Waving Not Drowning

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Waving Not Drowning Practical Tips for staying on
Top of Photo Field Data EPIC Conference Workshop,
25th September 2006
Jan Chipchase Nokia, Mobile HCI Group
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Attendees / Contributors

• Anjali Kelkar Illinois Institute of Technology
• Laura Forlano Communications, Columbia University
  
• Divya Jindal, Microsoft
• Dionne Smith Brand Trust
• Todd Wilkens Adaptive Path
• Roland Moore Pacific Institute for Research and
  Evaluation
• Jan Chipchase Nokia Mobile HCI Group, Tokyo
• Roberto Holguin in/situm, Mexico
• James Beebe Gonzaga University , Spokane
• Kathi Kitner Intel People Practices Group

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Why Are We Here?

• Field studies can involve multiple team members
  generating thousands of digital images. How do
  you move from photo data sitting unused and
  unloved on multiple hard-disks to being a valued,
  relevant resource?
• This workshop will cover how to
• Collect, manage and stay in control collections
  of thousands of photos
• Maintain data integrity
• Consider moral legal requirements
• Ensure that relevant data is easy to retrieve and
  use
• All within real-world constraints (i.e. running a
  field project, lack of sleep, intermittent power,
  crashing hard disks, buggy software, sullen
  subcontractors, etc.)

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Additional Topics to Cover

• How to store data?
• How to share? Within groups?
• Structure way of re-visiting data?
• How to notate photos?
• Process for taking photos pros and cons of
  different types of camera
• User identity, degrees of anonymisation
• Quality resolution, composition
• What makes a good ethnographic image?
• How to select what images to use for reporting?
• When to delete? What to delete?
• Backup strategies?
• Photos in storytelling?

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Software

• PC
• Irfanview (shareware) good for keyboard
  shortcuts, rapid image editing, and batch changes
• Adobe Elements (buggy, slow on laptop, good for
  setting up subcontractors to work in a hurry)
• Picasa (freeware)
• Qualitative data analysis
• ATLAS.ti (able to annotate selected parts of
  images)
• Nvivo 7
• Mac
• iPhoto
• Image Capture
• Photoshop
• Online
• Flickr (good for key word tagging)
• Shutterfly

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The Challenge

• right data
• right format
• right person
• right time
• How does this affect how we collect and manage
  the data?

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Sample Field Study

• 10 days in one location
• 6 team members
• Varying graphics skills
• Range of equipment pc, macs, camera brands
• Internal, external subcontractors
• Everyone has their preferred ways of doing things
• 20 study participants
• Participant diaries
• Variety of data collection methods shadowing,
  in-depth interviews, diaries, observations,
  wallet mapping, disposable camera studies, online
  blogs, Flickr photo diaries differing privacy
  requirements

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Collect, Manage Make Use of Photo Data
Data from various sources
Usable collections
1024 x 768 Presentations, web, email Original
size Print Backup
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Institutional Review Board (IRB)

• IRBs are also known as Committees for the
  Protection of Human Subjects
• Formal vs informal
• Age
• Different levels of vulnerability seen for
  minors, with more protections needed
• An element of rear covering!
• But actually a degree of flexibility when engaged
  an educational process for IRB members and
  applicant alike

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The Process
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Before We Get Started

• Camera setup
• Time date Nikon world time
• Sequential vs. restart
• File naming
• Folders
• Image resolution sufficient but not more
• Adding contact information inside memory
• Storage setup
• Folder naming
• Memory cards process me empties
• Data manager someone to take overall
  responsibility for photo processing

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Camera Equipment

• Good for ethnographicstyle work
• Good quality display for showing participants
  photos
• Small enough to carry anywhere
• Steady shot
• Good zoom
• Video good enough for presentations
• Responsive refresh times more time taking photos
  than waiting for camera to charge up
• Rapid on
• Rotating head supports more subtle photography
• Disable flash
• Night vision (Sony F717 pretty good, Sony F828
• Using
• SLRs, compacts, minis, camera phones,
  disposable, Polaroid

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Consent

• Data Consent
• USA Example
• UK Example
• India Example
• Photo ownership, rights separate from Data
  Consent
• Different rights according to how the photo is
  used
• What does it mean to sell an photo?
• Sold to magazine
• Appears on a web site that accepts adverts?
• On a web site?
• Used in a paid-for workshop at a conference?
• Model consent
• Consent to use photos featuring people

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Data Consent Low Literacy Users
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Process
Multiple Sources Team, participants
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Keeping Participants in Control

• Sufficiently aware of data collection process
• Asking permission prior to starting data
  collection
• Asking permission again in new privacy-breaching
  situations
• De-mystify equipment
• Allow participants to frame photos
• Rewards before data consent is signed
• Encouraged to delete any or all digital data
• Send participants a copy of the data post-study
• Print or digital

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Process
Multiple Sources Team, participants
Backup
Data Manager
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Secure Backup
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Process
Multiple Sources Team, participants
Backup
Clean up, filtering, basic metadata, re-naming
Data Manager
Working duplicates to team, clients, stakeholders
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Basic Metadata

• Sufficient metadata to make sorting easier later
  on
• Photographer
• Study
• Location
• Participant
• Data gathering method

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Photos As Metadata
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Filtering
Filtering
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Filtering
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Naming, Re-Naming

• Naming strategies
• projectname-location-participant-method-uniquenumb
  er
• optima-helsinki-f12-interview-001.jpg
• optima-hki-f12-ini-001.jpg
• optima-hki-f12-001.jpg
• optima-hki-001.jpg
• Demo batch re-naming

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Process
Multiple Sources Team, participants
Backup
Clean up, filtering, basic metadata, re-naming
Working duplicates to team, clients, stakeholders
Data Manager
Collections, detailed metadata
Collections
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Collection

• Personal favourites
• Themes
• Detailed metadata
• Things to watch out for

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Process
Multiple Sources Team, participants
Backup
Clean up, filtering, basic metadata, re-naming
Working duplicates to team, clients, stakeholders
Data Manager
Collections, detailed metadata
Collections
1024 x 768 Presentations, web, email Original
size Print Backup
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Batch Processing

• Versions
• Collections
• Writing metadata to files
• Awareness of who has what data
• Data clean up delete, delete, delete

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Use Reporting

• Process optimised for digital publishing
• Photo-rich Power point PDF
• Web
• Archive
• Email attachments naming
• NXPowerLite
• For compressing presentations

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Typical No-Stress Volumes
Multiple Sources Team, participants
6,000 photos
6,000
Clean up, filtering, basic metadata, re-naming
1,500
Data Manager
Collections, detailed metadata
400 50
Collections
1024 x 768 Presentations, web, email Original
size Print Backup
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Full Circle Returning Data To Participants
Multiple Sources Team, participants
Clean up, filtering, basic metadata, re-naming
Data Manager
Collections, detailed metadata
Collections
1024 x 768 Presentations, web, email Original
size Print Backup
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Process Recap
Multiple Sources Team, participants
Backup
Clean up, filtering, basic metadata, re-naming
Working duplicates to team, clients, stakeholders
Data Manager
Collections, detailed metadata
Collections
1024 x 768 Presentations, web, email Original
size Print Backup
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/learn to swim /end
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