This short talk addresses how to use VisuAlign to make nonlinear adjustments to 2D-to-3D registrations generated by QuickNII. 

This talk aims to provide guidance regarding the myriad labelling methods for histological image data. 

This lesson provides a cross-species comparison of neuron types in the rat and mouse brain. 

This lecture concludes the course with an outline of future directions of the field of neuroscientific research data integration. 

This lecture provides an introduction to the study of eye-tracking in humans. 

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 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 discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

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. 

In this lesson, you will learn how to utilize various features and tools included in the EBRAINS platform, particularly focusing on rodent brain atlases and how to incorporate them into your analyses. 

This talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform. 

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 talk gives a brief overview of current efforts to collect and share the Brain Reference Architecture (BRA) data involved in the construction of a whole-brain architecture that assigns functions to major brain organs. 

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks. 

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.