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.

Data on religion by gender and age for the population in private households in census divisions.

As of 2010, Christianity was the religion with the most followers worldwide, followed by Islam (Muslims) and Hinduism. In the forty years between 2010 and 2050, it is projected that the landscape of world religions will undergo some noticeable changes, with the number of Muslims almost catching up to Christians. The changes in population sizes of each religious group is largely dependent on demographic development, for example, the rise in the world's Christian population will largely be driven by population growth in Sub-Saharan Africa, while Muslim populations will rise across various regions of Africa and South Asia. As India's population is set to grow while China's goes into decline, this will be reflected in the fact that Hindus will outnumber the unaffiliated by 2050. In fact, India may be home to both the largest Hindu and Muslim populations in the world by the middle of this century.

This table contains 21 series, with data for years 1871 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Religious denominations (21 items: Total religious denominations; Baptist; Congregationalist; Anglican ...).

The table Gallup Religion Battery 1999-2024 is part of the dataset Gallup Religion Battery, available at https://stanford.redivis.com/datasets/14ch-9rmyd26gx. It contains 51959 rows across 118 variables.

Among the people surveyed in 26 countries around the world, a slight majority of the baby boomer generation were Christians. By comparison, only 42 percent of Generation Z stated that they were Christians. Millennials was the generation with the highest share of people stating that they had a religious belief other than Islam and Christianity.

The table "Religion; by region; 2000/2002 or 2003" shows the percentage of people aged 18 and over for each part of the country, province and COROP area who indicate that they belong to a specific religious denomination. The following categories of religious denomination are distinguished: Roman Catholic, Reformed, Dutch Reformed, Islam and no religious denomination. In addition to percentages, standard errors are also available. Depending on subject, data available on: 2000/2002 or 2003. Frequency: irregular Status of the figures: final Changes compared to the previous version: none When will new figures be released? As of January 1, 2008, this table has been discontinued.

Data on religion by gender and age for the population in private households in Canada, provinces and territories.

Population by religion by Electoral Divisions. (Census 2022 Theme 2 Table 4 )Census 2022 table 2.4 is the total population by religion. Details include population by religion. Census 2022 theme 2 is Migration, Ethnicity, Religion and Foreign Languages. Electoral Divisions (EDs) are the smallest legally defined administrative areas in the State. There are 3,440 legally defined EDs in the State. However, in order to render them suitable for the production of statistics, the CSO has amended some ED boundaries to ensure that statistical disclosure does not occur. This has had the effect of amalgamating some EDs and splitting others. The amending of the Cork City and Cork County boundaries necessitated a redrawing of Electoral Division boundaries within Cork to ensure all ED boundaries in the county were suitable for the production of statistical data. For Census 2022, the CSO is publishing data for 3,420 CSO Electoral Divisions. The CSO Electoral divisions are referred to by their established statutory names.Formally “District Electoral Divisions” (DEDs), under the 2001 Local Government Act, the names of Wards and the names of District Electoral Divisions were changed to Electoral Divisions. Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. CSO Electoral Divisions 2022

Population by religion by Local Authorities. (Census 2022 Theme 2 Table 4 )Census 2022 table 2.4 is the total population by religion. Details include population by religion. Census 2022 theme 2 is Migration, Ethnicity, Religion and Foreign Languages. The country is divided into 31 administrative counties/cities. Outside Dublin, there are 23 administrative counties and four cities: Cork, Limerick, Waterford and Galway. There are four local authority areas in Dublin: Dublin City and the three administrative counties of Dún Laoghaire-Rathdown, Fingal and South Dublin. The Local Government Reform Act 2014 Section 9 provided for the amalgamation of the city and county councils in Limerick, Waterford, and North Tipperary and South Tipperary County Councils.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. This dataset is provided by Tailte Éireann, Administrative Counties 2019

This statistic shows the feeling of belonging to a specific religion among people in France in a survey from 2022. It displays that half of respondents stated that they felt linked to Christianism, when around 40 percent of them declared they felt bound to no religion.

