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 presentation discusses the impact of data sharing in stroke.

This talks presents an overview of the potential for data federation in stroke research.

This talk focuses on the EAN Scientific Panel of Stroke, in particular on the aims and roles of the panel.

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research. 

This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience. 

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

This lecture provides an introduction to reproducibility issues within the fields of neuroimaging and fMRI, as well as an overview of tools and resources being developed to alleviate the problem.

This lecture provides a historical perspective on reproducibility in science, as well as the current limitations of neuroimaging studies to date. This lecture also lays out a case for the use of meta-analyses, outlining available resources to conduct such analyses. 

This workshop will introduce reproducible workflows and a range of tools along the themes of organisation, documentation, analysis, and dissemination. 

This lesson gives a description of the BrainHealth Databank, a repository of many types of health-related data, whose aim is to accelerate research, improve care, and to help better understand and diagnose mental illness, as well as develop new treatments and prevention strategies. 

This lesson corresponds to slides 46-78 of the PDF below. 

This talk goes over Neurobagel, an open-source platform developed for improved dataset sharing and searching. 

This lightning talk describes the heterogeneity of the MR field regarding types of scanners, data formats, protocols, and software/hardware versions, as well as the challenges and opportunities for unifying these datasets in a common interface, MRdataset.

This lesson describes the current state of brain-computer interface (BCI) standards, including the present obstacles hindering the forward movement of BCI standardization as well as future steps aimed at solving this problem. 

This lightning talk gives an outline of the DataLad ecosystem for large-scale collaborations, and how DataLad addresses challenges that may arise in such research cooperations.

In this lightning talk, you will learn about BrainGlobe, an initiative which exists to facilitate the development of interoperable Python-based tools for computational neuroanatomy.

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 conceptualize, design, implement, and maintain neuroscientific pipelines in via the cloud-based computational reproducibility platform Code Ocean.