World Religions Across Regions

Analyzing Adherence Across Regions, States and the Global System

By Correlates of War Project [source]

About this dataset

The World Religion Project (WRP) is an ambitious endeavor to conduct a comprehensive analysis of religious adherence throughout the world from 1945 to 2010. This cutting-edge project offers unparalleled insight into the religious behavior of people in different countries, regions, and continents during this time period. Its datasets provide important information about the numbers and percentages of adherents across a multitude of different religions, religion families, and non-religious affiliations.

The WRP consists of three distinct datasets: the national religion dataset, regional religion dataset, and global religion dataset. Each is focused on understanding individually specific realms for varied analysis approaches - from individual states to global systems. The national dataset provides data on number of adherents by state as well as percentage population practicing a given faith group in five-year increments; focusing attention to how this number evolves from nation to nation over time. Similarly, regional data is provided at five year intervals highlighting individual region designations with one modification – Pacific Ocean states have been reclassified into their own Oceania category according to Country Code Number 900 or above). Finally at a global level – all states are aggregated in order that we may understand a snapshot view at any five-year interval between 1945‐2010 regarding relationships between religions or religio‐families within one location or transnationally.

This project was developed in three stages: firstly forming a religions tree (a systematic classification), secondly collecting data such as this provided by WRP according to that classification structure – lastly cleaning the data so discrepancies may be reconciled and imported where needed with gaps selected when unknown values were encountered during collection process . We would encourage anyone wishing details undergoing more detailed reading/analysis relating various use applications for these rich datasets - please contact Zeev Maoz (University California Davis) & Errol A Henderson _(Pennsylvania State University)

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How to use the dataset

The World Religions Project (WRP) dataset offers a comprehensive look at religious adherence around the world within a single dataset. With this dataset, you can track global religious trends over a period of 65 years and explore how they’ve changed during that time. By exploring the WRP data set, you’ll gain insight into cross-regional and cross-time patterns in religious affiliation around the world.

Research Ideas

• Analyzing historical patterns of religious growth and decline across different regions
• Creating visualizations to compare religious adherence in various states, countries, or globally
• Studying the impact of governmental policies on religious participation over time

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: WRP regional data.csv | Column name | Description | |:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | Year | Reference year for data collection. (Integer) | | Region | World region according to Correlates Of War (COW) Regional Systemizations with one modification (Oceania category for COW country code ...

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 (as detailed below) 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 Regional Religion Dataset: The unit of analysis is the region, measured at five-year intervals. The Correlates of War regional breakdown is used with one modification: the Oceania category is added for Correlates of War nation numbers 900 and above.

World religion data in this dataset is from the World Religion Database.The map shows the percentage of the majority religion by provinces/states and also included in the database is Christian percentage by provinces/states. Boundaries are based on Natural Earth, August, 2011 modified to match provinces in the World Religion Database.*Originally titled

This dataset describes the world’s religious makeup in 2020 and 2010. We focus on seven categories: Christians, Muslims, Hindus, Buddhists, Jews, people who belong to other religions, and those who are religiously unaffiliated. This analysis is based on more than 2,700 sources of data, including national censuses, large-scale demographic surveys, general population surveys and population registers. For more information about this data, see the associated Pew Research Center report "How the Global Religious Landscape Changed From 2010 to 2020."

This folder consists of files for a case study of the methods used by Pew Research Center to make direct and indirect estimates for our report on The Religious Composition of the World's Migrants. Two subfolders demonstrate the procedures of the algorithm using two statistical programs, which mirror one another.

Contained within the 4th Edition (1974) of the Atlas of Canada is a set of three maps. The first two maps show the percentage of population, by census division, who identify with a particular religion. These two maps represent the first and second statistical ranks for the most common religions. The third map shows the diversity of religion by number of dominant religious denominations as well as a breakdown of the particular religions within each census division. A supplementary graph showing the percentage of religions by province is also provided.

