yuhangx / 2017-summer-workshop

Exercises, data, and more for our 2017 summer workshop (funded by the Estes Fund and in partnership with Project Jupyter and Berkeley's D-Lab)

Data on the Mind 2017 Workshop

Website: http://www.dataonthemind.org/2017-workshop
Videos: https://www.youtube.com/playlist?list=PLKW2Azk23ZtSOUnLafEn0W1FEf-Rw-8pi

Tackling the new data frontier

Big data and naturally occurring datasets (NODS) are increasingly of interest to cognitive scientists and psychologists. With the proper tools and mindset, these data can provide compelling evidence of human behavioral, cognitive, and social process in natural settings. Big data and NODS present an unprecedented opportunity to explore theory-driven questions -- questions rooted in theories developed in rigorous lab studies -- in real-word datasets. These naturalistic explorations can generate new ideas that can be further refined in follow-up lab studies, creating a virtuous cycle of theory development.

However, along with this unique opportunity comes unique challenges. Graduate students and postdoctoral researchers in cognitive science and psychology have often been trained to collect and handle data that are relatively small and that come from tightly controlled lab settings. Although early-career scientists have deep theoretical knowledge of their research areas that would be powerfully applied to big data and NODS, many lack experience dealing with the challenges posed by these messier (and often exponentially larger) datasets, including analysis selection, computational capacity, and data collection.

Our (free) 2017 workshop

To help cognitive scientists and psychologists tackle these issues, Data on the Mind was funded by the Estes Fund to create a 4-day workshop of hands-on introductions to topics that are essential for theory-driven research using big data and NODS. Each tutorial was taught by an expert in that area and included real code and other exercises meant to empower participants to immediately apply these techniques to their own research.

Videos

The workshop was live-streamed via YouTube, and videos from the live-stream are persistently available on the YouTube channel for the Berkeley Institute for Data Science: https://www.youtube.com/playlist?list=PLKW2Azk23ZtSOUnLafEn0W1FEf-Rw-8pi

Getting started

To get started, you'll need to download Docker: https://docker.com

After you've done that, you can clone this repository on your machine to access the workshop materials in a self-enclosed environment:

1. Open a terminal or command line window.
2. Pull the Docker image by pasting this command into the command line: docker pull aculich/data-on-the-mind-2017-summer-workshop
3. Press enter.
4. Wait for the download to complete.
5. Initialize the Docker container by pasting this into the command line: docker run aculich/data-on-the-mind-2017-summer-workshop
6. Pull the GitHub repository by pasting this into the command line: git clone https://github.com/Data-on-the-Mind/2017-summer-workshop/
7. Press enter.
8. Wait for the download to be complete.

Scope of the workshop

This workshop includes programming in both R and Python, and tutorials assume that all participants will start out with at least a beginner's level of programming in both languages. All tutorials should be accessible to anyone with a beginner's level of programming in R and Python. Check out our list of tutorials here: http://www.dataonthemind.org/2017-workshop/schedule

Pleaes keep in mind that we will not be covering any introductions to basic programming in our workshop, so be sure to complete some basic R and/or Python tutorials before attending in person or remotely. We link to some free online basic programming tutorials here: http://www.dataonthemind.org/2017-workshop/introductions-r-and-python

Questions?

Contact Alex Paxton at <paxton [dot] alexandra [at] berkeley [dot] edu>.

Organizing committee

• Tom Griffiths (University of California, Berkeley)
• Alexandra Paxton (University of California, Berkeley)
• Michael C. Frank (Stanford University)
• Todd Gureckis (New York University)

Our funding partners

We'd like to thank our funding partners who made this possible:

• The Estes Fund: http://www.psychonomic.org/page/estesfund
• D-Lab: http://dlab.berkeley.edu/
• Project Jupyter: http://jupyter.org/