance types, thereby referencing a place, person, religion, or time group. A reference to the annotation is stored in the instance, and evidences are constructed from annotations by connecting the respective instances in an evidence tuple.

Census: Population: by Religion: Christian: Madhya Pradesh: Female data was reported at 107,985.000 Person in 03-01-2011. This records an increase from the previous number of 85,025.000 Person for 03-01-2001. Census: Population: by Religion: Christian: Madhya Pradesh: Female data is updated decadal, averaging 96,505.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 107,985.000 Person in 03-01-2011 and a record low of 85,025.000 Person in 03-01-2001. Census: Population: by Religion: Christian: Madhya Pradesh: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE004: Census: Population: by Religion: Christian.

The increase in religiously motivated hate on social media is clear and ongoing. These platforms have become fertile ground for the dissemination of hate speech directed at religious communities, resulting in tangible repercussions in the real world. Much of the current research concerning the automated identification of hateful content on social media focuses on English-language content. There is comparatively less exploration in low-resource languages such as Hindi. As social media users increasingly utilize their regional languages for expression, it becomes crucial to dedicate appropriate research efforts to hate speech detection in these languages.

Hence, this work aims to fill this research void by introducing a meticulously curated and annotated dataset of YouTube comments in Hindi-English code-mixed language, specifically designed to identify instances of religious hate.

Citation: Sharma, D., Singh, A., & Singh, V. K. (2024). THAR-Targeted Hate Speech Against Religion: A high-quality Hindi-English code-mixed Dataset with the Application of Deep Learning Models for Automatic Detection. ACM Transactions on Asian and Low-Resource Language Information Processing. (https://doi.org/10.1145/3653017)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15070006%2Fda30b3bab0ba1ad16529f6b0be33f2c3%2Fxian.jpg?generation=1698558286455813&alt=media" alt=""> Great Mosque of Xi'an - China

Context & Inspiration

Seize this invaluable opportunity to immerse yourself in the sacred verses of the Quran, a source of profound wisdom, spiritual guidance, and cultural significance in the Islamic world. Whether you're a dedicated researcher, a passionate data enthusiast, or simply an individual with a thirst for knowledge and a desire to connect with diverse cultures, this dataset opens doors to a treasure trove of information.

By exploring '**The Holy Quran - English Translation Dataset**' sourced from online Quranic resources, you not only gain access to a comprehensive English translation but also a deeper understanding of the Islamic faith, its historical context, and the values it embodies. This endeavor goes beyond data analysis; it's an invitation to engage with a rich cultural heritage that spans centuries, fostering cross-cultural appreciation, and illuminating the shared human experience.

Unearth the profound beauty and timeless depth of the Quran's verses, and embark on a transformative journey of enlightenment through the power of data analysis and exploration. Let your exploration of this dataset inspire a greater appreciation for the wisdom and teachings contained within, ultimately enriching your understanding of Islam and its global impact.

Disclaimer

Please use 'The Holy Quran - English Translation Dataset' responsibly and with utmost sensitivity. This dataset contains sacred and religious text, and its contents should be handled with the highest level of respect and consideration. It is important to note that the dataset provides the original English translation sourced from online Quranic resources, and it is not intended for any form of misuse, misrepresentation, or disrespectful usage.

We urge all users to approach this dataset with a deep understanding of the religious and cultural significance of the Quran. Any analysis, research, or exploration should be conducted in a manner that respects the sanctity of the text and the beliefs of those who hold it dear.

Furthermore, it is crucial to recognize that this dataset serves as a reference tool and does not endorse any particular interpretation or religious viewpoint. The dataset's purpose is to facilitate the study and understanding of the Quran's English translation, its historical context, and its cultural relevance.

We kindly request all users to exercise discretion, empathy, and cultural sensitivity when using this dataset. Please be aware of the potential impact your work may have and take every precaution to ensure that it promotes understanding and goodwill among all individuals, regardless of their beliefs or backgrounds.

