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Background: Science strives to provide high-quality evidence for all members of society, but there continues to be a considerable gender and diversity data gap, i.e., a systematic lack of data for traditionally underrepresented groups. Gender and other diversity domains are related to morbidity, mortality, and social and economic participation, yet comprehensive measures as well as evidence regarding how these domains intersect are missing. We propose a brief, efficient Diversity Minimal Item Set (DiMIS) for routine data collection in empirical studies to contribute to closing the diversity and gender data gap. We focus on the example of health but consider the DiMIS applicable across scientific disciplines.Methods: To identify items for the DiMIS across diversity domains, we performed an extensive literature search and conducted semi-structured interviews with scientific experts and community stakeholders in ten diversity domains. Using this information, we created a minimal item set of self-report survey items for each domain.Findings: Items covering ten diversity domains as well as discrimination experiences were compiled from a variety of sources and modified as recommended by experts. The DiMIS focuses on an intersectional approach, i.e., studying gender, age, socioeconomic status, care responsibilities, sexual orientation, ethnicity, religion, disability, mental and physical health, and their intersections. It allows for data sets with comparable assessments of gender and diversity across multiple projects to be combined, creating samples large enough for meaningful analyses. Interpretation: In proposing the DiMIS, we hope to advance the conversation about closing the gender and diversity data gap in science.

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A major goal of stigma and health research is to elucidate the link between patients' lived experiences of stigma and measurable aspects of health (e.g., quality of life and health outcomes). However, "lived experience" and "measurable health outcomes" are often at methodological odds, with the former typically characterized qualitatively and the latter typically characterized using quantified measures. One way to bridge this methodological gap is to collect and report quantitative measures as part of a qualitative study, pool both the quantitative and qualitative data, and scale the combined qualitative and quantitative data for meta-analysis. This article will discuss the advantages and disadvantages of this approach, applications in healthcare, and offer suggestions for putting this method into practice.