This lesson consists of lecture and tutorial components, focusing on resources and tools which facilitate multi-scale brain modeling and simulation. 

In this talk, challenges of handling complex neuroscientific data are discussed, as well as tools and services for the annotation, organization, storage, and sharing of these data. 

This lecture describes the neuroscience data respository G-Node Infrastructure (GIN), which provides platform independent data access and enables easy data publishing. 

This lesson provides an introduction to the course Neuroscience Data Integration Through Use of Digital Brain Atlases, during which attendees will learn about concepts for integration of research data, approaches and resources for assigning anatomical location to brain data, and infrastructure for sharing experimental brain research data. 

This talk covers the various concepts, motivations, and trends within the neuroscientific community related to the sharing and integration of brain research data. 

This lesson focuses on the neuroanatomy of the human brain, delving into macrostructures like cortices, lobes, and hemispheres, and microstructures like neurons and cortical laminae.

This lesson provides an introduction to the European open research infrastructure EBRAINS and its digital brain atlas resources.

In this lesson, attendees will learn about the challenges in assigning experimental brain data to specific locations, as well as the advantages and shortcomings of current location assignment procedures. 

This lesson covers the inherent difficulties associated with integrating neuroscientific data, as well as the current methods and approaches to do so. 

Attendees of this talk will learn about QuickNII, a tool for user-guided affine registration of 2D experimental image data to 3D atlas reference spaces, which also facilitates data integration through standardized coordinate systems. 

This lesson provides an overview of DeepSlice, a Python package which aligns histology to the Allen Brain Atlas and Waxholm Rat Atlas using deep learning.

This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.

This primer on optogenetics primer discusses how to manipulate neuronal populations with light at millisecond resolution and offers possible applications such as curing the blind and "playing the piano" with cortical neurons.

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.

In this lesson, you will learn how to understand data management plans and why data sharing is important. 

This quick visual walkthrough presents the steps required in uploading data into a brainlife project using the graphical user interface (GUI). 

This short walkthrough documents the steps needed to find a dataset in OpenNeuro, a free and open platform for sharing MRI, MEG, EEG, iEEG, ECoG, ASL, and PET data, and import it directly to a brainlife project. 

This lesson describes and shows four different ways one may upload their data to brainlife.io.

This lecture covers why data sharing and other collaborative practices are important, how these practices are developed, and the challenges involved in their development and implementation.

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.