Human-Data Interactions in Personal Digital Data Engagements
Data Sense: People’s Engagements
with Their Personal Digital Data
Deborah Lupton
News & Media Research Centre
Faculty of Arts & Design
University of Canberra
Twitter@DALupton
Blog: This Sociological Life
Living Digital Data research program
•
How to people use and conceptualise their personal
digital data?
•
What do they know of how their data are used by
others?
•
How do they use other people’s data?
•
What are the intersections of lively devices, lively
data and human life itself?
The vitality of digital data
Data sense
Cycling Data Assemblages project
Elements to explore
(from Vannini)
•
Relations of humans-nonhumans
•
Doings (practices, actions,
performances, habits, routines)
•
The spoken and the unspoken
•
Affective resonances
•
New forms of life
•
Backgrounds and atmospheres
Data collection for Cycling Data Assemblages
Project
Research team Deborah Lupton, Sarah Pink, Shanti
Sumartojo, Christine Heyes Labond
1.
Interview 1 (talk to participant about their self-
tracking and cycling practices)
2.
Enactment of participant getting ready for a ride
and finishing a ride
3.
Participant records GoPro footage of a ride
4.
Interview 2 (watch the GoPro footage with the
participant and look at the self-tracked data they
collected on their ride)
GoPro action camera
Participant getting ready for a ride
Participant about to start ride
Other cycle path users
Participant shows and talks about his
cycling data on his phone app
Participant shows and talks about his
cycling data on his computer screen
Findings
What can self-tracked data do?
–
provide ‘documented proof’ that a ride took place and
how long and fast it was
–
‘confirm how you are feeling’
–
‘I’m seeing myself getting fitter’
–
‘you can see how your physiology is responding’
–
seeing heart rate ‘tells me how much work I’m doing’
–
help explain why you felt a certain way about a ride
–
remind you of how you felt during the ride
Data show you feel
Findings
What can self-tracked data do?
–
motivate by giving ‘external validation’
–
‘make me work harder’ (when viewed while riding)
–
distance travelled ‘gives a sense of achievement’
–
‘make me feel like part of a community even when
riding alone’
Data make you feel
Findings
What can self-tracked data do?
–
make you more aware of parts of the ride (e.g.
Strava ‘segments’)
–
make you more aware of other cyclists (on the
same app/platform)
–
assist riding technique (noticing speed,
anticipating gear changes)
Data sensitise
Findings
What
can’t
self-tracked data do?
–
always be accurate
–
record + acknowledge many aspects of
embodiment
Data must be negotiated/made sense of
Findings
How to make sense of the data …
–
assess against previous experiences and previous
data
–
assess against weather conditions, bike
affordances and how body is feeling
When data do not make sense
•
stuck data
•
disloyal data
•
obsolete data
•
unreliable data
•
dirty data
•
decayed data
•
dead data
•
lost data
•
vulnerable data
Delve into the intricate world of human-data interactions in the realm of personal digital data engagements. Investigate how individuals use and perceive their digital data, exploring the intersections of technology, data usage, and human life. Discover the vitality of digital data in shaping our social lives, impacting our existence, and possessing biovalue. Uncover the significance of data sense, human senses, digital sensors, and sense-making in the realm of data interpretation and utilization.
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Presentation Transcript
Data Sense: Peoples Engagements with Their Personal Digital Data Deborah Lupton News & Media Research Centre Faculty of Arts & Design University of Canberra Twitter@DALupton Blog: This Sociological Life
Living Digital Data research program How to people use and conceptualise their personal digital data? What do they know of how their data are used by others? How do they use other people s data? What are the intersections of lively devices, lively data and human life itself?
The vitality of digital data social lives of data data impact on life data possess biovalue data about life lively data
Data sense human senses digital sensors sense- making data sense
Cycling Data Assemblages project human space/place bicycle digital device emotion human senses digital data
Elements to explore (from Vannini) Relations of humans-nonhumans Doings (practices, actions, performances, habits, routines) The spoken and the unspoken Affective resonances New forms of life Backgrounds and atmospheres
Data collection for Cycling Data Assemblages Project Research team Deborah Lupton, Sarah Pink, Shanti Sumartojo, Christine Heyes Labond 1. Interview 1 (talk to participant about their self- tracking and cycling practices) 2. Enactment of participant getting ready for a ride and finishing a ride 3. Participant records GoPro footage of a ride 4. Interview 2 (watch the GoPro footage with the participant and look at the self-tracked data they collected on their ride)
Participant shows and talks about his cycling data on his phone app
Participant shows and talks about his cycling data on his computer screen
Findings What can self-tracked data do? provide documented proof that a ride took place and how long and fast it was confirm how you are feeling I m seeing myself getting fitter you can see how your physiology is responding seeing heart rate tells me how much work I m doing help explain why you felt a certain way about a ride remind you of how you felt during the ride Data show you feel
Findings What can self-tracked data do? motivate by giving external validation make me work harder (when viewed while riding) distance travelled gives a sense of achievement make me feel like part of a community even when riding alone Data make you feel
Findings What can self-tracked data do? make you more aware of parts of the ride (e.g. Strava segments ) make you more aware of other cyclists (on the same app/platform) assist riding technique (noticing speed, anticipating gear changes) Data sensitise
Findings What can t self-tracked data do? always be accurate record + acknowledge many aspects of embodiment Data must be negotiated/made sense of
Findings How to make sense of the data assess against previous experiences and previous data assess against weather conditions, bike affordances and how body is feeling
When data do not make sense stuck data disloyal data obsolete data unreliable data dirty data decayed data dead data lost data vulnerable data
