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The data in the U of M’s Clinical Data Repository comes from the electronic health records (EHRs) of more than 2 million patients seen at 8 hospitals and more than 40 clinics. For each patient, data is available regarding the patient's demographics (age, gender, language, etc.), medical history, problem list, allergies, immunizations, outpatient vitals, diagnoses, procedures, medications, lab tests, visit locations, providers, provider specialties, and more.
OHSU Digital Commons is a repository for the scholarly and creative work of Oregon Health & Science University. Developed by the OHSU Library, Digital Commons provides the university community with a platform for publishing and accessing content produced by students, faculty, and staff. OHSU Digital Commons documents the history and growth of the university, as well as current progress in education, research, and health care.
The CDHA assists researchers to create, document, and distribute public use microdata on health and aging for secondary analysis. Major research themes include: midlife development and aging; economics of population aging; inequalities in health and aging; international comparative studies of health and aging; and the investigation of linkages between social-demographic and biomedical research in population aging. The CDHA is one of fourteen demography centers on aging sponsored by the National Institute on Aging.
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The Canadian Longitudinal Study on Aging (CLSA) is a large, national, long-term study of more than 50,000 individuals who were between the ages of 45 and 85 when recruited. These participants will be followed until 2033 or death. The aim of the CLSA is to find ways to help us live long and live well, and understand why some people age in healthy fashion while others do not.
Knoema is a knowledge platform. The basic idea is to connect data with analytical and presentation tools. As a result, we end with one uniformed platform for users to access, present and share data-driven content. Within Knoema, we capture most aspects of a typical data use cycle: accessing data from multiple sources, bringing relevant indicators into a common space, visualizing figures, applying analytical functions, creating a set of dashboards, and presenting the outcome.