neuroinformatics
Stroke Patient Recovery Research Database (SPReD)
SPReD is a comprehensive electronic database used to store clinical patient data from various sites across Ontario. SPReD includes demographic, genetic, biomarker, proteomic, and imaging data. It is a project of the Heart & Stroke Foundation Centre for Stroke Recovery. This database will provide a huge repository of data from which researchers can examine and use to test out novel ideas to benefit persons with neurodegenerative disorders. The Strother team manages access to SPReD, provides user support, and aggregates data for quality control purposes. |
Spotfire
Spotfire is a software that allows you to manipulate, analyze and display your data. The display of the data is what we call the dashboard. Spotfire has a built-in tool that allows you create and execute scripts the way you would in other programming software such as R. Furthermore, there are many ways you can display data on Spotfire. For example, you can display data through tables, plots, pie charts, and bar charts. This software can do almost everything that Microsoft Excel can do. However, Spotfire will not accept Excel files as input files; it only accepts CSV files.
We have utilized Spotfire for many different purposes in this lab. We use it to display quality control reports of the data on SPReD. We also use it to organize and filter the information that we gather from SPReD. Spotfire is also being used to provide data release status updates for ONDRI leads.
Spotfire is a software that allows you to manipulate, analyze and display your data. The display of the data is what we call the dashboard. Spotfire has a built-in tool that allows you create and execute scripts the way you would in other programming software such as R. Furthermore, there are many ways you can display data on Spotfire. For example, you can display data through tables, plots, pie charts, and bar charts. This software can do almost everything that Microsoft Excel can do. However, Spotfire will not accept Excel files as input files; it only accepts CSV files.
We have utilized Spotfire for many different purposes in this lab. We use it to display quality control reports of the data on SPReD. We also use it to organize and filter the information that we gather from SPReD. Spotfire is also being used to provide data release status updates for ONDRI leads.
XTxGate
XTxGate is a middle-ware server providing a variety of services to support BrainCODE which include:
XTxGate is a middle-ware server providing a variety of services to support BrainCODE which include:
- Notify - An EVENT initiated specialized ETL service to move data of differing types between external data sources and BrainCODE (SPReD
- SPReDInventory - SPReD data inventory generator
- SFRequest - Spotfire data aggregation and download support services
Brain Imaging Data Structure (BIDS)
Brain Imaging Data Structure (BIDS) is a new standard for organizing neuroimaging and behavioural data. Prior to the definition of BIDS made standard by neuroimaging world-leading scientists, researchers had different layouts for arranging the brain imaging data obtained in different experiments. Inconsistency in data structures was leading to misunderstanding and extra work on adjusting the scripts for a certain data type. Therefore, most neuroimaging organizations are moving toward adopting BIDS as their standard of data sharing and storage. The Strother lab, as manager of the
Brain-CODE informatics pillar, is working on converting the current Brain-CODE neuroimaging database into BIDS format. We are also developing a robust pipeline for our primary data-management platform, namely XNAT, to automate the conversion of data directly from XNAT to BIDS.
Brain Imaging Data Structure (BIDS) is a new standard for organizing neuroimaging and behavioural data. Prior to the definition of BIDS made standard by neuroimaging world-leading scientists, researchers had different layouts for arranging the brain imaging data obtained in different experiments. Inconsistency in data structures was leading to misunderstanding and extra work on adjusting the scripts for a certain data type. Therefore, most neuroimaging organizations are moving toward adopting BIDS as their standard of data sharing and storage. The Strother lab, as manager of the
Brain-CODE informatics pillar, is working on converting the current Brain-CODE neuroimaging database into BIDS format. We are also developing a robust pipeline for our primary data-management platform, namely XNAT, to automate the conversion of data directly from XNAT to BIDS.
Neuroinformatics and Biostatistics (NIBS)
Many members of the Strother Lab are part of the Neuroinformatics and Biostatistics (NIBS) platform for the Ontario Neurodegenerative Disease Research Initiative (ONDRI). The NIBS team is responsible for harmonizing data across the measurement platforms within ONDRI (neuroimaging, neuropsychology, genetics, clinical, eye tracking, ocular imaging, gait & balance). The NIBS team also contributes to and develop tools to store, manage, process, provide quality control (QC), and distribute ONDRI data. One of the most active current projects within the NIBS team is the development of a comprehensive battery of multivariate outlier detection techniques. These techniques play a critical role in the QC to identify potential sources of errors in the data, and to identify observations with unique patterns. Additionally, the NIBS team also develops and implements analytical and statistical tools for complex, multi-modal, and large scale data.
Many members of the Strother Lab are part of the Neuroinformatics and Biostatistics (NIBS) platform for the Ontario Neurodegenerative Disease Research Initiative (ONDRI). The NIBS team is responsible for harmonizing data across the measurement platforms within ONDRI (neuroimaging, neuropsychology, genetics, clinical, eye tracking, ocular imaging, gait & balance). The NIBS team also contributes to and develop tools to store, manage, process, provide quality control (QC), and distribute ONDRI data. One of the most active current projects within the NIBS team is the development of a comprehensive battery of multivariate outlier detection techniques. These techniques play a critical role in the QC to identify potential sources of errors in the data, and to identify observations with unique patterns. Additionally, the NIBS team also develops and implements analytical and statistical tools for complex, multi-modal, and large scale data.