Population by religion by Limistéir Pleanála Teanga. (Census 2022 Theme 2 Table 4 )Census 2022 table 2.4 is the total population by religion. Details include population by religion. Census 2022 theme 2 is Migration, Ethnicity, Religion and Foreign Languages. Teorainneacha na Limistéar Pleanála Teanga Gaeltachta. I gcomhréir le forálacha Acht na Gaeltachta 2012, tá 26 Limistéar Pleanála Teanga Gaeltachta sainaitheanta ag an Aire Ealaíon, Oidhreachta agus Gaeltachta. Faoin Acht, athainmneofar an Ghaeltacht atá ann faoi láthair mar Limistéir Pleanála Teanga Ghaeltachta ach pleananna teanga a bheith aontaithe ag pobail sna limistéir éagsúla de réir na gcritéar pleanála teanga atá forordaithe faoin Acht. Tá Údarás na Gaeltachta freagrach faoin Acht as tacú le heagraíochtaí maidir le hullmhú agus cur i bhfeidhm na bpleananna teanga sna Limistéir Pleanála Teanga Ghaeltachta. Gaeltacht Language Planning Area Boundaries. In line with the provisions of the Gaeltacht Act 2012, the Minister for Arts, Heritage and the Gaeltacht has identified 26 Gaeltacht Language Planning Areas. Under the Act, the existing Gaeltacht will be redesignated as Gaeltacht Language Planning Areas provided that language plans are agreed by the communities in the various areas in accordance with the language planning criteria prescribed under the Act. Údarás na Gaeltachta is responsible under the Act for supporting organisations with regard to the preparation and implementation of the language plans in the Gaeltacht Language Planning Areas. Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. This dataset is provided by Tailte Éireann, Limistéir Pleanála Teanga 2015.

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.

IntroductionAs demonstrated in previous research and clinical observations, the personal value system is subject to disintegration as a consequence of schizophrenia. Patients with schizophrenia are sometimes religious and use religious coping mechanisms. A number of studies emphasize the benefits of positive religious coping as a part of clinical treatment for patients with schizophrenia and schizoaffective disorder. However, the contribution of these patients’ religious faith to the structure and composition of their personal value systems remains unexplored. The present study attempts to answer this question.MethodThe factorial design (2x2) included two conditionally independent variables: mental illness (absent/present) and religious faith (absent/present). We sampled four groups (N = 65) for the study: mentally ill believers of Orthodox Christian faith, mentally ill non-believers, healthy believers, and healthy non-believers. We analyzed the structure and composition of the participants’ personal values employing the following tools underpinned by G. Kelly’s personal construct theory: the triad method, Hinkle’s laddering and repertory grid methods. Correlation and factor analyses were then conducted within each group to find the relationships between the personal values identified. Subsequently, we compared the personal value systems of each group with each other.ResultsThe outcomes of the study reveal that personal values of healthy non-believers are less differentiated than those of healthy Orthodox believers and can be divided into two clusters of meta-values: spiritual and material. Mental illness in non-religious individuals is likely to contribute to disintegration of their personal value systems. Healthy believers have distinctly differentiated and hierarchical personal value systems, while mentally ill believers retain both the general hierarchy and key structures of their personal value systems.DiscussionThe relative stability of the personal value systems of mentally ill believers is explained by their attitude toward illness as a form of trial, which is integrated within the framework of their religious worldview grounded in the Orthodox Christian doctrine. In this way, illness is not regarded as a hindrance to achieving life goals and personal meanings inspired by religion.

Table showing the numbers and percentage of resident population (all ages) broken down into six faiths, plus no religion and any other religion. Data is taken from the Annual Population Survey (ONS).

The data covers: Christian, Buddhist, Hindu, Jewish, Muslim, Sikh, any other religion and no religion at all.

95% Confidence Intervals are shown.

Or alternatively, faith data from the 2011 Census is able to show numbers for each of the main religions.

Population: Religion: Female: Taoism data was reported at 165.600 Person th in 2015. This records a decrease from the previous number of 172.222 Person th for 2010. Population: Religion: Female: Taoism data is updated yearly, averaging 165.600 Person th from Jun 2000 (Median) to 2015, with 3 observations. The data reached an all-time high of 172.222 Person th in 2010 and a record low of 105.267 Person th in 2000. Population: Religion: Female: Taoism data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G002: Population by Religion .

This file contains all of the cases and variables that are in the original 2014 Baylor Religion Survey, but is prepared for easier use in the classroom. Changes have been made in two areas. First, to avoid confusion when constructing tables or interpreting basic analysis, all missing data codes have been set to system missing. Second, many of the continuous variables have been categorized into fewer categories, and added as additional variables to the file.

Wave IV of the Baylor Religion Survey (2014), also known as "The Values and Beliefs of the American Public - A National Study," was administered by Gallup and funded by the John Templeton Foundation. It covers topics of religious behaviors and attitudes; morality, gender roles, and politics; family and religiosity; sexual orientation; work; race and ethnicity; guns and society; surveillance; science and the supernatural; and basic demographics.

This package includes the do files and dta files required to replicate regression tables and figures in "Religion and Persecution" accepted at the Journal of Economic Growth, by Umair Khalil and Laura Panza

2009 Census Volume 2 Table 12 Population by Religious Affiliation