2017 Data on the Mind Workshop
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.
Accessing our workshop materials
We are excited to announce our entire workshop — which was publicly broadcast live via YouTube — is permanently and freely available through YouTube, GitHub, and Docker. Edited and transcribed versions of these broadcasts will be turned into a series of online tutorials, but the raw broadcast feeds are now up for anyone to use. Find out more here.
The tutorials relied on both R and Python and covered a range of topics (see all of our tutorials and instructors here). All tutorials were designed to be accessible to anyone with a beginner's level of programming in both languages. Basic introductions to programming were not covered in the tutorials, but you can find a list of some free online basic programming tutorials here.
If you're on Twitter, let everyone know you've found us with our hashtag: #dataonthemind
Contact Alex Paxton at <paxton [dot] alexandra [at] berkeley [dot] edu>.
- 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: