Background: The cohort was commenced to examine women's health from midlife (45-55 years) before the menopausal transition and into ageing. Methods: Randomised selection and assessment of 2,001 women living in the Melbourne metropolitan area was conducted by the Roy Morgan Centre in 1990/91. Of the 779 women who met the entry criteria for the longitudinal follow-up (aged 45-55 years, menstruating, having a uterus and at least one ovary and not taking hormone therapy) 438 agreed to be seen annually across the menopausal transition from 1992 to 1999. Longitudinal prospective follow-up since 2000 has continued intermittently (2002/03, 2004/05, 2012/13, 2014/15). Data collection has included fasting biomarkers in each year since 1992, clinical assessment, lifestyle and quality of life data, physical measures and validated questionnaire data. Participants have consented to data linkage and, to date, mammogram and BioGrid data have been accessed. Biobank storage including serum, deoxyribonucleic acid (DNA) storage and PAXgene tubes are maintained. Discussion: The WHAP has contributed to over 200 published research findings, several books, and book chapters in a variety of areas, including: health and wellbeing; mental and cognitive health; bone health; lifestyle, vascular risk and prevention; women's health and hormonal transition; and cross-cultural research. With all participants now aged over 70 years, the cohort is ideally placed to answer key questions of healthy ageing in women. With more than 25 years of longitudinal prospective follow-up this Australian dataset is unique in its duration, breadth and detail of measures including clinical review and specialized disease-specific testing and biomarkers. Ongoing follow-up into older ages for this long-running cohort will enable the association between mid to late-life factors and healthy ageing to be determined. This is particularly valuable for the examination of chronic diseases which have a 20-30 year prodrome and to provide knowledge on multiple morbidities. The dataset has a unique opportunity to improve our understanding of temporal relationships and the interactions between risk factors and comorbidities.