IntroductionOpen science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies.MethodsThe NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles.ResultsThe NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section “Methods” of the article. Articles returned from NeuroQuery based on the same search are also presented.ConclusionThe NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user’s research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering “enough data of the right kind,” ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.

中文翻译：

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NeuroBridge：用于发现长尾神经影像数据的原型平台

简介开放科学举措使得共享大量已收集的数据成为可能。然而，在如何找到适当的数据（包括科学长尾中存在的未充分利用的数据）方面仍然存在重大差距。我们展示了 NeuroBridge 原型及其搜索 PubMed Central 全文论文以获取与从精神分裂症和成瘾研究中收集的神经影像数据相关的信息的能力。方法 NeuroBridge 架构包含以下组件：（1）用于建模研究元数据的可扩展本体：受试者群体，成像技术以及相关的行为、认知或临床数据。本期特刊的配套论文中描述了详细信息；(2) 基于自然语言的文档处理器，利用小样本文档语料库上预先训练的深度学习模型，为每篇文章建立有效的表示，作为机器识别的本体术语的集合；(3) 使用本体驱动的相似性来查询 PubMed Central 和 NeuroQuery 的集成搜索，提供 fMRI 激活图以及 PubMed 源文章。 结果 NeuroBridge 原型包含 2018 年至 2021 年 356 篇描述精神分裂症和成瘾神经影像研究的论文的语料库，其中186 个用 NeuroBridge 本体进行了注释。NeuroBridge 网站上的搜索门户https://neurobridges.org/提供交互式查询构建器，用户可以通过选择 NeuroBridge 本体术语来构建查询，以保留本体树结构。对于每个返回条目，都会提供 PubMed 摘要以及 PMC 全文文章（如果有）的链接。对于每篇返回的文章，我们提供了文章“方法”部分中描述的临床评估列表。还显示了基于相同搜索从 NeuroQuery 返回的文章。结论 NeuroBridge 原型将基于本体的搜索与自然语言文本挖掘方法相结合，以证明可以识别与用户研究问题相关的论文。NeuroBridge 原型朝着识别全文论文中描述的潜在神经影像数据迈出了第一步。为了实现发现“足够的正确类型的数据”的总体目标，正在进行的工作包括使用更大的语料库验证文档处理器、扩展本体以包括详细的成像数据、从返回的出版物中提取有关数据可用性的信息并合并 XNAT-基于神经影像数据库以增强数据可访问性。