In 2007, the Pew Research Center conducted the "/data-archive?fid=MUSLIMS" Target="_blank">first-ever nationwide survey of Muslim Americans. As the 10th anniversary of 9/11 approached, it seemed an appropriate time to survey Muslim Americans again and take stock of any important changes in the attitudes, opinions and experiences of this growing segment of U.S. society. The 2011 survey repeats many key questions from the 2007 poll. It also closely follows the methodology of the previous survey, including the use of random-digit-dialing to screen a large number of households (more than 41,000) to obtain a representative national sample of Muslims. As in 2007, interviews were conducted not only in English but also in Arabic, Urdu and Farsi, helping to ensure coverage of parts of the heavily immigrant Muslim American population that could be missed by an English-only survey.

The Pew Research Center study was able to complete interviews with 1,033 Muslim American adults 18 years old and older from a probability sample consisting of three sampling frames. Interviews were conducted by telephone between April 14 and July 22, 2001 by the research firm Abt SRBI.

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...

This is the third national probability survey of American Muslims conducted by Pew Research Center (the first was conducted in "https://www.thearda.com/data-archive?fid=MUSLIMS" Target="_blank">2007, the second in "https://www.thearda.com/data-archive?fid=MUSAM11" Target="_blank">2011). Results from this study were published in the "https://www.pewresearch.org/" Target="_blank">Pew Research Center report '"https://www.pewresearch.org/religion/2017/07/26/findings-from-pew-research-centers-2017-survey-of-us-muslims/" Target="_blank">U.S. Muslims Concerned About Their Place in Society, but Continue to Believe in the American Dream.' The report is included in the materials that accompany the public-use dataset.

The survey included interviews with 1,001 adult Muslims living in the United States. Interviewing was conducted from January 23 to May 2, 2017, in English, Arabic, Farsi and Urdu. The survey employed a complex design to obtain a probability sample of Muslim Americans. Before working with the dataset, data analysts are strongly encouraged to carefully review the 'Survey Methodology' section of the report.

In addition to the report, the materials accompanying the public-use dataset also include the survey questionnaire, which reports the full details on question wording. Data users should treat the questionnaire (and not this codebook) as the authoritative reflection of question wording and order.

In the aftermath of the attacks on September 11, 2001, and subsequent terrorist attacks elsewhere around the world, a key counterterrorism concern was the possible radicalization of Muslims living in the United States. The purpose of the study was to examine and identify characteristics and practices of four American Muslim communities that have experienced varying levels of radicalization. The communities were selected because they were home to Muslim-Americans that had experienced isolated instances of radicalization. They were located in four distinct regions of the United States, and they each had distinctive histories and patterns of ethnic diversity. This objective was mainly pursued through interviews of over 120 Muslims located within four different Muslim-American communities across the country (Buffalo, New York; Houston, Texas; Seattle, Washington; and Raleigh-Durham, North Carolina), a comprehensive review of studies an literature on Muslim-American communities, a review of websites and publications of Muslim-American organizations and a compilation of data on prosecutions of Muslim-Americans on violent terrorism-related offenses.

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...

SSRS conducted a survey of Muslims, Jews, and the general population for the "https://www.ispu.org/" Target="_blank">Institute for Social Policy and Understanding from March 17 through April 22, 2020. The study investigated the opinions of Muslims, Jews, and the general population regarding politics, important issues facing the country, faith customs, and religious discrimination.

For the survey, SSRS interviewed 801 Muslim respondents, 351 Jewish respondents, and 1,015 general population adult respondents. A total of 2,167 respondents were surveyed.

In 2007, the Pew Research Center conducted what is believed to be the first-ever national telephone survey of a probability sample of Muslim Americans, a rare, dispersed, and highly diverse population. The study examined the political and social values, religious beliefs and practices, and life experiences of Muslims living in the U.S. today. The survey also contrasts the views of the Muslim population as a whole with those of the U.S. general population, and with the attitudes of Muslims all around the world, including Western Europe. Finally, findings from the survey make important contributions to the debate over the total size of the Muslim American population.

