Alharbi Ebtisam, Skeva Rigina, Juty Nick, Jay Caroline, Goble Carole
College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia.
Department of Computer Science, University of Manchester, Manchester, UK.
Drug Discov Today. 2023 Apr;28(4):103510. doi: 10.1016/j.drudis.2023.103510. Epub 2023 Jan 27.
The FAIR (findable, accessible, interoperable and reusable) principles are data management and stewardship guidelines aimed at increasing the effective use of scientific research data. Adherence to these principles in managing data assets in pharmaceutical research and development (R&D) offers pharmaceutical companies the potential to maximise the value of such assets, but the endeavour is costly and challenging. We describe the 'FAIR-Decide' framework, which aims to guide decision-making on the retrospective FAIRification of existing datasets by using business analysis techniques to estimate costs and expected benefits. This framework supports decision-making on FAIRification in the pharmaceutical R&D industry and can be integrated into a company's data management strategy.
FAIR（可查找、可访问、可互操作和可重用）原则是旨在提高科学研究数据有效利用率的数据管理和监管指南。在制药研发（R&D）中管理数据资产时遵循这些原则，可为制药公司提供将此类资产价值最大化的潜力，但这一努力成本高昂且具有挑战性。我们描述了“FAIR-Decide”框架，该框架旨在通过使用业务分析技术来估计成本和预期收益，从而指导对现有数据集进行追溯性FAIR化的决策。此框架支持制药研发行业中关于FAIR化的决策，并可整合到公司的数据管理策略中。
