33 datasets found
  1. World's Muslims Data Set, 2012

    • thearda.com
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    James Bell, World's Muslims Data Set, 2012 [Dataset]. http://doi.org/10.17605/OSF.IO/C2VE5
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    Dataset provided by
    Association of Religion Data Archives
    Authors
    James Bell
    Dataset funded by
    The Pew Charitable Trusts
    The John Templeton Foundation
    Description

    "Between October 2011 and November 2012, Pew Research Center, with generous funding from The Pew Charitable Trusts and the John Templeton Foundation, conducted a public opinion survey involving more than 30,000 face-to-face interviews in 26 countries in Africa, Asia, the Middle East and Europe. The survey asked people to describe their religious beliefs and practices, and sought to gauge respondents; knowledge of and attitudes toward other faiths. It aimed to assess levels of political and economic satisfaction, concerns about crime, corruption and extremism, positions on issues such as abortion and polygamy, and views of democracy, religious law and the place of women in society.

    "Although the surveys were nationally representative in most countries, the primary goal of the survey was to gauge and compare beliefs and attitudes of Muslims. The findings for Muslim respondents are summarized in the Religion & Public Life Project's reports The World's Muslims: Unity and Diversity and The World's Muslims: Religion, Politics and Society, which are available at www.pewresearch.org. [...] This dataset only contains data for Muslim respondents in the countries surveyed. Please note that this codebook is meant as a guide to the dataset, and is not the survey questionnaire." (2012 Pew Religion Worlds Muslims Codebook)

  2. Religious Populations Worldwide

    • kaggle.com
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    Updated Dec 8, 2023
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    The Devastator (2023). Religious Populations Worldwide [Dataset]. https://www.kaggle.com/datasets/thedevastator/religious-populations-worldwide
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    zip(481071 bytes)Available download formats
    Dataset updated
    Dec 8, 2023
    Authors
    The Devastator
    Description

    Religious Populations Worldwide

    Religious Populations Worldwide by Year and Category

    By Throwback Thursday [source]

    About this dataset

    The dataset includes data on Christianity, Islam, Judaism, Buddhism, Hinduism, Sikhism, Shintoism, Baha'i Faith, Taoism, Confucianism, Jainism and various other syncretic and animist religions. For each religion or denomination category, it provides both the total population count and the percentage representation in relation to the overall population.

    Additionally, - Columns labeled with Population provide numeric values representing the total number of individuals belonging to a particular religion or denomination. - Columns labeled with Percent represent numerical values indicating the percentage of individuals belonging to a specific religion or denomination within a given population. - Columns that begin with ** indicate primary categories (e.g., Christianity), while columns that do not have this prefix refer to subcategories (e.g., Christianity - Roman Catholics).

    In addition to providing precise data about specific religions or denominations globally throughout multiple years,this dataset also records information about geographical locations by including state or country names under StateNme.

    This comprehensive dataset is valuable for researchers seeking information on global religious trends and can be used for analysis in fields such as sociology, anthropology studies cultural studies among others

    How to use the dataset

    Introduction:

    • Understanding the Columns:

    • Year: Represents the year in which the data was recorded.

    • StateNme: Represents the name of the state or country for which data is recorded.

    • Population: Represents the total population of individuals.

    • Total Religious: Represents the total percentage and population of individuals who identify as religious, regardless of specific religion.

    • Non Religious: Represents the percentage and population of individuals who identify as non-religious or atheists.

    • Identifying Specific Religions: The dataset includes columns for different religions such as Christianity, Judaism, Islam, Buddhism, Hinduism, etc. Each religion is further categorized into specific denominations or types within that religion (e.g., Roman Catholics within Christianity). You can find relevant information about these religions by focusing on specific columns related to each one.

    • Analyzing Percentages vs. Population: Some columns provide percentages while others provide actual population numbers for each category. Depending on your analysis requirement, you can choose either column type for your calculations and comparisons.

    • Accessing Historical Data: The dataset includes records from multiple years allowing you to analyze trends in religious populations over time. You can filter data based on specific years using Excel filters or programming languages like Python.

    • Filtering Data by State/Country: If you are interested in understanding religious populations in a particular state or country, use filters to focus on that region's data only.

    Example - Extracting Information:

    Let's say you want to analyze Hinduism's growth globally from 2000 onwards:

    • Identify Relevant Columns:
    • Year: to filter data from 2000 onwards.
    • Hindu - Total (Percent): to analyze the percentage of individuals identifying as Hindus globally.

    • Filter Data:

    • Set a filter on the Year column and select values greater than or equal to 2000.

    • Look for rows where Hindu - Total (Percent) has values.

