88 datasets found
  1. Share of Muslim population in Africa 2024, by country

    • statista.com
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    Statista, Share of Muslim population in Africa 2024, by country [Dataset]. https://www.statista.com/statistics/1239494/share-of-muslim-population-in-africa-by-country/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Africa
    Description

    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.

  2. Countries with the largest Muslim population in 2020

    • statista.com
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    Statista, Countries with the largest Muslim population in 2020 [Dataset]. https://www.statista.com/statistics/374661/countries-with-the-largest-muslim-population/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    World
    Description

    In 2020, Indonesia recorded the largest population of Muslims worldwide, with around 239 million. This was followed with around 226.88 million Muslims in Pakistan and 213 million Muslims in India.

  3. Muslim populations in European countries 2016

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). Muslim populations in European countries 2016 [Dataset]. https://www.statista.com/statistics/868409/muslim-populations-in-european-countries/
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    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2016
    Area covered
    Europe
    Description

    This statistic shows the estimated number of Muslims living in different European countries as of 2016. Approximately **** million Muslims were estimated to live in France, the most of any country listed. Germany and the United Kingdom also have large muslim populations with **** million and **** million respectively.

  4. G

    Percent Muslim by country, around the world | TheGlobalEconomy.com

    • theglobaleconomy.com
    csv, excel, xml
    Updated Jan 17, 2015
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    Globalen LLC (2015). Percent Muslim by country, around the world | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/rankings/muslim/
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    xml, csv, excelAvailable download formats
    Dataset updated
    Jan 17, 2015
    Dataset authored and provided by
    Globalen LLC
    License

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

    Time period covered
    Dec 31, 1960 - Dec 31, 2013
    Area covered
    World
    Description

    The average for 2013 based on 128 countries was 34.3 percent. The highest value was in Algeria: 100 percent and the lowest value was in Angola: 0 percent. The indicator is available from 1960 to 2013. Below is a chart for all countries where data are available.

  5. Data set: 50 Muslim-majority countries and 50 richest non-Muslim countries...

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

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

    Description

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

  6. Isotope analyses to explore diet and mobility in a medieval Muslim...

    • plos.figshare.com
    xlsx
    Updated Jun 1, 2023
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    Iranzu Guede; Luis Angel Ortega; Maria Cruz Zuluaga; Ainhoa Alonso-Olazabal; Xabier Murelaga; Miriam Pina; Francisco Javier Gutierrez; Paola Iacumin (2023). Isotope analyses to explore diet and mobility in a medieval Muslim population at Tauste (NE Spain) [Dataset]. http://doi.org/10.1371/journal.pone.0176572
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Iranzu Guede; Luis Angel Ortega; Maria Cruz Zuluaga; Ainhoa Alonso-Olazabal; Xabier Murelaga; Miriam Pina; Francisco Javier Gutierrez; Paola Iacumin
    License

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

    Area covered
    Spain
    Description

    The Islamic necropolis discovered in Tauste (Zaragoza, Spain) is the only evidence that a large Muslim community lived in the area between the 8th and 10th centuries. A multi-isotope approach has been used to investigate the mobility and diet of this medieval Muslim population living in a shifting frontier region. Thirty-one individuals were analyzed to determine δ15N, δ13C, δ18O and 87Sr/86Sr composition. A combination of strontium and oxygen isotope analysis indicated that most individuals were of local origin although three females and two males were non-local. The non-local males would be from a warmer zone whereas two of the females would be from a more mountainous geographical region and the third from a geologically-different area. The extremely high δ15N baseline at Tauste was due to bedrock composition (gypsum and salt). High individual δ15N values were related to the manuring effect and consumption of fish. Adult males were the most privileged members of society in the medieval Muslim world and, as isotope data reflected, consumed more animal proteins than females and young males.

  7. Estimated Muslim population of England and Wales, by region

    • statista.com
    Updated Dec 16, 2021
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    Statista (2021). Estimated Muslim population of England and Wales, by region [Dataset]. https://www.statista.com/statistics/868696/muslim-population-in-england-and-wales/
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    Dataset updated
    Dec 16, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Wales, United Kingdom, England
    Description

    In 2019, there were estimated to be approximately **** million Muslims living in London, making it the region of England and Wales with the highest Muslim population. Large Muslim populations also live in other English regions, such as the West Midlands, the North West, and Yorkshire.

  8. Muslim population in Africa 2024, by country

    • statista.com
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    Statista, Muslim population in Africa 2024, by country [Dataset]. https://www.statista.com/statistics/1368589/muslim-population-in-africa-by-country/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Africa
    Description

    In 2024, Nigeria had the largest Muslim population in Africa, with around 105 million people who belonged to an Islamic denomination. Egypt and Algeria followed with 90.4 million and 39.4 million Muslims, respectively. Muslims have a significant presence in Africa, with an estimated 50 percent of the continent's population identifying as Muslim. The spread of Islam in Africa began in the 7th century with the arrival of Arab traders, and it continued through Islamic scholars and missionaries.

