Introduces semantic modelling through tabular data and RDF, covering relational databases, schema migration, future-proof schemata, SPARQL querying, and metaknowledge limitations.
Delves into Big Data in neuroscience, analyzing large datasets and addressing challenges in data organization, standardization, integration, and visualization.
Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.
Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.