    • Analyze Results: You can now visualize and calculate the growth of Hinduism worldwide after filtering out irrelevant data. Use statistical methods or graphical representations like line charts to understand trends over time.

    Conclusion: This guide has provided you with an overview of how to use the Rel

    Research Ideas

    • Comparing religious populations across different countries: With data available for different states and countries, this dataset allows for comparisons of religious populations across regions. Researchers can analyze how different religions are distributed geographically and compare their percentages or total populations across various locations.
    • Studying the impact of historical events on religious demographics: Since the dataset includes records categorized by year, it can be used to study how historical events such as wars, migration, or political changes have influenced religious demographics over time. By comparing population numbers before and after specific events, resea...
  3. Data set: 50 Muslim-majority countries and 50 richest non-Muslim countries...

    • figshare.com
    txt
    Updated Jun 1, 2023
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    Ponn P Mahayosnand; Gloria Gheno (2023). Data set: 50 Muslim-majority countries and 50 richest non-Muslim countries based on GDP: Total number of COVID-19 cases and deaths on September 18, 2020 [Dataset]. http://doi.org/10.6084/m9.figshare.14034938.v2
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    txtAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Ponn P Mahayosnand; Gloria Gheno
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

  4. Global Religious Demographics

    • kaggle.com
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    Updated Dec 19, 2023
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    The Devastator (2023). Global Religious Demographics [Dataset]. https://www.kaggle.com/datasets/thedevastator/global-religious-demographics
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    zip(481071 bytes)Available download formats
    Dataset updated
    Dec 19, 2023
    Authors
    The Devastator
    Description

    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...

  5. Islam & AI

    • kaggle.com
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    Updated Apr 9, 2024
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    Ali Zahid Raja (2024). Islam & AI [Dataset]. https://www.kaggle.com/datasets/alizahidraja/quran-nlp
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    zip(233249519 bytes)Available download formats
    Dataset updated
    Apr 9, 2024
    Authors
    Ali Zahid Raja
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

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

    Dataset Structure

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

    Motivation

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

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

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

    Looking forward to do something great with Quran & NLP

    مَنْ أَنصَارِىٓ إِلَى ٱللَّهِ

    Work till now

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

    Future Goals

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

    Important Note

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

    How To Contribute

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

  6. Dataset of Global Religious Composition Estimates for 2010 and 2020

    • pewresearch.org
    Updated 2025
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    Conrad Hackett; Marcin Stonawski; Yunping Tong; Stephanie Kramer; Anne Fengyan Shi (2025). Dataset of Global Religious Composition Estimates for 2010 and 2020 [Dataset]. http://doi.org/10.58094/vhrw-k516
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    Dataset updated
    2025
    Dataset provided by
    Pew Research Centerhttp://pewresearch.org/
    datacite
    Authors
    Conrad Hackett; Marcin Stonawski; Yunping Tong; Stephanie Kramer; Anne Fengyan Shi
    License

    https://www.pewresearch.org/about/terms-and-conditions/https://www.pewresearch.org/about/terms-and-conditions/

    Dataset funded by
    John Templeton Foundationhttp://templeton.org/
    Pew Charitable Trusts
    Description

    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."

  7. I

    India Census: Population: by Religion: Muslim: Urban

    • ceicdata.com
    Updated Apr 7, 2022
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    CEICdata.com (2022). India Census: Population: by Religion: Muslim: Urban [Dataset]. https://www.ceicdata.com/en/india/census-population-by-religion/census-population-by-religion-muslim-urban
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    Dataset updated
    Apr 7, 2022
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 1, 2001 - Mar 1, 2011
    Area covered
    India
    Variables measured
    Population
    Description

    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.

  8. Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A...

    • zenodo.org
    csv
    Updated Mar 13, 2024
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    Gunther Jikeli; Gunther Jikeli; Sameer Karali; Sameer Karali; Katharina Soemer; Katharina Soemer (2024). Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A Dataset for Machine Learning and Text Analytics [Dataset]. http://doi.org/10.5281/zenodo.10812805
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    csvAvailable download formats
    Dataset updated
    Mar 13, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Gunther Jikeli; Gunther Jikeli; Sameer Karali; Sameer Karali; Katharina Soemer; Katharina Soemer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

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

    Description

    The dataset is a product of a research project at Indiana University on biased messages on Twitter against ethnic and religious minorities. We scraped all live messages with the keywords "Asians, Blacks, Jews, Latinos, and Muslims" from the Twitter archive in 2020, 2021, and 2022.

