taxonomy | Translating Data

Definitions

Of Living: Biological
Data whose origin is directly linked to something that is alive. Data that occurs without conscious origin (i.e. not from a human typing). Often from sensors. Examples: a) species migration reported by a sensor; b) quantified self data such as output from a heart-rate monitor; c) a bird-call.

Of Living: Environmental
Data whose origin is directly linked to the natural world. Often from sensors. Examples: a) ocean temperature, b) solar storm activity, c) seed bank information.

Of Non-Living: Object
Data whose origin is a physical object or device. Object data is often generated for machine to machine communication, however, the Internet of Things will see a greater machine to (human) consumer communication. Examples: a) a fridge’s energy use; b) a CCTV camera; c) a smart watch.

Of Social Context: Commercial
Data produced by or about a corporate entity. Examples: a) 10 years of financial information about a company; b) the expiry date on a chocolate bar.

Of Social Context: Personal
Data produced by or about an individual. Certain types will have restricted access, and some legal and technical protections. Other will be accessible by some, if not all, of the general public. Examples: a) Google’s search analysis profile of a non-anonymised individual’s interests; b) International travel logs held at border controls; c) a recording of a private telephone conversation; d) family photos publicly tagged on Flickr; e) your social network feed.

Of Social Context: Social
Data produced by or about a social group or society. Examples; a) global number of births each day; b) voting preference in a London borough; c) immigration figures.

Of Social Context: State
Data produced by or about a government or ruling authority. Examples: a) the economy of the eurozone; b) legislation documents.

Of Licence: Closed
Closed data is generally only accessible to people within an organisation or to certain individuals. Examples: a) company personnel files; b) national security documents.

Of Licence: Open
Open data can be accessed, used, and shared by anyone. Examples: a) state-owned weather records, b) earthquake monitoring data.

Of Licence: Shared
Shared data is data available to a specific group of people for a specific purpose. Examples: a) the electoral register; b) anonymized supermarket shopping patterns.

Of Time/Space: Geospatial
Data describing, is relevant to, or is derived from a space or geographic area. Examples: a) GPS coordinates from a cross-country walk; b) the number of people visiting the Tate Modern art gallery; c) the area of a baseball pitch; d) longitude and latitude.

Of Time/Space: Live
Data which is, or was, captured in real-time. The recording does not necessarily get played-back at the same rate, or in the same moment. Examples: a) a football match on TV; b) animal tracking data.

Of Time/Space: Real-time
Data that is created, captured and disseminated in an immediate (ish) time-frame relative to the context of its use; it changes over time. Examples: a) smart-meter reporting electricity usage every 30 seconds (real-time data acquisition with a relevant-time display); b) feeds from sensors such as a webcam on a birds nest, a GPS location of a mobile phone, or a humidity reading in an gallery space.

Of Time/Space: Static
Data in which the items do not change once created, but the dataset can grow over time. Includes historical datasets and archive indexes. Examples: a) historical global population size; b) a recording in the sound archive at the British Library.

Of Time/Space: Temporal
Data which is time-based in its nature, relevant to a specific time or which may only exist for a short time period (transient). Examples: a) the value of a kilogram of rice over time; b) your date of birth; c) the radio signals received from an exploding star.

Of Type: Anecdata
Anecdotal information gathered and then presented as evidence. Anecdata is often not precisely measurable, has no reliable provenance, is hard to compare, and /or cannot be unproven by the scientific method. Examples: a) a collection of comments on a product website; b) proverbs such as ”Never look a gift horse in the mouth”.

Of Type: Causal
Data in which it is (or is made) obvious to the observer what its origin is. Example: a vocal recording.

Of Type: Generated
Data created by a software program. Examples: a) algorithmic music; b) cellular automaton; c) a model of a galaxy exploding.

Of Type: Metadata
Data about data. Data which describes information about other data. Examples: a) the number of rows in a database; b) the time and date a phone call was made.

Of Type: Processed
Data which has been calculated, altered or processed in some way. Examples: a) a sonification of stock market figures; b) aggregated statistics; c) a colourful digital photograph reduced to black and white.

