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

"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)

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...

The Carnegie Middle East Governance and Islam Dataset was created by "https://lsa.umich.edu/polisci/people/faculty/tessler.html" Target="_blank">Mark Tessler at the "https://umich.edu/" Target="_blank">University of Michigan. The data set includes both individual-level and country-level variables. Data on individual-level variables are drawn from 35 surveys carried out in 12 Arab countries, Turkey and Iran. Most of the surveys were carried out either as the first wave of the "https://www.arabbarometer.org/" Target="_blank">Arab Barometer, the third, fourth and fifth waves of the "https://www.worldvaluessurvey.org/wvs.jsp" Target="_blank">World Values Survey, or a project on attitudes related to governance carried out by Mark Tessler with funding from the "https://www.nsf.gov/" Target="_blank">National Science Foundation.

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!

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

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

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

This dataset contains a collection of images showcasing some of the world's most popular mosques. With a total of six folders, each dedicated to a specific mosque, this dataset provides a diverse visual exploration of these architectural wonders.

Data Collection I scraped the data from different websites using Selenium and Beautiful Soup then I filtered them to select suitable images. You can look at the scraping codes here.

Folders: - Al-Haram Mosque, Mecca 🇸🇦 - Prophet's Mosque, Madinah 🇸🇦 - Al-Aqsa Mosque, Jerusalem 🇵🇸 - Al-Azhar Mosque, Cairo 🇪🇬 - Umayyad Mosque, Damascus 🇸🇾 - Qolsharif, Kazan 🇷🇺

Contents: - High-resolution images capturing the intricate details and grandeur of each mosque. - Diverse angles and perspectives showcasing the architecture, interiors, and surroundings. - Aesthetic shots capturing the spiritual essence and cultural significance of these landmarks.

Potential Use Cases: - Research and analysis in the fields of architecture, art, and cultural studies. - Educational materials for students learning about Islamic architecture and history. - Visual content for travel guides, tourism promotions, and cultural exhibitions. - Explore the beauty and diversity of these iconic mosques through this comprehensive image dataset. - Implement ML algorithms to classify the mosques.

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.

The CIDII dataset is a binary classification, consisting of two classes of correct information and disinformation related to Islamic issues. The CIDII dataset belongs to our research (DISINFORMATION DETECTION ABOUT ISLAMIC ISSUES ON SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES) published in MJCS journal in the link below: https://ejournal.um.edu.my/index.php/MJCS/article/view/41935 This dataset consists of five columns: 1- ID: Each article has a unique ID. 2- Article: The article contains text that is either facts related to Islamic issues if the information is correct, or posts targeting the Islamic religion if the information is false. Most posts contain only the body without a title. 3- Propagation Source: The source refers to the source of the article content, as it contains a Facebook link in the event that the post is disinformation, or it contains a link to Islamic websites in the event that the article refers to correct information (an explanation of a verse, a hadith, or an article related to the Islamic religion). 4- Article Type: This column contains the type of article published. Is it a post if the article is disinformation, or is it an Islamic article, a Quranic interpretation, or a hadith, in case of that the information is correct? 5- Class Type: This column shows whether the article belongs to the category of correct information or disinformation.