    Random samples of 600 tweets were created for each keyword and year, including retweets. The samples were annotated in subsamples of 100 tweets by undergraduate students in Professor Gunther Jikeli's class 'Researching White Supremacism and Antisemitism on Social Media' in the fall of 2022 and 2023. A total of 120 students participated in 2022. They annotated datasets from 2020 and 2021. 134 students participated in 2023. They annotated datasets from the years 2021 and 2022. The annotation was done using the Annotation Portal (Jikeli, Soemer and Karali, 2024). The updated version of our portal, AnnotHate, is now publicly available. Each subsample was annotated by an average of 5.65 students per sample in 2022 and 8.32 students per sample in 2023, with a range of three to ten and three to thirteen students, respectively. Annotation included questions about bias and calling out bias.

    Annotators used a scale from 1 to 5 on the bias scale (confident not biased, probably not biased, don't know, probably biased, confident biased), using definitions of bias against each ethnic or religious group that can be found in the research reports from 2022 and 2023. If the annotators interpreted a message as biased according to the definition, they were instructed to choose the specific stereotype from the definition that was most applicable. Tweets that denounced bias against a minority were labeled as "calling out bias".

    The label was determined by a 75% majority vote. We classified “probably biased” and “confident biased” as biased, and “confident not biased,” “probably not biased,” and “don't know” as not biased.

    The stereotypes about the different minorities varied. About a third of all biased tweets were classified as general 'hate' towards the minority. The nature of specific stereotypes varied by group. Asians were blamed for the Covid-19 pandemic, alongside positive but harmful stereotypes about their perceived excessive privilege. Black people were associated with criminal activity and were subjected to views that portrayed them as inferior. Jews were depicted as wielding undue power and were collectively held accountable for the actions of the Israeli government. In addition, some tweets denied the Holocaust. Hispanic people/Latines faced accusations of being undocumented immigrants and "invaders," along with persistent stereotypes of them as lazy, unintelligent, or having too many children. Muslims were often collectively blamed for acts of terrorism and violence, particularly in discussions about Muslims in India.

    The annotation results from both cohorts (Class of 2022 and Class of 2023) will not be merged. They can be identified by the "cohort" column. While both cohorts (Class of 2022 and Class of 2023) annotated the same data from 2021,* their annotation results differ. The class of 2022 identified more tweets as biased for the keywords "Asians, Latinos, and Muslims" than the class of 2023, but nearly all of the tweets identified by the class of 2023 were also identified as biased by the class of 2022. The percentage of biased tweets with the keyword 'Blacks' remained nearly the same.

    *Due to a sampling error for the keyword "Jews" in 2021, the data are not identical between the two cohorts. The 2022 cohort annotated two samples for the keyword Jews, one from 2020 and the other from 2021, while the 2023 cohort annotated samples from 2021 and 2022.The 2021 sample for the keyword "Jews" that the 2022 cohort annotated was not representative. It 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 2021 sample for the keyword "Jews" that the 2023 cohort annotated was drawn proportionally for each trimester of 2021 for the keyword "Jews".

    Content

    Cohort 2022

    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.

    Cohort 2023

    The dataset contains 5363 tweets with the keywords “Asians, Blacks, Jews, Latinos and Muslims” from 2021 and 2022. 261 tweets (4.9 %) are labeled as biased, and 5102 tweets (95.1 %) were labeled as not biased. 975 tweets (18.1 %) were labeled as calling out or denouncing bias.

    1068 out of 5363 tweets (19.9 %) contain the keyword "Asians," 559 were posted in 2021 and 509 in 2022. 42 tweets (3.9 %) are biased against Asian people. 280 tweets (26.2 %) call out bias against Asians.

    1130 out of 5363 tweets (21.1 %) contain the keyword "Blacks," 586 were posted in 2021 and 544 in 2022. 76 tweets (6.7 %) are biased against Black people. 146 tweets (12.9 %) call out bias against Blacks.

    971 out of 5363 tweets (18.1 %) contain the keyword "Jews," 460 were posted in 2021 and 511 in 2022. 49 tweets (5 %) are biased against Jewish people. 201 tweets (20.7 %) call out bias against Jews.

    1072 out of 5363 tweets (19.9 %) contain the keyword "Latinos," 583 were posted in 2021 and 489 in 2022. 32 tweets (2.9 %) are biased against Latines. 108 tweets (10.1 %) call out bias against Latines.

    1122 out of 5363 tweets (20.9 %) contain the keyword "Muslims," 576 were posted in 2021 and 546 in 2022. 62 tweets (5.5 %) are biased against Muslims. 240 tweets (21.3 %) 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.

