Parciak Marcel, Bender Theresa, Sax Ulrich, Bauer Christian Robert
Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Niedersachsen, Germany.
Methods Inf Med. 2019 Dec;58(6):229-234. doi: 10.1055/s-0040-1709158. Epub 2020 Apr 29.
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.
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.
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.
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.
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.
生物医学信息学研究中的研究数据管理需要可靠的数据治理规则来确保可持续运行，因为它通常涉及多个专业和多个地点。由于生物医学研究涉及的每个学科都应用其自己的一套工具和方法，研究数据以及所应用的方法往往会扩展为众多中间数据对象和输出数据对象，这使得很难重现研究结果。
本文概述了我们在科学数据管理和 stewardship 的可查找性、可访问性、互操作性和可重用性（FAIR）指导原则应用于我们的研究数据管理流程方面的实施现状，重点关注正在使用的软件工具。
我们分析了在使整个数据管理流程 FAIR 化方面的进展，从处理非 FAIR 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。
我们能够提高数据集成以及数据存储解决方案的 FAIR 程度，但在关于数据使用方面缺乏更多的 FAIR 指导原则。现有的关于 FAIR 指导原则的评估方法（FAIRmetrics）不适用于我们对软件工具的分析。
使用 FAIR 指导原则，我们使研究数据管理流程的相关部分 FAIR 化，提高了数据集和研究结果的可查找性、可访问性、互操作性和可重用性。我们旨在将 FAIRmetrics 应用于我们的数据管理基础设施，并在需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，以实现我们研究数据管理流程更高程度的 FAIR 性。