In this manuscript, we review the literature to date on Muslims’ descriptive and substantive representation in American politics. We then evaluate how Members of Congress discussed Muslims from 2011-2017 by turning to their tweets during this time period. We find that Muslims were most discussed by non-White Democratic legislators, and contrary to expectations, White Republicans tweeted about Muslims far less than their White Democratic counterparts. But when White Republicans did mention Muslims, their tweets were much more negative in tone than Democrats of any racial background.

This dataset is about book subjects. It has 5 rows and is filtered where the books is Muslim American women on campus : undergraduate social life and identity. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

These are the Stata and R data and replication code for "The Media Matters: Muslim American Portrayals and the Effects on Mass Attitudes."

The Cellular Towers in USA dataset is a collection of data that provides information about cellular towers located in the United States. The dataset includes information about the location, ownership, and technical specifications of over 47,000 cellular towers.

Each record in the dataset contains the following information:

• Object ID
• Unique System ID
• Licensee
• Latitude
• Longitude
• Structure Type
• Location Address
• Location City
• Location State
• URL

This dataset can be useful for a variety of purposes, including:

• Analyzing the coverage and quality of cellular networks in the United States
• Identifying areas with a high density of cellular towers
• Evaluating the market share of different cellular tower owners

This dataset is about books. It has 1 row and is filtered where the book is Burqas, baseball, and apple pie : being Muslim in America. It features 5 columns: author, publication date, book publisher, and BNB id.

Spain has a long history of Islamic tradition under its belt. From cuisine to architecture, the southern European country has been linked to the North of Africa through many common elements. At the end of 2023, there were approximately 2.41 million Muslims in Spain, most of them of Spanish and Moroccan nationality, with upwards of eight hundred thousand believers in both cases. With a Muslim population of more than 660,000 people, Catalonia was home to the largest Muslim community in Spain as of the same date.

The not so Catholic Spain

Believers of a religion other than Catholicism accounted for approximately 3 percent of the Spanish population, according to the most recent data. Although traditionally a Catholic country, Spain saw a decline in the number of believers over the past years. Compared to previous years, when the share of believers accounted for slightly over 70 percent of the Spanish population, the Catholic community lost ground, while still being the major religion for the foreseable future.

A Catholic majority, a practicing minority

Going to mass is no longer a thing in Spain, or so it would seem when looking at the latest statistics about the matter: 50 percent of those who consider themselves Catholics almost never attend any religious service in 2024. The numbers increased until 2019, from 55.5 percent of the population never attending religious services in 2011 to 63.1 percent in 2019. The share of population that stated to be practicing believers and go to mass every Sunday and on the most important holidays accounted for only 15.5 percent.

This dataset is about book subjects. It has 3 rows and is filtered where the books is American heretics : Catholics, Jews, Muslims, and the history of religious intolerance. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Replication data and code for "Religion or Race? Using Intersectionality to Examine the Role of Muslim Identity and Evaluations on Belonging in the United States." All variables used in main text and supplemental materials included.

Comprehensive dataset containing 11 verified Indian Muslim restaurant businesses in New York, United States with complete contact information, ratings, reviews, and location data.

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

In January 2024, the ARDA added 21 religious tradition (RELTRAD) variables to this dataset. These variables start at variable #8 (TOTCNG_2020). Categories were assigned based on pages 88-94 in the original "https://www.usreligioncensus.org/index.php/node/1638" Target="_blank">2020 U.S. Religion Census Report.

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

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

Comprehensive dataset containing 6 verified Indian Muslim restaurant businesses in Ohio, United States with complete contact information, ratings, reviews, and location data.

To fully understand U.S. Muslims' experiences in comparison to other U.S. groups, we need extensive data from both Muslims and non-Muslims, along with detailed questioning on a broad range of sociopolitical issues. Fortunately, the 2020 CMPS offers a unique chance to dive deeper into the experiences of U.S. Muslims compared to other minoritized groups, and assess their experiences within American democracy. This article highlights several lessons in best practices from the CMPS Muslim oversample.