This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
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 covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
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.
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.
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 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.
This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.
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.
This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
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 lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity.
This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity.