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The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. This lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

Difficulty level: Beginner
Duration: 18:56
Speaker: : Tom Johnstone

As researchers develop new non-invasive direct-to-consumer technologies that read and stimulate the brain, society must consider the appropriate uses of such devices. Will these brain technologies eventually allow enhancement of abilities beyond human capabilities? In what settings are people using these devices outside the purview of researchers or clinicians? Should consumers be allowed to ‘hack’ their own brain in order to improve performance?

To explore these challenges and the ethical issues raised by advances in do-it-yourself (DIY) neurotechnology, the Emerging Issues Task Force of the International Neuroethics Society organized a virtual panel discussion. The panel discussed neurotechnologies such as transcranial direct current stimulation (tDCS) and electroencephalogram (EEG) headsets and their ability to change the way we understand and alter our brains. Particular attention will be given to the use of neurotechnology by everyday people and the implications this has for regulatory oversight and citizen neuroscience. 

Panelists included:

  • Marcello Ienca, ETH Zurich
  • Karola Kreitmair, University of Wisconsin–Madison
  • Anna Wexler, University of Pennsylvania
  • Ishan Dasgupta, University of Washington (moderator)
Difficulty level: Beginner
Duration: 1:00:59

This module covers many of the types of non-invasive neurotech and neuroimaging devices including Electroencephalography (EEG), Electromyography (EMG), Electroneurography (ENG), Magnetoencephalography (MEG), functional Near-Infrared Spectroscopy (fNRIs), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography

Difficulty level: Beginner
Duration: 13:36
Speaker: : Harrison Canning
Course:

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

 

Difficulty level: Beginner
Duration: 1:09:16
Speaker: : Aaron J. Newman

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

Difficulty level: Beginner
Duration: 13:16
Speaker: : Kelly Shen

Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools. Rather than rigid distinctions, in the data science of neuroinformatics, these activities and approaches intersect and interact in dynamic ways. Together with a panel of cutting-edge neuro-data-scientist speakers, we will explore these dynamics

 

This lecture covers how brainlife.io works, and how it can be applied to neuroscience data.

Difficulty level: Beginner
Duration: 10:14
Speaker: : Franco Pestilli

As a part of NeuroHackademy 2020, Tara Madhyastha (University of Washington), Andrew Crabb (AWS), and Ariel Rokem (University of Washington) give a lecture on Cloud Computing, focusing on Amazon Web Services

 

This video is provided by the University of Washington eScience Institute.

 

Difficulty level: Beginner
Duration: 01:43:59
Speaker: :

Shawn Brown presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance-computing facilities across Canada and around the world.

 

This talk was given in the context of a Ludmer Centre event in 2019.

 

 

Difficulty level: Beginner
Duration: 56:07
Speaker: :

This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research. 

Difficulty level: Beginner
Duration: 57:52
Speaker: : Satrajit Ghosh

An agent for reproducible neuroimaging

Difficulty level: Beginner
Duration: 1:00:10
Speaker: : David Kennedy

The Human Connectome Project aims to provide an unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve never before realized conclusions about the living human brain.

Difficulty level: Advanced
Duration: 59:06
Speaker: : Jennifer Elam

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

Difficulty level: Beginner
Duration: 15:14

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

Difficulty level: Beginner
Duration: 14:19
Speaker: : Michael Denker

The course is an introduction to the field of electrophysiology standards, infrastructure, and initiatives. 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.

Difficulty level: Beginner
Duration: 8:11
Speaker: : Thomas Wachtler
Course:

This session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.

 

 

Difficulty level: Beginner
Duration: 38:36
Course:

This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community.

 

This lecture provides an overview of The Virtual Brain Simulation Platform.

 

Difficulty level: Beginner
Duration: 9:36
Speaker: : Petra Ritter

Demo of the BRIAN Simulator. BRIAN is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible.

Difficulty level: Beginner
Duration: 1:27:32
Speaker: : Marcel Stimberg

NeuroFedora is a volunteer driven initiative to provide a ready to use Fedora based Free/Open Source Software platform for neuroscience. We believe that similar to Free Software, science should be free for all to use, share, modify, and study. The use of Free Software also aids reproducibility, data sharing, and collaboration in the research community. By making the tools used in the scientific process easier to use, NeuroFedora aims to take a step to enable this ideal. The CompNeuro Fedora Lab was specially to enable computational neuroscience. It includes everything you will need to get your work done—modelling software, analysis tools, general productivity tools—all well integrated with the modern GNOME platform to give you a complete operating system.

Difficulty level: Beginner
Duration: 1:06:08
Speaker: : Ankur Sinha

neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area. This network model can be used to simulate whole-brain dynamics. neurolib provides a simulation and optimization framework which allows you to easily implement your own neural mass model, simulate fMRI BOLD activity, analyse the results and fit your model to empirical data.

Difficulty level: Beginner
Duration: 1:06:53
Speaker: : Çağlar Çakan