This lesson provides an introduction to the lifecycle of EEG/ERP data, describing the various phases through which these data pass, from collection to publication.

In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle. 

This lesson provides an overview of the current regulatory measures in place regarding experimental data security and privacy. 

In this lesson, you will learn the appropriate methods for collection of both data and associated metadata during EEG experiments.

This lesson goes over methods for managing EEG/ERP data after it has been collected, from annotation to publication. 

In this final lesson of the course, you will learn broadly about EEG signal processing, as well as specific applications which make this kind of brain signal valuable to researchers and clinicians. 

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. 

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 lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.

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 lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

This session discussed the secret life of your dataset metadata: the ways in which, for many years to come, it will work non-stop to foster the visibility, reach, and impact of your work. We explored how metadata will help your dataset travel through the global research infrastructure, and how data repositories and discovery services can use this metadata to help launch your dataset into the world.

This lesson provides information on developing data management plans (DMPs), including an overview of how DMPs contribute to effective research efforts, as well as specific development resources and DMP examples. 

In this session, participants will take an in-depth look at the newly launched DMP Assistant 2.0, including all of its enhanced key features for both end-users and institutional administrators, as well as a brief look at the future of the platform.

This lesson provides a short overview of the main features of the Canadian Open Neuroscience Platform (CONP) Portal, a web interface that facilitates open science for the neuroscience community by simplifying global access to and sharing of datasets and tools. The Portal internalizes the typical cycle of a research project, beginning with data acquisition, followed by data processing with published tools, and ultimately the publication of results with a link to the original dataset.

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.