  9. e

    Bradford Council populations

    • data.europa.eu
    html, pdf
    Updated Sep 25, 2021
    + more versions
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    City of Bradford Metropolitan District Council (2021). Bradford Council populations [Dataset]. https://data.europa.eu/data/datasets/bradford-council-populations
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    pdf, htmlAvailable download formats
    Dataset updated
    Sep 25, 2021
    Dataset authored and provided by
    City of Bradford Metropolitan District Council
    License

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

    Area covered
    Bradford
    Description

    The latest population figures produced by the Office for National Statistics (ONS) on 28 June 2018 show that an estimated 534,800 people live in Bradford District – an increase of 2,300 people (0.4%) since the previous year.

    Bradford District is the fifth largest metropolitan district (in terms of population) in England, after Birmingham, Leeds, Sheffield and Manchester although the District’s population growth is lower than other major cities.

    The increase in the District’s population is largely due to “natural change”- there have been around 3,300 more births than deaths, although this has been balanced by a larger number of people leaving Bradford to live in other parts of the UK than coming to live here and a lower number of international migrants. In 2016/17 the net internal migration was -2,700 and the net international migration was 1,700.

    A large proportion of Bradford’s population is dominated by the younger age groups. More than one-quarter (29%) of the District’s population is aged less than 20 and nearly seven in ten people are aged less than 50. Bradford has the highest percentage of the under 16 population in England after the London Borough of Barking and Dagenham, Slough Borough Council and Luton Borough Council.

    The population of Bradford is ethnically diverse. The largest proportion of the district’s population (63.9%) identifies themselves as White British. The district has the largest proportion of people of Pakistani ethnic origin (20.3%) in England.

    The largest religious group in Bradford is Christian (45.9% of the population). Nearly one quarter of the population (24.7%) are Muslim. Just over one fifth of the district’s population (20.7%) stated that they had no religion.

    There are 216,813 households in the Bradford district. Most households own their own home (29.3% outright and 35.7% with a mortgage). The percentage of privately rented households is 18.1%. 29.6% of households were single person households.

    Information from the Annual Population Survey in December 2017 found that Bradford has 228,100 people aged 16-64 in employment. At 68% this is significantly lower than the national rate (74.9%). 91,100 (around 1 in 3 people) aged 16-64, are not in work. The claimant count rate is 2.9% which is higher than the regional and national averages.

    Skill levels are improving with 26.5% of 16 to 74 year olds educated to degree level. 18% of the district’s employed residents work in retail/wholesale. The percentage of people working in manufacturing has continued to decrease from 13.4% in 2009 to 11.9% in 2016. This is still higher than the average for Great Britain (8.1%).

  10. I

    India Census: Population: by Religion: Muslim: Uttarakhand

    • ceicdata.com
    Updated Aug 7, 2020
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    CEICdata.com (2020). India Census: Population: by Religion: Muslim: Uttarakhand [Dataset]. https://www.ceicdata.com/en/india/census-population-by-religion-muslim/census-population-by-religion-muslim-uttarakhand
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    Dataset updated
    Aug 7, 2020
    Dataset provided by
    CEICdata.com
    License

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

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

    Census: Population: by Religion: Muslim: Uttarakhand data was reported at 1,406,825.000 Person in 03-01-2011. This records an increase from the previous number of 1,012,141.000 Person for 03-01-2001. Census: Population: by Religion: Muslim: Uttarakhand data is updated decadal, averaging 1,209,483.000 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 1,406,825.000 Person in 03-01-2011 and a record low of 1,012,141.000 Person in 03-01-2001. Census: Population: by Religion: Muslim: Uttarakhand data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE003: Census: Population: by Religion: Muslim.

  11. G

    Percent Muslim in Europe | TheGlobalEconomy.com

    • theglobaleconomy.com
    csv, excel, xml
    Updated Sep 19, 2019
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    Globalen LLC (2019). Percent Muslim in Europe | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/rankings/muslim/Europe/
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    excel, xml, csvAvailable download formats
    Dataset updated
    Sep 19, 2019
    Dataset authored and provided by
    Globalen LLC
    License

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

    Time period covered
    Dec 31, 1960 - Dec 31, 2013
    Area covered
    World
    Description

    The average for 2013 based on 27 countries was 12.7 percent. The highest value was in Turkey: 99 percent and the lowest value was in Belarus: 0 percent. The indicator is available from 1960 to 2013. Below is a chart for all countries where data are available.

  12. Muslims in Spain 2023, by nationality

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). Muslims in Spain 2023, by nationality [Dataset]. https://www.statista.com/statistics/989902/muslims-in-spain-by-nationality/
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    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Spain, Spain
    Description

    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.