This dataset contains the Arab-West Report special reports that were published in 2007. This dataset mainly contains the writings of Cornelis Hulsman, Drs., among other authors on topics related to Muslim- Christian relations and interfaith dialogue. Additionally, this dataset features certain reports related to the Christian faith in Egypt, Monastic life and Coptic traditions.Some of the articles address the media coverage of Muslim-Christian incidents and sectarian tensions, and how biased media reporting can often exacerbate existing tensions between groups. These articles feature a number of interviews conducted by Arab West Report with prominent social figures and scholars.Additionally, reports from this dataset discuss conversion cases and interfaith meetings that were held at the time. This dataset also contains media critique from Arab West Report Editor-in-Chief Cornelis Hulsman, Drs.Some of the themes that characterize this dataset include:- Authors report on their trip to see the celebration of the Holy Family crossing the Nile River in a village in Upper Egypt. They reflect on their experiences and the need to improve dialogue between Muslims and Christians in Egypt.- An overview of a forum organized by the Center for Civilizational Studies and Dialogue between Cultures at Cairo University to introduce a book written by Father Christian van Nispen, sj, entitled, ‘Christians and Muslims: Brothers before God.’ van Nispen’s principle argument is that both Muslims and Christians worship one and the same God, but according to different understandings.- Another report highlighted the second conference on bias, entitled: ‘The International Conference for Dialogue between Civilizations and the Different Tracks of Knowledge.’ The 4-day conference, was sponsored by the Program for Civilizational Studies and Dialogue between Cultures at Cairo University, and the International Institute of Islamic Thought.- The Arab West Report annual report: The Center for Arab-West Understanding presents its annual report for 2006.Media critique:- “Minister Of Awqaf Dr. Hamdi Zakzouk Falsely Accused Of Calling For The Death Penalty For Apostates From Islam”: Arab-West Report responds to media claims that Dr. Hamdi Zakzouk called for the death penalty for apostates from Islam.-In another report, the authors stress that misguided media reporting often only serves to further tensions, particularly in cases of sectarian strife. Another article presents the transcript of a lecture for the Arab Thought Forum. It considers media distortions and mis-representation in the media that only serve to further antagonize Muslim-Christian relations and the perception of Islam / the Arab world in the West. Cornelis Hulsman, Drs., explains the role of the Center for Arab West Understanding, and the importance of constructive, unbiased, and fully researched journalism.-Hulsman stressed in one of his articles that media frequently manipulate headlines in an effort to present stories in the context they desire. Headlines are also frequently sensationalized in an effort to attract a larger number of readers, but if this also distorts a story this should be questioned. Cornelis Hulsman, Drs., stresses the danger of ignorant media reporting, and the damage that inaccurate fact-checking can cause. He provides a number of examples from various intellectuals, commenting on stories that have been sensationalized in the media, and the negative effects this reporting had on Arab-West relations and on furthering dialogue between the Islamic and Arab world and the West.Interviews:-“An interview With Father Basilius About Father Matta Al-Maskin”: Father Basilius discusses the history and theological philosophies of Father Mattá al-Maskīn. The interview is mainly focused on theology and the practices of clergymen.- An interview with Tarek Heggy at CIDT where Drs. Cornelis Hulsman and staff members discuss sensitive issues throughout the Arab world.- An interview by AWR/ CIDT interns with Dr. Hala Mustafa, where she comments on her role in the National Democratic Party’s Policies Committee, her opinions on reform in Egypt, critiques the role of Egyptian security, and outlines the necessary steps needed for reform to take effect.- “Saad Eddin Ibrahim Meets With CIDT Interns To Discuss How Islamists Have Changed”: Saad Eddin Ibrahim, is one of the most outspoken critics of the Egyptian government, who was imprisoned from 2000-2003 for his critique. Saad Eddin Ibrahim is a liberal secularist, but as a result of his strong democratic stance, he defends the rights of all groups in society, including Islamists, to participate in the politics of the country. CIDT-interns met with him for a talk about his life and his views.-A review of the Annual Anglican-Al Azhar Interfaith Meeting Dialogue held in All-Saints Cathedral which implicitly dealt with dialogue and means of furthering it.-A report on church response to poverty in Egypt and specifically how this...

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.

Between 2011 and 2016 field excavations and surveys in the frame of the Danish-German Jerash Northwest Quarter Project have brought to light a wealth of material and results presented in numerous publications, many of these in the same series as this volume. The quality of the data collected seems most promising with regard to an innovative evaluation and classification of the material. Many of the structures excavated, of which some were built in Roman times, were subsequently successively extended over decades and rebuilt until their destruction by a violent earthquake in 749 CE. This incident left large areas covered in debris preserving the contexts mostly undisturbed. This volume provides an in-depth analyses on the ceramic material found at the Northwest Quarter, which is typo-chronological evaluated and contextually analysed. Based on this dataset the authors provides insight into the micro and macro networks of ancient Gerasa in Roman to Early Islamic times and focusses on the question how finely meshed exchange can be pursued on a micro-regional level and what conditions must exist in order to trade.

Directed by Frédérick Madore, the Islam West Africa Collection (IWAC) is a collaborative, open-access digital database that currently contains over 5,000 archival documents, newspaper articles, Islamic publications of various kinds, audio and video recordings, and photographs on Islam and Muslims in Burkina Faso, Benin, Niger, Nigeria, Togo and Côte d'Ivoire. Most of the documents are in French, but some are also available in Hausa, Arabic, Dendi, and English. The site also indexes over 800 references to relevant books, book chapters, book reviews, journal articles, dissertations, theses, reports and blog posts. This project, hosted by the Leibniz-Zentrum Moderner Orient (ZMO) and funded by the Berlin Senate Department for Science, Health and Care, is a continuation of the award-winning Islam Burkina Faso Collection created in 2021 in collaboration with LibraryPress@UF.

This dataset contains all the metadata of the items in the Collection, the Jupyter notebooks that were used to create the visualisations that showcase the possibilities of digital humanities with the IWAC, and a copy of the spreadsheets that were used to create the digital exhibits using Timeline JS.