This Religion and State-Minorities (RASM) dataset is supplemental to the Religion and State Round 2 (RAS2) dataset. It codes the RAS religious discrimination variable using the minority as the unit of analysis (RAS2 uses a country as the unit of analysis and, is a general measure of all discrimination in the country). RASM codes religious discrimination by governments against all 566 minorities in 175 countries which make a minimum population cut off. Any religious minority which is at least 0.25 percent of the population or has a population of at least 500,000 (in countries with populations of 200 million or more) are included. The dataset also includes all Christian minorities in Muslim countries and all Muslim minorities in Christian countries for a total of 597 minorities. The data cover 1990 to 2008 with yearly codings.

These religious discrimination variables are designed to examine restrictions the government places on the practice of religion by minority religious groups. It is important to clarify two points. First, these variables focus on restrictions on minority religions. Restrictions that apply to all religions are not coded in this set of variables. This is because the act of restricting or regulating the religious practices of minorities is qualitatively different from restricting or regulating all religions. Second, this set of variables focuses only on restrictions of the practice of religion itself or on religious institutions and does not include other types of restrictions on religious minorities. The reasoning behind this is that there is much more likely to be a religious motivation for restrictions on the practice of religion than there is for political, economic, or cultural restrictions on a religious minority. These secular types of restrictions, while potentially motivated by religion, also can be due to other reasons. That political, economic, and cultural restrictions are often placed on ethnic minorities who share the same religion and the majority group in their state is proof of this.

This set of variables is essentially a list of specific types of religious restrictions which a government may place on some or all minority religions. These variables are identical to those included in the RAS2 dataset, save that one is not included because it focuses on foreign missionaries and this set of variables focuses on minorities living in the country. Each of the items in this category is coded on the following scale:

0. The activity is not restricted or the government does not engage in this practice.
1. The activity is restricted slightly or sporadically or the government engages in a mild form of this practice or a severe form sporadically.
2. The activity is significantly restricted or the government engages in this activity often and on a large scale.

A composite version combining the variables to create a measure of religious discrimination against minority religions which ranges from 0 to 48 also is included.

ARDA Note: This file was revised on October 6, 2017. At the PIs request, we removed the variable reporting on the minority's percentage of a country's population after finding inconsistencies with the reported values. For detailed data on religious demographics, see the "/data-archive?fid=RCSREG2" Target="_blank">Religious Characteristics of States Dataset Project.

Abstract

The Religion Battery is a consolidated list of items focused on religion in the United States. The dataset includes responses from 1999-2024.

Methodology

The Religion Battery leverages the same methodology as the Gallup Poll Social Series (GPSS).

Gallup interviews a minimum of 1,000 U.S. adults aged 18 and older living in all 50 states and the District of Columbia using a dual-frame design, which includes both landline and cellphone numbers. Gallup samples landline and cellphone numbers using random-digit-dial methods. Gallup purchases samples for this study from Survey Sampling International (SSI). Gallup chooses landline respondents at random within each household based on which member had the next birthday. Each sample of national adults includes a minimum quota of 70% cellphone respondents and 30% landline respondents, with additional minimum quotas by time zone within region. Gallup conducts interviews in Spanish for respondents who are primarily Spanish-speaking.

Gallup weights samples to correct for unequal selection probability, nonresponse, and double coverage of landline and cellphone users in the two sampling frames. Gallup also weights its final samples to match the U.S. population according to gender, age, race, Hispanic ethnicity, education, region, population density, and phone status (cellphone only, landline only, both, and cellphone mostly).

Demographic weighting targets are based on the most recent Current Population Survey figures for the aged 18 and older U.S. population. Phone status targets are based on the most recent National Health Interview Survey. Population density targets are based on the most recent U.S. Census.

Usage

Previous versions of the Religion Battery have more rows and columns than the current version (v. 3.0). This is because the previous data releases contained fields unrelated to religion. The current release was cleaned/streamlined to reflect the topic of interest and isolate the surveys related to that topic.

For more information about included variables, please see

Supporting Files.

Bulk Data Access

Data access is required to view this section.

This dataset provide a count of Veteran by their religious affiliation and state of residence. The dataset set covers all 50 states, District of Columbia and other territories.

This dataset provides Census 2021 estimates that classify usual residents in Birmingham by ethnic group, by religion, and by age.

