Ten simple rules for queer data collection and analysis by STEM researchers.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-05-28 eCollection Date: 2025-05-01 DOI:10.1371/journal.pcbi.1013091
Dori M Grijseels, M Banqueri, Keerthana Iyer, Lee Hanlin, Melanie Ortiz Alvarez de la Campa, David Pagliaccio, Bittu K Rajaraman, Eitan Schechtman
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引用次数: 0

Abstract

Queer people are still underrepresented both as STEM researchers and participants, partially due to a dearth of accurate data on this demographic. The lack of consideration for queer identities in data collection and dissemination causes a vicious cycle of exclusion. To address this invisibility, it is important to collect and report data in an inclusive and accurate manner across all areas of research, including in studies that are not specifically focused on queer populations. However, STEM researchers are often unsure of how to properly collect data in a manner that fairly represents queer people. We have developed a list of Ten Simple rules to aid researchers to perform data collection on queer individuals, focusing on study design and data dissemination. We address several issues in queer data, such as language use, dealing with small populations, and balancing demands. We also discuss how to extend this inclusive practice for studies on animal populations. These rules are aimed at anybody surveying populations which may contain queer individuals, including for example research studies and inclusivity surveys for conferences. By providing practical tips, we hope to alleviate insecurity and confusion around this topic.

STEM研究人员收集和分析酷儿数据的十条简单规则。
在STEM研究人员和参与者中,酷儿群体的代表性仍然不足,部分原因是缺乏关于这一人口统计的准确数据。在数据收集和传播中缺乏对酷儿身份的考虑,导致了排斥的恶性循环。为了解决这一问题,重要的是要以包容和准确的方式收集和报告所有研究领域的数据,包括那些不是专门针对酷儿群体的研究。然而,STEM研究人员往往不确定如何以公平代表酷儿人群的方式正确收集数据。我们制定了十项简单规则,以帮助研究人员对酷儿个体进行数据收集,重点是研究设计和数据传播。我们解决了酷儿数据中的几个问题,如语言使用、处理小群体和平衡需求。我们还讨论了如何将这种包容性实践扩展到动物种群的研究中。这些规则是针对任何调查可能包含酷儿个体的人群的人,包括例如研究研究和会议的包容性调查。通过提供实用技巧,我们希望减轻围绕这个主题的不安全感和困惑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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