Pan-Neurotrauma Data Commons

泛神经创伤数据共享

• 批准号: 10684922
• 负责人: ADAM R FERGUSON
• 金额: $ 73.76万
• 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/10684922
• 关键词: Acceleration; Adoption; Affect; Anatomy; Animal Model; Behavioral; Big Data; Big Data Methods; Brain; Central Nervous System; Certification; Collaborations; Common Data Element; Communities; Complex; Controlled Vocabulary; Data; Data Analyses; Data Commons; Data Pooling; Data Set; Dedications; Disease; Disparate; Ecosystem; Educational workshop; FAIR principles; Feedback; Functional disorder; Future; Generations; Goals; Health; Healthcare; Heterogeneity; Human; Impairment; Individual; Infrastructure; Injury; International; Internet; Knowledge; Laboratories; Licensing; Life Cycle Stages; Medical; Metadata; Monitor; Multiple Trauma; Mus; National Institute of Neurological Disorders and Stroke; Nervous System Trauma; Neurosciences; Ontology; Oranges; Outcome Assessment; Outcome Measure; Persons; Physiology; Police; Policies; Positioning Attribute; Predictive Value; Prevalence; Process; Productivity; Publication Bias; Publications; Quality Control; Rattus; Recovery; Recovery of Function; Reporting; Reproducibility; Reproducibility of Results; Research; Research Personnel; Severities; Site; Spinal Cord; Spinal cord injury; Standardization; Structure; Syndrome; System; Terminology; Testing; 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; body system; central nervous system injury; clinical implementation; clinically relevant; community engagement; complex data; cost; 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; novel marker; open data; open source; operation; pre-clinical; precision medicine; preservation; productivity loss; prospective; public repository; quality assurance; repaired; repository; skills; spinal cord and brain injury; success; symposium; therapeutic development; therapeutic evaluation; tool; translational medicine; translational therapeutics; 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/文摘