Data Science for Diverse Scholars in Down Syndrome Research (DS3)

PROJECT SUMMARY. The mission of the NIH INCLUDE Project is to accelerate research that addresses the critical health and quality of life needs of individuals with Down syndrome (DS). A key aspect of this mission is to ensure that the many large and valuable multidimensional datasets that are being generated by INCLUDE-funded projects are

available and amenable to a diverse community of researchers. Therefore, to increase diversity, equity, inclusion, and accessibility in the INCLUDE Project in particular and in the DS research community more broadly, we propose to develop an immersive summer course known as the Data Science for Diverse Scholars in Down

Syndrome Research (DS3). This course will provide training in the generation, identification, and collection of high-content multidimensional datasets; their management, analysis, and visualization, including cloud-based analytics; and the development of professional skills required for the career advancement of diverse trainees.

Led by a diverse teaching team, the DS3 will be developed along the following Specific Aims: Aim 1. To teach students how to perform FAIR research in the INCLUDE Data Hub and beyond. We will teach the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) and demonstrate the FAIR

use of datasets available in the INCLUDE Data Hub and other synergistic public repositories. We will tutor students about where to find datasets, how to frame answerable questions, considerations in using public data, and how to analyze these datasets in a cloud-based environment. Aim 2. To train students in the skills necessary for big data management, analysis, and visualization.

Using short-read next-generation sequencing (NGS) data as a foundational data type, students will learn the basics of high-performance computing skills (e.g., Unix/Linux, cluster computing skills, file management and data analysis workflows). Students will be trained on the integration of -omics data types with clinical metadata, and

also receive training in data visualization and biostatistical tools using Python and R Studio. Aim 3. To empower diverse trainees with professional skills necessary for career advancement. We will provide workshops on how to write a compelling Specific Aims page, how to develop clear presentations, and

how to prepare an impactful curriculum vitae and NIH biosketch. Furthermore, we will hold networking events with prominent DS researchers and self-advocates who are invited as guest speakers. Aim 4. To develop a portfolio of DS3 training resources for dissemination at a global scale. We will develop

a DS3 website connecting to public libraries of DS3 video tutorials, example analyses, presentations, slide decks, datasets, and code for sharing with the global community. Altogether, this training program will enhance diversity in the pool of young DS researchers, while also elevating data science literacy to accelerate clinical research that will benefit people with DS.