Conducting an Epidemiologic Study and Making It FAIR: Reusable Tools and Procedures from a Population-Based Cohort...

Area of Science:

• Epidemiology
• Health Informatics
• Data Science

Background:

• Large-scale epidemiologic studies require robust software for data management and participant tracking.
• There is a growing demand for research data to be findable, accessible, interoperable, and reusable (FAIR).
• Existing reusable software tools from major studies are often not widely known to the research community.

Purpose of the Study:

• To provide an overview of the software tools used in the Study of Health in Pomerania (SHIP).
• To describe approaches implemented to enhance the FAIRness of SHIP data.
• To highlight the importance of software and FAIR principles in large population-based research.

Main Methods:

• Utilized powerful software for electronic data capture, data management, quality assessment, and participant management.
• Implemented deep phenotyping and formalized data capture to data transfer processes.
• Focused on cooperation and data exchange to improve study and data FAIRness.

Main Results:

• The Study of Health in Pomerania (SHIP) employs a suite of tools for comprehensive data handling.
• Approaches to enhance data FAIRness have been successfully integrated into the study's workflow.
• The study has facilitated over 1500 published papers, demonstrating broad scientific impact.

Conclusions:

• Effective software and adherence to FAIR principles are crucial for successful large-scale epidemiologic research.
• Formalized processes and a focus on data exchange significantly contribute to a study's scientific reach.
• Sharing knowledge about tools and FAIR approaches can benefit the wider research community.