"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)

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

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

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

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

These data were collected for a study of how the characteristics of political parties influence women's chances in assuming leadership positions within the parties' inner structures. Data were compiled by Fatima Sbaity Kassem for a case-study of Lebanon and by national and local researchers for 25 other countries in Asia, Africa and Europe. The researchers collected raw data on women in politics from party administrators and government officials. Researchers gathered information about parties' year of origin, number of seats in parliament, political platform, and all gender-disaggregated party data (in percentages) on overall party membership, shares in executive and decision-making bodies, and nominations on electoral lists. A key variable measures party religiosity, which refers to the religious components on their political platforms or the extent to which religion penetrates their political agendas.

Only parties that have at least one seat in any of the last three parliaments were included. These are referred to as 'relevant' parties. The four data sets combined cover 330 political parties in Lebanon plus 12 other Arab countries (Algeria, Bahrain, Comoros, Djibouti, Egypt, Jordan, Kuwait, Mauritania, Morocco, Palestine, Tunisia, and Yemen), seven non-Arab Muslim-majority countries (Albania, Afghanistan, Bangladesh, Bosnia-Herzegovina, Indonesia, Senegal, and Turkey), five European countries with dominant Christian democratic parties (Austria, Belgium, Italy, Germany, and the Netherlands), and Israel.

This repository contains historical data collected in the digital humanities project Dhimmis & Muslims – Analysing Multireligious Spaces in the Medieval Muslim World. The project was funded by the VolkswagenFoundation within the scope of the Mixed Methods initiative. The project was a collaboration between the Institute for Medieval History II of the Goethe University in Frankfurt/Main, Germany, and the Institute for Visualization and Interactive Systems at the University of Stuttgart, and took place there from 2018 to 2021. The objective of this joint project was to develop a novel visualization approach in order to gain new insights on the multi-religious landscapes of the Middle East under Muslim rule during the Middle Ages (7th to 14th century). In particular, information on multi-religious communities were researched and made available in a database accessible through interactive visualization as well as through a pilot web-based geo-temporal multi-view system to analyze and compare information from multiple sources. The code for this visualization system is publicly available on GitHub under the MIT license. The data in this repository is a curated database dump containing data collected from a predetermined set of primary historical sources and literature. The core objective of the data entry was to record historical evidence for religious groups in cities of the Medieval Middle East. In the project, data was collected in a relational PostgreSQL database, the structure of which can be reconstructed from the file schema.sql. An entire database dump including both the database schema and the table contents is located in database.sql. The PDF file database-structure.pdf describes the relationship between tables in a graphical schematic. In the database.json file, the contents of the individual tables are stored in JSON format. At the top level, the JSON file is an object. Each table is stored as a key-value pair, where the key is the database name, and the value is an array of table records. Each table record is itself an object of key-value pairs, where the keys are the table columns, and the values are the corresponding values in the record. The dataset is centered around the evidence, which represents one piece of historical evidence as extracted from one or more sources. An evidence must contain a reference to a place and a religion, and may reference a person and one or more time spans. Instances are used to connect evidences to places, persons, and religions; and additional metadata are stored individually in the instances. Time instances are connected to the evidence via a time group to allow for more than one time span per evidence. An evidence is connected via one or more source instances to one or more sources. Evidences can also be tagged with one or more tags via the tag_evidence table. Places and persons have a type, which are defined in the place type and person type tables. Alternative names for places are stored in the name_var table with a reference to the respective language. For places and persons, references to URIs in other data collections (such as Syriaca.org or the Digital Atlas of the Roman Empire) are also stored, in the external_place_uri and external_person_uri tables. Rules for how to construct the URIs from the fragments stored in the last-mentioned tables are controlled via the uri_namespace and external_database tables. Part of the project was to extract historical evidence from digitized texts, via annotations. Annotations are placed in a document, which is a digital version of a source. An annotation can be one of the four instance types, thereby referencing a place, person, religion, or time group. A reference to the annotation is stored in the instance, and evidences are constructed from annotations by connecting the respective instances in an evidence tuple.

