# A tibble: 1 × 9
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# ℹ 4 more variables: datasets_shared <int>, n_files <int>, hours <dbl>,
# TB <dbl>Do they know…where they’re going to?
Sharing & analyzing data from wearable sensors
Rick O. Gilmore
Preliminaries
About me
- B.A., Cognitive Science, Brown University
- M.S. & Ph.D., Psychology (Cognitive Neuroscience), Carnegie Mellon University
- Human brain development, perception & action, computational modeling, machine vision, big data, open science
- Founding Director of Human Imaging, Penn State Social, Life & Engineering Sciences Imaging Center (SLEIC)
- Co-Founder/Co-Director of Databrary.org data library
- gilmore-lab.github.io
- Co-organizer & faculty R Bootcamp (17, 18, 19), Open Science Bootcamp 2023
- clawhammer banjo player, actor, cyclist, backpacker, poet, ham (W3TM)
- Judge of Elections, Precinct 26 • State College East 3
- native Coloradoan, husband, dad, grandpa
HTML slides
Agenda
- Wherefore perception?
- Case study: The development of motion perception
- Issues in openness, transparency, reproducibility
Wherefore perception?
Opening The Doors1
“If the doors of perception were cleansed every thing would appear to man as it is, Infinite. For man has closed himself up, till he sees all things thro’ narrow chinks of his cavern.”
What must animals perceive?
What must animals do?
Perception inextricably linked to action
Ecological Psychology
James J. and Eleanor J. Gibson
Behavioral priorities (evolutionary view)
- Secure sustenance (food & drink)…
- Avoid harm
- (Maybe, just maybe…) reproduce
Behavioral primitives
- Locomotion (wayfinding, steering, & balance)
- Object interaction/manipulation
- Communication (vocalizing & gesturing)
Perception informs…
- Where things are; where they’re moving
- Who/what’s out there
- Where am I positioned/moving
- What should I do next
Psychophysics
Virtues of \(\Psi\) functions
Change in visual acuity from https://gilmore-lab.github.io/visual-acuity/
(Figure 1 from Qian, Berenbaum, & Gilmore, 2022)
Limitations of psychophysical methods
- Costly to measure
- Are thresholds consistent (within observers, across situations)?
- Lab (control high, detail lower)
- vs. real world (control lower, detail high)
Case study: The development of motion perception
Optic flow
- Visual motion generated by observer movement
https://www.shutterstock.com/video/clip-1068753407-first-person-pov-snowboarding-fast-on-ski
- Self-motion <-> optic flow pattern
- Forward/backward: radial flow
- Side to side/Up or down: lateral flow
- Somersault: rotational flow
Wikipedia
Can be studied in infants & children…
- Using psychophysics (Gilmore, Baker, & Grobman, 2004; Gilmore & Rettke, 2003; Qian, Seisler, & Gilmore, 2021)
- And psychophysiology (Gilmore, Hou, Pettet, & Norcia, 2007; Gilmore, Thomas, & Fesi, 2016; Hou, Gilmore, Pettet, & Norcia, 2009)
- Development prolonged
- Brain activity immature
- What else affects developmental change?
A: Posture!
(Kretch, Franchak, & Adolph, 2014)
- Why? Geometry
Simulating effects of postural change
| Parameter | Crawling Infant | Walking Infant |
|---|---|---|
| Eye height | 0.30 m | 0.60 m |
| Locomotor speed | 0.33 m/s | 0.61 m/s |
| Head tilt | 20 deg | 9 deg |
| Type of Locomotion | Ground Plane | Room | Side Wall | Two Walls |
|---|---|---|---|---|
| Crawling | 14.41 | 14.42 | 14.43 | 14.62 |
| Walking | 9.38 | 8.56 | 7.39 | 9.18 |
- Environmental geometry doesn’t matter (much)
But, what’s the input?
- The real input?
- And how to measure it?
A: Head-mounted cameras
- Head-mounted eye trackers (Franchak, Kretch, Soska, & Adolph, 2011; Kretch & Adolph, 2015)
- Head cameras (Jayaraman & Smith, 2014)
(Figure 1 from Kretch & Adolph, 2015)
https://nyu.databrary.org/slot/7739/0,26634/asset/16747/download?inline=true
https://nyu.databrary.org/slot/7739/0,26134/asset/16749/download?inline=true
Flow patterns
Speed distribution
Findings
- Infant (passengers) experience faster visual speeds than mother
- Fast speeds common in infant visual experience
- Motion “priors” for infants ≠ mothers
Does culture (India vs. Indiana) influence?
(Figure 3 from Gilmore et al., 2015)
(Figure 4 from Gilmore et al., 2015)
A: Not much!
