Institute for Biomedical Informatics, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
Research Group Neuroscience, IZKF, Department of Neurophysiology, RWTH Aachen University, Aachen, Germany.
Stud Health Technol Inform. 2024 Aug 30;317:40-48. doi: 10.3233/SHTI240836.
The Local Data Hub (LDH) is a platform for FAIR sharing of medical research (meta-)data. In order to promote the usage of LDH in different research communities, it is important to understand the domain-specific needs, solutions currently used for data organization and provide support for seamless uploads to a LDH. In this work, we analyze the use case of microneurography, which is an electrophysiological technique for analyzing neural activity.
After performing a requirements analysis in dialogue with microneurography researchers, we propose a concept-mapping and a workflow, for the researchers to transform and upload their metadata. Further, we implemented a semi-automatic upload extension to odMLtables, a template-based tool for handling metadata in the electrophysiological community.
The open-source implementation enables the odML-to-LDH concept mapping, allows data anonymization from within the tool and the creation of custom-made summaries on the underlying data sets.
This concludes a first step towards integrating improved FAIR processes into the research laboratory's daily workflow. In future work, we will extend this approach to other use cases to disseminate the usage of LDHs in a larger research community.
本地数据中心（LDH）是一个用于公平分享医学研究（元）数据的平台。为了促进不同研究社区使用 LDH，了解特定领域的需求、当前用于数据组织的解决方案以及为无缝上传到 LDH 提供支持非常重要。在这项工作中，我们分析了微神经生理学的用例，这是一种用于分析神经活动的电生理学技术。
在与微神经生理学研究人员进行对话后进行需求分析，我们为研究人员提出了概念映射和工作流程，以转换和上传他们的元数据。此外，我们为 odMLtables 实现了一个半自动上传扩展，odMLtables 是一个用于处理电生理学社区中元数据的基于模板的工具。
开源实现支持 odML 到 LDH 的概念映射，允许在工具内部对数据进行匿名化，并创建底层数据集的自定义摘要。
这标志着将改进的 FAIR 流程集成到研究实验室日常工作流程中的第一步。在未来的工作中，我们将把这种方法扩展到其他用例，以在更大的研究社区中推广 LDH 的使用。
