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)

Islam is the major religion in many African countries, especially in the north of the continent. In Comoros, Libya, Western Sahara, at least 99 percent of the population was Muslim as of 202. These were the highest percentages on the continent. However, also in many other African nations, the majority of the population was Muslim. In Egypt, for instance, Islam was the religion of 79 percent of the people. Islam and other religions in Africa Africa accounts for an important share of the world’s Muslim population. As of 2019, 16 percent of the Muslims worldwide lived in Sub-Saharan Africa, while 20 percent of them lived in the Middle East and North Africa (MENA) region. Together with Christianity, Islam is the most common religious affiliation in Africa, followed by several traditional African religions. Although to a smaller extent, numerous other religions are practiced on the continent: these include Judaism, the Baha’i Faith, Hinduism, and Buddhism. Number of Muslims worldwide Islam is one of the most widespread religions in the world. There are approximately 1.9 billion Muslims globally, with the largest Muslim communities living in the Asia-Pacific region. Specifically, Indonesia hosts the highest number of Muslims worldwide, amounting to over 200 million, followed by India, Pakistan, and Bangladesh. Islam is also present in Europe and America. The largest Islamic communities in Europe are in France (5.72 million), Germany (4.95 million), and the United Kingdom (4.13 million). In the United States, there is an estimated number of around 3.45 million Muslims.

Context

The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content

This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset

Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Structure

This dataset (world_population_data.csv) covering from 1970 up to 2023 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the World Population Review. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

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

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))Birk, A.S. (Intern-Center for Arab West Understanding (CAWU))Anwar, Dr. S. (Intern-Center for Arab West Understanding (CAWU))Gabra Ayoub Khalil, S. (Centre for Arab-West Understanding (CAWU))Koehler, K. (Intern-Center for Arab West Understanding (CAWU))Fastenrath, C. (Intern-Center for Arab-West Understanding (CAWU))Levine, Dr. L.F.Muhammad al-Duwīnī, W. (Intern-Center for Arab-West Understanding (CAWU))Rezzonico, M. (Intern-Center for Arab-West Understanding (CAWU))Akselbo Holm, M. (Intern-Center for Arab-West Understanding (CAWU))Richards-Benson, S. (Center for Intercultural Dialogue and Translation (CIDT))

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 project had two main dimensions: the first is theoretical and the second is empirical, focusing on three case studies (Moscow, Tatarstan and Dagestan). The theoretical aspect of the project examines two main sets of questions: First, how the general concepts of extremism and moderation, and the associated concept of radicalization, are understood in the Russian context. How is radicalization linked to identity politics(ethnicity, nationalism and religion) and radical ideological movements? Second, how these concepts - moderation, extremism, and radicalization- applied in discourses and policies towards Muslim communities in Russia? What are the presumed internal and external influences? What are the comparisons and links with elite discourse in other European countries with significant Muslim communities, such as UK and France?

The empirical aspect of the project examines how these general concepts and approaches help to illuminate and explains developments in regions of Russian where there exist sizeable Muslim communities. The three case studies chosen include a) the city of Moscow, where it is estimated that there are 1-2 million Muslims, representing at least 10% of the population; b) Tatarstan, which has an ethnic Tatar Muslim plurality and which is often taken to be the best example of the influence of moderate Islam; c) Dagestan, which is regularly taken to be the region with the greatest potential danger, apart form Chechnya, of Islamic radicalization.

The dataset was originally intended to include transcriptions of elite interviews which would have been in the format of elite interview-audio files. However, as we warned might be the case, it did not prove possible to gain consent to recording the interviews.

This project investigates the causes of Islamic radicalisation within Russia and their consequences for Russia's relevant domestic policies (for example ethnic, regional, immigration policies, and domestic democratisation), as well as its foreign policy response towards the Muslim world in the context of the global 'War on Terror'. There are four principal research questions:(1) How Russian policy-making and academic elites conceptualise the idea of 'radicalisation' and political violence. (2) How these discourses are translated into state practice and policy. (3) How these state-driven practices feed or undermine underlying processes of radicalisation. (4) How Russia's domestic context of combating radicalisation drives its foreign policy. The project methodology includes a discourse analysis of academic and journalistic writings and three regional case studies of Russian state policy towards Islam (Moscow, Tatarstan and Dagestan). Each case study relies on discourse analysis of public and media approaches, content analysis of relevant legal and state policy documents, and semi-structured elite interviews. The project co-ordinators will work with local institutes in Russia and will invite scholars from these institutes to the UK as research fellows. The project findings will be disseminated by four journal articles, policy briefings and a co-authored monograph.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Iran, Islamic Rep. is 1005.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

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.

