Enabling Scientific Collaboration at UCI Yassa Lab
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Flywheel research data management platform
- Flywheel's DICOM connector
- Flywheel gears
技术栈
- Google Cloud Platform
- MATLAB
- Python
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Innovation Output
- Digital Expertise
技术
- 平台即服务 (PaaS) - 数据管理平台
- 基础设施即服务 (IaaS) - 云计算
适用行业
- 医疗保健和医院
- 教育
适用功能
- 产品研发
- 质量保证
用例
- 远程协作
服务
- 云规划/设计/实施服务
- 数据科学服务
- 系统集成
关于客户
The customer in this case study is the Yassa Lab at the University of California, Irvine (UCI). The lab is led by Dr. Michael Yassa, Ph.D., Director of the Translational Neurobiology Lab. The Yassa Lab studies changes in memory over a lifetime, methods for diagnosing progressive brain diseases and mood disorders, and paths to treatment. The lab is involved in a multicenter collaboration studying biomarkers of Alzheimer's disease in Down syndrome. The study investigates whether immunological factors active in Down syndrome are also involved in Alzheimer's Disease. The lab routinely collects and analyzes large, complex imaging data from a variety of MRI systems, including Siemens, Philips, and Bruker scanners.
挑战
The Yassa Lab at the University of California, Irvine (UCI), led by Dr. Michael Yassa, was facing several challenges. They were struggling with managing multi-center collaboration involving the collection of large data sets, quality control, analysis, and submission to NIH databases. The growing data and analytic complexity were impeding data reuse and scientific reproducibility. They were also looking for ways to best support and collaborate with other labs in the UC Irvine community. The lab was involved in a multicenter collaboration studying biomarkers of Alzheimer's disease in Down syndrome, which required secure sharing and processing of a variety of data.
解决方案
The Yassa Lab adopted the Flywheel research data management platform to streamline collaboration and core operations. The platform was deployed on Google Cloud Platform to simplify data transfer from collaborators and automate and document computational processing. The Flywheel system allowed data to be pushed directly from their MRI scanner through Flywheel's DICOM connector. Remote users could easily transfer data to the project via command line tools or a drag and drop web uploader. Once data landed in the system, rules controlled automated conversion from DICOM to NIFTI format using Dcm2Niix, execution of quality control algorithms, such as MRIQC, and analytic applications such as Freesurfer and fMRIPrep. All data and analytic results were available to project members via a secure web browser interface with access controls.
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