The production of large, shareable datasets is increasingly prioritized for a wide range of research purposes. In biomedicine, especially in the United States, calls to enhance representation of historically underrepresented populations in databases that integrate genomic, health history, demographic and lifestyle data have also increased in order to support the goals of precision medicine. Understanding the assumptions and values that shape the design of such datasets and the practices through which they are constructed are a pressing area of social inquiry. We examine how diversity is conceptualized in U.S. precision medicine research initiatives, specifically attending to how measures of diversity, including race, ethnicity, and medically underserved status, are constructed and harmonized to build commensurate datasets. In three case studies, we show how symbolic embrace of both diversity and harmonization efforts can compromise the utility of diversity data. Although big data and diverse population representation are heralded as the keys to unlocking the promises of precision medicine research, these cases reveal core tensions between what kinds of data are seen as central to ‘the science’ and which are marginalized.

中文翻译：

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边缘化措施：精准医学研究中多样性的协调


出于广泛的研究目的，制作大型、可共享的数据集越来越受到重视。在生物医学领域，尤其是在美国，为了支持精准医疗的目标，在整合基因组、健康历史、人口统计和生活方式数据的数据库中，提高历史上代表性不足的人群的代表性的呼声也有所增加。了解塑造此类数据集设计的假设和价值观以及构建它们的实践是社会探究的一个紧迫领域。我们研究了美国精准医学研究计划中如何概念化多样性，特别是关注如何构建和协调多样性的衡量标准，包括种族、民族和医疗服务不足的地位，以建立相应的数据集。在三个案例研究中，我们展示了象征性地接受多元化和协调努力如何损害多元化数据的效用。尽管大数据和多样化的人群代表性被誉为开启精准医学研究前景的关键，但这些案例揭示了哪些类型的数据被视为“科学”的核心数据与哪些被边缘化之间的核心紧张关系。