共享始于家庭:持续和无处不在的 FAIRness 如何提高研究效率和数据再利用。

Harvard data science review Pub Date : 2022-01-01 Epub Date: 2022-07-28 DOI:10.1162/99608f92.44d21b86
William Dempsey, Ian Foster, Scott Fraser, Carl Kesselman
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引用次数: 0

摘要

人们普遍认为,广泛共享研究数据对于提高科学研究的速度、质量、可获取性和完整性至关重要。尽管鼓励数据共享的力度不断加大,但共享数据的质量和数据再利用的频率仍然很低。我们在此认为,造成这种不幸状况的一个重要原因是,以可查找、可访问、可互操作和可重复使用(FAIR)的形式组织研究成果以满足重复使用的要求,往往被推迟到研究项目结束、准备出版物时才进行。因此,我们提出了一种研究信息学方法,在这种方法中,FAIR 原则从研究项目一开始就被持续应用于实验或计算所产生的每项数据资产。我们认为,这项看似具有挑战性的任务可以通过采用简单的工具来实现,例如轻量级标识符(确保每项数据资产都能被找到)、打包方法(便于理解数据内容)、数据访问方法以及元数据组织和结构化工具(支持模式开发和演化)。我们以实验神经科学为例,说明这些方法如何在实践中发挥作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.

Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.

The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the findable, accessible, interoperable, and reusable (FAIR) form required for reuse is too often deferred to the end of a research project when preparing publications-by which time essential details are no longer accessible. Thus, we propose an approach to research informatics in which FAIR principles are applied continuously, from the inception of a research project and ubiquitously, to every data asset produced by experiment or computation. We suggest that this seemingly challenging task can be made feasible by the adoption of simple tools, such as lightweight identifiers (to ensure that every data asset is findable), packaging methods (to facilitate understanding of data contents), data access methods, and metadata organization and structuring tools (to support schema development and evolution). We use an example from experimental neuroscience to illustrate how these methods can work in practice.

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