Pan-Neurotrauma Data Commons

泛神经创伤数据共享

• 批准号: 10269617
• 负责人: ADAM R FERGUSON
• 金额: $ 77万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10269617
• 关键词: Adoption; Affect; Anatomy; Animal Model; Behavioral; Big Data; Big Data Methods; Brain; Certification; Collaborations; Common Data Element; Communities; Complex; Controlled Vocabulary; Data; Data Commons; Data Pooling; Data Set; Disease; Ecosystem; Educational workshop; FAIR principles; Feedback; Functional disorder; Future; Generations; Goals; Health; Healthcare; Heterogeneity; Human; Impairment; Individual; Infrastructure; Injury; International; Internet; Knowledge; Laboratories; Lead; Licensing; Medical; Metadata; Monitor; Multiple Trauma; Mus; National Institute of Neurological Disorders and Stroke; Nervous System Trauma; Neuraxis; Neurosciences; Ontology; Oranges; Outcome Assessment; Outcome Measure; Physiology; Police; Policies; Positioning Attribute; Predictive Value; Prevalence; Process; Publication Bias; Publications; Quality Control; Rattus; Recovery; Reporting; Reproducibility; Reproducibility of Results; Research; Research Personnel; Severities; Site; Spinal Cord; Spinal cord injury; Standardization; Structure; Syndrome; System; Terminology; Testing; Therapeutic; Tissue Expansion; Translations; Trauma; Traumatic Brain Injury; Traumatic CNS injury; Treatment Efficacy; U-Series Cooperative Agreements; United States National Institutes of Health; Visualization; Work; advanced analytics; base; body system; central nervous system injury; clinical implementation; clinically relevant; community engagement; complex data; dashboard; data complexity; data format; data interoperability; data management; data mining; data quality; data repository; data resource; data reuse; data sharing; data standards; data visualization; data wrangling; digital object identifier; diverse data; economic cost; economic impact; experience; experimental study; human data; improved; information framework; insight; interoperability; large scale data; neuroinformatics; new technology; novel; open data; open source; operation; pre-clinical; preservation; productivity loss; prospective; public repository; quality assurance; repaired; repository; skills; spinal cord and brain injury; success; symposium; therapeutic development; therapeutic evaluation; tool; trustworthiness; virtual laboratory; web portal; webinar

PROJECT SUMMARY/ABSTRACT Trauma to the central nervous system (CNS: spinal cord and brain) together affect more than 2.5 million people per year in the US, with economic costs of $80 billion in healthcare and loss-of-productivity. Yet, the precise pathophysiological processes impairing recovery remain poorly understood. This lack of knowledge is exacerbated by poor reproducibility of findings in animal models and limits translation of therapeutics across species and into humans. Part of the problem is that neurotrauma is intrinsically complex, involving heterogeneous damage to the central nervous system (CNS), by far the most complex organ system in the body. This results in a multifaceted CNS syndrome reflected across heterogeneous endpoints and multiple scales of analysis. Multi-scale heterogeneity makes traumatic brain injury (TBI) and spinal cord injury (SCI) difficult to understand using traditional analytical approaches that focus on a single endpoint for testing therapeutic efficacy. Single endpoint-testing provides a narrow window into the complex system of changes that describe SCI and TBI. Understanding these disorders involves managing datasets that include high volume anatomy data, high velocity physiology decision-support data, the high variety functional/behavioral data, and assessing correlations among these endpoints. In this sense, neurotrauma is fundamentally a data management problem that involves the classic ‘3Vs of big data’ (volume, velocity, variety). Of these, variety is perhaps the greatest data challenge in neurotrauma research for reproducibility in basic discovery, cross-species translation, and ultimately clinical implementation. For the proposed Data Repositories Cooperative Agreement (U24) we will build on our prior work managing data variety in the Open Data Commons for SCI (odc-sci.org) and TBI (odc-tbi.org) to make neurotrauma data Findable, Accessible, Interoperable, and Reusable (FAIR). The milestone-driven aims will: 1) further develop and harden our data lifecycle management system with end-to-end data version control and provenance tracking, data certification, and data citation; 2) develop in-cloud data dashboards and visualizations to monitor data quality and to promote data reuse, exploration, and hypothesis generation; 3) establish a pan- neurotrauma (PANORAUMA) data commons that brings together separate data assets currently supported by our multi-PI (MPI) team by aligning a patchwork of governance structures and policies. The goal of the proposed project is to develop a pooled repository for preclinical discovery, reproducibility testing, and translational discovery both within and across neurotrauma types. Our team is well-positioned to execute this project given that we developed some of the largest multicenter, multispecies neurotrauma data repositories of neurotrauma to-date (N>10,000 subjects 20,000 curated variables); the Neuroscience Information Framework (NIF); data terminologies and standards for these fields (MIASCI, NIFSTD); and policy work with the International Neuroinformatics Coordinating Facility (INCF). The PANORAUMA cooperative agreement is highly responsive to PAR-20-089, leveraging early successes in SCI and TBI data sharing to improve quality and sustainability.

项目总结/摘要 中枢神经系统（CNS：脊髓和大脑）的创伤共同影响超过250万人 在美国，每年有800亿美元的经济成本用于医疗保健和生产力损失。然而， 损害恢复的病理生理过程仍然知之甚少。这种知识的缺乏， 由于动物模型中发现的重现性差而加剧，并限制了治疗方法在动物模型中的转化。 物种和人类。部分问题在于神经创伤本质上是复杂的， 中枢神经系统（CNS）是迄今为止身体中最复杂的器官系统。 这导致了多方面的CNS综合征，反映了异质性终点和多个量表。 分析.多尺度异质性使得创伤性脑损伤（TBI）和脊髓损伤（SCI）难以被诊断。 了解使用传统的分析方法，专注于单一终点来测试治疗效果。 单端点测试提供了一个狭窄的窗口，可以看到描述SCI和 创伤性脑损伤了解这些疾病涉及到管理数据集，包括大量解剖数据，高 速度生理学决策支持数据，多种功能/行为数据，以及评估相关性 在这些端点中。从这个意义上说，神经创伤基本上是一个数据管理问题， 经典的“大数据的3V”（数量、速度、多样性）。其中，多样性可能是最大的数据挑战 在神经创伤研究的基础发现，跨物种的翻译，并最终临床重现性 实施.对于拟议的数据存储库合作协议（U24），我们将在之前的基础上 在SCI（odc-sci.org）和TBI（odc-tbi.org）的开放数据共享中管理数据多样性， 神经创伤数据可查找、可解释、可互操作和可重用（FAIR）。里程碑驱动的目标将：1） 通过端到端数据版本控制进一步开发和强化我们的数据生命周期管理系统， 来源跟踪、数据认证和数据引用; 2）开发云数据仪表板和可视化 监控数据质量，促进数据重用、探索和假设生成; 3）建立泛 神经创伤（PANORAUMA）数据共享，汇集了目前由 我们的多PI（MPI）团队通过调整治理结构和政策的拼凑。建议的目标 项目是开发一个汇集的库，用于临床前发现，再现性测试和翻译 在神经创伤类型内和跨神经创伤类型的发现。我们的团队有能力执行这个项目， 我们开发了一些最大的多中心，多物种神经创伤数据库， 迄今为止（N> 10，000受试者20，000策划变量）;神经科学信息框架（NIF）;数据 这些领域的术语和标准（MIASCI，NIFSTD）;与国际 神经信息学协调机构（INCF）。PANORAUMA合作协议反应热烈 PAR-20-089，利用SCI和TBI数据共享的早期成功，提高质量和可持续性。