The UO site for Brainhack Global is being organized by Kate Mills, a post-doc in the Developmental Social Neuroscience lab (along with help from many others). Don’t let the Brain in Brainhack fool you — this is an event for anyone who wants to work on programming, analysis, and data related to the study of brain, mind and behavior!

We are hosting a Brainhack on March 4-5 as part of a global initiative, and we are inviting you to join us save the date!

This event will bring together neuroscientists, psychologists, biologists, and computer scientists to collaborate on participant-directed projects related to open science, software, and data. Come with a project idea of your own, or just excitement to collaborate with others.

Brainhack Global 2017 will unite regional events occurring the same week at 40+ different sites. We are participating as the only site in Oregon.

If you’re interested in attending and want to receive updates on Brainhack 2017, please fill out this form.

We hope to see you there!
Kate, Dani, John, Jenn, Theresa, Nandi and the rest of the DSN Lab

FAQ:
What’s going to happen at Brainhack?
Prior to our event, we will collect project ideas from attendees. Attendees can pitch project ideas to work on or join proposed project teams. We will all contribute to projects during times for open hacking and present our progress at the end of Brainhack. There will also be mini-unconferences, which are an opportunity to discuss topics of interest with other attendees, related to their areas of expertise.

What kind of projects can I work on?
Current project pitches include contributing to open science programs, such as NeuroVault and Brain Imaging Data Structure Apps. We welcome any projects related to the study of the brain and/or behavior.

I have a project idea! How can I let others know about it?
Great! Please fill out this form to let others know about your project idea! Since the event is only two days long, project pitches should be submitted here prior to the start of the event so that we can hit the ground running.

I don’t have a project idea. What should I do?
It’s okay if you don’t have a project idea of your own, because other projects will need your skills and support. Take a look at this spreadsheet to look at current project ideas. All skills are valued at a Brainhack–you can always be a beta tester!

I don’t have a background in neuroscience -and/or- I don’t have strong programming skills. Can I still attend?
Yes! All are welcome. The purpose of Brainhack is to bring together people with different skills to learn from one another.

Today we’ll start with a consultation:

I am running some machine learning “decoding” analyses on EEG data, and am trying to figure out the best way to assess these effects using statistical tests. Each subject produces something similar to a correlation matrix (confusion matrix), and I need to figure out what statistical test will be appropriate considering the non-independence of the observations.

Then we’ll move on to machine learning in R. Last week we compiled a bunch of learning resources. This week we’ll come up with a plan for traversing this stuff, and begin!

Last week we decided that everyone was interested in learning machine learning in R, and that we’d end up getting more proficient with version control along the way. The rest of the session was spent identifying resources — here’s a list we’ll add to as the quarter continues:

• Why choose prediction? Tal and Jake have an argument for that.
• Data sets for machine learning competitions at Kaggle
• R package for machine learning: Caret
• caretEnsemble
• What’s an ensemble?
• Textbook (pdf) and R code from one of the field’s leaders: An Introduction to Statistical Learning
• 15 HOURS OF EXPERT VIDEOS
• tangential: What natural language processing tells us about heavy metal

R Club is on Thursdays from 2:00-3:30p in 008 STB

As always, R Club is available for anyone interested in spend a bit more time in any given week on scientific computing. Come with a question you’d like help with, or a cool new thing you’d like to show and tell. The best way to get the conversation started is via the SlackR group here: https://uorclub.slack.com. Sign up with your uoregon.edu email address for immediate access.

The guiding structure this winter quarter is going to be pseudo-hackathon style. We’re going to try to form a few different working groups with the goal of learning a new scientific computing skill, and use R Club as protected time to hack away at those skills. Today, we’ll pitch ideas, and people can choose to join up. Having a folks working on building these skills all in the same room will hopefully provide a supportive environment where questions and difficulties can be overcome quickly.

Here are some possible working-group topics:

• Machine learning in R (or Python?)*
• Version control*
• R Markdown → APA Formatted manuscripts
• Text Mining
• Shiny Apps

* This one is definitely happening!

Lou Moses is going to present today on how to use the metafor package today.

Being able to do a meta-analysis in depth, or on the fly, is something I’ve come to view as nearly as important a basic skill as the t-test. It’s indispensable whether you’re trying to get a quick estimate of effect size across a literature for a power analysis, concerned about publication bias, or attempting to produce a definitive summary of a literature. And metafor in R is a really easy to use tool for doing that.

This week we’re going to talk about one of my favorite things: simulation!

Simulation is one of the most useful tools I’ve come across. You can use it to test how your data and statistical tests behave when certain assumptions are violated, how much power you have to detect a true effect, and more generally it helps you think about what you expect from the data generating process you’re interested in.

Also, for those of us who haven’t written a formal mathematical proof in awhile, it’s a simple way to demonstrate statistical problems and solutions without slogging through complicated equations.

Our strategy for today:

We’ll start by looking at how to draw numbers from distributions (e.g., the normal distribution), how to do this repeatedly to simulate sampling from a population, and then how to do that repeatedly to simulate running multiple studies (hopefully this builds up a crucial bit of insight about how frequentist statistics work, too).

After that, we’ll look at some tools (lavaan and simsem) that you can use to simulate more complicated experimental designs.

Resources

• Hadley’s simulation lecture (part 1)
• Hadley’s simulation lecture (part 2)
• Daniel Laken’s power simulation code from this coursera course
• P Curves
• Exploring false positive rates
• Power to detect mediation (lavaan and simsem)
• Model comparison power (more advanced simsem)
• Distributions!

I just set up a slack team for R Club. The focus right now is on doing a little planning for the coming quarter, but it will also serve as a place for our UO R community to ask questions and maybe get help from fellow students on the fly. All you need to join is a UO email address.

uorclub.slack.com

Today at R club, we’ll help Jessica with a fun problem: she has a bunch of multi-level models written in SAS that she want’s to be able to play with in R. The motivation is that she can find R on pretty much any computer she sits down at these days, but only has access to one computer that runs SAS. For convenience, and because she is learning R, she wants to be able to work with these models using lme4 or nlme.

The data sounds really cool:

This project is looking at diurnal cortisol (4 times measurements per day per participant over the course of 2 days) in a cross sectional sample, ages 4-16 years old, specifically examining how two groups differ (previously institutionalized versus biologically reared). The second component of the project is examining how a 3rd variable (sleep) may moderate these effects.

Her data is already in long format, so we can get right into the modeling.