Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle

Objective: Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets...

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Autores principales:	Ari Gofman, Sam A. Leif, Hannah Gunderman, Nina Exner
Formato:	article
Lenguaje:	EN
Publicado:	University of Massachusetts Medical School, Lamar Soutter Library 2021
Materias:	gender demographics nonbinary transgender data collection Bibliography. Library science. Information resources Z
Acceso en línea:	https://doaj.org/article/01861a9b3d41496a8fef7be2b96a9ee9
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spelling	oai:doaj.org-article:01861a9b3d41496a8fef7be2b96a9ee92021-11-18T17:54:23ZDo I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle10.7191/jeslib.2021.12192161-3974https://doaj.org/article/01861a9b3d41496a8fef7be2b96a9ee92021-11-01T00:00:00Zhttps://escholarship.umassmed.edu/jeslib/vol10/iss4/6/https://doaj.org/toc/2161-3974Objective: Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets represent nonbinary persons and how research involving gender data should approach the curation of this data at each stage of the research lifecycle. Methods: In this article, we examine some of the known challenges of gender inclusion in datasets and summarize some solutions underway. Using a critical lens, we examine the difference between current practice and inclusive practice in gender representation, describing inclusive practices at each stage of the research lifecycle from writing a data management plan to sharing data. Results: Data structures that limit gender to “male” and “female” or ontological structures that use mapping to collapse gender demographics to binary values exclude nonbinary and gender diverse populations. Some data collection instruments attempt inclusivity by adding the gender category of “other,” but using the “other” gender category labels nonbinary persons as intrinsically alien. Inclusive change must go farther, to move from alienation to inclusive categories. We describe several techniques for inclusively representing gender in data, from the data management planning stage, to collecting data, cleaning data, and sharing data. To facilitate better sharing of gender data, repositories must also allow mapping that includes nonbinary genders explicitly and allow for ontological mapping for long-term representation of diverse gender identities. Conclusions: A good practice during research design is to consider two levels of critique in the data collection plan. First, consider the research question at hand and remove unnecessary gendering from the data. Secondly, if the research question needs gender, make sure to include nonbinary genders explicitly. Allies must take on this problem without leaving it to those who are most affected by it. Further, more voices calling for inclusionary practices surrounding data rises to a crescendo that cannot be ignored.Ari GofmanSam A. LeifHannah GundermanNina ExnerUniversity of Massachusetts Medical School, Lamar Soutter Libraryarticlegender demographicsnonbinarytransgenderdata collectionBibliography. Library science. Information resourcesZENJournal of eScience Librarianship, Vol 10, Iss 4, p 1219 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gender demographics nonbinary transgender data collection Bibliography. Library science. Information resources Z
spellingShingle	gender demographics nonbinary transgender data collection Bibliography. Library science. Information resources Z Ari Gofman Sam A. Leif Hannah Gunderman Nina Exner Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle
description	Objective: Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets represent nonbinary persons and how research involving gender data should approach the curation of this data at each stage of the research lifecycle. Methods: In this article, we examine some of the known challenges of gender inclusion in datasets and summarize some solutions underway. Using a critical lens, we examine the difference between current practice and inclusive practice in gender representation, describing inclusive practices at each stage of the research lifecycle from writing a data management plan to sharing data. Results: Data structures that limit gender to “male” and “female” or ontological structures that use mapping to collapse gender demographics to binary values exclude nonbinary and gender diverse populations. Some data collection instruments attempt inclusivity by adding the gender category of “other,” but using the “other” gender category labels nonbinary persons as intrinsically alien. Inclusive change must go farther, to move from alienation to inclusive categories. We describe several techniques for inclusively representing gender in data, from the data management planning stage, to collecting data, cleaning data, and sharing data. To facilitate better sharing of gender data, repositories must also allow mapping that includes nonbinary genders explicitly and allow for ontological mapping for long-term representation of diverse gender identities. Conclusions: A good practice during research design is to consider two levels of critique in the data collection plan. First, consider the research question at hand and remove unnecessary gendering from the data. Secondly, if the research question needs gender, make sure to include nonbinary genders explicitly. Allies must take on this problem without leaving it to those who are most affected by it. Further, more voices calling for inclusionary practices surrounding data rises to a crescendo that cannot be ignored.
format	article
author	Ari Gofman Sam A. Leif Hannah Gunderman Nina Exner
author_facet	Ari Gofman Sam A. Leif Hannah Gunderman Nina Exner
author_sort	Ari Gofman
title	Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle
title_short	Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle
title_full	Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle
title_fullStr	Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle
title_full_unstemmed	Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle
title_sort	do i have to be an “other” to be myself? exploring gender diversity in taxonomy, data collection, and through the research data lifecycle
publisher	University of Massachusetts Medical School, Lamar Soutter Library
publishDate	2021
url	https://doaj.org/article/01861a9b3d41496a8fef7be2b96a9ee9
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Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle

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