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药物发现和开发中 FAIR 数据选择:选择哪些数据、为什么选择以及如何选择?

Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

机构信息

Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK; College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia.

Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany.

出版信息

Drug Discov Today. 2022 Aug;27(8):2080-2085. doi: 10.1016/j.drudis.2022.05.010. Epub 2022 May 17.

Abstract

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost-benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.

摘要

尽管在学术和工业领域采用可发现、可访问、可互操作和可重用(FAIR)原则具有直观的价值,但在资源配置、平衡长期与短期优先事项以及实现技术实施方面仍存在挑战。当涉及到 FAIR 决策时,由于成本和收益评估的机制不明确,这种情况更加严重。科学和研究与开发(R&D)领导层需要可靠的潜在利益证据,以及有关有效实施机制和补救策略的信息。在本文中,我们描述了成本效益评估的程序,并确定了支持 FAIR 实施决策过程的最佳实践方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f9e/9236643/1fab2fee13f7/gr1.jpg

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