Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

In this presentation by the OHBM OpenScienceSIG, Tom Shaw and Steffen Bollmann cover how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers. They demonstrate how to build docker containers from scratch, using Neurodocker, and cover how to use containers on an HPC with singularity.

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

NWB: An ecosystem for neurophysiology data standardization

Learn how to build and share extensions in NWB

Learn how to build custom APIs for extension

Learn how to handle writing very large data in PyNWB

Learn how to handle writing very large data in MatNWB

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives.

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

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives.

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.

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. This lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience.  FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible,  Interoperable and Reusable.  But FAIR is not a specification;  it leaves many of the specifics up to individual scientific disciplines to define.  INCF has been leading the way in promoting, defining and implementing FAIR data practices for neuroscience.  We have been bringing together researchers, infrastructure providers, industry and publishers through our programs and networks.  In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience.  We will engage in a discussion on questions such as:  how is neuroscience doing with respect to FAIR?  What have been successes?  What is currently very difficult? Where does neuroscience need to go?

This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).

This lecture will highlight our current understanding and recent developments in the field of neurodegenerative disease research, as well as the future of diagnostics and treatment of neurodegenerative diseases