Abstract

Background Managing research data in biomedical informatics research requires solid data governance rules to guarantee sustainable operation, as it generally involves several professions and multiple sites. As every discipline involved in biomedical research applies its own set of tools and methods, research data as well as applied methods tend to branch out into numerous intermediate and output data objects, making it very difficult to reproduce research results.

Objectives This article gives an overview of our implementation status applying the Findability, Accessibility, Interoperability and Reusability (FAIR) Guiding Principles for scientific data management and stewardship onto our research data management pipeline focusing on the software tools that are in use.

Methods We analyzed our progress FAIRificating the whole data management pipeline, from processing non-FAIR data up to data usage. We looked at software tools for data integration, data storage, and data usage as well as how the FAIR Guiding Principles helped to choose appropriate tools for each task.

Results We were able to advance the degree of FAIRness of our data integration as well as data storage solutions, but lack enabling more FAIR Guiding Principles regarding Data Usage. Existing evaluation methods regarding the FAIR Guiding Principles (FAIRmetrics) were not applicable to our analysis of software tools.

Conclusion Using the FAIR Guiding Principles, we FAIRificated relevant parts of our research data management pipeline improving findability, accessibility, interoperability and reuse of datasets and research results. We aim to implement the FAIRmetrics to our data management infrastructure and—where required—to contribute to the FAIRmetrics for research data in the biomedical informatics domain as well as for software tools to achieve a higher degree of FAIRness of our research data management pipeline.

中文翻译：

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应用公平：重新设计生物医学信息学研究数据管理管道。

摘要

背景 技术在生物医学信息学研究中管理研究数据需要可靠的数据治理规则来保证可持续的运行，因为它通常涉及多个专业和多个站点。由于涉及生物医学研究的每个学科都使用自己的一套工具和方法，因此研究数据以及应用的方法往往会分支为众多的中间数据和输出数据对象，从而很难重现研究结果。

目标 本文概述了将科学数据管理和管理的可发现性，可访问性，互操作性和可重用性（FAIR）指导原则应用到我们的研究数据管理管道中（以正在使用的软件工具为基础）的实施状态概述。

方法 我们分析了公平处理整个数据管理管道的进展，从处理非公平数据到数据使用。我们研究了用于数据集成，数据存储和数据使用的软件工具，以及FAIR指导原则如何帮助为每个任务选择合适的工具。

结果 我们能够提高数据集成和数据存储解决方案的公平性，但是缺乏启用更多关于数据使用的公平指导原则。关于FAIR指导原则（FAIRmetrics）的现有评估方法不适用于我们对软件工具的分析。

结论 根据FAIR指导原则，我们对研究数据管理管道的相关部分进行了公平化处理，从而提高了数据集和研究结果的可发现性，可访问性，互操作性和重用性。我们旨在将FAIRmetrics应用于我们的数据管理基础架构，并在必要时为生物医学信息学领域的研究数据以及软件工具的FAIRmetrics做出贡献，以实现研究数据管理流程更高程度的FAIR。