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

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.

Context

Hadith (an Arabic word) refers to the words and actions of Prophet Mohammed. Those collections of Hadiths have been transmitted through generations of Muslim scholars until they have been collected and written in big collections. The chain of narrators is a main area of study in Islamic scholarship because a single hadith may have multiple chains of narrators (that may or may not overlap). However, it has mainly remained a qualitative field where scholars of Hadith try to determine the authenticity of Hadiths by investigating and validating the chains of narrators who transmitted a given hadith. Further, the raw texts of Hadiths have not yet been used in qualitative approaches in data analysis. I hope this dataset makes it easier to further progress in this direction.

Content

Hadith dataset contains the set of all Hadiths from the six primary hadith collections. The data is scraped from http://qaalarasulallah.com/. Note that the chain_indx column refers to scholar_indx column in Hadith Narrators Dataset.

Notably, this is a very draft version of the dataset as it is not validated. For example, the number of Hadiths in this dataset is much higher than the real number of Hadiths contained in those sources. This may be due to a bug in my script. Further actions will be taken to further clean up this dataset. However, as it is right now, it can be used to prototype certain analyses in those areas.

Disclaimer: I scraped the data and I hold no responsibility for its accuracy or validation. Use at your own risk!

Acknowledgements

This dataset wouldn't have been possible without the great people who have already transcribed this dataset from primary sources and bibliographies to muslimscholars.info & qaalarasulallah.com database. I only scraped this database with a Python script plus very minimal cleanup.

This Religion and State-Minorities (RASM) dataset is supplemental to the Religion and State Round 2 (RAS2) dataset. It codes the RAS religious discrimination variable using the minority as the unit of analysis (RAS2 uses a country as the unit of analysis and, is a general measure of all discrimination in the country). RASM codes religious discrimination by governments against all 566 minorities in 175 countries which make a minimum population cut off. Any religious minority which is at least 0.25 percent of the population or has a population of at least 500,000 (in countries with populations of 200 million or more) are included. The dataset also includes all Christian minorities in Muslim countries and all Muslim minorities in Christian countries for a total of 597 minorities. The data cover 1990 to 2008 with yearly codings.

These religious discrimination variables are designed to examine restrictions the government places on the practice of religion by minority religious groups. It is important to clarify two points. First, these variables focus on restrictions on minority religions. Restrictions that apply to all religions are not coded in this set of variables. This is because the act of restricting or regulating the religious practices of minorities is qualitatively different from restricting or regulating all religions. Second, this set of variables focuses only on restrictions of the practice of religion itself or on religious institutions and does not include other types of restrictions on religious minorities. The reasoning behind this is that there is much more likely to be a religious motivation for restrictions on the practice of religion than there is for political, economic, or cultural restrictions on a religious minority. These secular types of restrictions, while potentially motivated by religion, also can be due to other reasons. That political, economic, and cultural restrictions are often placed on ethnic minorities who share the same religion and the majority group in their state is proof of this.

This set of variables is essentially a list of specific types of religious restrictions which a government may place on some or all minority religions. These variables are identical to those included in the RAS2 dataset, save that one is not included because it focuses on foreign missionaries and this set of variables focuses on minorities living in the country. Each of the items in this category is coded on the following scale:

0. The activity is not restricted or the government does not engage in this practice.
1. The activity is restricted slightly or sporadically or the government engages in a mild form of this practice or a severe form sporadically.
2. The activity is significantly restricted or the government engages in this activity often and on a large scale.

A composite version combining the variables to create a measure of religious discrimination against minority religions which ranges from 0 to 48 also is included.

ARDA Note: This file was revised on October 6, 2017. At the PIs request, we removed the variable reporting on the minority's percentage of a country's population after finding inconsistencies with the reported values. For detailed data on religious demographics, see the "/data-archive?fid=RCSREG2" Target="_blank">Religious Characteristics of States Dataset Project.