  13. u

    A Translation of a Script in Arabic on The History of The Jaxanka People,...

    • fdr.uni-hamburg.de
    Updated Jul 18, 2022
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    NCAC/RDD; NCAC/RDD (2022). A Translation of a Script in Arabic on The History of The Jaxanka People, The Islamisation of West Africa, The History of Niani And Sandu (NCAC_RDD_TAPE_0168A_B) [Dataset]. http://doi.org/10.25592/uhhfdm.10315
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    Dataset updated
    Jul 18, 2022
    Dataset provided by
    National Centre for Arts and Culture, The Gambia
    Authors
    NCAC/RDD; NCAC/RDD
    Area covered
    Niani, West Africa, Africa
    Description

    Side A: History of The Jaxanka People

    When the Arabs left from Arabia, they headed to Africa through Babel Mandabi.

    Islam came through Sudan to Bahrulahon Asobia Habsa country and then to Misira (Egypt) and in 642 after the death of Christ.

    Islam started spreading from there to Nobatou a town located by sea where Ham, son of Noah, had his twins. They were black in complexion and that was embarrassing to him and he absconded to another land.

    When the children grew older they went to search for their father and they arrived in Noba at a time when Islam got there, and that was at 652 after the death of Christ. War broke out between Noba and the Roman Catholics in 1323 at Baja.

    The Arabs went to Sudan through Misira and then to Darfur. Islam spread in Darfur and to Chad and then to the West African coast and to other parts of Africa. Islam spread in these regions during 668 after the death of Christ.

    The first Jihad leaders were Uthba Bin Nafiyou, Asan Bin Noman and Tarik Bin Jab.

    Arabs became rivals and some left and headed to Africa. Jihad broke out in the Sahara region at Jenne and Asab as well.

    Islam got to Gana to the Yorubas, Hausas and Fulani. The Hausas spread Islam in Sierra Leone and the Mandinkas spread Islam in Gana, Guinea up to the coast where the two Islamic parties met.

    Gambia emanated from the Gana empire. The king then was Basarang Colley who was around Mali. Mali and Songhai were together.The first king in Mali was Sunjata Keita in 1545, he was the most popular king in Mali. He conquered Jenne and the Mossi ruler.

    Mansa Musa the king of Kankan died on his way from performing pilgrimage in Mecca. Sulayman took over the throne in 1500 and 1570 at Kawong.

    When Askia Muhamed was the king, he opened many Islamic learning centers in 1600. He ruled for some time. Later the king of Morocco took over the land and that was Gao, Jenne and Sudan.

    Mamadi Coulibally was the ruler of Sikasso, Jenne and Timbuktu in 1650. Alhagi Umaru from Sundan fought Mamadi Coulibally and killed him. He left Madina to Segou and conquered Segou and then settled his son Amadou at Hamdallaye and continued to Banjagara. After 60 years at Banjagara, Amadou Segou left to Hausa land.

    When the Europeans came to the region, they invited all the rulers and warned them to seize the war between them. Amadou and the Europeans fought for 18 years. They met at Bisandugu and he fought with Colonel Arsenal at Kankan. One of Amadou’s sons supported the Europeans and fought the ruler of Sikasso who was referred to as, very cruel.

    Before the arrival of the Europeans, there were Muslims and non-Muslims in the region. The most popular kingdom was Ndungu Sine. The ruler Mansa Bari Camara was in Kataba, a small town East of Kuntaur. There was a ruler called Wally at Niani who settled a village called Walliya.

    People came from the East to the West Africa. They first settled at Kupantum Mandinka Kunda were Wally Camara was the ruler. Members of the Camara clan wer known to be the first settlers of Niani. Other people settled at Kissang, Jambur and Jumbiya and the capital was Ndungu Sine.

    The first Jaxanka that settled in the region came from Masina and were known to be knowledgeable and Islamic scholars. When they left Masina, they first settled in Bambuku Jarka in Tukulor land. Some of them left to Fouta and some went to Bundu. After a while some left Bundu because of the war and headed to the Gambia.This was during Jihad war of Sheik Muhamed Lamin Drammeh.

    Sheik Muhamed Lamin Drammeh, a Soninke, who hailed from Gunjur was fighting a holy war against the Non-Muslims. He was attacked by the Europeans and fled to Bundu. While he was there the Europeans attacked him again and he left but he was blocked by Bundu Seykunda and he fought with them and both sides suffered.

    Muhamed Janteh was a great Islamic scholar at Seykunda who left to Niani Sukuta and wanted to cross to the other side of the river but could not and he turned to Niani Kayai.

    The people of Niani attacked and killed him. They took his head to the Europeans and they buried his body at Yonna. Now his grave cannot be traced.

