## <span class="censor">Data Studies 2020 // S02</span>
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S02/record.png" width=50%>
Pablo Velasco // Information Studies // [pablov.me](https://pablov.me)
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## Plan for the day:
* Raw / Cooked data
* Data taxonomies
* Data contexts: information
* Mini-project B1
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*Data is all kinds of **information**, that can tell something about something.*
*Stored **knowledge**.*
*...information of some sorts, about an artifact or several artefacts, **whether it being humans, machines or others**.*
*...data can tell **a story** about something...*
*Data is everything that can be written down and then **used later for some purpose**.*
*...Normal memories are stored in **the brain**, where data are stored in some form of **technological storage-unit**...*
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### "Raw" data:
* Neither “transparent” nor “self-evident”
* Objectivity is historical
* Data vs fact (Rosenberg)
* Before, during, after
### Data != Capta
* Data != capta (Jensen 1950 in Bekker 1952)
* Not passive acceptance, but active construction (Drucker 2011)
* Knowledge as produced, more than discovered (Gitelman 2013)
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### Data taxonomies
* **Data *is* (Rosenberg 2013):**
* Abstract: an abstraction of "reality"
* Discrete: it consists of finite elements
* Aggregative: can be combined and accumulated
* Meaningful: conveys some meaning
* **Data *is* (Floridi 2010):**
* Taxonomical: can be ordered or related
* Typological: primary, derived, metadata
* Genetic: not interpretable
* **But also differs on perspectives:**
* Epistemic (a collection of "facts")
* Informational (information not related to "facts")
* Computational (*tansmittable binary elements*)
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S02/codex_s.jpeg" width="40%">
<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S02/voynich.jpg" width="45%">
<small>Codex Seraphinianus (1976-1978) // Voynich manuscript 1404-1438)</small>
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* **Data *can* be (Kitchin and Tate 1999):**
* Quantitative
* Nominal: categories
* Ordinal: ranks, scales
* Qualitative
* Structure
* Structured: consistent format
* Semi-structured: irregular format
* Unstructured
* Source
* Captured: observation, measurement
* Exhaust: produced by a machine
* Derived: from additional processing
* Type
* Indexical: includes identifiers
* Attribute: attributes of the identifiers
* Metadata: data about data
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### "well-formed" data vs *rightly*-formed data
* **Data *should* be FAIR (Wilkinson et al 2018)**
* Findability
* Accessibility
* Interoperability
* Reusability
* **Data *should* be Smart (Schöch 2013):**
* Structured (or semi-structured)
* Enriched
* Small
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<img src="https://i.imgur.com/iUupQlk.jpg" width="30%">
<img src="https://i.imgur.com/TuMZ7go.png" width="65%">
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* van Dijck (2014)
* *Dataification*:
* social actions turned into quantified data (e.g. Moll's Dating Brokers project https://datadating.tacticaltech.org/viz)
* *Dataveillance*:
* continous tracking of (meta)data for unstated purposes
* *Dataism*
* ideology of neutrality
> Dataism thrives on the assumption that gathering data happens outside any preset framework (...) and data analysis happens without a preset purpose
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### Short activity
**What about *good* data?**
Choose one (1-3 people):
* Explore the [*noonies*](https://noonies.hackernoon.com/award/cjxvrv4p26gd40b40cdlfmwwy). These prizes are given to "emerging tech to advance social or environmental progress". Do they involve the use of open/fair data? Is it clear how they could improve the social life of others (beyond stating it)?
* Check the [open data index](https://index.okfn.org/ ) for Data in Denmark, and compare it to other country you are familiar with. How data differs? Is it readable? Open licensed? Accessible? Are the formats machine readable? Public? Free?
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## Data Contexts: information
<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S03/DIKW.png" alt="drawing" width="80%">
*Knowledge pyramid (Kitchin, adapted from Adler 1986 and McCandless 2010)*
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#### Cybernetics
* **Shannon** (1948)
* information is about signals, not meaning
* information must be finite
> “Information is a probability, a function, with no dimensions, no materiality, and no necessary connection with meaning”
* **Weiner** (1961)
* information as organised communication
* (the more probable the message, the less information it gives)
> “Information is information, not matter or energy”
* **Capurro and Hjørland** (2003)
* information as the act of communicating meaning
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# A line is a dot that went for a walk(Paul Klee)
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S03/new_harmony.jpg" alt="drawing" width="36%">
<img src="https://i.imgur.com/Ts72Iz2.png" alt="drawing" width="58%">
<small>*New Harmony (Klee 1936)* // [*The million dollar homepage](http://www.milliondollarhomepage.com/) (Tew 2005/2006)*</small>
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S03/PlaceFinal.png" alt="drawing" width="60%">
<small>(**Click the image for a bigger version. You can also find the gif [here](https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S03/place.gif)**)</small>
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## Short activity:
1. Identify “things” that you recognise in Place.
2. What do you think is/are the main difference(s) between place and the million dollar homepage?
3. Note down a research question for Place (i.e. what would you like to *know* about it?).
**Write your RQ of point 3 in our messy notes** (no need to put your name): https://hackmd.io/@ds20/B1shHLM7v/edit
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### Data contexts (POST)
* STS/ANT
* Media theory (Kittler 1999 [1986])
* Cultural analytics
* Digital humanities
* Software studies
* Digital Methods / Issue mapping
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# [Mini project B1](https://hackmd.io/@ds20/rJfOkOL4D)
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