A CONCISE TAXONOMY FOR DESCRIBING
DATA AS AN ART MATERIAL
Julie Freeman, Media & Arts Technologies, School of
Electronic Engineering & Computer Science, Queen Mary
University of London, U.K. E-mail:
Geraint Wiggins. E-mail:
Gavin Starks, Open Data Institute.
Mark Sandler. E-mail:
Vedere
associated with this issue.
Submitted: 27 Luglio 2016
Astratto
How can we describe data when used as an art material? As the num-
ber of artists using data in their work increases, so too must our ability
to describe the material in a way that is understood by both specialist
and general audiences alike. Based on a review of existing vocabular-
ies, glossaries and taxonomies of data, we propose our own concise
taxonomy. To conclude, we propose the adoption of this concise tax-
onomy by artists, critics and curators, and suggest that ongoing re-
finement of the taxonomy takes place through crowdsourced
knowledge sharing on the Web.
introduzione
Data is no longer just in the domain of engineers and scientists.
In fact it never was; designers and cartographers have been
visualizing data for around 3,000 years [1]. Today, data are
deeply embedded in all subject domains and within our daily
lives. From the mundane to the specialist, whether 3D printing
a kidney [2], doing your washing [3], scheduling a meeting,
designing a city [4] or finding a partner [5], it takes some con-
sideration to find an activity that does not involve data.
As electricity is pervasive in many societies, so too is digital
dati [6]: It has become a layer of essential infrastructure [7].
For clarity, we will use the word data in this paper to refer to
digital (binary) data specifically: machine-readable, represent-
ing a set of distinct pieces of information (datum) in a particu-
lar structure and format that describe something.
So what do data mean to us? Again, like electricity, data are
invisible yet necessary components in many of the systems that
surround us. Enablers and disablers, data can inform decisions,
help solve problems and provide insight. In their raw format
they are sets of individual values that can be manipulated, Rif-
configured and transformed. This highly flexible, malleable
substance is an ideal art material.
Artists need to understand any material they work with so
that they can use it effectively to convey their ideas. The same
applies to data, which are not usually framed as an art material.
This lack of conceptualizing data as an art material has led us
to notice that it does not often receive adequate depth of de-
scription when mentioned in interpretation texts supporting
artworks. There is a difference between experiencing works
that incorporate real-time data as opposed to historical data,
or that depict a so-called truth garnered from a sample size of
five participants versus 50,000 participants. To interpret the
work fully, these differences should be made accessible to
any audience.
in questo documento, we consider why artists use data as a material
and how data can be translated. Based on existing vocabularies
used specifically within the arts, we propose a concise taxon-
omy for use in the description of data as an art material, Di-
signed for artists, curators, critics and associated general
audiences. We conclude that although there are many taxono-
mies and vocabularies for cataloguing art, they are not easily
adoptable tools in this context and that our concise taxonomy
is more practical.
Through the definition of this working taxonomy we hope to
encourage discourse around data as an art material and to ena-
ble comparison and critical review in a consistent manner. Nostro
formal way of describing data can help reveal deeper under-
standing of the inclusion of data in artistic processes and ena-
ble us to gain insight into differences and similarities between
artists in their conceptualization, approach and implementation
of data in their work.
On Data
Data is a broad term that refers to collections of values that
help us understand a phenomenon more deeply. It is used as a
conceptual container for the reader to fill with facts and fig-
ures. Data are measurements of all kinds and can be used to
generate more data. Euclid’s book of propositions from around
300 B.C.E., Data [8], was written to “facilitate and promote
the method of resolution or analysis,” in other words to clarify
what we can do with the data we have. His propositions (come
as if X then Y) take givens (existing datum) and enable the de-
duction or inference of new data—a process we are very famil-
iar with. Datum is a Latin term meaning “something given.” In
The Data Revolution [9] we read a quote by Jensen from 1950
that explains that really we should be referring to data as “cap-
ta,” from the Latin capere, meaning “to take” [10]. It could be
argued that we have lost the idea that data are a collection of
things to be given, as opposed to taken [11].
Data (with their perception of benevolent evidence) can
hold the promise of a new perspective, a digital version of
the Overview Effect [12], and can be the foundation of many
different outputs and experiences, such as graphic visualiza-
zioni, artworks, animations, sound and music, narratives,
tactile experiences, objects, scent, textiles and even personal-
ized cosmetics.
Why Use Data as an Art Material?
