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Welcome to the

Online Brain Intensive 

The Online Brain Intensive is an online course aimed at preparing students to participate in their first Brainhack.  Through this course students will learn to use openly available online tools to gain a foundation of technical knowledge for neuroimaging and computational neuroscience, with a strong focus on reproducibility and version control.  This course is free, fully online and open to anyone who wants to participate.

week

one

The what, why and how of Brainhacks

August 28 - September 1

Project: Run Docker pulled from git that runs Jupyter notebook with analysis, and modify a classifier

Takeaway: Students should understand how to do basic reproducible science

Topic Specialist: Git / Python
Topic Specialist : Shell Scripting

Introduction

to Brainhacks

Cameron Craddock,

child mind institute

Estimated Time to Complete:

40 Minute Lecture

Estimated Time to Complete:

35 Minute Lecture 

​

Reproducibility

in brain science

kirstie whitaker,

cambridge univeristy

Estimated Time to Complete:

30 Minute 

30 Minute Set-up

 

Brainhack101 refers to:

Brainhack

101

greg kiar,

mcgill university

week

two

Week 2: Brain Imaging Pipelines

September 4 - September 8

Project: Make a workflow for data with an existing Nipype pipeline

Takeaway: Students should have a better understanding of the landscape for fMRI data and analysis

Estimated Time to Complete:

35 Minute Lecture

​

Learning through

data mining on

open data sets

emily finn,

National Institutes

of health

Estimated Time to Complete:

22 Minute Lecture

40 Minute Tutorial 

​

 

​

getting started

with Nipype

chris gorgolewski,

stanford university

Estimated Time to Complete:

50 Minute Lecture 

60 Minute Tutorial 

 

​

Decentralized

Distribution

and Sharing of

Scientific

Datasets

yaroslav halchenko,

dartmouth college

week

three

Week 3: Exploring and Visualizing Data

September 11 - September 15

Project: Run Docker pulled from git that runs Jupyter notebook with analysis, and modify a classifier

Takeaway: Students should understand how to do basic reproducible science

efficiency in fMRI: increasing power for a fixed sample size

jeanette mumford,

university of

Wisconsin-Madison  

​

Estimated Time to Complete:

40 Minute Lecture

30 Minute Tutorial 

​

​

Estimated Time to Complete:

40 Minute Lecture

​

how not to

fool yourself

with

statistics

regina nuzzo,

gallaudet university

Estimated Time to Complete: 

15 Minute Lecture

​

data

visualization 

as exploratory

analysis

greg kiar,

mcgill university

week

four

Week 4: Hypothesis Testing and Model Building

September 18 - September 22

Project: Do a hypothesis test and train a model

Takeaway: Awareness of common pitfalls while doing computational analysis

Estimated Time to Complete:

35 Minute Lecture 

20 Minute Tutorial - Orientation Spatial Frequency  

20 Minute Tutorial - Generative Models 

 

​

building a

generative

model

megan peters,

uc riverside

Estimated Time to Complete:

15 Minutes Lecture 

 

​

T

TBD

Rapid Rounds

for Reproducible 

Science

​

sara kimmich,

nih

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