    The Jaxanka clans that first came to the Gambia were great Islamic scholars like, Kabaa, Jakite, Gasama and Jaabi. The most popular among the Gassama clan was Sambu who settled at Wulli Sutukoba. He had a big family and among his family was Yusufa his last son whom he loved a lot. When he grew up, he went to study with Alhagi Salim Suware. When Yusufa came to visit his family he returned with his father and some of the family members went to settle in Fata Tenda. Others left to settle in Foni and they became the Kujaabi last names. Sambu continued with Yusufa to his teacher Alhagi Salim Suware, on their way Yusufa threw his father’s idol in the river. Sambu later converted to Islam and married Salim Suware’s daughter. They got children and they were called Foday Yonankoolu. The children of Yusupha were called Yamayankoolu.

    History of Niani

    The ruler of Niani Kataba used to get the best share of all what the people got. After a long time in that state of oppression on the people of Niani the Muslims made plans to find a solution to their condition. The first European who came there was Mungo Park, who was killed and buried at Karantaba Tenda in Sandu.

    The Camara clan rule became so terrible for the Muslims and there was a marabout called Hafoday Jawla at Kupantum. Hafoday went to the East to seek blessings and found and old man who prayed for him so as to keep him from spilling the blood of Muslims. Hafoday later went to settle at Kunting and became the village chief.

    When the Non-Muslims suffered so much in the hands the Niani ruler who was at Lamin Koto, they went to Fouta Jallon to look for spiritual support. The found a marabout there who prayed for them and God accepted his prayers.

    After that, the people of Kunting went for fishing and they caught a big fish. One of the ruler’s men wanted to take the big fish for the ruler but Hafoday refused to give it out. Kolli, the ruler’s man, went to the Mansa Wally Camara and told him about it and he was angered by that.

    Hafoday knew well that there would be war between him and the ruler, so he mobilized the men of Kunting and the attacked the ruler on that night. They silently crept in and knocked at his door and he opened and they grabbed and killed him.

    When they killed him they left to Kunting. On their way through Kujaw they met Foday Alhagi Kanyi, Karang Kajali’s uncle, and they told him that they killed the Mansa Wally Camara and he asked them to go for his head. They went back and cut off his head and took it along.

    References to entities made in the recording

    Culture: Jaxanka, Mandinka, Soninke, Ajamaataw

    Language: Translated from Arabic to Mandinka

    Persons: Ham, Uthba Bin Nafiyou, Asan Bin Noman, Tarik Bin Jab, Andrusi, Basarang Colley, Sunjata Keita, Mossi ruler, Mansa Musa, Mamadi Coulibaly, Alhagi Umaru, Colonel Arsenal, Amadou, Mansa Bari Camara, Sheik Muhamed Lamin Drammeh, Muhamed Janteh, Kaba Jaiteh, Sambu, Yusufa, Alhagi Salim Suwareh

    Relationships:

    Mamadi Coulibaly was the king of Sikasso

    Amadou was the son of Alhagi Umaru

    Basarang Colley was the ruler of Ghana Empire

    Mansa was the king at Kankan and was succeeded by Sulayman

    Andrusi was an Arab king at Marakisa

    Places: Babel Mandiba, Bahrulahon Asobia Habsa, Sudan, Darfur, Egypt, Tunisia, Noba, Baja, Chad, Gambia, Ghana, Sierra Leone, Guinea, Nigeria, Mali, Masina, Gao, Timbuktu, Sikasso,Segou, Mandina, Hamdallaye, Banjagara, Bisadugu, Marakisa, Ndungu Sine, Kataba, Kuntaur, Niani, Walliya, Kupantum Mandika Kunda, Kissang, Jambur, Jumbiya, Bundu Jarka, Fouta, Bundu, Bundu Jannah, Bakkel. Bundu Seykunda, Niani Sukuta, Niani Kayai, Yonna, Wulli Sutukoba, Fata Tenda and Foni

    Movements:

    Sambu and his son Yusufa left Sutukoba a went to live with Alhagi Salim Suware

    Some of Sambu’s family members went to settled at Fara Tenda and others went to Foni

    Actions:

    Alhagi Umaru killed Mamadi Coulibaly the ruler at Sikasso

    Mansa Musa died on his way back from pilgrimage at Mecca

    Sambu converted to Islam while living with Alhagi Salim Suware

    Time references: None

    Side B: History of Sandu

    After Mansa Wally was killed, the men from Kunting called for a war against the non-Muslims to vacate the land. They called the rulers Mansa Wally and Kolli and informed them that their next target was Mansa Bari Camara at Kataba. Hafoday mentioned that fighting him would not be prolonged, and Karang Kajali advised them to wait.

    He went on in the hills to pray for days, and God accepted his prayers. He warned Hafoday and his men to that they should wait forty-three days before attempting to fight Mansa Bari Camara. Hafoday ignored his advice and went on to fight him, but he was defeated and killed.