Issues in openness, transparency, reproducibility
Sharing identifiable data, especially video
- Costly to anonymize
- Alteration reduces reuse potential
- File sizes large
- Long-term storage $3-5K/TB
- Adolph, Franchak, Kretch, Jayaraman willing to share with me
- What about others?
Databrary
- Restricted access data library
- Based at NYU, global user community
Consistent sharing release levels
- Encourages open sharing (data owner/depositor permission not required for reuse)
Databrary
- Free (but moving to subscription model)
- Share unaltered video + other data
- Open, unrestricted research uses (broad consent)
- All data & metadata accessible via API
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asset_name
1 Introduction
2 Databrary 1.0 plan
3 Datavyu
4 Slides
5 Databrary demo
6 Overview and Policy Update
7 Private Beta demo
8 04-07-Slides
9 Positioning Databrary for the future
10 Board Meeting
11 slides
12 Databrary demo
13 SLIDES: Open video data sharing
14 Open video data sharing
15 Datavyu tutorial
16 SLIDES: Best practices in video coding
17 Lunch discussion
18 Best practices in video coding
19 Welcome slides
20 SLIDES: Lunch discussion
21 video
22 slides
23 Slides: Video reuse, management, and sharing
24 Slides: Best practices in behavioral video coding and introduction to Datavyu
25 Slides: Policies and best practices for sharing and managing video data
26 Policies & best practices for sharing & managing video data
27 Agenda
28 Introduction to Datavyu: Video coding & best practices
29 Introduction to Databrary
30 SLIDES: Policies & best practices for sharing & managing video data
31 SLIDES: Introduction to Datavyu: Video coding & best practices
32 SLIDES: Introduction to Databrary
33 logo
34 NSF Proposal Narrative
35 NIH Proposal Narrative
36 counting (demo video)
37 counting (demo Datavyu file)
38 volumes-citations-monthly
39 databrary-institutions-investigators
40 SLIDES: Introduction to Databrary
41 SLIDES: Policies & best practices for sharing & managing video data
42 SLIDES: Introduction to Datavyu: Video coding & best practices
43 Introduction to Datavyu: Video coding & best practices
44 Policies & best practices for sharing & managing video data (Part 1)
45 Policies & best practices for sharing & managing video data (Part 2)
46 Introduction to Databrary
47 SLIDES: Introduction to Databrary
48 Workshop attendees survey results
49 Introduction to Databrary
50 SLIDES: Managing Data to Accelerate Discovery
51 SLIDES: Policies for Sharing Identifiable Video
52 SLIDES: Datavyu & Best Practices for Coding Video
53 Policies for Sharing Identifiable Video
54 Managing Data to Accelerate Discovery
55 Survey results
56 Policies for Sharing Identifiable Video
57 Managing Data with Databrary
58 Datavyu & Best Practices for Coding Video
59 SLIDES: Datavyu & Best Practices for Coding Video
60 SLIDES: Policies for Sharing Identifiable Video
61 SLIDES: Introduction to Databrary
62 SLIDES: Managing Data with Databrary
63 Introduction to Databrary
64 SLIDES: Managing Data with Databrary
65 SLIDES: Introduction to Databrary
66 SLIDES: Datavyu & Best Practices for Coding Video
67 Databrary Pre-conference
68 SLIDES: Policies for Sharing Video Data
69 ICIS Survey Results
70 VIDEO: Databrary Board Meeting - June 13, 2016
71 SLIDES: Databrary Board Meeting - June 13, 2016
72 Policies for sharing video data
73 Managing data with Databrary
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75 Introduction to Databrary
76 SLIDES: Introduction to Databrary
77 SLIDES: Policies for Sharing
78 SLIDES: Managing data with Databrary
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10 10991 Regional Workshop at Penn State 2016-03-25 PUBLIC
# ℹ 15 more rowsPlay & Learning Across a Year (PLAY)
- What does behavior look like in natural home environments?
- Recruiting site for parents
- Public researcher site
- Openly shared protocol
Lessons learned
- Openness -> detailed quality assurance
Lessons learned
- Openness -> (Hyper)-active data curation (Soska et al., 2021)
Lessons learned
- Openness -> continuous integration
Parent-reported age of child crawling onset vs. walking onset. Source: https://play-behaviorome.github.io/KoBoToolbox/
Parent reports of child illnesses. Source: https://play-behaviorome.github.io/KoBoToolbox/
Most common words parents report 18-mo-olds know or say. Source: https://play-behaviorome.github.io/KoBoToolbox/
Take-homes
Development of optic flow perception
- Lab + real-world data
- Confirmations & surprises
- Data sharing essential
Sharing identifiable data
- Possible
- Easier than many think, but still hard
- Databrary model: Restricted access + consistent permission
Plan your work; work your plan
- Plan to share from the outset
- Ask permission to share
- Script everything you can
- Be kind to your future (forgetful) self
- DRY WIT
- Don’t repeat yourself; write it down.
