# Increasing Diversity of the Genomics Workforce Through Accessible Data and Visualization > **NIH NIH K99** · HARVARD MEDICAL SCHOOL · 2024 · $144,087 ## Abstract Project Summary Despite efforts to increase diversity and inclusion, opportunities in genomics education and research are still unequally offered to people with disabilities. While making data and visualization resources accessible and useful is vital in genomics education, research, and clinical se ings, genomics resources such as data portals, visualizations, and research papers largely fail to meet accessibility standards according to our preliminary evaluation (96.3% of 2,936 evaluated portals), making essential resources rather inaccessible to people with visual impairments. My overarching goal is to include disability in the genomics workforce by enhancing the accessibility of data and visualization resources. This project proposes three specific aims: (1) conducting accessibility evaluations of biomedical resources, (2) developing accessible visual and textual representations for genomics data and novel tools based on them, and (3) implementing accessible graphical user interfaces for genomics data analysis. I will conduct a series of comprehensive accessibility evaluations of biomedical resources with input from users. Based on the evaluation results, I will build accessibility guidelines for biomedical resources and their priorities based on different use cases which will complement general accessibility guidelines. The evaluations will unveil critical aspects of potential improvements and offer guidance toward accessible biomedical resources. Second, by extending my earlier work on the Gosling grammar-based genomics data visualization, I will help content creators to build accessible genomics data tables and visualizations which will offer smart accessibility defaults and out-of-the-box accessibility features. Based on the extended Gosling, I will develop an assistive toolkit that will automatically generate missing metadata and refine the structure of web pages to enable viewing currently inaccessible data tables and figures in existing web-based genomics resources. Given the complexity and scale of genomics data, user interactions, such as zooming and panning, are essential techniques for genomics data analysis. However, traditional mouse-based interactions are largely inaccessible without accurate vision. As the last aim, I propose to design accessible and intuitive user interactions tailored for genomics visualization, such as keyboard-based interactions. Building on top of the accessible interactions, I will build an accessibility-friendly graphical user interface (GUI) platform that enables people with visual impairments to create visualizations and analyze genomics data. The novel tools I will develop will not only help content creators to efficiently and accurately create accessible visualizations but also enable current and prospective genomics students, researchers, and clinicians with visual impairments to access, interpret, and analyze genomics data, making data-driven genomics research more inclusive. ## Key facts - **NIH application ID:** 10984200 - **Project number:** 1K99HG013348-01A1 - **Recipient organization:** HARVARD MEDICAL SCHOOL - **Principal Investigator:** Sehi LYi - **Activity code:** K99 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $144,087 - **Award type:** 1 - **Project period:** 2024-09-03 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10984200 ## Citation > US National Institutes of Health, RePORTER application 10984200, Increasing Diversity of the Genomics Workforce Through Accessible Data and Visualization (1K99HG013348-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10984200. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*