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Introduction

Background

FHS is an epidemiologic study beginning in Framingham, Massachusetts in 1948 with 5,209 men and women. Since that time the FHS has studied three generations of participants, with an addition of two ethnically and racially diverse populations and related individuals from 1994 – a total of six study cohorts, resulting in biological specimens and data over 15,000 participants. This clinically and genetically well-characterized population is a valuable scientific source that is maintained under the joint stewardship of Boston University and the NHLBI.

  • Tsao CW, Vasan RS. Cohort Profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology. Int J Epidemiol. 2015 Dec;44(6):1800-13. doi: 10.1093/ije/dyv337. PMID: 26705418; PMCID: PMC5156338.

Data documentation plays a crucial role for all FHS-BAP data management processes by providing clarity, transparency, and credibility to the data presented.​

  1. Transparency: Data documentation ensures that researchers understand the source, collection methods, and processing techniques used to generate the data.

  2. Credibility: Including data documentation in the introduction section enhances the credibility of the FHS-BAP Data. Researchers are more likely to trust and rely on information that is accompanied by thorough documentation, as it indicates that the data is well-researched and accurately represented.

  3. Accuracy: Data documentation helps researchers understand the limitations and potential biases associated with the data. It provides insights into any assumptions made during data collection and analysis, helping researchers interpret the information accurately and avoid misinterpretations.

  4. Accessibility: By including these data documentations, researchers have easy access to relevant information about the FHS-BAP data. This accessibility is essential for users who want to delve deeper into the data or verify its accuracy independently.

  5. Promote Collaboration: Data documentation facilitates collaboration among different stakeholders, such as data analysts, researchers, and developers. By documenting the data in a clear and structured manner, it becomes easier for researchers to communicate, share insights, and collaborate on data-related projects.