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39 results for “Muslim”
Replication files for "Building social cohesion between Christians and Muslims through soccer in post-ISIS Iraq"
<p>Replication files for main analyses, and supplementary analyses (comparison group, Muslim player attitudes, fan attitudes, match-level data) for:</p> <p><strong>Mousa, Salma. </strong>"<a href="https://science.sciencemag.org/content/369/6505/866">Building social cohesion between Christians and Muslims through soccer in Post-ISIS Iraq</a>.<strong>" <em>Science</em>. </strong>Vol. 369, Issue 6505, pp. 866-870. DOI: 10.1126/science.abb3153</p> <p>Each R file describes the needed datasets at the top of the script. </p>
Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A Dataset for Machine Learning and Text Analytics
<h1>Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset on bias against Asians, Blacks, Jews, Latines, and Muslims </h1> <div> <h2> </h2> <h2>Description </h2> </div> <div> <p>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.</p> <p>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 <a href="https://annotationportal.com/" target="_blank" rel="noreferrer noopener">Annotation Portal</a> (Jikeli, Soemer and Karali, 2024). The updated version of our portal, <a href="https://portal2.annotationportal.com/" target="_blank" rel="noreferrer noopener">AnnotHate</a>, 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. </p> </div> <div> <p>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 <a href="https://isca.indiana.edu/publication-research/social-media-project/Research-Report-BIAS-on-Twitter-against-Asians--Blacks-Jews-Latinos-Muslims-final-002.pdf" target="_blank" rel="noreferrer noopener">2022</a> and <a href="https://isca.indiana.edu/documents/BIAS%20Against%20Asian-Black-Hispanic-Jewish-and-%20Muslim-People%20on%20X-Twitter%20in%202021%20and%202022.pdf" target="_blank" rel="noreferrer noopener">2023</a>. 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". </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>*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". </p> </div> <div> <h2> </h2> <h2>Content</h2> <h3>Cohort 2022 </h3> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <h3>Cohort 2023 </h3> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <p>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. </p> </div> <div> <h2> </h2> <h2>File Description</h2> </div> <div> <p>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: </p> <p>'TweetID': Represents the tweet ID. </p> </div> <div> <p>'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet. </p> </div> <div> <p>'Text': Represents the full text of the tweet (not pre-processed). </p> </div> <div> <p>'CreateDate': Represents the date the tweet was created. </p> </div> <div> <p>'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0). </p> </div> <div> <p>'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0). </p> </div> <div> <p>'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username. </p> </div> <div> <p> ‘Cohort’: Represents the year the data was annotated (class of 2022 or class of 2023) </p> </div> <div> <h2> </h2> <h2>Acknowledgements </h2> </div> <div> <p>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, Aurora Young, Sydney Allen, Hundre Askie, Norah Bardol, Olivia Baren, Samuel Barth, Emma Bender, Noam Biron, Kendyl Bond, Graham Brumley, Kennedi Bruns, Leah Burger, Hannah Busche, Morgan Butrum-Griffith, Zoe Catlin, Angeli Cauley, Nathalya Chavez Medrano, Mia Cooper, Suhani Desai, Isabella Flick, Samantha Garcez, Isabella Grady, Macy Hutchinson, Sarah Kirkman, Ella Leitner, Elle Marquardt, Madison Moss, Ethan Nixdorf, Reya Patel, Mickey Racenstein, Kennedy Rehklau, Grace Roggeman, Jack Rossell, Madeline Rubin, Fernando Sanchez, Hayden Sawyer, Diego Scheker, Lily Schwecke, Brooke Scott, Megan Scott, Samantha Secchi, Jolie Segal, Katherine Smith, Constantine Stefanidis, Cami Stetler, Madisyn West, Alivia Yusefzadeh, Tayssir Aminou, Karen Fecht, Luciana Orrego-Hoyos, Hannah Pickett, and Sophia Tracy. </p> </div> <div> <p>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. </p> </div> <div> <p> </p> </div>