    When the forty-three days ended, Karang Kajali attacked Ndungu Sine captured Mansa Bari Camara, and killed him. They traveled to Kujaw, and raised the Islamic flag in Niani.

    When Karang Kajali killed Mansa Bari Camara, there was a disagreement over whom taxes should be paid to after Karang Kajali killed Mansa Bari Camara. Then the people of Niani began to pay their taxes to Karang Kajali, who shared the tax between his uncle Foday lilay Kanyi and the people of Kunting.

    Following the death of Mansa Bari, the spread of Islam became

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

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

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

    Description

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

    Description

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

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

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

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

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

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

    *Due to a sampling error for the keyword "Jews" in 2021, the data are not identical between the two cohorts. The 2022 cohort annotated two samples for the keyword Jews, one from 2020 and the other from 2021, while the 2023 cohort annotated samples from 2021 and 2022.The 2021 sample for the keyword "Jews" that the 2022 cohort annotated was not representative. It has only 453 tweets from 2021 and 147 from the first eight months of 2022, and it includes some tweets from the query with the keyword "Israel". The 2021 sample for the keyword "Jews" that the 2023 cohort annotated was drawn proportionally for each trimester of 2021 for the keyword "Jews".

    Content

    Cohort 2022

    This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias.

    1180 out of 5880 tweets (20.1 %) contain the keyword "Asians," 590 were posted in 2020 and 590 in 2021. 39 tweets (3.3 %) are biased against Asian people. 370 tweets (31,4 %) call out bias against Asians.

    1160 out of 5880 tweets (19.7%) contain the keyword "Blacks," 578 were posted in 2020 and 582 in 2021. 101 tweets (8.7 %) are biased against Black people. 334 tweets (28.8 %) call out bias against Blacks.

    1189 out of 5880 tweets (20.2 %) contain the keyword "Jews," 592 were posted in 2020, 451 in 2021, and ––as mentioned above––146 tweets from 2022. 83 tweets (7 %) are biased against Jewish people. 220 tweets (18.5 %) call out bias against Jews.

    1169 out of 5880 tweets (19.9 %) contain the keyword "Latinos," 584 were posted in 2020 and 585 in 2021. 29 tweets (2.5 %) are biased against Latines. 181 tweets (15.5 %) call out bias against Latines.

    1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims.

    Cohort 2023

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

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

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

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

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

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

    File Description

    The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns:

    'TweetID': Represents the tweet ID.

    'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet.

    'Text': Represents the full text of the tweet (not pre-processed).

    'CreateDate': Represents the date the tweet was created.

    'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0).

    'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0).

    'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.

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

    Acknowledgements

    We are grateful for the technical collaboration with Indiana University's Observatory on Social Media (OSoMe). We thank all class participants for the annotations and contributions, including Kate Baba, Eleni Ballis, Garrett Banuelos, Savannah Benjamin, Luke Bianco, Zoe Bogan, Elisha S. Breton, Aidan Calderaro, Anaye Caldron, Olivia Cozzi, Daj Crisler, Jenna Eidson, Ella Fanning, Victoria Ford, Jess Gruettner, Ronan Hancock, Isabel Hawes, Brennan Hensler, Kyra Horton, Maxwell Idczak, Sanjana Iyer, Jacob Joffe, Katie Johnson, Allison Jones, Kassidy Keltner, Sophia Knoll, Jillian Kolesky, Emily Lowrey, Rachael Morara, Benjamin Nadolne, Rachel Neglia, Seungmin Oh, Kirsten Pecsenye, Sophia Perkovich, Joey Philpott, Katelin

  15. Share of adults who agree with certain anti-Muslim stereotypes U.S. 2022, by...

    • boostndoto.org
    Updated Dec 18, 2024
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    Statista Research Department (2024). Share of adults who agree with certain anti-Muslim stereotypes U.S. 2022, by religion [Dataset]. https://www.boostndoto.org/?p=2379579
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    Dataset updated
    Dec 18, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    United States
    Description

    According to a survey conducted in 2022, Muslims were the most likely religious group to agree with certain anti-Muslim stereotypes about Muslims living in the United States. 24 percent of surveyed Muslims agreed with the statement that most Muslims living in the United States were more prone to violence than other people, compared to only nine percent of the general public who shared this belief.

