This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

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 contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

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.

Maximize Your Research With Cloud Workspaces is a talk aimed at researchers who are looking for innovative ways to set up and execute their life science data analyses in a collaborative, extensible, open-source cloud environment. This panel discussion is brought to you by MetaCell and scientists from leading universities who share their experiences of advanced analysis and collaborative learning through the Cloud.

This talk describes the relevance and power of using brain atlases as part of one's data integration pipeline. 

This brief talk goes into work being done at The Alan Turing Institute to solve real-world challenges and democratize computer vision methods to support interdisciplinary and international researchers. 

This is the second of three lectures around current challenges and opportunities facing neuroinformatic infrastructure for handling sensitive data. 

This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.

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 learn about current efforts towards integrating multimodal human brain data using the open source SCORE HED library schema. 

This lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas. 

This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects. 

This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

This video will demonstrate how to create and launch a pipeline using FreeSurfer on brainlife.io.

This lecture gives an introduction to simulation, models, and the neural simulation tool NEST. 

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture covers the description and characterization of an input-output relationship in a information-theoretic context. 

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.