NeuroJSON - A Scalable, Searchable and Verifiable Neuroimaging Data Platform

NeuroJSON - 可扩展、可搜索和可验证的神经影像数据平台

• 批准号: 10476470
• 负责人: Qianqian Fang
• 金额: $ 37.31万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10476470
• 关键词: Address; Adopted; Adoption; Archives; Automation; Back; Collaborations; Communities; Complex; Computer software; Data; Data Analyses; Data Files; Data Set; Data Storage and Retrieval; Databases; Documentation; Educational workshop; Electroencephalography; Foundations; Funding; Future; Growth; Human; Image; Industry; Information Technology; Letters; Libraries; Linux; Magnetic Resonance Imaging; Maintenance; Modernization; Neurosciences; Online Systems; Output; Performance; Privatization; Publishing; Readability; Records; Reproducibility; Research; Series; Solid; Specific qualifier value; Standardization; Technology; Text; Training; United States National Institutes of Health; Validation; Vision; Writing; cloud based; computerized data processing; data analysis pipeline; data dissemination; data exchange; data format; data integration; data management; data resource; data sharing; file format; flexibility; forging; functional near infrared spectroscopy; hackathon; improved; large datasets; large scale data; light weight; multimodality; neuroimaging; next generation; open source; open source tool; prototype; response; scale up; software development; success; tool

Traditional file-based neuroimaging data management and integration strategies have shown increasing limitations in accommodating the meteoric growth in both the scale and complexity of today’s neuroimaging data. The sophisticated software and hardware pipelines required in many of today’s neuroimaging studies have produced numerous platform-specific data files that are increasingly difficult to parse, exchange, and understand by the broader research community. Modern neuroimaging studies are hampered by not only the challenge of parsing and managing rigid and diverse file types, but also the lack of a unified interface for systematic data validation, query, manipulation and integration, thereby limiting its ability to handle large datasets. Creating a future-proof, highly scalable, and low-maintenance data storage and dissemination platform is highly desirable for the broad and rapidly growing neuroimaging community. The future of neuroimaging data management must be scalable, searchable, verifiable, and capable of accommodating highly complex hierarchical data generated from complex paradigms involving multi-modal inputs. Inspired by the great success of NoSQL database platforms, we envision that a unified database interface for managing complex neuroimaging data and exchanging human-readable hierarchical data records will be highly suitable to address the urgent needs of next-generation neuroimaging data management. In this project, we aim to solidify a series of easy-to-adopt, easy-to-extend, human-readable JSON (http://json.org) based data file specifications to systematically assist the storage, exchange and integration of existing and emerging neuroimaging datasets. These JSON-encoded universal data files readily enable users to utilize highly scalable and high-performance NoSQL databases, such as CouchDB and MongoDB, to rapidly disseminate large, NIH-funded neuroimaging public datasets, and enable validation and automation. Our group has been a major contributor to JSON-based scientific data storage since 2011. We have published open- source specifications (http://openjdata.org) to standardize the exchange of neuroimaging data for common formats such as NIfTI/GIfTI/SNIRF, building a solid foundation for application-specific adoptions. In this project, we seek to further develop, solidify, and disseminate JSON-based data exchange specifications and NoSQL databases. We have built collaborations to major neuroimaging data analysis stakeholders, such as FreeSurfer, SPM, FieldTrip, HOMER, BrainStorm. At the end of this project, we will be able to 1) develop a set of stable universal file formats that greatly modernize data sharing in neuroimaging applications, easing future maintenance and extension, and 2) provide open-source tools for users to build NoSQL database backends to facilitate integration and automation of public/private databases, enabling query, validation, and scale-up. Success in this project will result in a robust data exchange platform to facilitate convenient data sharing, promote reproducible research, and forge efficient collaborations among a broad neuroimaging community.

传统的基于文件的神经成像数据管理和集成策略显示 在适应当今规模和复杂性的快速增长方面越来越多的限制 神经成像数据。当今许多企业所需的复杂的软件和硬件管道 神经成像研究已经产生了大量特定于平台的数据文件，这些文件越来越困难 被更广泛的研究社区解析、交流和理解。现代神经成像研究是 不仅因为解析和管理僵化和多样化的文件类型的挑战，而且还因为缺乏 用于系统数据验证、查询、操作和集成的统一接口，从而限制其 能够处理大型数据集。创建面向未来、高度可扩展且维护成本低的数据存储 而传播平台对于广泛且快速增长的神经影像社区是非常可取的。这个 神经影像数据管理的未来必须是可扩展、可搜索、可验证和能够 适应从涉及多模式的复杂范例生成的高度复杂的分层数据 投入。受NoSQL数据库平台的巨大成功的启发，我们设想一个统一的数据库 用于管理复杂的神经成像数据和交换人类可读的分层数据记录的接口 将非常适合解决下一代神经成像数据管理的迫切需求。在这 项目，我们的目标是固化一系列易于采用、易于扩展、人类可读的JSON(http://json.org) 基于数据文件规范，系统地帮助存储、交换和集成现有和 新兴的神经成像数据集。这些JSON编码的通用数据文件使用户能够利用 高度可扩展的高性能NoSQL数据库，如CouchDB和MongoDB，以快速 传播由美国国立卫生研究院资助的大型神经成像公共数据集，并实现验证和自动化。我们的团队 自2011年以来一直是基于JSON的科学数据存储的主要贡献者。我们已经出版了公开的- 源规范(http://openjdata.org)用于标准化通用的神经成像数据交换 Nifti/GIfTI/SNIRF等格式，为特定应用的采用奠定了坚实的基础。在这个项目中， 我们寻求进一步开发、巩固和传播基于JSON的数据交换规范和 NoSQL数据库。我们已经与主要的神经成像数据分析利益相关者建立了合作关系，例如 自由冲浪，SPM，实地考察，本垒打，头脑风暴。在这个项目结束时，我们将能够1)开发一套 稳定的通用文件格式，极大地现代化了神经成像应用程序中的数据共享，为未来提供了便利 维护和扩展，2)为用户构建NoSQL数据库后台提供开源工具 促进公共/私有数据库的集成和自动化，支持查询、验证和纵向扩展。 该项目的成功将带来一个强大的数据交换平台，以促进方便的数据共享， 促进可重复的研究，并在广泛的神经成像社区中建立有效的合作。