  16. Share of Muslim population SEA 2023, by country

    • statista.com
    Updated Nov 29, 2025
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    Statista (2025). Share of Muslim population SEA 2023, by country [Dataset]. https://www.statista.com/statistics/1113906/southeast-asia-muslim-population-forecasted-share-by-country/
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    Dataset updated
    Nov 29, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Asia, APAC
    Description

    In 2023, it was estimated that approximately ** percent of the Indonesian population were Muslim, accounting for the highest share of Muslims in any Southeast Asian country. Indonesia also has the world's largest Muslim population, with an estimated *** million Muslims. Demographics of Indonesia The total population of Indonesia was estimated to reach around *** million in 2028. The median age of the population in the country was at an all-time high in 2020 and was projected to increase continuously until the end of the century. In 2020, the population density in Indonesia reached its highest value recorded at about ***** people per square kilometer. Shopping behavior during Ramadan in Indonesia Nearly all Muslims in Indonesia celebrated Ramadan in 2022. During the month of Ramadan, ** percent of Indonesian users utilized online applications to order food. Many Indonesians planned to shop online or offline during Ramadan, with around ** percent of online users planning to purchase fashion wear and accessories. Shopee was the most used app for shopping purposes during that period.

  17. I

    India Census: Population: by Religion: Muslim: Urban

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

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

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

    India Census: Population: by Religion: Muslim: Urban data was reported at 68,740,419.000 Person in 2011. This records an increase from the previous number of 49,393,496.000 Person for 2001. India Census: Population: by Religion: Muslim: Urban data is updated yearly, averaging 59,066,957.500 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 68,740,419.000 Person in 2011 and a record low of 49,393,496.000 Person in 2001. India Census: Population: by Religion: Muslim: Urban data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE001: Census: Population: by Religion.

  18. d

    Survey of Muslims in Canada (Dec 2006)

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Environics Research Group (2023). Survey of Muslims in Canada (Dec 2006) [Dataset]. http://doi.org/10.5683/SP3/AM9Y5P
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Environics Research Group
    Area covered
    Canada
    Description

    In December 2006, Environics Research conducted a major national survey of Muslims and multiculturalism in Canada, as part of its ongoing syndicated FOCUS CANADA research program. The research consisted of two national telephone public opinion surveys: - National survey with a representative sample of 2,045 Canadians (18 years plus) - National survey with a representative sample of 500 Muslims living in Canada The focus of this research is on the presence and experience of Muslims in this country, and draws direct comparisons with similar research conducted in 13 other countries by the Pew Research Center (many of the same research questions were used to provide for direct country-to-country comparisons). The Pew research included Muslim over-samples in Great Britain, France, Germany and Spain. Some of the topics covered in this research: General Public: - General attitudes about immigration in Canada - Personal contact with different ethnic groups (including Muslims) - Perceived discrimination against ethnic groups - General attitudes towards Muslims - Concerns about Muslims and terrorism - Islamic identity and extremism among Muslims - Integration of Muslims and other ethnic minorities into Canadian society - Canadian foreign policy and the mission in Afghanistan Muslims - Experience of being Muslim in Canada - Concern about the future of Muslims in Canada - Self identification within the Muslim community - The role and rights of women in ethnic communities - Islamic identity and extremism among Muslims - Integration of Muslims and other ethnic minorities into Canadian society - Canadian foreign policy and the mission in Afghanistan Please note, the cases in this dataset are comprised only of Muslim respondents. Data from the other component of this survey - the survey of the general population - may be found in the dataset titled "EFC064." Environics Focus Canada - Survey of Muslims in Canada (Dec 2006) Study Overview: http://queensu.ca/cora/_files/Environics%20Muslims%20in%20Canada%20-%20Overview.pdf Environics Focus Canada - Survey of Muslims in Canada (Dec 2006) Methodology: http://queensu.ca/cora/_files/Methodology%20for%20Survey%20of%20Muslims.pdf Copyright (c) 2007 - Environics Research Group

  19. Z

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

    • data-staging.niaid.nih.gov
    • data.niaid.nih.gov
    Updated Oct 26, 2023
    + more versions
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    Jikeli, Gunther; Karali, Sameer; Soemer, Katharina (2023). Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A Dataset for Machine Learning and Text Analytics [Dataset]. https://data-staging.niaid.nih.gov/resources?id=zenodo_8147307
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    Dataset updated
    Oct 26, 2023
    Dataset provided by
    Indiana University Bloomington
    Authors
    Jikeli, Gunther; Karali, Sameer; Soemer, Katharina
    License

