Explores the concept of Knowledge Graphs and their role in data integration and semantic understanding, showcasing real-world examples and applications.
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.
Covers neurophysiological data analysis, including AP detection, firing rate computation, and spectral analysis, with a focus on predicting cell classes.
Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.