    ‘Cohort’: Represents the year the data was annotated (class of 2022 or class of 2023)

    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

  9. England and Wales Census 2021 - Religion by highest qualification level

    • statistics.ukdataservice.ac.uk
    xlsx
    Updated Mar 24, 2023
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    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service. (2023). England and Wales Census 2021 - Religion by highest qualification level [Dataset]. https://statistics.ukdataservice.ac.uk/dataset/england-and-wales-census-2021-religion-by-highest-qualification-level
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    xlsxAvailable download formats
    Dataset updated
    Mar 24, 2023
    Dataset provided by
    Northern Ireland Statistics and Research Agency
    Office for National Statisticshttp://www.ons.gov.uk/
    UK Data Servicehttps://ukdataservice.ac.uk/
    Authors
    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service.
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    England, Wales
    Description

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

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

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

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

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

    Quality notes can be found here

    Quality information about Education can be found here

    Religion

    The 8 ‘tickbox’ religious groups are as follows:

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

    No qualifications

    No qualifications

    Level 1

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

    Level 2

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

    Apprenticeship

    Apprenticeship

    Level 3

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

    Level 4 +

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

    Other

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

  10. Islam QA Dataset

    • kaggle.com
    zip
    Updated Aug 13, 2024
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    Md. Abdullah Al Mamun (2024). Islam QA Dataset [Dataset]. https://www.kaggle.com/datasets/mamun18/islam-qa-dataset/data
    Explore at:
    zip(1504297 bytes)Available download formats
    Dataset updated
    Aug 13, 2024
    Authors
    Md. Abdullah Al Mamun
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

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

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

  11. D

    Arab West Report 2004, Weeks 01-52: Insights into Muslim-Christian Relations...

    • ssh.datastations.nl
    pdf, zip
    Updated Jan 16, 2017
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    C. Hulsman; C. Hulsman (2017). Arab West Report 2004, Weeks 01-52: Insights into Muslim-Christian Relations and Interfaith Dialogue [Dataset]. http://doi.org/10.17026/DANS-Z45-MRUZ
    Explore at:
    pdf(159043), pdf(4956), pdf(143497), pdf(195047), pdf(71236), pdf(154014), pdf(141406), pdf(186031), pdf(125028), pdf(69241), pdf(73973), pdf(10674), pdf(117441), pdf(85068), pdf(110014), pdf(74867), pdf(87546), pdf(8919), pdf(133845), pdf(81638), pdf(139130), pdf(92908), pdf(75489), pdf(167343), pdf(260113), pdf(161149), pdf(144667), pdf(154353), pdf(108532), pdf(90795), pdf(215962), pdf(69065), pdf(129687), pdf(153102), pdf(141511), pdf(146346), zip(104687), pdf(132767), pdf(133815), pdf(17761), pdf(70850), pdf(85244), pdf(154558), pdf(64951), pdf(125732), pdf(89462), pdf(90945), pdf(86837), pdf(370623), pdf(118044), pdf(91190), pdf(105135), pdf(148669), pdf(83533), pdf(76428), pdf(82756), pdf(75522), pdf(80243), pdf(95429), pdf(87591), pdf(86999), pdf(7037), pdf(89276), pdf(77732), pdf(224327), pdf(84230), pdf(143559), pdf(7815), pdf(102487), pdf(82038), pdf(99911)Available download formats
    Dataset updated
    Jan 16, 2017
    Dataset provided by
    DANS Data Station Social Sciences and Humanities
    Authors
    C. Hulsman; C. Hulsman
    License

    https://doi.org/10.17026/fp39-0x58https://doi.org/10.17026/fp39-0x58

    Description

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

  12. f

    Data from: Genomic diversity of the Muslim population from Telangana (India)...

    • datasetcatalog.nlm.nih.gov
    • tandf.figshare.com
    Updated Oct 19, 2020
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    Kumawat, Ramkishan; Chaubey, Gyaneshwer; Rani, Hanumanth Surekha; Shrivastava, Pankaj; Srivastava, Varsha (2020). Genomic diversity of the Muslim population from Telangana (India) inferred from 23 autosomal STRs [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000482936
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    Dataset updated
    Oct 19, 2020
    Authors
    Kumawat, Ramkishan; Chaubey, Gyaneshwer; Rani, Hanumanth Surekha; Shrivastava, Pankaj; Srivastava, Varsha
    Area covered
    Telangana, India
    Description

    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.

  13. f

    Data from: A modest proposal for conducting future research on media...

