ODC-TBI

ODC-TBI aggregates and harmonizes subject-level preclinical traumatic brain injury (TBI) datasets to enable pooled multivariate analyses of inflammation, oxidative stress, and related pathophysiology.

Key Features:

• Data sharing and reuse: Enables sharing and reuse of individual subject–level datasets for granular big-data and machine learning analyses.
• Data citation with persistent identifiers: Supports data citation using persistent identifiers to track dataset provenance and reuse.
• FAIR Data Principles: Implements FAIR (Findable, Accessible, Interoperable, Reusable) principles to support dataset discoverability and reuse.
• Data harmonization: Promotes harmonization across different data elements to integrate heterogeneous datasets from multiple studies.
• Multivariate analytics: Supports multivariate approaches including principal component analysis (PCA) to identify patterns across pooled datasets.
• Analytical methods: Enables descriptive statistics and unsupervised machine learning on aggregated preclinical datasets.
• Collaboration and transparency: Facilitates collaborative data sharing and transparent reporting to support multidimensional analyses.

Scientific Applications:

• Pooled statistical analysis: Performs descriptive statistics on aggregated subject-level data from multiple preclinical TBI studies.
• Unsupervised machine learning: Applies unsupervised machine learning to pooled datasets to reveal latent structure across studies.
• Inflammation and oxidative stress profiling: Identifies persistent inflammatory patterns and enhances sensitivity of analyses related to inflammation and oxidative stress across different TBI models in mice and rats.

Methodology:

Computational methods explicitly include data aggregation/pooling, descriptive statistics, unsupervised machine learning, multivariate analytics, and principal component analysis (PCA).