This study, designed and carried out by the "http://www.asarb.org/" Target="_blank">Association of Statisticians of American Religious Bodies (ASARB), compiled data on 372 religious bodies by county in the United States. Of these, the ASARB was able to gather data on congregations and adherents for 217 religious bodies and on congregations only for 155. Participating bodies included 354 Christian denominations, associations, or communions (including Latter-day Saints, Messianic Jews, and Unitarian/Universalist groups); counts of Jain, Shinto, Sikh, Tao, Zoroastrian, American Ethical Union, and National Spiritualist Association congregations, and counts of congregations and adherents from Baha'i, three Buddhist groupings, two Hindu groupings, four Jewish groupings, and Muslims. The 372 groups reported a total of 356,642 congregations with 161,224,088 adherents, comprising 48.6 percent of the total U.S. population of 331,449,281. Membership totals were estimated for some religious groups.

In January 2024, the ARDA added 21 religious tradition (RELTRAD) variables to this dataset. These variables start at variable #12 (TOTCNG_2020). Categories were assigned based on pages 88-94 in the original "https://www.usreligioncensus.org/index.php/node/1638" Target="_blank">2020 U.S. Religion Census Report.

Visit the "https://www.thearda.com/us-religion/sources-for-religious-congregations-membership-data" Target="_blank">frequently asked questions page for more information about the ARDA's religious congregation and membership data sources.

Analysis of ‘🎦 Academy Awards Demographics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/academy-awards-demographicse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

A data set concerning the race, religion, age, and other demographic details of all Oscar winners since 1928 in the following categories: * Best * Actor

• Best Actress
• Best Supporting Actor
• Best Supporting Actress
• Best Director For further information on this data set, please read our resulting blog post For further information on this data set, please read our resulting blog post.

Source: https://www.crowdflower.com/data-for-everyone/

This dataset was created by CrowdFlower and contains around 400 samples along with Birthplace:confidence, Sexual Orientation Gold, technical information and other features such as: - Date Of Birth - Religion - and more.

How to use this dataset

• Analyze Year Of Award Gold in relation to Trusted Judgments
• Study the influence of Sexual Orientation on Date Of Birth:confidence
• More datasets

Acknowledgements

If you use this dataset in your research, please credit CrowdFlower

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Study question: To what extent do financial, demographic, and cultural determinants explain the vast cross-national differences in assisted reproductive technology (ART) treatments in Europe?

Summary answer: The normative cultural acceptance of ART is a major driver of ART treatments in Europe, above and beyond differences in country wealth, demographic aspects, and religious composition.

What is known already: There are vast differences in the number of ART treatments across European countries, which are to some extent related to country affluence, regulation, and insurance coverage and costs. The role and impact of cultural and normative factors has not been explored in a larger cross-national comparison.

Study design, size, duration: A descriptive and comparative cross-national analysis of ART treatment prevalence in over 30 European countries in 2010, with the outcome defined as the total number of ART cycles per million women of reproductive age (15–44 years). Data is drawn from multiple sources (ICMART, US Census Bureau Library, World Bank, Barro–Lee Educational Attainment Dataset, IFFS Surveillance reports, European Values Study, and World Religion Database).

Participants/materials, setting, methods: Our sample includes data from 35 European countries, where we describe the associations between demographic and cultural factors and the prevalence of ART treatments. Bivariate correlation and ordinary least squares (OLS) multiple regression analysis serves to establish the relationships between predictor variables and the number of ART treatments per million women aged 15–44 years in a country.

Main results and the role of chance: A one-percent increase in national GDP is associated with 382 (95% CI: 177–587) additional ART procedures per million women of reproductive age, yet this effect is reduced to 99 (-92–290) treatments once cultural values are accounted for. In our fully adjusted model, normative cultural values about the acceptability of ART are the strongest predictor of ART usage, with a one-point increase of average approval in a country associated with 276 (167–385) additional ART treatments per million women of reproductive age.

Limitations, reasons for caution: Findings are based on a cross-sectional, cross-national analysis, making formal tests of causality impossible and prohibiting inferences to the individual level.