    • figshare.com
    • data.niaid.nih.gov
    • +2more
    pdf
    Updated Dec 28, 2021
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    Harits Masduqi (2021). A modest proposal for conducting future research on media portrayals of Islam and Muslims in Indonesia [Dataset]. http://doi.org/10.6084/m9.figshare.16681825.v1
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    pdfAvailable download formats
    Dataset updated
    Dec 28, 2021
    Dataset provided by
    figshare
    Authors
    Harits Masduqi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Indonesia
    Description

    Recent issues on politics have been dominant in Indonesia that people are divided and become more intolerant of each other. Indonesia has the biggest Muslim population in the world and the role of Islam in Indonesian politics is significant. The current Indonesian government claim that moderate Muslims are loyal to the present political system while the opposing rivals who are often labelled’intolerant and radical Muslims’ by Indonesian mass media often disagree with the central interpretation of democracy in Indonesia. Studies on contributing factors and discourse strategies used in news and articles in secular and Islamic mass media which play a vital role in the construction of Muslim and Islamic identities in Indonesia are, therefore, recommended.

  14. g

    EVS - European Values Study 1990 - Integrated Dataset

    • search.gesis.org
    • da-ra.de
    Updated Nov 20, 2011
    + more versions
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    Zulehner, Paul M.; Dobbelaere, Karel; Kerkhofs, Jan; Raichev, Andrei; Stoichev, Kancho; Nevitte, Neil; Rehak, Jan; Gundelach, Peter; Riis, Ole; Saar, Andrus; Lotti, Leila; Pehkonen, Juhani; Riffault, Hélène; Klingemann, Hans-Dieter; Köcher, Renate; Barker, David; Harding, Stephen; Heald, Gordon; Timms, Noel; Hankiss, Elemer; Manchin, Robert; Jónsson, Fridrik H.; Fogarty, Michael; Kennedy, Kieran; Whelan, Chris; Gubert, Renzo; Capraro, Giuseppe; Zepa, Brigita; Alishauskiene, Rasa; Cachia-Caruana, Richard; Inganuez, Fr. Joe; Halman, Loek; Heunks, Felix; de Moor, Ruud; Listhaug, Ola; Jasinska-Kania, Aleksandra; Franca, Luis de; Vala, Jorge; Zamfir, Catalin; Tos, Niko; Elzo, Javier; Orizo, Francisco Andrés; Pettersson, Thorleif; Inglehart, Ronald (2011). EVS - European Values Study 1990 - Integrated Dataset [Dataset]. http://doi.org/10.4232/1.10790
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    application/x-spss-sav(24341244), application/x-stata-dta(19131359)Available download formats
    Dataset updated
    Nov 20, 2011
    Dataset provided by
    GESIS Data Archive
    GESIS search
    Authors
    Zulehner, Paul M.; Dobbelaere, Karel; Kerkhofs, Jan; Raichev, Andrei; Stoichev, Kancho; Nevitte, Neil; Rehak, Jan; Gundelach, Peter; Riis, Ole; Saar, Andrus; Lotti, Leila; Pehkonen, Juhani; Riffault, Hélène; Klingemann, Hans-Dieter; Köcher, Renate; Barker, David; Harding, Stephen; Heald, Gordon; Timms, Noel; Hankiss, Elemer; Manchin, Robert; Jónsson, Fridrik H.; Fogarty, Michael; Kennedy, Kieran; Whelan, Chris; Gubert, Renzo; Capraro, Giuseppe; Zepa, Brigita; Alishauskiene, Rasa; Cachia-Caruana, Richard; Inganuez, Fr. Joe; Halman, Loek; Heunks, Felix; de Moor, Ruud; Listhaug, Ola; Jasinska-Kania, Aleksandra; Franca, Luis de; Vala, Jorge; Zamfir, Catalin; Tos, Niko; Elzo, Javier; Orizo, Francisco Andrés; Pettersson, Thorleif; Inglehart, Ronald
    License

    https://www.gesis.org/en/institute/data-usage-termshttps://www.gesis.org/en/institute/data-usage-terms

    Variables measured
    weight_g - weight, year - survey year, cntry_y - country_year, country - country code, q716 - sex respondent (Q716), c_abrv - country abbreviation, version - GESIS archive version, language - language of interview, q480 - opinion on society (Q480), q719b - age: respondent (Q 717b), and 380 more
    Description

    The online overview offers comprehensive metadata on the EVS datasets and variables.

    The variable overview of the four EVS waves 1981, 1990, 1999/2000, and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

    This overview can be found at: Online Variable Overview.

    Moral, religious, societal, political, work, and family values of Europeans.

    Replication of the EVS survey carried out in 1981.

