Open! (Says Me)

Rick O. Gilmore

2018-09-05 16:58:01


Karen Adolph (NYU)

Joy Kennedy (Databrary)

Jeff Spies (Databrary,


  • Why share data & materials?
  • How to share data & materials
  • Why open sharing of data & materials will advance psychological science


…meta-analyses from your desktop

…visualization of task data accumulated across studies & labs

…machine-learning-assisted analysis

Source: Ori Ossmy (NYU)

…“scriptable” analyses and visualizations from centrally stored & shared data

…easy downloading and reuse of others’ materials

Cole, P.M., Gilmore, R.O., Scherf, K.S. & Perez-Edgar, K. (2016). The Proximal Emotional Environment Project (PEEP). Databrary.

…reproduction of others’ procedures through video protocols

The PLAY Project Wiki:

…A cumulative psychological science


  • Findings accumulate
  • Theories are advanced, accepted, expanded, or rejected
  • Phenomena become increasingly predictable
  • Discovery accelerates


Psychological science is harder than physics

Studies are underpowered

“Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature.”

(Szucs & Ioannides, 2017)

Published papers have errors

(Nuijten et al., 2015)

Confusion about data ownership

  • Institutions
  • Taxpayers
  • Researchers
  • Participants

Eagerly share findings but not data or materials

Blinded from seeing the whole elephant

Fall victim to the toothbrush problem (Mischel, 2009)

…psychologists tend to treat other peoples’ theories like toothbrushes; no self-respecting individual wants to use anyone else’s.

The toothbrush culture undermines the building of a genuinely cumulative science, encouraging more parallel play and solo game playing, rather than building on each other’s directly relevant best work.


Mischel 2009

  • Common tools
  • Robust, replicable, consequential findings
  • Boundary crossing and bridge building

Make open data & materials sharing

…the norm not the exception

Plan for sharing

  • From the earliest stages
  • Data Management Plans (NSF and NIH proposals)
  • Data as a “first order” research product

What to share

  • Data
    • & analysis code/scripts (R, Python, SPSS, SAS, …)
    • Rawest possible (trial-level, individual, …)
  • Displays (& code to generate)
  • Protocols & procedures
    • Video as gold standard

The PLAY Project Wiki:

Where to share

With whom

  • Public
    • Risk of reidentification
    • Can you really anonymize?
  • Researchers
    • ICPSR, Databrary, & OpenNeuro
  • People you select & vet


  • Soon after you collect it
  • On manuscript submission
  • On acceptance or publication
  • End of grant
  • When I’m damn good and ready…


  • “FAIRly”
  • Findable, Accesible, Interoperable, and Reusable (Wilkinson et al., 2016)
    • Easier to find in repository
    • Interoperable formats
    • Codebooks


  • Ask permission to share (especially for sensitive, identifiable data)
  • Don’t promise to destroy data (but GDPR?)
  • Don’t unduly restrict future reuses

the principles of human subject research require an analysis of both risks and benefits…such an analysis suggests that researchers may have a positive duty to share data in order to maximize the contribution that individual participants have made.

(Brakewood & Poldack, 2013)


  • Without restriction on others’ reuse
  • Without quid pro quo, pre-approval, or requirement of co-authorship
  • With expectation of ethical use AND proper citation


…build platforms for discovery

that will make physicists jealous

…acknowledge the elephant in the room

…build a

  • Robust
  • Transparent
  • Reproducible
  • Powerful
  • Openly shared
  • Cumulative
  • (Awesome!)
  • Psychological Science

Open! (Says me)

This talk was produced on 2018-09-05 16:58:02 in RStudio 1.1.383 using R Markdown and the reveal.JS framework. The code and materials used to generate the slides may be found at Information about the R Session that produced the slides is as follows:

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