• An open science manifesto
• Databrary.org
• Let’s strengthen developmental neuroscience

“The first principle is that you must not fool yourself, and you are the easiest one to fool.”

– Richard P. Feynmann

• Shows your work
• Data + analysis code + tasks
• What’s the effect size for…

• Wastes energy, time, money
• Hasn’t somebody tried X before?
• How big is the ‘file drawer effect’?

• Improves reproducibility
• Reveals errors faster
• Permits verification, re-analysis; strengthens meta-analysis
• Boosts sample sizes & increases power

“…yield more robust and reliable findings that others can readily build upon.”

(SRCD, 2019)

“essential for the conduct of research and its application to practice and policy.”

(SRCD, 2019)

“The advancement of detailed and diverse knowledge about the development of the world’s children is essential for improving the health and well-being of humanity.”

(SRCD, 2019)

• Sharing difficult, time-consuming
• Openness not yet rewarded, highly valued
• Collecting new data better (for me) than cleaning-up finished studies to share them
• I’ll share with X but not with Y

• I’ll change when..
  • the field does/I get tenure/I’m finished with my data…
• I can’t share because…
  • I don’t have permission from IRB/participants/collaborators
• I own my data…
  • don’t I?

• World’s only data library specialized for storing and sharing video and audio + related data from research on humans
• Hosted at New York University
• Opened 2014
• 588 institutions; 1746 researchers; 58,893 hours of video + other data; 577 shared projects

• Faces & voices
• Names, personal locations
• Behaviors

as data…

• Spatially & temporally dense samples of behavior
• “Outputs” of biological processes

(Adolph, K.E., Gilmore, R.O., & Kennedy, J.L., 2017)

and as documentation…

• How procedures are actually done
• What computer-based tasks actually look or sound like

(Gilmore & Adolph, 2017)

• Shared only with participant permission (& IRB approval)
• Provide template language, scripts and procedure documentation

• Researchers require institutional authorization
• Formal access agreement
• Site-wide access, not dataset-specific
• Generic uses
• Fosters data re-use and contribution

• Restricted data sharing has long track-record (e.g., ICPSR)
• Consistent sharing permission clarifies nature of risk
• Empowers participants
• Researchers & institutions determine what to share & when

• Open, but not public, sharing
• More secure than public data and materials services or journal web pages
• Researchers, Institutions need not reinvent wheels
• More discoverable than personal websites or institutional repositories
• Allows smaller-scale programs of research to contribute

• Consistent curation (and sharing permissions) make reuse easier
• Works for data beyond video
• Secure data interaction via API

databraryapi::get_db_stats()

##                  date investigators affiliates
## 1 2020-09-09 12:44:24          1191        555
##   institutions datasets_total datasets_shared n_files
## 1          588           1138             577  125663
##      hours       TB
## 1 58893.01 26.19616

(R. Gilmore, 2020)

• Updated policy framework
• Rewriting in Node.js, Hasura/GraphQL, Vue.js/Quasar

• ABCD
• HBCD
• NDA
• Make productive use of large public investments

• to document and share procedures
• to capture and describe a broader range of participants’ behaviors

• Databrary
  • Data that should be restricted
  • Start with procedure videos, displays
• Open Science Framework (OSF)
• OpenNeuro

• Psychophysics Toolbox, Psychopy, E-Prime
• Github, Gitlab
• MATLAB, R, Python, shell scripts; but also SPSS, SAS, …

• Cite colleagues’ data & materials
  • e.g., (R Core Team, 2019); (Xie, Allaire, & Grolemund, 2018); (Allaire et al., 2020); (R. Gilmore, 2020)
• Add open science contributions to our websites, CVs, etc.
• Urge professional societies and journals to adopt open science policies, e.g. (SRCD, 2019)

Make our science open!

This talk was produced on 2020-09-09 in RStudio using R Markdown. The code and materials used to generate the slides may be found at https://github.com/gilmore-lab/2020-09-02-flux. Information about the R Session that produced the code is as follows:

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets 
## [6] methods   base     
## 
## other attached packages:
## [1] qrcode_0.1.1
## 
## loaded via a namespace (and not attached):
##  [1] png_0.1-7          assertthat_0.2.1  
##  [3] packrat_0.5.0      digest_0.6.25     
##  [5] R.methodsS3_1.8.0  R6_2.4.1          
##  [7] jsonlite_1.7.0     magrittr_1.5      
##  [9] evaluate_0.14      highr_0.8         
## [11] httr_1.4.2         rlang_0.4.7       
## [13] stringi_1.4.6      curl_4.3          
## [15] R.oo_1.23.0        R.utils_2.9.2     
## [17] keyring_1.1.0      databraryapi_0.2.3
## [19] rmarkdown_2.3      tools_3.6.2       
## [21] stringr_1.4.0      xfun_0.16         
## [23] yaml_2.2.1         compiler_3.6.2    
## [25] htmltools_0.5.0    knitr_1.29

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