    Themes: Important areas in life; feeling of happiness; state of health; ever felt: very excited or interested, restless, proud, lonely, pleased, bored, depressed, upset because of criticism; respect and love for parents; important child qualities: good manners, independance, hard work, felling of responsibility, imaginantion, tolerance, saving money, determination perseverance, religious faith, unselfishness, obedience; attitude towards abortion; frequency of political discussions; opinion leader; volentary engagement in: welfare service for elderly, education, labour unions, political parties, local political action, human rights, environment, animal rights, professional associations, youth work, sports, women´s group, peace movement, health group; reasons for voluntary work; characterisation of neighbourhood: people with a ciminal record, of a different race, heavy drinkers, emotionally unstable people, Muslims, Hindus, immigrants or foreign workers, people with AIDS, drug addicts, homosexuals, jews, left-wing or right-wing extremists, people with large families; general confidence; satisfaction with life; freedom of choice and control; willingness to give part of income for better environment; environment talks make people anxios; priority: for men, demestic people, able bodied and younger persons in case of scarce job situation; satisfaction with financial situation of the household; important values at work: good pay, not too much pressure, job security, a respected job, good hours, opportunity to use initiative, generous holidays, responsibility, interesting job, a job that meets one´s abilities, pleasant people, chances for promotion, useful job for society, meeting people; pride in one´s work; job satisfaction; freedom of decision taking in job; job orientation; fair payment; preferred management type; attitude towards following instructions at work; satisfaction with home life; sharing attitudes with partner and parents: towards religion, moral standards, social attitudes, polititcal views, sexual attitudes; ideal number of children; child needs a home with father and mother; a woman has to have children to be fulfilled; marriage as an out-dated institution; woman as a single parent; enjoy sexual freedom; important values for a successful marriage: faithfulness, adequate income, same social background, respect and appreciation, religious beliefs, good housing, agreement on politics, understanding and tolerance, apart from in-laws, happy sexual relationship, sharing household chores, children, taste and interests in common; gender role in job and family; willingness to fight for the own country; expected future changes of values; opinion about scientific advances; interest in politics; political action: signing a petition, joining in boycotts, attending lawful demonstrations, joining unofficial strikes and occupying buildings or factories; prefence for freedom or equality; self-positioning on a left-right scale; basic kinds of attitudes concerning society and economic system; income equality; wealth accumulation; idea of welfare state preferred aims of society and politics; postmaterialism; personal characteristics; conservatism and need for change in politics and economic system; confidence in institutions: churches, armed forces, education system, the press, labour unions, the police, parliament, the civil services, social secure system, major companies EU, NATO and the justice system; approval of: ecology movement, anti-nuclear energy movement, disarmament movement, human rights movement, women´s movement and anti-apartheid movement; party preference and identification; reasons for people living in need; opinion on terrorism; thinking about meaning and purpose of life; feeling that life is meaningless; thoughts about dead; attitude towards good and evil and religion and truth; religious denomination; former religious denomination; church attendence; raised religiously; importance of reli...

  15. m

    Data from: Dataset on the acceptance of Islamic microfinance in Kano state,...

    • data.mendeley.com
    Updated Feb 11, 2021
    + more versions
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    Surajo Musa Yakubu (2021). Dataset on the acceptance of Islamic microfinance in Kano state, Nigeria [Dataset]. http://doi.org/10.17632/pyvnbdsvgc.1
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    Dataset updated
    Feb 11, 2021
    Authors
    Surajo Musa Yakubu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Nigeria, Kano
    Description

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

  16. England and Wales Census 2021 - Religion by economic activity status and...

    • statistics.ukdataservice.ac.uk
    xlsx
    Updated Mar 24, 2023
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    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service. (2023). England and Wales Census 2021 - Religion by economic activity status and occupation [Dataset]. https://statistics.ukdataservice.ac.uk/dataset/england-and-wales-census-2021-religion-by-economic-activity-status-and-occupation
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Mar 24, 2023
    Dataset provided by
    Northern Ireland Statistics and Research Agency
    Office for National Statisticshttp://www.ons.gov.uk/
    UK Data Servicehttps://ukdataservice.ac.uk/
    Authors
    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service.
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    England, Wales
    Description

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

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

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

    This dataset shows population counts for usual residents aged between 16 to 64 years old only. This is to focus on religious affiliation differences among the working age. Population counts in these tables may be different from other publications which use different age breakdowns.

    Quality notes can be found here

    Quality information about Labour Market can be found here

    The Standard Occupation Classification 2020 code used can be found here

    Religion

    The 8 ‘tickbox’ religious groups are as follows:

    • Buddhist
    • Christian
    • Hindu
    • Jewish
    • Muslim
    • No religion
    • Sikh
    • Other religion
  17. g

    European Values Study 2008: Integrated Dataset (EVS 2008)