IntroductionAlthough the number of people receiving antiretroviral therapy (ART) in Indonesia has increased in recent years, little is known about the specific characteristics affecting adherence in this population. Indonesia is different from most of its neighbors given that it is a geographically and culturally diverse country, with a large Muslim population. We aimed to identify the current rate of adherence and explore factors that influence ART adherence.MethodsData were collected from ART-prescribed outpatients on an HIV registry at a North Jakarta hospital in 2012. Socio-demographic and behavioral characteristics were explored as factors associated with adherence using logistics regression analyses. Chi squared test was used to compare the difference between proportions. Reasons for missing medication were analyzed descriptively.ResultsTwo hundred and sixty-one patients participated, of whom 77% reported ART adherence in the last 3 months. The level of social support experienced was independently associated with adherence where some social support (p = 0.018) and good social support (p = 0.039) improved adherence compared to poor social support. Frequently cited reasons for not taking ART medication included forgetting to take medication (67%), busy with something else (63%) and asleep at medication time (60%).DiscussionThis study identified that an increase in the level of social support experienced by ART-prescribed patients was positively associated with adherence. Social support may minimize the impact of stigma among ART prescribed patients. Based on these findings, if social support is not available, alternative support through community-based organizations is recommended to maximize treatment success.

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.

Islam and Christianity form the two dominant religions in Nigeria. Since colonialism, approximately 90 percent of the Nigerian people identify themselves as Islamic or Christian. The northern region of Nigeria is predominately Islamic, while the southern region is predominately Christian.

Nigeria’s contact with Islam predated that of Christianity and European colonialism; its spread was facilitated into Sub-Saharan Africa through trade and commerce. The northern part of Nigeria is symbolic to the history of Islam, as it penetrated the area through the Kanem-Borno Empire in the 11th century before spreading to other predominately Hausa states. Islam was then introduced into the traditional societies of the Yoruba-speaking people of south-west Nigeria through their established commercial relationship with people of the north, particularly the Nupe and Fulani.

Christianity reached Nigeria in the 15th century with the visitation of Catholic missionaries to the coastal areas of the Niger-Delta region. Christianity soon recorded a boost in the southern region given its opposition to the slave trade and its promotion of Western education.

The distinct religious divide has instigated violence in present-day Nigeria, including the Sharia riot in Kaduna in 2000, ongoing ethno-religious violence in Jos since 2001, and the 2011 post-election violence that erupted in some northern states, particularly in the city of Maiduguri. Nigerians’ continued loyalty to religion compared to that of the country continues to sustain major political debate, conflict, and violent outbreaks between populations of the two faiths.

ISO3-International Organization for Standardization 3-digit country code

NAME-Name of religious institution

TYPE-Type of religious institution

CITY-City religious institution is located in

SPA_ACC-Spatial accuracy of site location 1- high, 2 – medium, 3 - low

SOURCE_DT-Source creation date

SOURCE-Primary source

SOURCE2_DT-Secondary source creation date

SOURCE2-Secondary source

Collection

This HGIS was created using information collected from the web sites GCatholic.org, Islamic Finder, Wikimapia, and BBBike.org, which uses OpenStreetMap, a crowd-source collaboration project that geo-locates sites throughout the world. After collection, all education institutions were geo-located.

The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe Analytics is not responsible for the accuracy and completeness of data compiled from outside sources.

Sources (HGIS)

BBBike, "Nigeria." Last modified 2013. Accessed March 19, 2013. http://extract.bbbike.org.

GCatholic.org, "Catholic Churches in Federal Republic of Nigeria." Last modified 2013. Accessed April 4, 2013. http://www.gcatholic.org/.

Islamic Finder, "Nigeria." Last modified 2013. Accessed April 4, 2013. http://islamicfinder.org/.