As an art material, data has a great many attributes, including
being low in cost (often free), widely available, easy to manip-
ulate and abundant. It can even self-replicate. This variety and
depth present a challenge to an artist who wishes to become
fully proficient with a material they cannot handle directly.
Although seemingly intangible, data can help illuminate and
make sense of things we cannot see, feel or hear with our hu-
man senses. For an artist, it is a particular medium via which to
be curious about the world.
There are many different ways data can be used in an art-
lavoro. Per esempio, it can generate the essence of the work,
allowing shapes and forms to be derived from the dataset itself
[13]. It can be: used as a driver to generate dynamics [14];
mapped conscientiously to communicate a message; used to
reveal patterns [15]; or misappropriated into artifice [16]. In
The Anti-Sublime Ideal in Data Art [17], Manovich discusses
mapping as the primary way of using data in art; this clearly
identifies data as process but not data as material, framing it in
computer science rather than fine art.
Given the ubiquity of digital technology, we argue that it is
a legitimate material through which to reflect our lives and
should be acknowledged as such. Data is at the heart of digital
culture. Without its prevalence, the systems we rely on—from
global finance through to personal communications—would
fail. It is integral to governance, economics, social accord (E
discord) and of course generation of, and access to, the arts.
© 2018 ISAST. Published under a Creative Commons Attribution 4.0 Unported (CC BY 4.0) licenza.
doi:10.1162/LEON_a_01414
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Like the steam engine as a catalyst of the industrial revolu-
zione, and TV and radio bringing democratization to education,
data is seen as the technology that will save us. How? By giv-
ing us the raw material with which to expose more knowledge
than ever before, questo è, to gain insight beyond expectations
of the past. And as we instrument the world through sensors
and mass-measurement, and data becomes infrastructure,
the language we use to describe and to criticize it becomes
paramount.
Translating Data
The impact of the delivery, type, properties and other charac-
teristics of data on the creation and experience of an artwork is
significant. If the work uses real-time data from a living
source, what are the consequences of the death of the source?
What does it suggest if the data transfer fails? If the data is
anecdotal, or fabricated, is that made obvious? Does it need to
be? Do preconceived ideas of data as evidence (real or not)
reinforce the artist’s intention? Does the intimacy of the work
increase if the data is personal, or does it heighten discomfort?
Is the temporal aspect of the work true to the data, or is the
artist manipulating time?
The design and construction of the work can also affect
how data is experienced. Obfuscation can take place within
code through filters, randomness, subjective programming or
biased algorithms. The aesthetic of the work can conceal or
alter meaning derived from the data if it is over-bearing or has
some strong characteristics. As Negroponte [18] says “the
signature of the machine can be too strong,” at the same time
acknowledging the benefits of working with digital materials,
in that “the process, not just the product, [can] be conveyed.”
These thoughts lead toward refinement of the way data art is
described, and the level of detail about the core material, IL
dati, that is included in those descriptions.
Existing Taxonomies and Vocabularies
Every taxonomy has a purpose—to elucidate information with-
in a field, to define an index, to enable meaningful relation-
ships to be made. Often they are created to work within
existing higher-level ontologies, removing accidental duplica-
tion and furthering standardization. Cataloguing art is a wide
and established field, particularly in media-based arts [19],
which are in constant flux, as the materials change continually,
even while part of a live work. Software and hardware redun-
dancy rates are high and protocols and interfaces change and
can become unusable very quickly [20]. In this oscillating cul-
ture, we can easily mislay important developments through an
inability to log, capture and retrieve them. Inoltre, the lack
of palpability of data elevates the need for careful metadata
tagging and permanent linking, as without physical actuality,
the retrieval of the work relies solely on future audiences being
able to establish its digital existence.
During the development of this taxonomy, tagging and cate-
gorizing techniques in significant online artwork archives of
net art, data art and media art were reviewed. Some visual tax-
onomies and relevant data glossaries were also studied. These
included: the Getty Vocabularies (e.g. CDWA, AAT, CONA);
the Dublin Core Metadata Initiative; Rhizome’s Artbase [21];
the Archive of Digital Art; Turbulence.org; the Rose Goldsen
Archive of New Media; Shneiderman’s Data Type Taxonomy
[22] and the updated version produced with Heer [23]; Visual-
ising.org; Lima’s Syntax of a New Language [24]; the U.S.