    • search.gesis.org
    Updated Jun 8, 2022
    + more versions
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    Gedeshi, Ilir; Zulehner, Paul M.; Rotman, David; Swyngedouw, Marc; Voyé, Liliane; Fotev, Georgy; Baloban, Josip; Roudometof, Victor; Rabusic, Ladislav; Gundelach, Peter; Saar, Andrus; Pehkonen, Juhani; Tchernia, Jean-François; Pachulia, Merab; Jagodzinski, Wolfgang; Voas, David; Gari, Aikaterini; Rosta, Gergely; Jónsson, Fridrik H.; Breen, Michael; Rovati, Giancarlo; Zepa, Brigita; Ziliukaite, Ruta; Hausman, Pierre; Petkovska, Antoanela; Troisi, Joseph; Petruti, Doru; Besic, Milos; European Values Study; Halman, Loek; Smith, Alan; Listhaug, Ola; Jasinska-Kania, Aleksandra; Vala, Jorge; Voicu, Malina; Bashkirova, Elena; Gredelj, Stjepan; Kusá, Zuzana; Tos, Niko; Silvestre Cabrera, María; Lundasen, Susanne; Joye, Dominique; Esmer, Yilmaz; Balakireva, Olga (2022). European Values Study 2008: Integrated Dataset (EVS 2008) [Dataset]. http://doi.org/10.4232/1.13841
    Explore at:
    (13535183), (10522392)Available download formats
    Dataset updated
    Jun 8, 2022
    Dataset provided by
    GESIS
    GESIS search
    Authors
    Gedeshi, Ilir; Zulehner, Paul M.; Rotman, David; Swyngedouw, Marc; Voyé, Liliane; Fotev, Georgy; Baloban, Josip; Roudometof, Victor; Rabusic, Ladislav; Gundelach, Peter; Saar, Andrus; Pehkonen, Juhani; Tchernia, Jean-François; Pachulia, Merab; Jagodzinski, Wolfgang; Voas, David; Gari, Aikaterini; Rosta, Gergely; Jónsson, Fridrik H.; Breen, Michael; Rovati, Giancarlo; Zepa, Brigita; Ziliukaite, Ruta; Hausman, Pierre; Petkovska, Antoanela; Troisi, Joseph; Petruti, Doru; Besic, Milos; European Values Study; Halman, Loek; Smith, Alan; Listhaug, Ola; Jasinska-Kania, Aleksandra; Vala, Jorge; Voicu, Malina; Bashkirova, Elena; Gredelj, Stjepan; Kusá, Zuzana; Tos, Niko; Silvestre Cabrera, María; Lundasen, Susanne; Joye, Dominique; Esmer, Yilmaz; Balakireva, Olga
    License

    https://www.gesis.org/en/institute/data-usage-termshttps://www.gesis.org/en/institute/data-usage-terms

    Time period covered
    Mar 27, 2008 - Mar 15, 2010
    Variables measured
    weight_g - weight, year - survey year, cntry_y - country_year, country - country code, intno - interviewer number, studyno - GESIS study number, c_abrv - country abbreviation, doi - digital object identifier, version - GESIS archive version, v25 - do you belong to: none (Q5a), and 122 more
    Description

    The European Values Study is a large-scale, cross-national and longitudinal survey research program on how Europeans think about family, work, religion, politics, and society. Repeated every nine years in an increasing number of countries, the survey provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe.

    The EVS 2008 wave maintains a persistent focus on a broad range of values. Questions are highly comparable across waves and regions, making EVS suitable for studying trends over time. A significant improvement in this fourth wave is the rich set of socio-demographic background variables added to the questionnaire, facilitating far-reaching analyses of the determinants of values.

    Moral, religious, societal, political, work, and family values of Europeans.

    Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; frequency of political discussions with friends; happiness; self-assessment of own health; memberships and unpaid work (volunteering) in: social welfare services, religious or church organisations, education, or cultural activities, labour unions, political parties, local political actions, human rights, environmental or peace movement, professional associations, youth work, sports clubs, women´s groups, voluntary associations concerned with health or other groups; tolerance towards minorities (people with a criminal record, of a different race, left/right wing extremists, alcohol addicts, large families, emotionally unstable people, Muslims, immigrants, AIDS sufferers, drug addicts, homosexuals, Jews, gypsies and Christians - social distance); trust in people; estimation of people´s fair and helpful behaviour; internal or external control; satisfaction with life.

    1. Work: reasons for people to live in need; importance of selected aspects of occupational work; employment status; general work satisfaction; freedom of decision-taking in the job; importance of work (work ethics, scale); important aspects of leisure time; attitude towards following instructions at work without criticism (obedience work); give priority to nationals over foreigners as well as men over women in jobs.

