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A FAIR-Decide framework for pharmaceutical R&D: FAIR data cost-benefit assessment
Ebtisam Alharbi1, Rigina Skeva2, Nick Juty2
1College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia.
Drug Discovery Today
|January 30, 2023
Summary
The FAIR-Decide framework aids pharmaceutical companies in deciding whether to make existing research data findable, accessible, interoperable, and reusable (FAIR). It uses business analysis to weigh the costs against the benefits of data FAIRification.
Area of Science:
- Pharmaceutical Research & Development
- Data Management
- Scientific Data Stewardship
Background:
- The FAIR principles enhance scientific data utility but implementing them in pharmaceutical R&D is challenging and costly.
- Effective data management is crucial for maximizing the value of pharmaceutical research assets.
Purpose of the Study:
- To introduce the FAIR-Decide framework for guiding decisions on retrospective FAIRification of datasets.
- To support pharmaceutical R&D companies in their data management strategies.
Main Methods:
- Utilizes business analysis techniques to estimate costs and benefits.
- Focuses on decision-making for retrospective FAIRification of existing datasets.
Main Results:
- Provides a structured approach to evaluating the financial and strategic implications of FAIRification.
- Enables informed decision-making regarding data management investments.
Conclusions:
- The FAIR-Decide framework offers a practical solution for the pharmaceutical industry to navigate the complexities of data FAIRification.
- Integration of FAIR-Decide can optimize data management strategies and enhance the value derived from research data assets.
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