White House’s Project Open Data glossary [25]; and the
U.K.’s
It is evident from reviewing these archives, vocabularies and
taxonomies, that there is a lack of consistency in the language
used when describing data art and data visualization. Moreo-
ver, it is only the open data resources that mention the type,
origin or delivery method of data. All the artwork archives fail
to comprehensively describe data despite them being a core
material in many works. It could be that not conceptualizing
data as a material has led to the exclusion of comprehensive
descriptors from the collections of terms referenced above.
A review of the substantial body of research on data visuali-
zation categorization and taxonomies that focus on the semiol-
ogy, syntax and visual meaning of graphics (including Tufte,
Bertin and von Engelhardt) is beyond the scope of this paper.
The large number of technical data taxonomies, including the
W3C data definitions and schema.org, are also beyond scope.
A Concise Taxonomy for Describing Data
Of living: Biological; Environmental
Of non-living: Object
Of social context: Commercial; Personale; Sociale; State
Of license: Closed; Open; Shared
Of time/space: Live; Real-time; Geospatial; Static; Temporal
Of type: Anecdata; Causal; Generated; Metadata; Processed;
Retrieved; Streamed
Of disclosure: Anonymized; Identifiable; Unknown
Within an artwork, as opposed to a visualization, the viewer
is allowed flexibility in translation. An artist may have the
intention of provoking emotion or passing comment on a sub-
ject, but we cannot assume that it is the role of the artwork to
convey a certain message due to the use of a particular dataset.
This taxonomy is designed for artists, curators, critics and con-
sumers of any art that incorporates data as a material. It is a
descriptive set of terms, questo è, it eschews some technical ac-
curacy for classifications that are more commonly understood
and easy to apply. To borrow from Guarino’s ontology defini-
zioni [26], we have worked in a philosophical manner to create
a set of words that form an informal conceptual system, Quale
is that the terms underlie a more specific knowledge base (come
as the CDWA). It is a challenge to represent all aspects of data
in a uniform way; Perciò, this taxonomy includes generic
terms that guide the reader toward a richer understanding of
the data and, perhaps, of why it is being used in the artwork.
We have aimed to create a concise taxonomy that enables
data to be described in an objective way. Its purpose is not to
describe subjective response of the viewer or listener; hence
we have not included terms that can be applied to the affective
descriptions of the experience of the work, such as evocative or
intimate. We have also avoided terms that describe the aesthet-
ic that the data yields in the artwork itself, such as dynamic or
abstract. We acknowledge that while useful for categorizing
and grouping art for some purposes, these more subjective
terms are often personal and user-defined (by the artist, cura-
tor, audience, or critic), which makes a controlled vocabulary
less effective and relevant.
The material (dati) is examined from a number of perspec-
tives—delivery method, how it emerged, format of existence,
which system it represents, the source or origin, the license. In
comparison, when considering a traditional art material, we
may ask: where it was made, who made it, where it is from,
what does it comprise, who owns it, how does it need to be
stored, does it transform or degrade? Any number of the terms
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in the taxonomy may be relevant to any one artwork, and it
should be used with this in mind. Per esempio, Listening
Post by Mark Hansen and Ben Rubin [27] would be tagged
personal, social, live, real-time, temporal, retrieved, pro-
cessed, anecdata.
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. Ex-
amples: (UN) species migration reported by a sensor; (B) quanti-
fied self data such as output from a heart-rate monitor; (C) UN
birdcall.
Of Living: Environmental—Data whose origin is directly
linked to the natural world. Often from sensors. Esempi:
(UN) ocean temperature; (B) solar storm activity; (C) seed bank
informazione.
Of Non-Living: Object—Data whose origin is a physical object
or device. Object data is often generated for machine-to-
machine communication; Tuttavia, the Internet of Things will
see a greater machine-to-(human) consumer communication.
Esempi: (UN) a fridge’s energy use; (B) a CCTV camera; (C) UN
smart watch.
Of Social Context: Commercial—Data produced by or about
a corporate entity. Esempi: (UN) 10 years of financial
information about a company; (B) the expiry date on a choco-
late bar.
Of Social Context: Personal—Data produced by or about an
individual. Certain types will have restricted access and some
legal and technical protections. Others will be accessible by
some, if not all, of the general public. Esempi: (UN) Google’s
search analysis profile of a non-anonymized individual’s inter-
ests; (B) International travel logs held at border controls; (C) UN
recording of a private telephone conversation; (D) family pho-
tos publicly tagged on Flickr; (e) your social network feed.