The Hajj Dataset 2021-2024: Ministry of Religious Affairs Malang City contains comprehensive data on the Hajj pilgrimage process for 2021 through 2024, gathered explicitly from the Malang City branch of Indonesia's Ministry of Religious Affairs (Kemenag). This dataset captures key information about the Hajj pilgrimage, including payment records, associated costs, and demographic details of the pilgrims, providing valuable insights into the financial aspects and trends over the four years. Key Data Features: Yearly Hajj Costs: Information on the financial breakdown of Hajj costs for each year, covering all components, including transportation, accommodation, and other mandatory fees. Pilgrim Demographics: Data on the number and characteristics of pilgrims from Malang City, including age, gender, and other socioeconomic indicators. Payment Status and History: Records of payments made by the pilgrims detailing the timing, amount, and any outstanding balances. Regulatory Changes: Information on changes in the regulations and policies of the Ministry of Religious Affairs (Kemenag) that may have impacted the cost structure or payment schedule during this period. Inflation and Currency Impact: Data reflecting the impact of national inflation rates or currency fluctuations, particularly the value of the Indonesian Rupiah (IDR) relative to the Saudi Riyal (SAR), on the overall pilgrimage cost. Hajj Quota and Registrations: The number of Hajj applicants from Malang City and the annual quota allocated to the region, including details on the selection process and waiting periods. Potential Use Cases: Cost Prediction: Analyze cost trends and predict future financial needs for the Hajj pilgrimage. Policy Analysis: Assess the impact of government policies on the affordability and accessibility of Hajj for pilgrims. Economic Analysis: Understand how national economic factors (inflation and and exchange rates) affect pilgrimage costs. Social Research: Study demographic patterns and regional participation in Hajj from Malang City. This dataset provides an essential resource for anyone interested in the economic, social, and policy dimensions of the Hajj pilgrimage in Indonesia, particularly in the context of Malang City's unique data.

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 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 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.

This statistic displays the distribution of jihad fighters in the Netherlands from 2012 to 2017, by descent. In 2017, the HSCC database contained the names of 207 Dutch nationals who had left for Iraq or Syria to join the jihad since 2012. Of the 206 jihad fighters whose nationality was known, the largest group was of Moroccan descent. Converted Muslims from Dutch descent ranked second, with approximately 17 percent of the total known jihad fighters.

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.

This is an Excel spreadsheet with 160 mosques and prayer places listed in chronological order along with GPS coordinates and other information. These places date starting from the very first mosques built in Medina onwards. I have tried to produce an exhaustive list for the first two centuries of Islam, then major mosques for the third century, followed by a few important later mosques. I have been updating this list on a regular basis for the last twenty years, but now that it is published here, I will post a new list only after any major additions. An active up-to-date list is published on my website: https://nabataea.net/ Follow the menu Founding of Islam /Qibla Tool. This tool provides a graphical overview of this spread-sheet. I have included one column in this spreadsheet with my own classifications.Dan Gibson January 2021

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.

Research finds that social media platforms' peer-to-peer structures shapes the public discourse, and increases citizens' likelihood of exposure to unregulated, false, and prejudicial content. Here, we test whether self-reported reliance on social media as a primary news source is linked to racialized policy support, taking the case of U.S. Muslims, a publicly visible but understudied group about whom significant false and prejudicial content is abundant on these platforms. Drawing on three original surveys and the Nationscape Dataset, we find a strong and consistent association between reliance on social media and support for a range of anti-Muslim policies. Importantly, reliance on social media is linked to policy attitudes across the partisan divide and for individuals who reported holding positive or negative feelings towards Muslims. These findings highlight the need for further investigation into the political ramification of information presented on contemporary social media outlets, particularly information related to stigmatized groups.