Of Type: Retrieved
Data made available on request by machine or user. Examples: a) compilation of weather data from the past 24 hours as a single CSV file; b) availability status of a library book.

Of Type: Streamed
The technical means of delivering real-time data as a contiguous stream. The primary use-cases are where there is no requirement for data storage, or that the data-sets involved are too large to be manipulated in any other manner (the entire Twitter back catalogue). Examples: a) real-time audio and video from a carnival procession; b) on-demand replay of a film from 1960; c) music playing from a digital radio.

Of Disclosure: Anonymised
Data that has had any identifiable in- formation about a person, animal, or thing removed. Examples: a) CCTV camera footage containing people which have been blurred or obfuscated; b) all bicycle hire users across a city with user IDs and names removed.

Disclosure: Identifiable
Data in which the direct source within it (person, animal, or thing) can be identified. Examples: a) a Facebook data export including friend names; b) a set of mobile phone numbers with owner address details.

Of Disclosure: Unknown
Data which contains information about a person, animal, or thing but in which it is not clear if it is adequately anonymised. Examples: a) a live Twitter feed containing some geolocated photos of people and animals; b) a sound recording from a public space that includes ambient conversation.

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julie 1:11 pm on July 21, 2015 Permalink
Tags: getty, taxonomy
Getty vocab usage
Intro to Getty’s cataloguing system. Really helpful page on the difference between Display (text that will be read by the end-user) and Indexing (text that uses a controlled vocabulary (AAT) for finding and accessing the works)

https://www.getty.edu/research/tools/vocabularies/intro_to_cco_cdwa.pdf [pdf]

DISPLAY VS INDEXING Patricia Harpring © 2015 J. Paul Getty Trust. For educational purposes only. Do not distribute.
- Display refers to how the data looks to the end user in the database, on a Web site, on a wall or slide label, or in a publication
- Information for display should be in a format that is easily read and understood by users
- Free-text or concatenated from controlled fields
- Indexing refers to the process of evaluating information and designating indexing terms by using controlled vocabulary that will aid in finding and accessing the cultural work record
- By human labor, not to the automatic parsing of data into a database index
julie 4:11 pm on July 20, 2015 Permalink
Tags: taxonomy

ADA | Archive of Digital Art
Home – ADA | Archive of Digital Art.

Use for analysis of descriptions of dat art
julie 10:44 am on July 7, 2015 Permalink
Tags: schema ( 2 ), taxonomy

re3data – schema
Schema for the Description of Research Data Repositories

Version 2.2 December 2014

doi: http://doi.org/10.2312/re3.006

http://gfzpublic.gfz-potsdam.de/pubman/item/escidoc:758898:3/component/escidoc:775891/re3data_schema_v2-2_public_final-2014-12-03.pdf

Research data are valuable and ubiquitous. The permanent access to research data is a challenge for all stakeholders in the scientific community. The long-term preservation and the principle of open access to research data offer broad opportunities for the scientific community. More and more universities and research centres are starting to build research data repositories allowing permanent access to data sets in a trustworthy environment. Due to disciplinary requirements, the landscape of data repositories is very heterogeneous. Thus it is difficult for researchers, funding bodies, publishers and scholarly institutions to select appropriate repositories for storage and search of research data. re3data.org is a global registry of research data repositories that covers research data repositories from different academic disciplines. It presents repositories for the permanent storage and access of data sets to researchers, funding bodies, publishers and scholarly institutions. re3data.org promotes a culture of sharing, increased access and better visibility of research data. The registry is funded by the German Research Foundation (DFG)1 and went online in autumn 2012.
julie 6:04 pm on June 29, 2013 Permalink
Tags: taxonomy, visualization ( 5 )

A Taxonomy of Data Visualization

For some time at Visualizing, we’ve been working on a commonsense taxonomy of data visualization. This is still a work in progress, but we wanted to involve the wider community in the discussion.

