Neuroscience Gateway to Enable Dissemination of Computational And Data Processing Tools And Software.

神经科学网关能够传播计算和数据处理工具和软件。

• 批准号: 10594344
• 负责人: Amitava Majumdar
• 金额: $ 33.28万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594344
• 关键词: Award; Brain; Characteristics; Clinical; Cognitive; Communities; Computer Models; Computer software; Data; Data Set; Data Store; Development; Disease; Doctor of Philosophy; Electroencephalography; Environment; FAIR principles; Funding; Future; Grant; Health; High Performance Computing; Hour; Internet; Metadata; Methods; Modeling; Neurophysiology - biologic function; Neurosciences; Ontology; Parents; Pilot Projects; Publications; Reproducibility; Research; Secure; Standardization; Time; Workload; base; blockchain; brain cell; computerized data processing; computerized tools; computing resources; experience; large datasets; nervous system disorder; open data; operation; simulation; success; supercomputer; tool

Summary The objective of this supplement proposal is to collect standardize provenance metadata of the Neuroscience Gateway (NSG) datasets and computational tools using a provenance ontology, which will enable them to be AI/ML ready. The NSG project began in 2012 to catalyze progress in neuroscience by reducing technical and administrative barriers that neuroscientists faced in large scale modeling projects which require high performance computing resources. NSG's success is reflected in the facts that its base of registered users (currently 1370) has grown continually since it started operation, every year the NSG team successfully acquires over 14,000,000 core hours of time on NSF funded supercomputers and it has contributed to large number of publications and Ph.D./MS thesis. Starting in 2017 experimentalists and cognitive neuroscientists began to use NSG for data processing/analysis and ML. NSG now provides over sixteen tools on supercomputers for simulation, data processing and ML. As promised in the parent U24 grant awarded in 2019, we have enhanced NSG by adding new features making it an efficient environment for development and dissemination of lab-developed neuroscience tools to the broader neuroscience community. It should be noted that currently there is no provision to record metadata or provenance information for any of the NSG tools and the data sets they produce. Currently the large number of NSG computational tools and the datasets lack standardized annotations that is needed to enable them to be integrated into ML workflows with support for explainable AI and reproducibility. In this supplement, we will first integrate a W3C PROV specification-based provenance ontology in NSG through a provenance interface to allow users to record provenance metadata using ontology classes. We will demonstrate the use of provenance ontology through a pilot project that will use a neuroscience software called the NeuroIntegrative Connectivity (NIC) tool that analyzes EEG data to compute functional brain networks in neurological disorders. The NIC tool has provenance metadata characteristics built into it, and will be the first NSG tool to carry the metadata provenance information from the beginning to the end of a dataset’s lifecycle. The ontology-based standardized description of both the NIC tool and data will enable NSG to make them findable, accessible, interoperable, and reusable. In this context, providing a secure method to efficiently share and verify the data and metadata is necessary for reuse of scientific data. To achieve this, we will utilize the NSF-funded Open Science Chain (OSC) project which provides a blockchain based solution to maintain the integrity and provenance for datasets and its metadata and provides a way to perform independent verification of the data stored in the blockchain. The experience gained via integrating a provenance ontology, the NIC tool and the OSC within the framework of NSG, will allow us in the future to integrate metadata provenance information of other NSG tools and make the NSG comprehensively more suitable for AI/ML workloads.

概括 该补充建议的目的是收集神经科学的标准化源 使用出处本体的网关（NSG）数据集和计算工具，这将使它们成为 AI/ML准备就绪。 NSG项目于2012年开始，以减少技术和 神经科学家在大规模建模项目中面临的行政障碍，需要高 性能计算资源。 NSG的成功反映在其注册用户基础的事实中 （目前1370年）自从开始运营以来一直在不断发展，NSG团队每年都会成功 在NSF资助的超级计算机上获得了超过14,000,000的核心时间，并为大型 出版物和博士学位论文的数量。从2017年开始实验者和认知神经科学家 开始使用NSG进行数据处理/分析和ML。 NSG现在提供了16多个工具 用于仿真，数据处理和ML的超级计算机。正如父母U24授予的授予 2019年，我们通过添加新功能增强了NSG，使其成为开发和 向更广泛的神经科学社区传播实验室开发的神经科学工具。应该注意 目前尚无任何规定记录任何NSG工具的元数据或出处信息 它们产生的数据集。目前，大量的NSG计算工具和数据集缺乏 需要将它们集成到ML工作流程中所需的标准化注释并支持 可解释的AI和可重复性。在此补品中，我们将首先整合基于W3C的基于规范 NSG中的出处本体通过预告片接口，允许用户记录proveance元数据 使用本体学课。我们将通过一个试点项目来证明出处本体的使用 使用称为神经共同连接（NIC）工具的神经科学软件，该软件将脑电图数据分析为 计算神经系统疾病中的功能性脑网络。 NIC工具具有出处元数据 它内置的特征，将是第一个携带元数据出处信息的NSG工具 开始到数据集的生命周期结束。 NIC工具的基于Ontoogogy的标准化描述 数据将使NSG能够使它们找到，可访问，可互操作和可重复使用。在这种情况下， 提供有效共享和验证数据和元数据的安全方法对于重复使用是必要的 科学数据。为了实现这一目标，我们将利用NSF资助的开放科学链（OSC）项目 提供基于区块链的解决方案，以维持数据集及其元数据的完整性和出处 并提供了一种对位于区块链中存储的数据进行独立验证的方法。经验 通过整合出处本体，NIC工具和OSC在NSG框架内获得的获得，将 将来允许我们整合其他NSG工具的元数据出处信息，并制作NSG 全面地适合AI/ML工作负载。