Manipulate the default connectome provided with TVB to see how structural lesions effect brain dynamics. In this hands-on session you will insert lesions into the connectome within the TVB graphical user interface (GUI). Afterwards, the modified connectome will be used for simulations and the resulting activity will be analysed using functional connectivity.
This tutorial is part 1 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are part of the same YouTube video; part 1 ends at 17:42.
This lesson introduces the practical usage of The Virtual Brain (TVB) in its graphical user interface and via python scripts. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator, and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of TVB.
This lesson provides a comprehensive introduction to the command line and 50 popular Linux commands. This is a long introduction (nearly 5 hours), but well worth it if you are going to spend a good part of your career working from a terminal, which is likely if you are interested in flexibility, power, and reproducibility in neuroscience research. This lesson is courtesy of freeCodeCamp.
This lecture covers visualizing extracellular neurotransmitter dynamics
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
This video will document the process of uploading data into a brainlife project using ezBIDS.
This short video walks you through the steps of publishing a dataset on brainlife, an open-source, free and secure reproducible neuroscience analysis platform.
This video will document the process of visualizing the provenance of each step performed to generate a data object on brainlife.
This video will document the process of downloading and running the "reproduce.sh" script, which will automatically run all of the steps to generate a data object locally on a user's machine.
This short video shows how a brainlife.io publication can be opened from the Data Deposition page of the journal Nature Scientific Data.
In this tutorial, you will learn the basic features of uploading and versioning your data within OpenNeuro.org.
This tutorial shows how to share your data in OpenNeuro.org.
Following the previous two tutorials on uploading and sharing data with OpenNeuro.org, this tutorial briefly covers how to run various analyses on your datasets.
This video introduces the key principles for data organization and explains how you could make your data FAIR for data sharing on EBRAINS.
This video explains what metadata is, why it is important, and how you can organize your metadata to increase the FAIRness of your data on EBRAINS.
This video introduces the importance of writing a Data Descriptor to accompany your dataset on EBRAINS. It gives concrete examples on what information to include and highlights how this makes your data more FAIR.
This lesson provides a hands-on tutorial for generating simulated brain data within the EBRAINS ecosystem.
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.