Wider implications of the findings: Results indicate that reproductive health policy should openly acknowledge the importance of cultural norms in informally shaping and regulating the wider availability of ART treatment.

Analysis of ‘🛟 Contraceptive Method Choice’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/contraceptive-method-choicee on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey.# Source:
Origin:
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey
Creator:
Tjen-Sien Lim (limt '@' stat.wisc.edu)
Donor:
Tjen-Sien Lim (limt '@' stat.wisc.edu)

Data Set Information:

This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics.

Attribute Information:

1. Wife's age (numerical) 2. Wife's education (categorical) 1=low, 2, 3, 4=high 3. Husband's education (categorical) 1=low, 2, 3, 4=high 4. Number of children ever born (numerical) 5. Wife's religion (binary) 0=Non-Islam, 1=Islam 6. Wife's now working? (binary) 0=Yes, 1=No 7. Husband's occupation (categorical) 1, 2, 3, 4 8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high 9. Media exposure (binary) 0=Good, 1=Not good 10. Contraceptive method used (class attribute) 1=No-use, 2=Long-term, 3=Short-term

Relevant Papers:

Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning. ( or )

Papers That Cite This Data Set1:

Earl Harris Jr. Information Gain Versus Gain Ratio: A Study of Split Method Biases. The MITRE Corporation/Washington C. 2001.

• Soumya Ray and David Page. Generalized Skewing for Functions with Continuous and Nominal Attributes. Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wis.
• Jos'e L. Balc'azar. Rules with Bounded Negations and the Coverage Inference Scheme. Dept. LSI, UPC.

Citation Request:

Please refer to the Machine Learning Repository's citation policy.
[1] Papers were automatically harvested and associated with this data set, in collaborationwith Rexa.info

Source: http://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice

This dataset was created by UCI and contains around 1000 samples along with 1.1, 2.1, technical information and other features such as: - 2 - 3.1 - and more.

How to use this dataset

• Analyze 3 in relation to 1.2
• Study the influence of 24 on 1
• More datasets

Acknowledgements

If you use this dataset in your research, please credit UCI

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Social relevance of Christian religion in the Netherlands.Social characteristics / intimate relationships / division of care and household work / education / employment / social integration / intra-generational occupational mobility / intergenerational educational and occupational mobility / educational and occupational homogamy / traditional achievement values / traditional family values / social criticism / hedonistic values / r.' s past and present church membership / r'.s past and present church attendance / r'.s religious upbringing / membership non-Christian religious group / parents' past and present church membership / partners past and present church membership / past and present church membership of best friends and family / Christian, transcendental and world-directed interpretation and denial existence ultimate reality Christian and world-directed interpretation and denial of meaning of life, suffering and death / Christian and world-directed interpretation of good and evil / salience of religion and world view / religious surroundings / anthropomorphic and non-anthropomorphic images of God, theism, and individual, social and cosmic panentheism / external, internal and quest religious orientation / religious and mystic experiences and prayer / economic and cultural conservatism / political party preference, (post)materialism, political orientation, interest in politics / legitimacy of government decisions / primary relationship / attitudes towards homosexuality / attitude towards ethnic minorities / subjectively perceived threat / ethnic distance / ethnic discrimination / authoritarianism / anomie / utilitarian individualism / social trust individual and group deprivation / policy matters with regard to ethnic minorities / exposure to ethnic minorities / received and given help behaviour / financial help behaviour / pro-social orientation / attitude towards relationship men and nature / interaction with nature / proximity of nature / action willingness for nature / physical and mental illness / perception of chronic stress / acute negative life-events / stressful events during childhood and adolescence / physical family history mental health / social functioning.The data- and documentation files of this dataset can be downloaded via the option Data Files.

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.