Of Social Context: Social—Data produced by or about a social
group or society. Esempi: (UN) global number of births each
day; (B) voting preference in a London borough; (C) immigra-
tion figures.
Of Social Context: State—Data produced by or about a gov-
ernment or ruling authority. Esempi: (UN) the economy of the
eurozone; (B) legislation documents.
Of License: Closed—Closed data is generally only accessible
to people within an organization or to certain individuals.
Esempi: (UN) company personnel files; (B) national security
documents.
Of License: Open—Open data can be accessed, used and
shared by anyone. Esempi: (UN) publicly funded research da-
ta; (B) earthquake monitoring data.
Of License: Shared—Shared data is data available to a specific
group of people for a specific purpose. Esempi: (UN) the elec-
toral register; (B) anonymized supermarket shopping patterns.
Of Time/Space: Live—Data that is, or was, captured in real
time. The recording does not necessarily get played back at the
same rate, or in the same moment. Esempi: (UN) a football
match on TV; (B) animal tracking data.
Of Time/Space: Real-time—Data that is created, captured and
disseminated in an immediate time-frame relative to the con-
text of its use; it changes over time. Esempi: (UN) smart-meter
reporting electricity usage every 30 seconds (real-time data
acquisition with a relevant-time display); (B) feeds from sen-
sors such as a webcam on a bird’s nest, a GPS location of a
mobile phone, or a humidity reading in a gallery space.
Of Time/Space: Geospatial—Data describing, is relevant to or
is derived from a space or geographic area. Esempi: (UN) GPS
coordinates from a cross-country walk; (B) the number of peo-
ple visiting the Tate Modern art gallery; (C) the area of a base-
ball pitch; (D) longitude and latitude.
Of Time/Space: Static—Data in which the items do not change
once created, but the dataset can grow over time. Includes his-
torical datasets and archive indexes. Esempi: (UN) historical
global population size; (B) a recording in the sound archive at
the British Library.
Of Time/Space: Temporal—Data that is time-based in its na-
ture, relevant to a specific time, or that may only exist for a
short time period (transient). Esempi: (UN) the value of a kilo-
gram 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 pre-
sented 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. Esempi: (UN) a collec-
tion 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. Esempio: a vocal recording.
Of Type: Generated—Data created by a software program.
Esempi: (UN) algorithmic music; (B) cellular automaton;
(C) a model of a galaxy exploding.
Of Type: Metadata—Data about data. Data that describes in-
formation about other data. Esempi: (UN) the number of rows
in a database; (B) the time and date a phone call was made.
Of Type: Processed—Data that has been calculated, altered or
processed in some way. Esempi: (UN) a sonification of stock
market figures; (B) aggregated statistics; (C) a colorful digital
photograph reduced to black and white.
Of Type: Retrieved—Data made available on request by ma-
chine or user. Esempi: (UN) compilation of weather data
from the past 24 hours as a single CSV file; (B) loan status of
a library book.
Of Type: Streamed—The technical means of delivering real-
time data as a continuous stream. The primary use-cases are
where there is no requirement for data storage, or the data-
sets involved are too large to be manipulated in any other
maniera (the entire Twitter back catalogue). Esempi: (UN) real-
time audio and video from a carnival procession; (B) SU-
demand replay of a film from 1960; (C) music playing from
a digital radio.
Of Disclosure: Anonymized—Data that has had any identifiable
information about a person, animal or thing removed. Exam-
ples: (UN) CCTV camera footage containing people that has
been blurred or obfuscated; (B) all bicycle hire users across a
city with user IDs and names removed.
Of Disclosure: Identifiable—Data in which the direct source
within it (persona, animal or thing) can be identified. Esempi:
(UN) a Facebook data export including friend names; (B) a set of
mobile phone numbers with owner address details.
Of Disclosure: Unknown—Data that contains information
about a person, animal or thing but in which it is not clear if it
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is adequately anonymized. Esempi: (UN) a live Twitter feed
containing some geolocated photos of people and animals; (B)
a sound recording from a public space that includes ambient
conversation.
Additional Dataset Parameters
There are aspects of data that are useful to explore in the pro-
cess of understanding datasets that are not included in the tax-
onomy. These tend toward more technical descriptions and are
used by archivists and preservation experts. The W3C Data on
the Web Best Practices Use Cases & Requirements Note [28],
recommends these elements are used for defining data: do-
mains, obligation/motivation, usage, quality, lineage, size,
type/format, rate of change, data lifespan, potential audience.