Ethnic Group: The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. Religion: The religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it. Age: A person's age on Census Day, 21 March 2021 in England and Wales.CoverageThis dataset is focused on the data for Birmingham at city level. About the 2021 CensusThe Census takes place every 10 years and gives us a picture of all the people and households in England and Wales.Protecting personal dataThe ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they:Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority.Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation.For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS Create a custom dataset tool.

Data Set Information:

Most of the sacred texts in this dataset were collected from Project Gutenberg. We herein provide the raw texts along with our pre-processed Document Term Matrices (DTM). For more details, please contact the authors

Attribute Information:

The attributes are just the words from the bag of words preprocessing of the mini-corpus made up of the 8 religious books considered in this study. There are 8265 words used

Relevant Papers:

@misc{sah2019asian, title={What do Asian Religions Have in Common? An Unsupervised Text Analytics Exploration}, author={Preeti Sah and Ernest Fokoué}, year={2019}, eprint={1912.10847}, archivePrefix={arXiv}, primaryClass={cs.CL} }

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 survey charts the religiosity of the Finns, their attitudes on religion, and their views on different religions and denominations. The dataset is the Finnish pre-test data for the ISSP 2008 survey. First, the respondents were asked whether they thought all religious groups should have equal rights, whether religion influences politics too much, whether people who are prejudiced against some nationality should be allowed to express their views in public, and whether religious extremists should be allowed to hold public meetings, publish books or Internet pages, and express their views on the media. They were also asked whether they would accept a person from a different religion to marry their relative or to be a candidate of the political party they prefer. Further questions probed the respondents' attitudes towards different religions and denominations and whether practicing a religion helps people to find inner peace and happiness, to maintain good family relations, to gain comfort, to make friends, to meet the right kind of people, or to improve one's financial status. They were also presented with a set of attitudinal statements on the similarity between the purposes of different religions, on worshipping deceased ancestors, on the human ability to find perfect freedom from earthly suffering by his/her own strength, and on men's higher status in religious meetings and ceremonies. Religiosity was charted by asking whether the respondents believed in life after death, Hell, Heaven, religious miracles, reincarnation, Nirvana, or the supernatural powers of deceased ancestors. They were also asked how often they thanked God or asked for something when praying, how people can find salvation (if it exists), how they would describe their own religiosity, how often they had felt the presence of a spiritual power, whether they had religious objects on display in their home, and how often they took part in the activities, organisations or services of a church. Further questions charted whether the respondents had made some personal sacrifice as an expression of their faith during the past year (e.g. fasting), how often they prayed, how religious or spiritual they considered themselves to be, and whether they belonged to a church or other religious group. Opinions on euthanasia and cloning humans were also canvassed. Background variables included the respondent's year of birth, marital status, education, spouse's education, household composition, household size, occupational status, industry of employment, trade union membership, annual household income, annual personal income, type of accommodation, financial circumstances, province of residence, region of residence (NUTS3 and NUTS2), type of municipality, social class, political party preference, and left-right political self-placement.

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.

IndQNER

IndQNER is a Named Entity Recognition (NER) benchmark dataset that was created by manually annotating 8 chapters in the Indonesian translation of the Quran. The annotation was performed using a web-based text annotation tool, Tagtog, and the BIO (Beginning-Inside-Outside) tagging format. The dataset contains:

3117 sentences

62027 tokens

2475 named entities

18 named entity categories

Named Entity Classes

The named entity classes were initially defined by analyzing the existing Quran concepts ontology. The initial classes were updated based on the information acquired during the annotation process. Finally, there are 20 classes, as follows:

Allah

Allah's Throne

Artifact

Astronomical body

Event

False deity

Holy book

Language

Angel

Person

Messenger

Prophet

Sentient

Afterlife location

Geographical location

Color

Religion

Food

Fruit

The book of Allah

Annotation Stage

There were eight annotators who contributed to the annotation process. They were informatics engineering students at the State Islamic University Syarif Hidayatullah Jakarta.