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

    Description

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

    The ISCA project compiled this dataset using an annotation portal, which was used to label tweets as either biased or non-biased, among other labels. Note that the annotation was done on live data, including images and context, such as threads. The original data comes from annotationportal.com. They include representative samples of live tweets from the years 2020 and 2021 with the keywords "Asians, Blacks, Jews, Latinos, and Muslims". A random sample of 600 tweets per year was drawn for each of the keywords. This includes retweets. Due to a sampling error, the sample for the year 2021 for the keyword "Jews" has only 453 tweets from 2021 and 147 from the first eight months of 2022 and it includes some tweets from the query with the keyword "Israel." The tweets were divided into six samples of 100 tweets, which were then annotated by three to seven students in the class "Researching White Supremacism and Antisemitism on Social Media" taught by Gunther Jikeli, Elisha S. Breton, and Seth Moller at Indiana University in the fall of 2022, see this report. Annotators used a scale from 1 to 5 (confident not biased, probably not biased, don't know, probably biased, confident biased). The definitions of bias against each minority group used for annotation are also included in the report. If a tweet called out or denounced bias against the minority in question, it was labeled as "calling out bias." The labels of whether a tweet is biased or calls out bias are based on a 75% majority vote. We considered "probably biased" and "confident biased" as biased and "confident not biased," "probably not biased," and "don't know" as not biased.
    The types of stereotypes vary widely across the different categories of prejudice. While about a third of all biased tweets were classified as "hate" against the minority, the stereotypes in the tweets often matched common stereotypes about the minority. Asians were blamed for the Covid pandemic. Blacks were seen as inferior and associated with crime. Jews were seen as powerful and held collectively responsible for the actions of the State of Israel. Some tweets denied the Holocaust. Hispanics/Latines were portrayed as being in the country illegally and as "invaders," in addition to stereotypical accusations of being lazy, stupid, or having too many children. Muslims, on the other hand, were often collectively blamed for terrorism and violence, though often in conversations about Muslims in India.

    Content:

    This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias. 1180 out of 5880 tweets (20.1 %) contain the keyword "Asians," 590 were posted in 2020 and 590 in 2021. 39 tweets (3.3 %) are biased against Asian people. 370 tweets (31,4 %) call out bias against Asians. 1160 out of 5880 tweets (19.7%) contain the keyword "Blacks," 578 were posted in 2020 and 582 in 2021. 101 tweets (8.7 %) are biased against Black people. 334 tweets (28.8 %) call out bias against Blacks. 1189 out of 5880 tweets (20.2 %) contain the keyword "Jews," 592 were posted in 2020, 451 in 2021, and ––as mentioned above––146 tweets from 2022. 83 tweets (7 %) are biased against Jewish people. 220 tweets (18.5 %) call out bias against Jews. 1169 out of 5880 tweets (19.9 %) contain the keyword "Latinos," 584 were posted in 2020 and 585 in 2021. 29 tweets (2.5 %) are biased against Latines. 181 tweets (15.5 %) call out bias against Latines. 1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims.

    File Description:

    The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns:
    'TweetID': Represents the tweet ID.
    'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet.
    'Text': Represents the full text of the tweet (not pre-processed). 'CreateDate': Represents the date the tweet was created.
    'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0). 'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0). 'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.

    Licences

    Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)

    Acknowledgements

    We are grateful for the technical collaboration with Indiana University's Observatory on Social Media (OSoMe). We thank all class participants for the annotations and contributions, including Kate Baba, Eleni Ballis, Garrett Banuelos, Savannah Benjamin, Luke Bianco, Zoe Bogan, Elisha S. Breton, Aidan Calderaro, Anaye Caldron, Olivia Cozzi, Daj Crisler, Jenna Eidson, Ella Fanning, Victoria Ford, Jess Gruettner, Ronan Hancock, Isabel Hawes, Brennan Hensler, Kyra Horton, Maxwell Idczak, Sanjana Iyer, Jacob Joffe, Katie Johnson, Allison Jones, Kassidy Keltner, Sophia Knoll, Jillian Kolesky, Emily Lowrey, Rachael Morara, Benjamin Nadolne, Rachel Neglia, Seungmin Oh, Kirsten Pecsenye, Sophia Perkovich, Joey Philpott, Katelin Ray, Kaleb Samuels, Chloe Sherman, Rachel Weber, Molly Winkeljohn, Ally Wolfgang, Rowan Wolke, Michael Wong, Jane Woods, Kaleb Woodworth, and Aurora Young. This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

  20. u

    An Interview About Bundu And The Spread of Islam in The Senegambia Region...

    • fdr.uni-hamburg.de
    Updated Mar 6, 2023
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    NCAC/RDD; NCAC/RDD (2023). An Interview About Bundu And The Spread of Islam in The Senegambia Region (NCAC_RDD_TAPE_0383A) [Dataset]. http://doi.org/10.25592/uhhfdm.11703
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    Dataset updated
    Mar 6, 2023
    Dataset provided by
    National Centre for Arts and Culture, The Gambia
    Authors
    NCAC/RDD; NCAC/RDD
    Description