Olanrewaju, Timothy. The Sun, "oko Haram attacks church in Maiduguri." Last modified 2013. Accessed April 9, 2013. http://sunnewsonline.com/.

Wikimapia, "Nigeria:Mosques/Churches." Last modified 2013. Accessed April 4, 2013. http://wikimapia.org/

World Watch Monitor, "Muslim Threat to Attack Church Raises Tensions." Last modified 2012. Accessed April 9, 2013. http://www.worldwatchmonitor.org/.

Sources (Metadata)

Danjibo, N.D. "Islamic Fundamentalism and Sectarian Violence: The "Maitatsine" and "Boko Haram" Crises in Northern Nigeria." manuscript., University of Ibadan, 2010. http://www.ifra-nigeria.org.

Olanrewaju, Timothy. The Sun, "oko Haram attacks church in Maiduguri." Last modified 2013. Accessed April 9, 2013. http://sunnewsonline.com/.

Onapajo, Hakeem. "Politics for God: Religion, Politics, and Conflict in Democratic Nigeria." Journal of Pan African Studies. 4. no. 9 (2012): 42-66. http://web.ebscohost.com (accessed March 26, 2013).

World Watch Monitor, "Muslim Threat to Attack Church Raises Tensions." Last modified 2012. Accessed April 9, 2013. http://www.worldwatchmonitor.org/.

This is my training data for kaggle's blood-vessel-segmentation contest. Original Data Source

I have volume-normalized the 24-bit images to 8-bit. Here is the link to the code used here, in case you want to check it out.

For now, I've just done kidney_1_dense and kidney_3_dense, as these are ones being used most commonly. If you'd like the other ones as well, please leave a comment and I'll make a new version with the rest of the dataset. Thanks ^_^

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.

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. Van
Uwaydah, Rev. Dr. S. (Coptic Evangelical Church Ismailia, Egypt)
van Agt, Dr. A.
EKD Press Service
Center for the Study of Christianity in Islamic Lands (CSCIL)
AWR Editorial Board
AWR Board of Advisors
Hulsman, Drs. C. Mr. (Center for Intercultural Dialogue and Translation

The Arabic dataset is scraped mainly from الموسوعة الشعرية and الديوان. After merging both, the total number of verses is 1,831,770 poetic verses. Each verse is labeled by its meter, the poet who wrote it, and the age which it was written in. There are 22 meters, 3701 poets and 11 ages: Pre-Islamic, Islamic, Umayyad, Mamluk, Abbasid, Ayyubid, Ottoman, Andalusian, era between Umayyad and Abbasid, Fatimid, and finally the modern age. We are only interested in the 16 classic meters which are attributed to Al-Farahidi, and they comprise the majority of the dataset with a total number around 1.7M verses. It is important to note that the verses diacritic states are not consistent. This means that a verse can carry full, semi diacritics, or it can carry nothing.

This dataset contains information about how Islamic boarding schools (pesantren) in Indonesia are involved in supporting the Sustainable Development Goals (SDGs). The data was collected in 2024 from two groups: ustadz (teachers) and santri (students). It focuses on four main areas: quality education (SDG 4), poverty reduction (SDG 1), economic activities in pesantren (SDG 8), and environmental care through waste and recycling (SDG 12).

The dataset is organized into several sheets, including background information on the respondents and their answers to questions about SDG practices in daily pesantren life. Most questions use a simple scale to measure opinions and actions, and some also include open comments.

This dataset is useful for researchers, educators, or policymakers who want to understand how Islamic schools contribute to sustainable development. It is open for public use under the CC-BY license and can help support programs or policies related to education, community development, and environmental awareness.

Iran (Islamic Republic of) administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: COD-PS incluces alternate UNHCR P-codes.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB requires improvements.

Sourced from UNHCR

Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Iran (Islamic Republic of) COD-PS.

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the COD Portal.

Administrative level 1 contains 31 feature(s). The normal administrative level 1 feature type is ""provincen (ostān)"".

Administrative level 2 contains 429 feature(s). The normal administrative level 2 feature type is ""district (baxš)"".

Recommended cartographic projection: Asia South Albers Equal Area Conic

This metadata was last updated on January 9, 2025.