Ten simple rules for queer data collection and analysis by STEM researchers
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.
Authors
- Dori M. Grijseels (ORCID: https://orcid.org/0000-0002-3822-5323)
- Keerthana Iyer (ORCID: https://orcid.org/0000-0002-4384-6781)
- David Pagliaccio (ORCID: https://orcid.org/0000-0002-1214-1965)
- Bittu Kaveri Rajaraman (ORCID: https://orcid.org/0000-0003-2435-7015)
- Eitan Schechtman (ORCID: https://orcid.org/0000-0002-8127-012X)
- Melanie Ortiz Alvarez de la Campa (ORCID: https://orcid.org/0000-0002-0894-2938)
- M. Banqueri
- Lee Hanlin
Institutions
- University of California, Irvine (US)
- Brown University (US)
- New York Psychoanalytic Society and Institute (US)
- Ashoka University (IN)
- University of California San Diego (US)
- Max Planck Institute for Brain Research (DE)
- FENS Kavli Network of Excellence (BE)
- New York State Psychiatric Institute
- Columbia University (US)
- University of Edinburgh (GB)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1371/journal.pcbi.1013091
- Citations
- 2
- Primary Topic
- Sex and Gender in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.76