    Summary

    Bundu consisted of people from different walks of life, these included weavers, blacksmiths, and Marabouts who were mainly teaching and preaching Islam. People from Masina Jaha were called Jahankoolu. Among them was the Jaiteh clan who was Muslim and other clans including Fofanna, Darbo and Dansoho who were traders. Generally, the Jahankoolu were mainly Muslims and farmers. The Singateh clan became rulers in Netebulu with the support of the Sey clan. The ancestors of the Jahankoolu Suwaibou came from Jaha Masina and settled Bambugu Jaha. He arrived along with great men while some members of his family remained at Jaha Masina. His sons went to settle at Wulli Sutukoba. Saikou Alhagi Salim Suwareh and Foday Touray were great Muslim scholars with a large number of students. Among their children were Mohamadu Fatuma and Omaru Tanda Touray. When they traveled from Jaha and settled at Bambugu Jaha a dispute occurred with the Bambara ruler called Dusuba. They then moved to Diddeh in Bundu and several journeyed to Bindangalang. After migrating to different places one family member went to study the Koran at Jenne. After completing his studies, he went to Fouta Jallon and was welcomed there by their ruler Abdukhadir. He settled in a place called Touba in Labe. He lived in Touba teaching and preaching Islam and was also occupied with farming. While he was there, the people from Tandaba attacked him and many of his people were captured. When the incident occurred, he wrote to the ruler about this and later left to Kanjori. Saihou Ibrayma Janneh, Foday Kasang and Foday Mohamadu Jawara were all in Kanjori. Subsequently, he left Kanjori where he was living for some time, and traveled to Jombo, for the purpose of studying with Umaru Touray. He then continued towards Jenne to study medicine. When he returned, he settled in a town called Taslima and later again moved to Kangkang. He lived there for three years and then left to do trading at Kallato in Manding. Bademba was the ruler, and Alfa Muhamadou Woyanke, a prominent Islamic scholar, lived there. He also preached Islam to the people of Timbo while residing there. He relocated to Labe with the help of a ruler named Modi Abdulay, where he taught and preach Islam. He journeyed to Wourry and settled in a town called Touba and was joined there by other Jahankoolu. After he was attacked there, he traveled to Binaney and resettled in a town called Touba Kuta. He served as the Muslim Leader (Imam) there for seven years. Due to regular attacks from some rulers, he had to migrate, hence, his frequent movements from place to place. The people of Kakandeh and Touba were primarily from Jaha. He married Aysatu Camara and they had a son named Muhamadou Taslima. When he died, he was laid to rest by the mosque and his son Muhamadou Taslima became the new leader (Kalipha). He was succeeded by his son Karamohoba Madi, who later died at Dabola. Many leaders were present when the Europeans arrived; including, Muhamadou Kassum, Muhamadou Mustafa and Muhamadou Taslima Junior. He was succeeded by Karamoho Kutubo who had a large family of about six hundred people including his students. He had a good relationship with Alfa Yaya the ruler of Labe. Captain Yusi constructed the first French post at Touba. He later died and was succeeded by Karamoho Arafaha eighty-nine years after Touba was settled. He was a prominent Islamic scholar who went on a pilgrimage to Mecca. Omaru Tanda Touray was also a great Islamic scholar who settled down in Diddeh Koto at Bundu. The village gradually transform into a large town. Mohamadu Fatuma settled in Kunting and began to teach and preach about Islam there. He later left and resettled at Kanjoro close to Khasso.

    References to entities made in the recording

    Culture: Mandinka

    Language: Mandinka

    Persons: Saikou Alhagi Salim Suwareh, Foday Touray, Mohamadu Fatuma and Omaru Tanda Touray. Dusuba, Abdukhadir, Saihou Ibrayma Janneh, Foday Kasang and Foday Mohamadu Jawara, Umaru Touray, Bademba, Alfa Muhamadou Woyanke, Modi Abdulay, Aysatu Camara, Muhamadou Taslima, Karamohoba Madi, Muhamadou Kassum, Muhamadou Mustafa and Muhamadou Taslima Junior, Karamoho Kutubo, Captain Yusi, Karamoho Arafaha, Omaru Tanda Touray, Mohamadu Fatuma

    Places: Bundu, Masina Jaha,Netebulu, Bambugu Jaha, Jaha Masina, Wulli Sutukoba, Diddeh in Bundu, Bindangalang, Touba in Labe, Tandaba, Kanjori, Jombo, Jenne, Taslima, Kallato in Manding, Timbo, Binaney, Touba Kuta, Kakandeh, Dabola, Labe, Diddeh Koto at Bundu, Kunting, Kanjoro

    Movements:

    The Jahankoolu traveled from Masina to Bambug Jaha and later dispersed to several places

    Actions:

    The Jahankoolu were attacked at Bambugu Jaha by the Bambara ruler Dusuba and they left.

    Time references:

    None

    Additional information: The Jahankoolu migrated to numerous places because of constant enemies’ attacks.

    Alternative names: Diakite, Cherno, Kajali, Sirra, Kebba, Sidibeh, Bakary, Bakari, Bakery

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Statista, Share of Muslim population in Africa 2024, by country [Dataset]. https://www.statista.com/statistics/1239494/share-of-muslim-population-in-africa-by-country/
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Share of Muslim population in Africa 2024, by country

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3 scholarly articles cite this dataset (View in Google Scholar)
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2024
Area covered
Africa
Description

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.

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