This dataset is a collection of 12,478 social media comments found on the official Facebook pages of ten Philippine newspapers, The Philippine Daily Inquirer, Manila Bulletin, The Philippine Star, The Manila Times, Sunstar Cebu, Sunstar Davao, Cebu Daily News, The Freeman, Sunstar Davao, MindaNews, and The Mindanao Times, spanning the years 2015, 2017 and 2019. The comments contain terms related to the Moro identity and the Mamasapano Clash, the Marawi Siege and the establishment of BARMM in the southern Philippines, allowing researchers to study semantic fields with regard to Muslims and the relationship between the texts and the source newspaper, their region of origin, and political administration, among other variables. All comments in the dataset were downloaded through Facebook's Graph API via Facepager (Jünger & Keyling, 2019).

One CSV file (MMB151719SOCMED_v2.csv) is provided, along with a codebook that contains descriptions of the variables and codes used in the CSV file, and a Readme document with a changelog.

Each social media comment is annotated with the following metadata:

object_id: identifier associated with the comment;

message: the textual string of the comment;

message_proc: the textual string of the comment after pre-processing;

lang_label: categorical value for the language of the comment (Tagalog (Filipino), Cebuano, English, Taglish, Bislog, Bislish, Trilingual or Other);

from_name: identifier of public pages (not profiles of individuals) leaving comments (NaN for profiles of individuals, 'NAME' for public pages besides the newspapers, otherwise, the page name of the newspaper);

created_time: Facebook Graph API's-generated string for the date and time the comment was posted;

month_year: categorical value in the form string+YY (e.g. Jun-15) of the month and year when the comment was posted;

year: numerical value in the form YY;

newspaper: categorical value for the newspaper Facebook page under which the comment was found;

corpus: categorical value for comments from the main corpus or the side (control) corpus;

administration: categorical value for political administration (pbsa = President Benigno Aquino III, prrd = President Rodrigo Roa Duterte);

count: numerical value referring to the number of string sequences without spaces;

The dataset may only be used for non-commercial purposes and is licensed under the CC BY-NC-SA 4.0 DEED.

V2 - 05/06/2024

Corrections

Corrections made to region to include Luzon, Visayas and Mindanao (as opposed to Mindanao, non-Mindanao);

Corrections made to administration coding.

This dataset is described by:

Cruz, F. A. (2024). A Multilingual Collection of Facebook Comments on the Moro Identity and Armed Conflict in the Southern Philippines. Journal of Open Humanities Data, 10(1), 41. DOI: https://doi.org/10.5334/johd.219

Bibiliography

Jünger, J., & Keyling, T. (2019). Facepager: An application for automated data retrieval on the web (4.5.3) [Computer software]. https://github.com/strohne/Facepager/

During the present time, COVID-19 situation is the topmost priority in our life. We are introducing a new dataset named Covid Face-Mask Monitoring Dataset which is based on Bangladesh perspective. We have a main concern to detect people who are using masks or not in the street. Furthermore, few people are not wearing masks properly which is harmful for other people and we have the intention to detect them also. Our proposed dataset contains 6,550 images and those images collected from the walking street, bus stop, street tea stall, foot-over bridge and so on. Among the full dataset, we selected 5,750 images for training purposes and 800 images for validation purposes. Our selected dimension is 1080 × 720 pixels for entire dataset. The percentage of validation data from the full dataset is almost 12.20%. We used a personal cell phone camera, DSLR for collecting frames and adding them into our final dataset. We have also planned to collect images from the mentioned place using an action camera or CCTV surveillance camera. But, from Bangladesh perspective it is not easy to collect clear and relevant data for research. To extend, CCTV surveillance cameras are mostly used in the university, shopping complex, hospital, school, college where using a mask is mandatory. But our goal of research is different. In addition, we want to mention that in our proposed dataset there are three classes which are 1. Mask, 2. No_mask, 3. Mask_not_in_position.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_421d597d78d9dab253ab491b13ff30f0/view

The dataset contains translations and interpretations of all verses of the Qur'an by 15 Large Language Models, collected in March-May, and November-December 2024.