In many places around the world, young voters participate in politics at low rates. What factors might increase youth political participation? We investigate one possibility: exposure to a religious message that emphasizes the possibility of change through faithful action. We argue that this message, which is common in religious groups that attract large numbers of youth around the world, addresses several barriers to political participation by young voting-age adults. Working in collaboration with the major religious coalitions in Zambia, we randomly assigned young adults (18-35 years old) into civic engagement workshops. Identical informational material, based on pre-existing, non-partisan curricula, was presented in each workshop. Workshops then concluded with one of two randomly assigned, pre-recorded Christian motivational messages based on existing religious programming in Zambia. In some workshops, the concluding message emphasized a Christian obligation to work towards the greater good. In other workshops, the message emphasized the power of faith to make change in the world. Materials in this dataset include the .do file replicating results presenting in the paper and the de-identified dataset produced by our implementing partner, IPA-Zambia, that was analyzed with that .do file. The READ.ME file includes descriptions of the attached files.

The Standard Cross-Cultural Sample of Religion is a product of the Database of Religious History (DRH). The DRH is a qualitative-quantitative encyclopedic database of historical religious data across time and space. Data are contributed to the project by academic experts and overseen by a panel of editors. The data take the form of answers (provided by experts) to a long list of standard questions grounded in time and space.

The Standard Cross-Cultural Sample of Religion is “standard” in a different way than its namesake, The Standard Cross-Cultural Sample (SCCS). The SCCS was designed to control for region and cultural relatedness. Because of our mostly bottom-up, expert-driven data gathering method, DRH data is heavily overweighted in certain time/space regions. Analysts will have to control for this as they see fit.

On the other hand, DRH data is “standard” in the sense that whatever Group, Place of Text is being portrayed, experts are answering a standardized set of questions, allowing a degree of comparison and quantitative analysis that has simply never been possible before. As the DRH grows, top-down data-gathering pushes will be targeted at underrepresented regions of the world, with the goal of making future versions of the SCCSR more and more comprehensive.

The Standard Cross-Cultural Sample of Religion (SCCSR.v2) is provided under CC-BY-4.0 license.

World Population World Population and top 20 Countries Live Clock. Population in the past, present, and future. Milestones. Global Growth Rate. World population by Region and by Religion. Population Density, Fertility Rate, Median Age, Migrants. All-time population total.

The World Values Survey (WVS) series was designed to enable a crossnational, crosscultural comparison of values and norms on a wide variety of topics and to monitor changes in values and attitudes across the globe. This dataset contains the survey data from the Australian component of the seventh wave of the World Values Surveys carried out in 2018. The World Values Survey is the world’s most comprehensive investigation of political and sociocultural change. It is an international survey with the 2018 ‘wave’ covering at least 95 countries. The purpose of the survey is to investigate worldwide political and sociocultural change and is conducted by a network of social scientists from leading universities around the world. Broad topics covered in the 2018, seventh wave include personal values, trust, gender roles, subjective wellbeing, volunteering, self-perceptions, social and economic environment, inequality, confidence in institutions, politics and democracy, religion, perceptions of older people, perceptions of crime and security, national identity, media and technology. Demographic information includes size of locality, region of residence, occupation of the head of household, and the respondent's age, sex, marital status, number of children, employment status, occupation, social class, country of birth, ethnicity, education, religion, religiosity, political party, and left-right political self-placement.

India Census: Population: by Religion: Hindu: Male data was reported at 498,306,968.000 Person in 2011. This records an increase from the previous number of 428,678,554.000 Person for 2001. India Census: Population: by Religion: Hindu: Male data is updated yearly, averaging 463,492,761.000 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 498,306,968.000 Person in 2011 and a record low of 428,678,554.000 Person in 2001. India Census: Population: by Religion: Hindu: Male 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.

Census: Population: by Religion: Muslim: Assam data was reported at 10,679,345.000 Person in 03-01-2011. This records an increase from the previous number of 8,240,611.000 Person for 03-01-2001. Census: Population: by Religion: Muslim: Assam data is updated decadal, averaging 9,459,978.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 10,679,345.000 Person in 03-01-2011 and a record low of 8,240,611.000 Person in 03-01-2001. Census: Population: by Religion: Muslim: Assam data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE003: Census: Population: by Religion: Muslim.