Video as data

Video as documentation

Open video sharing with Databrary

Video analysis with Datavyu

The future of video sharing

Adolph, K., Tamis-LeMonda, C. & Gilmore, R.O. (2017). PLAY Project: Pilot Data Collections. Databrary. Retrieved April 24, 2018 from https://nyu.databrary.org/volume/444.

Video…

Captures the complexities and nuances of behavior unlike any other measure

Can be readily repurposed to answer new questions

Contains faces & voices that make it hard(er) to share openly

Involves large file sizes that pose challenges to storage & streaming

The PLAY Project Wiki: https://dev1.ed-projects.nyu.edu/wikis/docuwiki/doku.php/landing

Bahrick, L.E. (2017). Multisensory Attention Assessment Protocol (MAAP). Databrary. http://doi.org/10.17910/B7.326.

Naigles, L. (2014). Children use syntax to learn verb meanings. Databrary. http://doi.org/10.17910/B7J01M.

Cole, P.M., Gilmore, R.O., Scherf, K.S. & Perez-Edgar, K. (2016). The Proximal Emotional Environment Project (PEEP). Databrary. http://doi.org/10.17910/B7.248.

Video (& audio)…

Reveal critical procedural details that text-based descriptions omit

Capture the complexities and nuances of experimenter behavior unlike any other measure

Are a “gold-standard” in documenting procedures, displays, & materials

Can document annotation/code definitions, supplementing text-based descriptions

The PLAY Project Wiki: https://dev1.ed-projects.nyu.edu/wikis/docuwiki/doku.php/landing

The PLAY Project Wiki: https://dev1.ed-projects.nyu.edu/wikis/docuwiki/doku.php/landing

Adolph, K.E., Gilmore, R.O., & Kennedy, J.L. (2017, October). Video data and documentation will improve psychological science. Psychological Science Agenda.

If a picture paints a thousand words, then why can’t I paint you? The words will never show the you I’ve come to know.

“If”, Bread

is…

A data library

Specialized for storing, sharing, browsing, and reusing video & audio recordings

Stores and shares…

Session (person + place + time) metadata + other types of data

Provides…

a policy framework for securely and ethically sharing identifiable data

Encourages (but does not require) …

Open (unrestricted) data sharing

No requirement for collaboration or co-authorship

Expectation of citation

Sharing identifiable data

Unaltered videos are most valuable for reuse

Videos are identifiable, so sharing requires participant permission

Restrict access: To researchers formally authorized by their institutions

Access agreement signed by researcher, institution

396 Institutions • 732 Investigators

Seek permission to share: Using standardized template language

https://www.databrary.org/resources/guide/investigators/release/release-levels.html

Store for research team/collaborator use; preservation

Share with other researchers (with participant permission)

Register for access

Databrary staff get in touch

Institution signs Databrary Access Agreement

Browsing

Log on

Search, filter, play

Most teaching & pre-research use cases do not require IRB approval

Sharing data

Upload as-as-you go (“self-curate”)

Add metadata, links to other resources

Give collaborators access

Share when you’re ready (paper goes to press, grant ends)

Secondary (re)use

Get IRB approval

Search, filter

Download

Code & use videos

Domains beyond developmental psychology

From data repository to analysis platform

Scriptable visualization

Visualize Datavyu timelines stored on Databrary

Machine-assisted audio & video analysis

Source: Ori Ossmy (NYU)

Source: LookIt, Kim Scott (MIT), & Rhodri Cusack (Trinity College Dublin)

Gilmore, R.O. & Adolph, K.E. (2017). Video can make behavioural science more reproducible. Nature Human Behaviour. doi:10.1038/s41562-017-0128

databrary.org, @databrary

datavyu.org, @datavyu

rogilmore@psu.edu

gilmore-lab.github.io

http://gilmore-lab.github.io/2018-04-25-introducing-databrary/

This talk was produced on 2018-04-25 08:30:04 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 https://github.com/gilmore-lab/2018-04-25-introducing-databrary/. Information about the R Session that produced the slides is as follows:

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## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
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## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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
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## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] databraryapi_0.1.2.9004
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.16    revealjs_0.9    digest_0.6.15   rprojroot_1.3-2
##  [5] R6_2.2.2        jsonlite_1.5    backports_1.1.2 magrittr_1.5   
##  [9] evaluate_0.10.1 httr_1.3.1      stringi_1.1.7   curl_3.2       
## [13] rmarkdown_1.9   tools_3.4.4     stringr_1.3.0   yaml_2.1.18    
## [17] compiler_3.4.4  htmltools_0.3.6 knitr_1.20