Workshop registration
Data management workshop
Data scientist wanted
- PLAY Project
- Databrary (especially databraryr and Python version)
- Part-time/hourly
Materials
The code and materials used to generate the slides may be found at https://github.com/penn-state-open-science/soda-501-2024-spring/.
Figure 1: Talk slides (HTML)
References
SoDA 501: Sharing identifiable data
(Figure 3 from Soska et al., 2021)
(Figure 1 from Soska et al., 2021)
Franchak, J. M., Kretch, K. S., Soska, K. C., & Adolph, K. E. (2011). Head-mounted eye tracking: A new method to describe infant looking. Child Development, 82(6), 1738–1750. https://doi.org/10.1111/j.1467-8624.2011.01670.x
Gilmore, R. O. (2014a, July). Four-Month-Olds’ discrimination of optic flow patterns depicting different directions of observer motion. Databrary. https://doi.org/http://doi.org/10.17910/B7Z593
Gilmore, R. O. (2014b, October). Children’s brain responses to optic flow vary by pattern type and motion speed. Databrary. https://doi.org/http://doi.org/10.17910/B7QG6W
Gilmore, R. O., Baker, T. J., & Grobman, K. H. (2004). Stability in young infants’ discrimination of optic flow. Developmental Psychology, 40(2), 259–270. https://doi.org/10.1037/0012-1649.40.2.259
Gilmore, R. O., Hou, C., Pettet, M. W., & Norcia, A. M. (2007). Development of cortical responses to optic flow. Visual Neuroscience, 24(06), 845–856. https://doi.org/10.1017/S0952523807070769
Gilmore, R. O., Raudies, F., & Jayaraman, S. (2015). What accounts for developmental shifts in optic flow sensitivity? In 2015 joint IEEE international conference on development and learning and epigenetic robotics (ICDL-EpiRob) (pp. 19–25). ieeexplore.ieee.org. https://doi.org/10.1109/DEVLRN.2015.7345450
Gilmore, R. O., & Rettke, H. J. (2003). Four-Month-Olds’ discrimination of optic flow patterns depicting different directions of observer motion. Infancy: The Official Journal of the International Society on Infant Studies, 4(2), 177–200. https://doi.org/10.1207/S15327078IN0402\_02
Gilmore, R. O., Thomas, A. L., & Fesi, J. (2016). Children’s brain responses to optic flow vary by pattern type and motion speed. PloS One, 11(6), e0157911. https://doi.org/10.1371/journal.pone.0157911
Hou, C., Gilmore, R. O., Pettet, M. W., & Norcia, A. M. (2009). Spatio-temporal tuning of coherent motion evoked responses in 4–6 month old infants and adults. Vision Research, 49(20), 2509–2517. https://doi.org/10.1016/j.visres.2009.08.007
Jayaraman, S., & Smith, R. O., L B & Gilmore. (2014, October). Natural scene statistics of visual experience across development and culture. Databrary. https://doi.org/http://doi.org/10.17910/B7988V
Kretch, K. S., & Adolph, K. E. (2015). Active vision in passive locomotion: Real-world free viewing in infants and adults. Developmental Science, 18(5), 736–750. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1111/desc.12251
Kretch, K. S., Franchak, J. M., & Adolph, K. E. (2014). Crawling and walking infants see the world differently. Child Development, 85(4), 1503–1518. https://doi.org/10.1111/cdev.12206
Qian, Y., Berenbaum, S. A., & Gilmore, R. O. (2022). Vision contributes to sex differences in spatial cognition and activity interests. Scientific Reports, 12(1), 17623. https://doi.org/10.1038/s41598-022-22269-y
Qian, Y., Seisler, A. R., & Gilmore, R. O. (2021). Children’s perceptual sensitivity to optic flow-like visual motion differs from adults. Developmental Psychology, 57(11), 1810–1821. https://doi.org/10.1037/dev0001227
Raudies, F., & Gilmore, R. O. (2014). Visual motion priors differ for infants and mothers. Neural Computation, 26(11), 2652–2668. https://doi.org/10.1162/NECO\_a\_00645
Soska, K. C., Xu, M., Gonzalez, S. L., Herzberg, O., Tamis-LeMonda, C. S., Gilmore, R. O., & Adolph, K. E. (2021). (Hyper)active data curation: A video case study from behavioral science. Journal of Escience Librarianship, 10(3). https://doi.org/10.7191/jeslib.2021.1208