We recommend considering the following, particularly for
retrieval, maintenance and archival purposes of the artwork:
Precisione: How exact are the individual data points (e.g. if it is
real-time data is there latency to acknowledge).
Utility: Does the data have potential to provide utility by
providing new content or insight, is this important to the work?
Provenance: Scientific datasets should be reproducible and
should be collated from, or by, reliable sources. Any bias
should be declared or detected.
Context: Does this dataset provide meaning through its
relationships to other datasets (for comparative interest, for
ratification)?
Relevancy: Are the data points relevant to each other, to some-
one or something (e.g. a machine)?
Accessibility: How and by whom can the dataset be accessed
and used (licensing rights, availability, database rights) and is
this reliable and future-proof?
Format: What is the structure and format (technical data struc-
ture and/or data definition, distribution)?
Dimensionality: How many dimensions are represented (e.g. UN
point against time, a number of parameters)?
Size: The order of magnitude of the number of data points, IL
sample size (e.g. 1 O 1 million). Often imprecisely referred to
as large (big) data or small data.
A Note on Licensing—The taxonomy includes reference to
open, shared and closed licenses. It is important to note that
datasets are nearly all issued under some form of restriction.
Even open datasets (available for free, to reuse, for any pur-
pose) can have attribution requirements. Within artwork,
which by default has copyright assigned to the artist, it is im-
perative that the use of a restricted material within it is
acknowledged. Freeman’s work We Need Us (2014) uses real-
time open data from zooniverse.org. As the core material in the
artwork is open, the ability for her to completely own the work
outright is impossible, ownership must be reconsidered, there-
fore, the work has a series of different licenses that apply to
various elements and uses of it [29].
A Note on Privacy and Anonymized Data—Much of the
data used within artwork can be directly attributed to its
source. Infatti, the revelation of the source often confers a
large part of the meaning of the artwork. In the taxonomy, IL
Of Disclosure category includes anonymized, identifiable and
unknown tags. Whereas in other categories unknown is not
specifically required, the declaration of using data in which it
is not known whether it is anonymized is important.
Paolo Cirio & Alessandro Ludovico’s work Face to Face-
book (2012) [30] uses shared, easy to acquire, but unauthorized
and identifiable scraped data to create a fictitious dating web-
site. The controversy of the action would not exist if the data
did not allow direct identification of real people.
The disclosure section of the taxonomy requires additional
consideration on whether animals and certain objects have
rights to privacy and whether re-identification possibilities
through merging multiple datasets renders absolute anonymity
possible.
Conclusione
The collaborative development and application of this taxono-
my has highlighted that artists describe data in different ways
making cross-referencing and comparison difficult, and that
there is a lack of standardized terms to refer to. We note that
the Getty vocabularies are complex, mainly for use by domain
experts. Our taxonomy aims to be an accessible and adoptable
way of categorizing data as an art material. We view the work
as an accompaniment to Heer and Shneiderman’s taxonomy of
interactive dynamics for visual analysis, and as a potential
addition to the Digital Art Archive.
The taxonomy is released on GitHub to encourage sugges-
tions for ongoing improvement [31]. Through this public col-
laboration we aspire to contribute to the Project Open Data
metadata schema, and perhaps the Getty vocabularies them-
selves. We also invite contributions to the data art database at
We conclude that the proposed taxonomy will be an aid to
those archiving and cataloguing works in the future, but more
importantly its lightweight nature should encourage use by
practitioners, those new to the field of data art and others. In
the words of Gillespie [32], we hope that it is “specific enough
to mean something and vague enough to work across multiple
areas for multiple audiences.” The taxonomy prompts us to
think about data as a material, and as such an essential compo-
nent of any artwork that demands full disclosure.
Ringraziamenti
Supported by RCUK Doctoral Training Centre EP/G03723X/1.
References and Notes
Based on a presentation given at VISAP’15, 19–30 October 2015, Chicago,
IL, U.S.A. The IEEE VIS Arts Program (VISAP) showcases innovative
artwork and research that explores the exciting and increasingly prominent
intersections between art and visualization. The theme of VISAP’15 was Data
Improvisations.
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28. Data on the Web Best Practices Use Cases & Requirements, W3C Working
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29. Julie Freeman (2014):
30. Paolo Cirio and Alessandro Ludovico (2012)
31. Data Taxonomy (2014),
32. T. Gillespie, “The Politics of Platforms,” New Media & Società 12, No. 3,
347–364 (2010).
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