The datasets of the DeQuDy project are raw data that allow the analysis of the codicological and textual characteristics of the Muslim holy book in the Iberian Peninsula. This dataset is made up of the most relevant characteristics of each Qur'anic work. These data can be seen in relation to the data of the ERC Synergy Grant project, The European Qur'an, led by Mercedes García-Arenal, as they are Here we publish five tables. Each represents blocks of characteristics of the manuscripts studied. The tables have the following properties:Typology, Current location, Institution, Collection, Present shelfmark /signature, Former shelfmarks/signature, Total pages/fols, Folio measures, Material composition, Codicological unit I, Writing support, Textual composition, Textual interval, Title on the manuscipt specimen, Title position, Attributed title, Language (Main), Script Incipit, Incipit position, Explicit, Explicit position, State of preservation, Released date, Released place, Other dates, Other places, Colophon, Colophon position, General decoration, Quranic structure decoration, Quranic reading, Illustrations, Marginalia, Bibliographical references, Link to library catalogue, Descriptive card, Type of relationship, Bookbinding type, Origin of the bookbinding, Bookbinding measures, Covering material Claps, Covering decoration, Writing support colour Watermark, Watermark identification, Catchwords, Presence of numeration, Numeration system, Origin of the numeration, Writing area measures, Number columns, Number of lines, Hands, Position, Text calligraphic style, Text vocalisation, Text ductus colour, Text vowels colour, Text alif elongation (madd colour), Text sukun colour, Text tashid colour, Text wasla colour, Text hamza colour, Headings calligraphic style, Headings vocalisation, Headings ductus colour, Headings vowels colour, Headings alif elongation colour, Headings sukun colour, Headings tashdid colour, Headings hamza colour, Colophon calligraphic style, Colophon vocalisation, Colophon ductus colour, Colophon vowels colour, Colophon alif elongation madd colour, Colophon sukun colour, Colophon tashdid colour, Decorations, Decoration position, Decoration colours, Illustrations, Illustration position, Marginalia, About the text, Hands of marginalia, Location, Position, Released date, Released place, Language, Script, Calligraphic style, Decoration Colour, Writing baseline orientation, Transcription, About the use and transmission of the specimen, Hands of marginalia, Location, Position, Date, Place, Language, Script, Calligraphic style, Decoration Colour, Writing baseline orientation, Transcription, Initial formula, Fol., 1 ayat division, Numbered, 1 ayat markers, 1 ayat calligraphic style, 1 ayat colour, 5 ayats division, 5 ayats markers, 5 ayats calligraphic style, 5 ayats colour, 10 ayats division, 10 ayats markers, 10 ayats calligraphic style, 10 ayats colour, Ayat al-arsh (throne) mark, Ayat al-arsh marker, Ayat al-arsh calligraphic style, Ayat al-arsh colours, Other ayats marks, Ayat number, Other ayats marker, Other ayats mark calligraphic style, Other ayats mark colour, Recitation marks, Recitation marks colour, Surah headings decoration, Surah headings deco colour, Surah headings deco remarks, HIZB location, HIZB decoration, HIZB decoration calligraphic style, HIZB decoration colours, Hizb subdivision location, Hizb subdivision deco, Hizb subdivision calligraphic, Hizb subdivision colour, 30 YUZ location, 30 YUZ decoration no-tipo, 30 YUZ decoration calligraphic, 30 YUZ decoration colours, 27 YUZ location , 27 YUZ decoration, 27 YUZ decoration calligraphic 27 YUZ decoration colours, Others divisions location, Others division decoration, Others deco calligraphic style, Others deco colours, SAYDA location, SAYDA decoration SAYDA deco calligrapic style, SAYDA deco colours

Replication data set (STATA format) and R code to reproduce analyses and figures in the paper. Abstract: What citizens think about Muslim immigrants is of great importance for some of the most pressing challenges facing Western democracies. To advance our understanding of what “Islamophobia” really is – i.e. whether it is a dislike based on immigrants` ethnic background, their religious identity or their specific religious behaviour – we fielded a representative online survey experiment in the UK in the summer 2015. Our results suggest that Muslims are not per se viewed more negatively than Christian immigrants. Instead, we provide evidence that citizens’ uneasiness with Muslim immigration is first and foremost the result of a rejection of fundamentalist forms of religiosity. This suggests that com-mon explanations, which are based on simple dichotomies between liberal supporters and conservative critics of immigration need to be re-evaluated. While the politically left and culturally liberal have more positive attitudes towards immigrants than right leaning and conservatives, they are also far more critical towards religious groups. We conclude that a large part of the current political controver-sy over Muslim immigration has to do with this double opposition. Importantly, the current political conflict over Muslim immigration is not so much about immigrants versus natives or even Muslim versus Christians as it is about political liberalism versus religious fundamentalism.

