This lecture on generating TVB ready imaging data by Paul Triebkorn is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Difficulty level: Intermediate

Duration: 1:40:52

Speaker: : Paul Triebkorn

Course:

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:

This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial 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: Intermediate

Duration: 50:44

Speaker: : Caterina Gratton

Course:

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

Course:

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner

Duration: 1:16:36

Speaker: : Tal Yarkoni

Course:

Agah Karakuzu takes a spaghetti script written in MATLAB and turns it into understandable and reusable code living happily in a powerful GitHub repository.

Difficulty level: Beginner

Duration: 02:08:19

Speaker: :

Course:

A quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science. Includes the use of readr, dplyr, tidyr, and ggplot2.

Difficulty level: Beginner

Duration:

Speaker: :

Serving as good refresher, Shawn Grooms explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

Difficulty level: Beginner

Duration: 01:00:07

Speaker: :

Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. As such, it underlies a huge variety of analyses in the neurosciences. This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

Difficulty level: Beginner

Duration: 01:21:30

Speaker: :

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Difficulty level: Beginner

Duration: 37:51

Speaker: : Barbara Sperner-Unterweger

Course:

Introduction to the FAIR Principles and examples of applications of the FAIR Principles in neuroscience. This lecture was 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: Beginner

Duration: 55:57

Speaker: : Maryann E. Martone

Tutorial on collaborating with Git and GitHub. This tutorial was 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: Intermediate

Duration: 2:15:50

Speaker: : Elizabeth DuPre

Course:

Next generation science with Jupyter. This lecture was 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: Intermediate

Duration: 50:28

Speaker: : Elizabeth DuPre

Course:

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner

Duration: 1:25:17

Speaker: : Fernando Perez

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 the description and brief history of data science and its use in neuroinformatics.

Difficulty level: Beginner

Duration: 11:15

Speaker: : Ariel Rokem

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 self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.

Difficulty level: Beginner

Duration: 25:50

Speaker: : Eva Dyer

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

- Notebooks (1)
- Electroencephalography (EEG) (9)
- BIDS (3)
- (-) Cognitive neuroinformatics (1)
- (-) Programming Languages (3)
- Data reuse (2)
- Clinical neuroinformatics (6)
- Connectomics (1)
- Standards and Best Practices (6)
- Brain computer interface (13)
- The Virtual Brain (1)
- Deep learning (28)
- Data integration (1)
- Data sharing (2)
- Neurodata Without Borders (2)
- Deep brain stiumlation (2)
- Event related potential (ERP) (6)
- Connectivity (2)
- DANDI archive (1)
- Digital brain atlasing (1)
- Data management (10)
- Machine learning (5)
- (-) Brain networks (2)
- FAIR (24)
- Quantitative Electroencephalogram (qEEG) (1)
- Data curation (2)
- Data standard (4)
- Databases (1)
- (-) Mathematics (2)
- NIDM (1)
- Neuroimaging (16)
- Glia (1)
- Data governance (3)
- Epilepsy (1)
- Ontologies (1)
- (-) Magnetoencephalography (MEG) (2)
- Simulation (3)
- Reproducibility (3)
- Electrophysiology (23)
- Cloud computing (5)
- Positron Emission Tomography (PET) (2)
- High performance computing (3)
- Data structures/models (2)
- Psychiatric disorders (1)
- Neuroanatomy (3)
- Standards and best practices (16)
- Tools (12)
- Neurobiology (4)
- Metadata (2)
- Neurodegeneration (1)
- Workflows (3)
- Neuroimmunology (1)
- Neural networks (2)
- Neuropharmacology (2)
- Neuronal plasticity (15)
- Animal models (1)
- Assembly 2021 (28)
- Brain-hardware interfaces (13)
- Clinical neuroscience (9)
- International Brain Initiative (2)
- Repositories and science gateways (5)
- Resources (4)
- General neuroscience (4)
- General neuroinformatics (1)
- Computational neuroscience (98)
- Computer Science (5)
- Genomics (1)
- (-) Data science (8)
- Open science (13)
- Project management (6)
- Education (1)
- Neuroethics (24)