Understanding Big Data in Neuroscience

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Explores text mining in neuroinformatics to extract brain connectivity data and annotate model parameters from scientific literature.

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Covers recent advances in structural learning for graphical models, including Gaussian models, mixed models, and extreme events.

Covers the fundamentals of spectroscopy, including selection rules and transitions.

Covers ontology as a knowledge repository with agreed-upon meanings, evolution, continuous updating, and smart ontology design.

Covers topics in Quantum Chemistry, including history, tools, color & draw techniques, shapes, and mathematical conversions.

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Explores the role of ontologies in neuroscience, focusing on organizing data and defining entity properties.

Covers energy contracts, market mechanisms, locational marginal pricing, and congestion management in power systems.

Covers the creation and encoding of ontologies, modeling RDF statements, syntax, and classification.