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.

Global Religious Demographics

Global religious demographics over time

By Throwback Thursday [source]

About this dataset

The dataset contains information on a wide range of religions, including Christianity, Judaism, Islam, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism, Zoroastrianism, Syncretic Religions (religious practices that blend elements from multiple faiths), Animism (belief in spiritual beings in nature), Non-Religious individuals or those without any religious affiliation.

For each religion and region/country combination recorded in the dataset we have the following information:

• Total population: The total population of the region or country.
• Religious affiliation percentages: The percentages of the population that identify with specific religious affiliations.
• Subgroup populations/percentages: The populations or percentages within specific denominations or sects of each religion.

The dataset also provides additional variables like Year and State Name (for regional data) for further analysis.

How to use the dataset

• Understanding the Columns

  The dataset contains several columns with different categories of information. Here's a brief explanation of some important columns:

  • Year: The year in which the data was recorded.
  • Total Population: The total population of a country or region.
  • State Name (StateNme): The name of the state or region.

  Each religion has specific columns associated with it, such as Christianity, Buddhism, Islam, Hinduism, Judaism, Taoism, Shintoism etc., representing its percentage and population for each category/denomination within that religion.

• Selecting Specific Data

  If you are interested in exploring data related to a particular religion or geographic location:

  • To filter data by Religion: Identify relevant columns associated with that religion such as 'Christianity', 'Buddhism', 'Islam', etc., and extract their respective percentage and population values for analysis.

    Example: If you want to analyze Christianity specifically, extract columns related to Christianity like 'Christianity (Percent)', 'Christianity (Population)', etc.

    Note: There might be multiple columns related to a specific religion indicating different categories or denominations within that religion.

  • To filter data by Geographic Location: Utilize the 'State Name' column ('StateNme') to segregate data corresponding to different states/regions.

    Example: If you want to analyze religious demographics for a particular state/region like California or India:

    i) Filter out rows where State Name is equal to California or India.

    ii) Extract relevant columns associated with your selected religion as mentioned above.

• Finding Trends and Insights

  Once you have selected the specific data you are interested in, examine patterns and trends over time or across different regions.

  • Plotting data using visualizations: Use graphical tools such as line charts, bar charts, or pie charts to visualize how religious demographics have changed over the years or vary across different regions.

  • Analyzing population proportions: By comparing the percentage values of different religions for a given region or over time, you can gather insights into changes in religious diversity.

• Comparing Religions

  If you wish to compare multiple religions:

Research Ideas

• Comparing religious affiliations across different countries or regions: With data on various religions such as Christianity, Islam, Buddhism, Judaism, Hinduism, etc., researchers can compare the religious affiliations of different countries or regions. This can help in understanding the cultural and religious diversity within different parts of the world.
• Exploring the growth or decline of specific religions: By examining population numbers for specific religions such as Jainism, Taoism, Zoroastrianism, etc., this dataset can be used to investigate the growth or decline of these religious groups over time. Researchers can analyze factors contributing to their popularity or decline in particular regions or countries

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: ThrowbackDataThursday 201912 - Religion.csv | Column name...

Pew Research Center conducted face-to-face surveys among 29,999 adults (ages 18 and older) across 26 Indian states and three union territories in 17 languages. The sample includes interviews with 22,975 Hindus, 3,336 Muslims, 1,782 Sikhs, 1,011 Christians, 719 Buddhists and 109 Jains. An additional 67 respondents belong to other religions or are religiously unaffiliated. Six groups were targeted for oversampling as part of the survey design: Muslims, Christians, Sikhs, Buddhists, Jains and those living in the Northeast region. Interviews were conducted under the direction of RTI International from November 17, 2019, to March 23, 2020. Data collection used computer-assisted personal interviews (CAPI) after random selection of households.

This project was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation.

Two reports focused on the findings from this data: •Religion in India: Tolerance and Segregation: https://www.pewresearch.org/religion/2021/06/29/religion-in-india-tolerance-and-segregation/ •How Indians View Gender Roles in Families and Society: https://www.pewresearch.org/religion/2022/03/02/how-indians-view-gender-roles-in-families-and-society/

This study aimed to investigate the genomic diversity and population structure in the Muslim community of Telangana, India, using 23 autosomal microsatellite genetic markers. We also examined genetic relatedness between Muslim and non-Muslim populations of India. A sample of 184 randomly selected unrelated healthy Muslim individuals from the Telangana state were included in this study. The genotyping of 23 autosomal STR markers included in PowerPlex® Fusion 6 C multiplex system (Promega)was done. A total of 273 alleles were observed in the studied population, and locus SE33 showed 37 observed alleles, which is the highest number of observed alleles among all the studied loci. Among all the studied loci the most polymorphic and discriminatory locus was SE33, with the values of polymorphic information content (PIC) = 9.411E–01 and power of discrimination (PD) = 9.865E–01. Observed heterozygosity ranged from 6.630E–01 (D22S1045) to 9.239E–01 (SE33). Discrimination power, exclusion power, matching probability and paternity index for all the studied loci were 1.00E + 00, 1.00E + 00, 2.01E–28, and 5.68E + 09, respectively. The studied Muslim population showed genetic relatedness with non-Muslim populations i.e. populations of central India, Jharkhand, and Uttar Pradesh, suggesting the conversion of Hindus during the Muslim invasion. Neighbor-joining (NJ) tree and principal component analysis (PCA) revealed that the studied population showed genetic affinity with communities of Jharkhand, Madhya Pradesh and Uttar Pradesh states. The genetic data of this study may be useful for forensic, medical, and anthropological studies.

Comprehensive YouTube channel statistics for Indian Muslim, featuring 1,650,000 subscribers and 86,248,328 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Education category and is based in IN. Track 311 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

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.

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

The dataset contain the primary census abstract categorised by religion in Assam. The list contains different religions including Hindu, Buddhist, Christian, Muslim, Jain, Sikh etc.. along with the region specifying whether it is urban or rural. The data is of the 2011 census.

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.