Summary Through this new NIH-funded biomedical center at the University of Kansas (KU), we aspire to leverage big data to improve women’s health. A strong and diverse inaugural group of Research Project Leaders, spanning the laboratory sciences and social/behavioral sciences would lead the research. Their goals include: improving the detection and treatment of ovarian cancer; developing a better understanding of estrogen’s link to neurodegeneration; providing women, who are at higher risk of Alzheimer’s Disease, with tools they can use to improve their lifestyle and reduce disease probability; additionally, two different projects seek to improve women’s health by studying lifestyles, overall health, and the impact of health-improving innovations (i.e telemedicine) in a geographic and sex-differentiated context so that limited public health funds can be leveraged for maximal public good. The Center would enable progress on these projects and others at the interface of data science and women’s health by providing a strong administrative structure organized around the following Specific Aims: 1) Advance five RPLs’ research programs (through mentoring, grant-writing support, and professional development) 2) Engage additional research faculty (through three new tenure-track hires and a unique Research-Engaged Faculty Fellows Program); 3)Bolster Big Data research with core lab support (through two initiatives, the development of a new core lab focused on Biomedical Datasets and Services, and a voucher program, supported by university matching funds, that leverages KU’s 17 existing centralized research core lab facilities.) The well-planned theme, excellent inaugural project leaders, close coordination with university administration, and experienced Center Director and Associate-Director all lend strong credibility that the goals would be achieved. The successful completion of these aims will markedly increase the biomedical research capabilities on our campus, establishing at our institution a unique center that leverages data science for advancing women’s health research.