This dataset contains the Arab West Report special reports published in the year 2003. The majority of the material in this dataset focuses on in depth analysis of Muslim-Christian relations in Egypt, however, Judaism is also the subject of a great deal of analysis in these reports. A number of the reports address relations between religious minorities such as 'dhimmi' status, and the complex relationship between national identity and religious identity. A number of reports are also media critique, a staple of AWR’s work.The AWR reports in this dataset also describe the early work of AWR, and introduce several of its early board members and affiliates. Authors include:- Cornelis Hulsman, Drs.- Sunni M. Khalid- Jeff Adams (Dr. Rev.)- Larry F. Levine (Dr.)- Victor M. Ordonez- Michael Reimer (Dr.)- Wolfram Reiss, (Rev. Dr.)- Johanna Pink (Dr.)- Nirmīn Fawzī- Hedda Klip- Munīr Hannā Anīs Armanius (Bishop)- Cassandra Chambliss- Adam Hannestad- David Weaver- Konrad Knolle (Rev.)- Usamah Wadi‘ al-Ahwani- Marjam Van Oort- Nawal al-Sa‘dawi- M.E. van Gent- Subhi ‘Uwaydah, (Rev. Dr.)- Andreas Van Agt, (Dr.)Institutional authors include AWR Editorial Board, AWR Board of Advisors, Center for the Study of Christianity in Islamic Lands (CSCIL), and EKD Presservice. All reports are written in English, though some reports feature Arabic text or cite Arabic sources.Team including job titles:Sparks, MA M.R. (Center for Intercultural Dialogue and Translation (CIDT))Adams, Rev.Dr. J. (Religious News Service from the Arab-World (RNSAW))Levine, Dr. L.Khalid, S.Reimer, Dr. M. (American University in Cairo)Ordonez, Dr. V.Reiss, Rev. Dr. W.Pink, Dr. J.Fawzi, N. (Religious News Service from the Arab World (RNSAW))Klip, Rev. H. (Swiss Reformed Church)Hannā Anīs Armanius, Bishop M. (Episcopal Church)Chambliss, C. (Intern-Center for Arab-West Understanding (CAWU))Hannestad, A.Weaver, D. (Church World Service, USA)Knolle, Rev. K. (German Reformed Church in Cairo)Al-Ahwani, U. (Religious News Service from the Arab-World (RNSAW))Oort, M. Van (Roos Foundation)Al-Sa'adawi, N.Gent, M.E. VanUwaydah, Rev. Dr. S. (Coptic Evangelical Church Ismailia, Egypt)van Agt, Dr. A.EKD Press ServiceCenter for the Study of Christianity in Islamic Lands (CSCIL)AWR Editorial BoardAWR Board of AdvisorsHulsman, Drs. C. Mr. (Center for Intercultural Dialogue and Translation

BackgroundPerinatal and paediatric autopsy rates are at historically low levels with declining uptake due to dislike of the invasiveness of the procedure, and religious objections particularly amongst Muslim and Jewish parents. Less invasive methods of autopsy including imaging with and without tissue sampling have been shown to be feasible alternatives. We sought to investigate attitudes including religious permissibility and potential uptake amongst members of the Muslim and Jewish communities in the United Kingdom.MethodsSemi-structured interviews with religious and faith-based authorities (n = 16) and bereaved parents from the Jewish community (n = 3) as well as 10 focus groups with community members (60 Muslim participants and 16 Jewish participants) were conducted. Data were analysed using thematic analysis to identify key themes.FindingsMuslim and Jewish religious and faith-based authorities agreed that non-invasive autopsy with imaging was religiously permissible because it did not require incisions or interference with the body. A minimally invasive approach was less acceptable as it still required incisions to the body, although in those circumstances where it was required by law it was more acceptable than a full autopsy. During focus group discussions with community members, the majority of participants indicated they would potentially consent to a non-invasive autopsy if the body could be returned for burial within 24 hours, or if a family had experienced multiple fetal/pregnancy losses and the information gained might be useful in future pregnancies. Minimally invasive autopsy was less acceptable but around half of participants might consent if a non-invasive autopsy was not suitable, with the exception of the Jewish Haredi community who unanimously stated they would decline this alternative.ConclusionsOur research suggests less invasive autopsy offers a viable alternative to many Muslim and Jewish parents in the UK who currently decline a full autopsy. The findings may be of importance to other countries with significant Muslim and/or Jewish communities as well as to other religious communities where concerns around autopsy exist. Awareness-raising amongst religious leaders and community members will be important if these methods become routinely available.

Time series data for the statistic Rank: Getting electricity (1=most business-friendly regulations) and country Iran, Islamic Rep.. Indicator Definition: