Enhancing the Pan-Neurotrauma Data Commons (PANORAUMA) to a complete open data science tool by FAIR APIs

通过 FAIR API 将泛神经创伤数据共享 (PANORAUMA) 增强为完整的开放数据科学工具

基本信息

批准号：
10608657
负责人：
ADAM R FERGUSON
金额：
$ 23.96万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
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/10608657
关键词：
Address Adopted Adoption Affect Anatomy Artificial Intelligence Award Back Behavioral Big Data Methods Brain Budgets Cloud Service Code Collaborations Communities Computer software Data Data Commons Data Pooling Data Science Data Scientist Data Sources Development Documentation Ecosystem Elements FAIR principles Funding Individual Infrastructure Injury Investigational Therapies Language Language Development Liquid substance Machine Learning Medical Metadata Modernization Molecular Multiple Trauma Nervous System Trauma Neuraxis Outcome Parents Patients Physiological Privatization Process Pythons Readiness Registries Reproducibility Research Research Personnel Research Subjects Resource Sharing Resources Scientific Inquiry Secure Services Severities Site Software Engineering Spinal Cord Spinal cord injury Strategic Planning Syndrome Translations Trauma Traumatic Brain Injury United States National Institutes of Health Update Writing application programming interface clinical practice community based participatory research community building community partnership computerized data processing cost data access data pipeline data repository data resource data reuse data sharing data space diverse data economic impact heterogenous data improved interoperability large scale data machine learning pipeline meetings neurological recovery novel open data operation parent grant parent project response skills socioeconomics success tool web based interface

项目摘要

Project Summary Neurotrauma (trauma to the spinal cord and brain) affects over 2.5 million individuals in the US, with an annual economic impact of $80 billion in medical and socioeconomic costs. Despite improved patient management in the last decades, there are limited viable options to promote neurological recovery. Spinal cord injury (SCI) and traumatic brain injury (TBI) result in multifaceted syndromes spanning heterogeneous data sources and multiple scales of analysis. In addition, these injuries often occur at various sites within the central nervous system, with graded severities producing heterogeneous injuries with diverse outcome trajectories. Making sense of this complexity requires pooling data across multiple injury severities, types, and scales of analysis ranging from molecular, anatomical, physiological, and behavioral levels. Large-scale data resources and big-data tools have the potential to help. By pooling and harmonizing diverse data at the individual level, it becomes possible to make neurotrauma data “Findable, Accessible, Interoperable, and Reusable” (FAIR). FAIR neurotrauma data can be harnessed using modern data workflows and analytics, directing novel discovery and accelerating translation. Moreover, FAIR data can set the stage for widespread adoption of artificial intelligence (AI) and machine learning (ML), and it is at the core of NIH Strategic Plan for Data Science and AI/ML-readiness initiatives like Bridge2A1 and AIM-AHEAD. Researchers and data scientists can use FAIR neurotrauma data to drive novel discoveries and build robust reproducibility and translation tools, such as data processing software and new analytical workflows and pipelines. The overarching objective of the Pan-Neurotrauma data commons parent project is to build a Pan- Neurotrauma (PANORAUMA) data commons infrastructure. The award aims at improving the efficiency, quality, and sustainability of the community-driven Open Data Commons for Spinal Cord Injury (odc-sci) and Traumatic Brain Injury (odc-tbi) by centralizing their operations and governance. The NOSI (NOT-OD-22-068) for this supplement provides an opportunity for “improving the quality and sustainability of research software from a software engineering perspective.” The supplement is vital for PANORAUMA sustainability and the expansion of the community of users to include research data scientists and research software developers in response to NIH’s strategic plan for data science which states that “accessible, well-organized, secure, and efficiently operated data resources are critical to modern scientific inquiry.” For this supplement, we propose to: 1) develop the Application Programming Interface (API) of PANORAUMA to better support data science activities in the cloud and optimize reusability, interoperability, and sustainability of data pipelines; 2) incorporate the SmartAPI FAIR standards to maximize the API’s FAIRness and documentation; 3) enhance the PANORAUMA-API interface with state-of-the-art, open-source data science coding languages R and Python; and 4) build community partnerships between developers and data scientists.