This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized. 

This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.

This lesson discusses the need for and approaches to integrating data across the various temporal and spatial scales in which brain activity can be measured. 

This lesson consists of lecture and tutorial components, focusing on resources and tools which facilitate multi-scale brain modeling and simulation. 

In this talk, challenges of handling complex neuroscientific data are discussed, as well as tools and services for the annotation, organization, storage, and sharing of these data. 

This lecture describes the neuroscience data respository G-Node Infrastructure (GIN), which provides platform independent data access and enables easy data publishing. 

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more.