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# System prepended metadata

title: 'Uncharted Data project :world_map:'

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# Uncharted Data project :world_map:

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## :seedling: Initial data collection format

After discussing what to track we came up with these parameters for tracking our liquid intake. Our goal was to minimise interacting with our tracking throughout the collecting, so to not disrupt our liquid intake by seeing it.

* Simple collection with Google Sheets
* Trying to make it disconnected from the context
* We hope not to manipulate with the collect or at least learn where such manipulations occured
* Should we include context? 
    * The environment of reason (Enigma of reason - Hugo Mercier, Dan Sperber)




![](https://i.imgur.com/AEi9Zan.png)

Downsides to our approach:
* We found it difficult to visualize our data in a meaningful way.
* It is very importanted to know what the data is gonna be used for if you dont want a headache cleaning the data. *(swapping around date and year on 150 data entries sounds more fun than it is)*
    * Tools constrain how different data syntax can be used.

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## :wrench: Visualization


### :bar_chart:  A simple comparison
We started off trying to make the simplest visualisation possible to illustrate our total liquid consumption during our tracking period. To visualize this we picked up a new visualisation tool called Tableau, which you can get a student Licens to for free. This program has a ton of features to visualize data sets.
*Link to tool: https://www.tableau.com/*

**Over all amount during the entire period:**
![](https://i.imgur.com/va3PCbv.png)


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Following the rather simple comparison of the data above we decided to make some visualisation with a bit more information about our data. We came up with this much more segmented variation, where essentially it's the same data but this time, its color coded, segmented on a day to day basis and there is a count of how many times each day a drink was poured by each of us.

**Times each day Asger and Andreas grab a drink:**

![](https://i.imgur.com/m01DYar.png)

A slightly alternative view on how you could visualize something in a more non-traditional way using circles instead of bars. In this example the size of the circle is relative to the amount if liquid on that paticular day.

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### :fire: Tired of stale static bar charts? Look no further
The animated charts have been created by using Tableau's Pages feature and making it into a GIF with ScreenToGif. Inspiration for the format has been drawn by the popularisation of the racing bar chart format.
If hard to see right click -> open Image in new tab :ok_hand:
 *https://www.screentogif.com/*

**Animated bar chart of Andreas Liquid intake during the tracking period:**
![](https://i.imgur.com/S11WLPF.gif)

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**Animated bar chart of Asger's liqiod intake during the tracking period:**

![](https://i.imgur.com/oukHwls.gif)
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## :chart_with_upwards_trend: Accumulative amount in order of most consumed
Last by not least we have we have made a visualisation displaying our most consumed liquid during the tracking period.
**Andreas:**
![](https://i.imgur.com/SwRLxEi.png)


**Asger:**
![](https://i.imgur.com/68pa2KR.png)

## Theory

### The Quantified Self
"Health-promotion organisations and agencies have developed apps and platforms of their own, custom-designed for such purposes, or else they advocate the use of health and fitness self-tracking apps and devices that are commercially available. These are represented as behavioural interventions designed to encourage adherence to health-promoting objectives (Lupton, 2012, 2015b)"(Lupton, 2016: 25).



"These people expected the data produced by the devices to have an effect on them, and several commented that this indeed was the effect of wearing them. Because they knew that the devices were monitoring their physical activity, they were more likely to be active. The findings also revealed that, at least in the initial stages of wearing a device, **people reported feeling more aware of their bodies than usual**" (Lupton, 2016: 41).

### Datafication of Health
"Other scholars prefer to regard the datafication of health as a form of neoliberal subjectification (Foucault 1991), emphasizing how tracking tools accelerate the withdrawal of the welfare state from citizens’ lives, **turning health care into self-care** (Ajana 2017; Depper & Howe 2017; Fotopoulou & O’Riordan 2016; Lupton 2013a,b; Mort et al. 2009; Rich & Miah 2014, 2017; Schull 2016a,b). **Subjecting oneself to data tracking becomes an avenue for performing “healthy citizenship”** and enacting cultural values of entrepreneurial, autonomous behavior" (Ruckenstein, 2017: 265).


"Whereas a biomedicalization framework tends to place a negative emphasis on the reductive, fragmenting, decontextualizing effects of quantification on selves, ethnographic studies show that self-quantification “rarely produces a definitive truth, a one-to-one representation of one’s life or one’s identity” (Sharon 2017); instead, it involves a **“situated objectivity”** (Pantzar & Ruckenstein 2017) in which certain prior experiences, understandings, and shared expectations come to matter"(Ruckenstein, 2017: 266).

"As social expectations of normality and health become embedded in tracking **devices’ target numbers, presentation of scores, and gamified incentives** (Depper & Howe 2017, Whitson 2013), a **“numerical ontology”** comes to suffuse everyday practices and “the ways in which people relate to their own bodies” (Oxlund 2012, p. 53; see also Jethani 2015, p. 40)" (Ruckensteain, 2017: 269).


### Making Up People
"Who we are is not only what we did, do, and will do but also what we might have done and may do. Making up people changes the space of possibilities for personhood.” (Hacking 1986: 168)

“Software systems change how we know, think, and what we know, and like algorithms ‘represent particular knowledge logic’” and are built on specific presumptions about what knowledge is and how one should identify its most relevant components" (Ruppert 2013)

"It was a disease created by a new (functional) understanding of disease."' Davidson is not denying that there have been odd people at all times. He is asserting that perversion, as a disease, and the pervert, as a diseased person, were created in the
the late nineteenth century. Davidson's claim, one of many now in circulation, illustrates what I call making up people.(Hacking, 1986; 161)

In regards to Hacking we made up a new categori of people when doctors made up the term pervert. This has since happened time and time again, and in recent times an example could be ADHD. Suddenly out of the blue a new group of people were made. This draws some paralels to our data tracking. A very real scenario could be that datafication of drinking habits could lead to the creatoin of a new categori/identity. ATCD?(Addicted to Carbonated Drinks)

## :dart: Narratives with a goal
### Lifesum
![](https://i.imgur.com/qlW4TCL.png) ![](https://i.imgur.com/YpMqefZ.png)
### Waterminder
![](https://i.imgur.com/ALj4pYK.png)