Science | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Science” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbG5iR3ZCZXJCZFRIemIwbE9IajIwZGpfUURaUXxBQ3Jtc0traWczREJacDV3U0FMamdjMEtDMC1lV1Z3Ml9iUmlXS0EwbEpWblo0VnJOcWxwVVEtSzV0eVpvVlVkdXk2RjI4TzI5VkNqZlBEeWduRzdpbjZwMjhTQkE4Q0hhTUNtdDlfRERVZ08xekowZ1lzZE81TQ&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=L3SqY0eQEhk">https://www.ecampusontario.ca/</a>.</p>
Civil Society | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Civil Society” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbmZhdnlWYU90Zm43Z3pRbEg1NkpPdXE3dUhnQXxBQ3Jtc0ttbUV4X1VrQ09DNEVkcXNxczU0blYzRkY5WHFEd2gydDlqQkdIY2tabGhHODkxaG9SUzN6aDduU05pVFBLbV9YQUJmWU5FOG9KTkFnbG1vS1ZnaVNTOHN5bzltb1lXVWs4WXk3OExQQXJjbklsaFJvMA&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=4vFUtAXWo-8">https://www.ecampusontario.ca/</a>.</p>
Studying Islam | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Studying Islam” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbEFDUmV3OVJDZWhIUTNQLXcwTTNzMDdicEZSQXxBQ3Jtc0tuWHhlTmRlNUJLbHVWYWh1MnBRQnVzMUhJa3otX0hkSFN4Z0RudHVtN2M2ODhiVEZLNDY1VzZaZ0lUY3pnZDJmenhBVS1CcEhMSzFic0YwYUFQamZhaFVFQVg1c3ZPcEhwQ1JDMjM1Smc1bWdvaWpYQQ&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=ym5DU64UyfQ">https://www.ecampusontario.ca/</a>.</p>
Shariʿah: The Path to Water | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Shariʿah: The Path to Water” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbWlnZHlBdDBNRXNTb1FOUTZWZkZCVmo1WF8td3xBQ3Jtc0tsNC1MdWNsSmNRdWZHS0tSSkRhSUZuMGg5UUxzZFk2andIUk9sdUJSclcyWjBFZFplRHVwbFIzWWpRUXZmdWZNNURGalphUHZoX3FXWmhnYzAzTGZzMHBtNUdiU0dySDlybXViTWxULWlhMXExUEZzVQ&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=GGwwz-0kXZo">https://www.ecampusontario.ca/</a>.</p>
Ethics: Purification of the Soul | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Ethics: Purification of the Soul” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa2xPSmF1azkydjFmejdBZjhZN014eXlmYkVhQXxBQ3Jtc0ttZVhNR1ExSFZFV2dkWGVGSERBaE5GcHlJb2plM0RpamdLQUgzSUVzMV9WMWtvZDFRdHZnWjNsb2trNmU3NlJZSEt0NGlwbHkxLXNGZUJmSnRneElXVF9VdER6R2p2dk1sMm9vS05XeUJ0dFNDaThDVQ&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=FYCDjMkTrrg">https://www.ecampusontario.ca/</a>.</p>
Heroes: Do All of Them Wear Capes? | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Heroes: Do All of Them Wear Capes?” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa29hdmEydkxSVkdmMk9BUmV5S29lS1c0dGFvd3xBQ3Jtc0tsNXc4M2lGTlhIUkhITnZJbEp2WHhDMzZXamJFMG9fVkhld1RSTlNBcEtUbUFUNkhzaU04UmtJTnpVZ2EzdGJCWWRqcXdWRFNWaEM4endIYTFuYjk1Z3ZtU1hINHloU3k3cFhmRDUxMDQ2NnZnUVd0OA&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=9ch92zHt3To">https://www.ecampusontario.ca/</a>.</p>
Prophecy | Islam and Muslim Civilizations | iBrary
<p>This video introduces the module “Prophecy” from the course Islam and Muslim Civilizations, taught by Prof. Shafique N. Virani. You can download the complete online course, including this module, for free from <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbWYxLUx5SXZ4UGMyVl9OUk9sa2hxcGRab0lkUXxBQ3Jtc0ttYUo3eTcyb21sT3dVNjdSR3lpX2pCaDU2RkdndWZUdlVpd0QzZktkZ0tGX2ZJR29OUFlWSXVScy1qeEZuQkpiUHRkZTlCVU1Sd01pWXYtUE53R3VFajE1R0J4dDIxSUdTYTZSdVdGRjhTdTNqUkRqWQ&q=https%3A%2F%2Fwww.ecampusontario.ca%2F&v=lFtW3xMdB6Q">https://www.ecampusontario.ca/</a>.</p>