Anggita Maharani Gumay Putri

Muhammad Destamal Junas

Naufaldi Hafidhigbal

Nur Kholis Azzam Ubaidillah

Puspitasari

Septiany Nur Anggita

Wilda Nurjannah

William Santoso

Verification Stage

We found many named entity and class candidates during the annotation stage. To verify the candidates, we consulted Quran and Tafseer (content) experts who are lecturers at Quran and Tafseer Department at the State Islamic University Syarif Hidayatullah Jakarta.

Dr. Eva Nugraha, M.Ag.

Dr. Jauhar Azizy, MA

Dr. Lilik Ummi Kultsum, MA

Evaluation

We evaluated the annotation quality of IndQNER by performing experiments in two settings: supervised learning (BiLSTM+CRF) and transfer learning (IndoBERT fine-tuning).

Supervised Learning Setting

The implementation of BiLSTM and CRF utilized IndoBERT to provide word embeddings. All experiments used a batch size of 16. These are the results:

Maximum sequence length Number of e-poch Precision Recall F1 score

256 10 0.94 0.92 0.93

256 20 0.99 0.97 0.98

256 40 0.96 0.96 0.96

256 100 0.97 0.96 0.96

512 10 0.92 0.92 0.92

512 20 0.96 0.95 0.96

512 40 0.97 0.95 0.96

512 100 0.97 0.95 0.96

Transfer Learning Setting

We performed several experiments with different parameters in IndoBERT fine-tuning. All experiments used a learning rate of 2e-5 and a batch size of 16. These are the results:

Maximum sequence length Number of e-poch Precision Recall F1 score

256 10 0.67 0.65 0.65

256 20 0.60 0.59 0.59

256 40 0.75 0.72 0.71

256 100 0.73 0.68 0.68

512 10 0.72 0.62 0.64

512 20 0.62 0.57 0.58

512 40 0.72 0.66 0.67

512 100 0.68 0.68 0.67

This dataset is also part of the NusaCrowd project which aims to collect Natural Language Processing (NLP) datasets for Indonesian and its local languages.

How to Cite

@InProceedings{10.1007/978-3-031-35320-8_12,author="Gusmita, Ria Hariand Firmansyah, Asep Fajarand Moussallem, Diegoand Ngonga Ngomo, Axel-Cyrille",editor="M{\'e}tais, Elisabethand Meziane, Faridand Sugumaran, Vijayanand Manning, Warrenand Reiff-Marganiec, Stephan",title="IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran",booktitle="Natural Language Processing and Information Systems",year="2023",publisher="Springer Nature Switzerland",address="Cham",pages="170--185",abstract="Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world's largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research's domain range.",isbn="978-3-031-35320-8"}

Contact

If you have any questions or feedback, feel free to contact us at ria.hari.gusmita@uni-paderborn.de or ria.gusmita@uinjkt.ac.id

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsReligionThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by religion. The estimates are as at Census Day, 21 March 2021.Definition: The religion people connect or identify with (their religious affiliation), whether or not they practice 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.This variable classifies responses into the eight tick-box response options. Write-in responses are classified by their "parent" religious affiliation, including 'No Religion', where applicable.This dataset contains details for Leicester City and England overall. There is also a dashboard that has been produced to show a selection of Census statistics for the city of Leicester which can be viewed here: Census 21 - Leicester dashboard.

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.

Idea

The idea for making this dataset is came to me when I was searching project for submitting on jovian.ml platform as a part of their task. (This is very good site for beginners who want to learn data science python skills, they arranged course in collaboration with freecodecamp). I thought to make unique project for that I need my own dataset. That's why I created this dataset.

What's Inside

Contains God names with meaning in Sanskrit , translated in English for better understanding. I collected this data from different scriptures & Sanskrit literatures. More will be added soon.

Acknowledgements

Thanks to my school Sanskrit teacher Mr. V. B. Patil Sir, which introduced us to this language.

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

This dataset provides Census 2021 estimates that classify households in Birmingham constituencies by religious group. The estimates are as at Census Day, 21 March 2021.The religion question in the Census refers to the religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it.CoverageThis dataset is focused on the data for Birmingham at 2021 constituency level. About the 2021 CensusThe Census takes place every 10 years and gives us a picture of all the people and households in England and Wales.Protecting personal dataThe ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they:Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority.Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation.For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS Create a custom dataset tool.