There are already all-inclusive glossaries of specific techniques, and there are several academic approaches to classification (Bertin, Schneiderman, etc.). But we’re looking for something in between: a general, top-level language to describe the forms used in visualization and information graphics. It should be useful to experts and non-experts alike, and so requires a balance between familiar words and ideas on the one hand and rigorous thinking on the other.

via A Taxonomy of Data Visualization | visualizing.org.
julie 6:57 pm on June 28, 2013 Permalink
Tags: taxonomy, visualisation ( 6 )

A Periodic Table of Visualization Methods.

Data Visualisation taxonomy of sorts in a rollover graphic format. Ironically it is very ugly, and the design requires some serious typographic overhaul. Anyhow – a useful mapping of data visualisations.
julie 5:55 pm on June 27, 2013 Permalink
Tags: taxonomy

dataists » A Taxonomy of Data Science
5 steps of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and Interpret, by Hilary Mason (bit.ly chief scientist):

Both within the academy and within tech startups, we’ve been hearing some similar questions lately: Where can I find a good data scientist? What do I need to learn to become a data scientist? Or more succinctly: What is data science?

We’ve variously heard it said that data science requires some command-line fu for data procurement and preprocessing, or that one needs to know some machine learning or stats, or that one should know how to `look at data’. All of these are partially true, so we thought it would be useful to propose one possible taxonomy — we call it the Snice* taxonomy — of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret (or, if you like, OSEMN, which rhymes with possum).

Different data scientists have different levels of expertise with each of these 5 areas, but ideally a data scientist should be at home with them all. We describe each one of these steps briefly

Since histograms of real-valued data are contingent on choice of binning, we should remember that they an art project rather than a form of analytics in themselves.

via dataists » A Taxonomy of Data Science.
julie 4:22 pm on June 27, 2013 Permalink
Tags: taxonomy

A Taxonomy of Data Types
A Taxonomy of Data Types – Statistical Machine Learning and Visualization.

A table of data types that refers to distribution, for example:

type: categoric atom example: word in Eng Lang distribution example: Multinomial (1,theta)

This taxonomy at the technical end of the data type spectrum and is unlikely to be used by artists.

—

It is useful to separate machine learning and visualization techniques (k-NN, PCA, etc.) from specific data domains (text, images, etc.). We should be able to come up with a taxonomy of data types on one hand and a library of techniques suitable for each data type on the other hand. Then, given a specific data domain we can identify the appropriate data type and follow up with one or more appropriate analysis/visualization techniques.

This is by no means completely satisfactory as each data domain has its own peculiarities and any attempt to come up with a short taxonomy is bound to be a “lossy approximation”. But as many approximations I believe it is one that is useful and worthy of consideration.

Translating Data

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julie 1:11 pm on July 21, 2015 Permalink Tags: getty, taxonomy

Getty vocab usage

julie 4:11 pm on July 20, 2015 Permalink Tags: taxonomy

ADA | Archive of Digital Art

julie 10:44 am on July 7, 2015 Permalink Tags: schema ( 2 ), taxonomy

re3data – schema

julie 6:04 pm on June 29, 2013 Permalink Tags: taxonomy, visualization ( 5 )

A Taxonomy of Data Visualization

julie 6:57 pm on June 28, 2013 Permalink Tags: taxonomy, visualisation ( 6 )

julie 5:55 pm on June 27, 2013 Permalink Tags: taxonomy

dataists » A Taxonomy of Data Science

julie 4:22 pm on June 27, 2013 Permalink Tags: taxonomy

A Taxonomy of Data Types

julie 1:11 pm on July 21, 2015 Permalink
Tags: getty, taxonomy

julie 4:11 pm on July 20, 2015 Permalink
Tags: taxonomy

julie 10:44 am on July 7, 2015 Permalink
Tags: schema ( 2 ), taxonomy

julie 6:04 pm on June 29, 2013 Permalink
Tags: taxonomy, visualization ( 5 )

julie 6:57 pm on June 28, 2013 Permalink
Tags: taxonomy, visualisation ( 6 )

julie 5:55 pm on June 27, 2013 Permalink
Tags: taxonomy

julie 4:22 pm on June 27, 2013 Permalink
Tags: taxonomy