Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom

<p dir="ltr">“U.S. defaultism”—the assumption that American contexts, units, and perspectives are universal—manifests in many ways in political science. In this article, I describe how toy datasets commonly employed in quantitative methods courses exemplify this problem. Using custom...

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المؤلف الرئيسي: Paul Musgrave (12982827) (author)
منشور في: 2025
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author Paul Musgrave (12982827)
author_facet Paul Musgrave (12982827)
author_role author
dc.creator.none.fl_str_mv Paul Musgrave (12982827)
dc.date.none.fl_str_mv 2025-10-15T09:00:00Z
dc.identifier.none.fl_str_mv 10.1080/15512169.2025.2572320
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/Defaulting_to_Inclusion_Producing_Sample_Datasets_for_the_Global_Data_Science_Classroom/31241554
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Human society
Political science
Information and computing sciences
Data management and data science
Statistical education
U.S. defaultism
international pedagogy
inclusive teaching
dc.title.none.fl_str_mv Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
dc.type.none.fl_str_mv Text
Journal contribution
info:eu-repo/semantics/publishedVersion
text
contribution to journal
description <p dir="ltr">“U.S. defaultism”—the assumption that American contexts, units, and perspectives are universal—manifests in many ways in political science. In this article, I describe how toy datasets commonly employed in quantitative methods courses exemplify this problem. Using customary units, for instance, is unsuitable for an internationalized higher education system. To address these limitations, I introduce the Qatar Cars (qcars) dataset, a freely available alternative toy dataset that uses International System (SI) units, reflects current global automotive market trends (such as the rise of Chinese manufacturers and electric vehicles), and avoids ethnocentric classifications such as labeling the non-U.S. world “foreign.” Created through collaborative data collection with students, the Qatar Cars dataset maintains the pedagogical advantages of earlier datasets, improves statistical instruction by removing barriers for international audiences, and provides opportunities to discuss data-generating processes and research ethics.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Journal of Political Science Education<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/15512169.2025.2572320" target="_blank">https://dx.doi.org/10.1080/15512169.2025.2572320</a></p>
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oai_identifier_str oai:figshare.com:article/31241554
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spelling Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science ClassroomPaul Musgrave (12982827)Human societyPolitical scienceInformation and computing sciencesData management and data scienceStatistical educationU.S. defaultisminternational pedagogyinclusive teaching<p dir="ltr">“U.S. defaultism”—the assumption that American contexts, units, and perspectives are universal—manifests in many ways in political science. In this article, I describe how toy datasets commonly employed in quantitative methods courses exemplify this problem. Using customary units, for instance, is unsuitable for an internationalized higher education system. To address these limitations, I introduce the Qatar Cars (qcars) dataset, a freely available alternative toy dataset that uses International System (SI) units, reflects current global automotive market trends (such as the rise of Chinese manufacturers and electric vehicles), and avoids ethnocentric classifications such as labeling the non-U.S. world “foreign.” Created through collaborative data collection with students, the Qatar Cars dataset maintains the pedagogical advantages of earlier datasets, improves statistical instruction by removing barriers for international audiences, and provides opportunities to discuss data-generating processes and research ethics.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Journal of Political Science Education<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/15512169.2025.2572320" target="_blank">https://dx.doi.org/10.1080/15512169.2025.2572320</a></p>2025-10-15T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1080/15512169.2025.2572320https://figshare.com/articles/journal_contribution/Defaulting_to_Inclusion_Producing_Sample_Datasets_for_the_Global_Data_Science_Classroom/31241554CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/312415542025-10-15T09:00:00Z
spellingShingle Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
Paul Musgrave (12982827)
Human society
Political science
Information and computing sciences
Data management and data science
Statistical education
U.S. defaultism
international pedagogy
inclusive teaching
status_str publishedVersion
title Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
title_full Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
title_fullStr Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
title_full_unstemmed Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
title_short Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
title_sort Defaulting to Inclusion: Producing Sample Datasets for the Global Data Science Classroom
topic Human society
Political science
Information and computing sciences
Data management and data science
Statistical education
U.S. defaultism
international pedagogy
inclusive teaching