Egg Carton From Muslim Farms
Smithsonian source data can be found [here](https://ids.si.edu/ids/media_view?id=3d_package:060947f2-4cb8-4f3b-98d7-61a7254842b0) This media file is in the public domain (free of copyright restrictions). You can copy, modify, and distribute this work without contacting the Smithsonian. For more information and to review the 3D disclaimer, visit the Smithsonian's [Terms of Use](https://www.si.edu/Termsofuse) page. Egg Carton From Muslim Farms Manufactured by: Muslim Farms, Inc., American Used by: Nation of Islam, American, founded 1930 Medium: ink on cardboard Dimensions: H x W: 2 1/2 x 11 1/8 x 3 3/4 in. (6.4 x 28.3 x 9.5 cm) Type: egg baskets Place Used: Cassopolis, Cass County, Michigan, United States, North and Central America Date: 1968 Credit Line: Collection of the Smithsonian National Museum of African American History and Culture, Gift of the family of Becca Nu'Mani Data Source: National Museum of African American History and Culture EDAN-URL: edanmdm:nmaahc_2013.39.7 Source: Objaverse 1.0 / Sketchfab
A modest proposal for conducting future research on media portrayals of Islam and Muslims in Indonesia
<p>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.</p>
Code Status Discussions in Muslim ICU Patients: Insights Into Physician-Family Communication
ClinicalTrials.gov study NCT07243041. IPD Sharing: NO. Countries: 1. Publications: 16.
Brick by Brick Bias: Arab Muslim Experience of Intersectionality in Housing
<p>Experimental data on intersectional discrimination against Arab Muslims in the Swedish rental housing market. Definitions for variables are in the Excel data file. The Stata do-file contains the complete analysis in accordance with the paper. The published paper can be accessed here: <a href="https://doi.org/10.1080/1369183X.2024.2366319">https://doi.org/10.1080/1369183X.2024.2366319</a>. </p>
Supplementary materials of The Dynamics of The Relationship between Religious Identity and Fundamentalism in Predicting Muslim Prejudice against Christian in Indonesia
<p>Supplementary materials of <em><strong>The Dynamics of The Relationship between Religious Identity and Fundamentalism in Predicting Muslim Prejudice against Christian in Indonesia</strong> </em>published in <em><strong>Islamic Guidance and Counseling Journal (IGCJ) Vol. 7, No. 2, 2024</strong></em></p>
Critical appraisal of qualitative studies of Muslim females' perceptions of physical activity barriers and facilitators
<p>Excel file of 56 studies that investigated Muslim women's perceptions of physical activity barriers and facilitators in westernized, high-income countries. Characteristics of studies as well as application of Consolidated criteria for reporting qualitative research (COREQ) checklist across studies and 26 COREQ items.</p>
A Mosque-Based Intervention to Promote Physical Activity in South Asian Muslim Women
ClinicalTrials.gov study NCT02124967. IPD Sharing: UNDECIDED. Countries: 1. Publications: 14.
Islamically Integrated Chair-Work for Bereaved Muslims
ClinicalTrials.gov study NCT07193732. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Integrated Mindfulness-Based Cognitive Therapy for Singapore Malay Muslims
ClinicalTrials.gov study NCT05237336. IPD Sharing: NO. Countries: 1. Publications: 3.
Informing American Muslims About Living Donation
ClinicalTrials.gov study NCT04443114. IPD Sharing: NO. Countries: 1. Publications: 2.
Safety And Efficacy Of Empagliflozin In Pakistani Muslim Population With Type Ii Diabetes Mellitus
ClinicalTrials.gov study NCT04665284. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.