Course:

This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.

Difficulty level: Beginner

Duration: 01:28:03

Speaker: : Daniel Aharoni, Phil Dong

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.

Difficulty level: Beginner

Duration: 1:16:04

Speaker: : Erin Dickie and Sejal Patel

Course:

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

Difficulty level: Beginner

Duration: 1:16:36

Speaker: : Tal Yarkoni

Course:

This lesson gives a general introduction to the essentials of navigating through a Bash terminal environment. The lesson is based on the Software Carpentries "Introduction to the Shell" and was given in the context of the BrainHack School 2020.

Difficulty level: Beginner

Duration: 1:12:22

Speaker: : Ross Markello

Course:

This lesson covers Python applications to data analysis, demonstrating why it has become ubiquitous in data science and neuroscience. The lesson was given in the context of the BrainHack School 2020.

Difficulty level: Beginner

Duration: 2:38:45

Speaker: : Ross Markello

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

Course:

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

Difficulty level: Intermediate

Duration:

Speaker: :

Course:

This Jupyter Book is a series of interactive tutorials about quantitative T1 mapping, powered by qMRLab. Most figures are generated with Plot.ly – you can play with them by hovering your mouse over the data, zooming in (click and drag) and out (double click), moving the sliders, and changing the drop-down options. To view the code that was used to generate the figures in this blog post, hover your cursor in the top left corner of the frame that contains the tutorial and click the checkbox “All cells” in the popup that appears.

Jupyter Lab notebooks of these tutorials are also available through MyBinder, and inline code modification inside the Jupyter Book is provided by Thebelab. For both options, you can modify the code, change the figures, and regenerate the html that was used to create the tutorial below. This Jupyter Book also uses a Script of Scripts (SoS) kernel, allowing us to process the data using qMRLab in MATLAB/Octave and plot the figures with Plot.ly using Python, all within the same Jupyter Notebook.

Difficulty level: Intermediate

Duration:

Speaker: :

Course:

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

Difficulty level: Intermediate

Duration: 5:17

Speaker: : Mike X. Cohen

Course:

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

Difficulty level: Intermediate

Duration: 11:37

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram.

Difficulty level: Intermediate

Duration: 5:31

Speaker: : Mike X. Cohen

Course:

Followers of this lesson will learn how to compute, visualize and quantify the tuning curves of individual neurons.

Difficulty level: Intermediate

Duration: 13:48

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to programmatically generate a spatial map of neuronal spike counts using MATLAB.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn about human brain signals as measured by electroencephalography (EEG), as well as associated neural signatures such as steady state visually evoked potentials (SSVEPs) and alpha oscillations.

Difficulty level: Intermediate

Duration: 8:51

Speaker: : Mike X. Cohen

Course:

This lecture describes the principles of EEG electrode placement in both 2- and 3-dimensional formats.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

This tutorial walks users through performing Fourier Transform (FFT) spectral analysis of a single EEG channel using MATLAB.

Difficulty level: Intermediate

Duration: 13:39

Speaker: : Mike X. Cohen

Course:

This tutorial builds on the previous lesson's demonstration of spectral analysis of one EEG channel. Here, users will learn how to compute and visualize spectral power from all EEG channels using MATLAB.

Difficulty level: Intermediate

Duration: 12:34

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn more about the steady-state visually evoked potential (SSEVP), as well as how to create and interpret topographical maps derived from such studies.

Difficulty level: Intermediate

Duration: 9:10

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how to extract edogenous brain waves from EEG data, specifically oscillations constrained to the 8-12 Hz frequency band, conventionally named alpha.

Difficulty level: Intermediate

Duration: 13:23

Speaker: : Mike X. Cohen

Course:

In the final lesson of this module, users will learn how to correlate endogenous alpha power with SSVEP amplitude from EEG data using MATLAB.

Difficulty level: Intermediate

Duration: 12:36

Speaker: : Mike X. Cohen

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- Standards and best practices (4)
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- Repositories and science gateways (1)
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