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The tutorial is intended primarily for beginners, but it will also beneficial to experimentalists who understand electroencephalography and event related techniques, but need additional knowledge in annotation, standardization, long-term storage and publication of data.

Difficulty level: Beginner
Duration: 35:30

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

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

As a part of NeuroHackademy 2020, Elizabeth DuPre gives a lecture on "Nilearn", a python package that provides flexible statistical and machine-learning tools for brain volumes by leveraging the scikit-learn Python toolbox for multivariate statistics.  This includes predictive modelling, classification, decoding, and connectivity analysis.

 

This video is courtesy of the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 01:49:18
Speaker: : Elizabeth DuPre

Estefany Suárez provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:22:18
Speaker: :

Gael Varoquaux presents some advanced machine learning algorithms for neuroimaging, while addressing some real-world considerations related to data size and type.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:17:14
Speaker: :

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

Difficulty level: Beginner
Duration: 02:09:22
Speaker: :

Learn how to handle writing very large data in MatNWB

Difficulty level: Advanced
Duration: 16:18
Speaker: : Ben Dichter

Overview of the CaImAn package, and demonstration of usage with NWB

Difficulty level: Intermediate
Duration: 44:37

Overview of the SpikeInterface package, including demonstration of data loading, preprocessing, spike sorting, and comparison of spike sorters

Difficulty level: Intermediate
Duration: 1:10:28
Speaker: : Alessio Buccino

Overview of the NWBWidgets package, including coverage of different data types, and information for building custom widgets within this framework

Difficulty level: Intermediate
Duration: 47:15
Speaker: : Ben Dichter

Elizabeth Dupre provides reviews some standards for project management and organization, including motivation in the view of the FAIR principles and improved reproducibility.

Difficulty level: Beginner
Duration: 01:08:34
Speaker: : Elizabeth DuPre

Introductory presentation on how data science can help with scientific reproducibility.

Difficulty level: Beginner
Duration:
Speaker: : Michel Dumontier

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

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