    2. Religion: individual or general clear guidelines for good and evil; religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; importance of religious celebration at birth, marriage, and funeral; self-assessment of religiousness; churches give adequate answers to moral questions, problems of family life, spiritual needs and social problems of the country; belief in God, life after death, hell, heaven, sin and re-incarnation; personal God versus spirit or life force; own way of connecting with the divine; interest in the sacred or the supernatural; attitude towards the existence of one true religion; importance of God in one´s life (10-point-scale); experience of comfort and strength from religion and belief; moments of prayer and meditation; frequency of prayers; belief in lucky charms or a talisman (10-point-scale); attitude towards the separation of church and state.

    3. Family and marriage: most important criteria for a successful marriage (scale); attitude towards childcare (a child needs a home with father and mother, a woman has to have children to be fulfilled, marriage is an out-dated institution, woman as a single-parent); attitude towards marriage, children, and traditional family structure (scale); attitude towards traditional understanding of one´s role of man and woman in occupation and family (scale); attitude towards: respect and love for parents, parent´s responsibilities for their children and the responsibility of adult children for their parents when they are in need of long-term care; importance of educational goals; attitude towards abortion.

    4. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale); self-responsibility or governmental provision; free decision of job-taking of the unemployed or no permission to refuse a job; advantage or harmfulness of competition; liberty of firms or governmental control; equal incomes or incentives for individual efforts; attitude concerning capitalism versus government ownership; postmaterialism (scale); expectation of future development (less emphasis on...

  18. England and Wales Census 2021 - Religion by general health, disability and...

    • statistics.ukdataservice.ac.uk
    xlsx
    Updated Mar 24, 2023
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    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service. (2023). England and Wales Census 2021 - Religion by general health, disability and unpaid care [Dataset]. https://statistics.ukdataservice.ac.uk/dataset/england-and-wales-census-2021-religion-by-general-health-disability-and-unpaid-care
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    xlsxAvailable download formats
    Dataset updated
    Mar 24, 2023
    Dataset provided by
    Northern Ireland Statistics and Research Agency
    Office for National Statisticshttp://www.ons.gov.uk/
    UK Data Servicehttps://ukdataservice.ac.uk/
    Authors
    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service.
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    England, Wales
    Description

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

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

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

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

    Quality notes can be found here

    Religion

    The 8 ‘tickbox’ religious groups are as follows:

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

    General health

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

    Disability

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

    Unpaid care

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

  19. Radical Islamic Terrorism Attacks 2015-2019

    • kaggle.com
    zip
    Updated Aug 26, 2019
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    ma7555 (2019). Radical Islamic Terrorism Attacks 2015-2019 [Dataset]. https://www.kaggle.com/ma7555/radical-islamic-terrorism-attacks-20152019
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    zip(306073 bytes)Available download formats
    Dataset updated
    Aug 26, 2019
    Authors
    ma7555
    Description

    Context

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

    Content

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

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

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

    Acknowledgements

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

    Inspiration

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

  20. COVID-19 vaccination rates and odds ratios by socio-demographic group

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Jun 10, 2021
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    Office for National Statistics (2021). COVID-19 vaccination rates and odds ratios by socio-demographic group [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthinequalities/datasets/covid19vaccinationratesandoddsratiosbysociodemographicgroup
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    xlsxAvailable download formats
    Dataset updated
    Jun 10, 2021
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Vaccination rates and odds ratios by socio-demographic group among people living in England.

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James Bell, World's Muslims Data Set, 2012 [Dataset]. http://doi.org/10.17605/OSF.IO/C2VE5
Organization logo

World's Muslims Data Set, 2012

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101 scholarly articles cite this dataset (View in Google Scholar)
Dataset provided by
Association of Religion Data Archives
Authors
James Bell
Dataset funded by
The Pew Charitable Trusts
The John Templeton Foundation
Description

"Between October 2011 and November 2012, Pew Research Center, with generous funding from The Pew Charitable Trusts and the John Templeton Foundation, conducted a public opinion survey involving more than 30,000 face-to-face interviews in 26 countries in Africa, Asia, the Middle East and Europe. The survey asked people to describe their religious beliefs and practices, and sought to gauge respondents; knowledge of and attitudes toward other faiths. It aimed to assess levels of political and economic satisfaction, concerns about crime, corruption and extremism, positions on issues such as abortion and polygamy, and views of democracy, religious law and the place of women in society.

"Although the surveys were nationally representative in most countries, the primary goal of the survey was to gauge and compare beliefs and attitudes of Muslims. The findings for Muslim respondents are summarized in the Religion & Public Life Project's reports The World's Muslims: Unity and Diversity and The World's Muslims: Religion, Politics and Society, which are available at www.pewresearch.org. [...] This dataset only contains data for Muslim respondents in the countries surveyed. Please note that this codebook is meant as a guide to the dataset, and is not the survey questionnaire." (2012 Pew Religion Worlds Muslims Codebook)

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