The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

中文翻译：

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NIDM-Terms：基于社区的术语管理，用于改进神经影像数据集描述和查询

生物医学研究界有动力共享和重用资助机构和出版商的研究和项目的数据。有效地组合和重用来自公开数据集的神经影像数据，需要能够跨数据集查询，以便识别符合神经影像和临床/行为数据标准的队列。实施此类查询的关键障碍部分包括广泛使用未定义的研究变量，且注释有限或没有注释，这使得在不与原作者进行大量交互的情况下很难理解可用的数据。使用脑成像数据结构 (BIDS) 来组织神经影像数据，使得跨研究查询特定图像类型成为可能。然而，在 BIDS 中，除了文件命名和严格控制的成像目录结构之外，对辅助变量命名/含义或特定于实验的元数据几乎没有限制。在这项工作中，我们提出了 NIDM-Terms，这是一套用户友好的术语管理工具和相关软件，可以更好地管理各个实验室术语并帮助注释 BIDS 数据集。使用这些工具使用神经影像数据模型 (NIDM) 语义网络表示来注释 BIDS 数据，从而能够跨数据集进行查询，以识别具有特定神经影像和临床/行为测量的队列。本手稿描述了整体信息学结构，并演示了如何使用工具来注释 BIDS 数据集以执行集成的跨队列查询。