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Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combinationto aid meaningful interpretation and analysis of such data.The eTRANSAFE Consortium's effort to drive integrative drug safetyresearch at a large scale hereby present the lessons learnt and theproposal of solution at the guidelines in practice at this InnovativeMedicines Initiative (IMI) project. Recommendations in theseguidelines were compiled from feedback received from keystakeholders in regulatory agencies, EFPIA companies, and academicpartners. The research reproducibility guidelines presented in thisstudy lay the foundation for a comprehensive data sharing andknowledge management plans accounting for research datamanagement in the drug safety space - FAIR data sharing guidelines,and the model verification guidelines as generic deliverables that bestpractices that can be reused by other scientific community members at large.FAIR data sharing is a dynamic landscape that rapidly evolves withfast-paced technology advancements. The research reproducibility indrug safety guidelines introduced in this study provides a reusableframework that can be adopted by other research communities thataim to integrate public and private data in biomedical research space