Data as assemblage
| DOI | https://doi.org/10.1108/JD-08-2021-0159 |
| Published date | 03 March 2022 |
| Date | 03 March 2022 |
| Pages | 1338-1352 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Ceilyn Boyd |
Data as assemblage
Ceilyn Boyd
Simmons University, Boston, Massachusetts, USA
Abstract
Purpose –A definition of data called data as assemblage is presented. The definition accommodates different
forms and meanings of data; emphasizes data subjects and data workers; and reflects the sociotechnical
aspects of data throughout its lifecycle of creation and use. A scalable assemblage model describing the
anatomy and behavior of data, datasets and data infrastructures is also introduced.
Design/methodology/approach –Data as assemblage is compared to common meanings of data. The
assemblagemodel’s elementsand relationships also are defined, mapped to the anatomy of a US Census dataset
and used to describe the structure of research data repositories.
Findings –Replacing common data definitions with data as assemblage enriches information science and
research data management (RDM) frameworks. Also, the assemblage model is shown to describe datasets and
data infrastructures despite their differences in scale, composition and outward appearance.
Originality/value –Data as assemblage contributes a definition of data as mutable, portable, sociotechnical
arrangements of material and symbolic components that serve as evidence. The definition is useful in
information science and research data management contexts. The assemblage model contributes a scale-
independentway to describe the structure and behavior of data, datasets and data infrastructures and supports
analyses and comparisons involving them.
Keywords Assemblage theory, Data, Data definitions, Information science, Research data management,
Theoretical models
Paper type Research paper
Introduction
What is data? Those of us who study data, data infrastructures and their sociocultural
consequences know there is a definition for every occasion. The variety reflects data’s
complexity as a concept and phenomenon, how we encounter and engage with it and the
vantage points from which researchers have studied it.
Furner (2016) identifies nine historical and contemporary meanings of data, including data
as gifts, mathematical premises, evidence and digital bits. Kitchin (2014) enumerates assorted
kinds of data, including quantitative and qualitative data; structured, semistructured and
unstructured data; captured, exhaust, transient and derived data and primary, secondary and
tertiary data (pp. 4–8). Data refers to physical samples (Devaraju et al., 2017), capta (Drucker,
2011), documents, metadata, Big Data (Kitchin and McArdle, 2016) or information on
properties about units of analysis (Hjørland, 2018). Research data is relational and portable
(Lenonelli, 2015); it may be aggregated into datasets or databases and shared in repositories.
Data has an unstable relationship with information, knowledge and wisdom (Buckland, 1991;
Frick
e, 2008) and may be perceived as “lacks of uniformity in the real world”(Floridi, 2010,p.
23). Data may be thought of as a when rather than a what, which is neither neutral nor raw
(Borgman, 2015, p. 26; Gitelman, 2013), or it may serve as a rhetorical gesture towards
anything given before an argument (Rosenburg, 2013, p. 36).
According to Capurro and Hjørland (2003), definitions “are not true or false, but more or less
fruitful”(p. 345). Furthermore, “scientific definitions of terms like information depend on the
roleswe give them in our theories; in otherwords, the type of methodologicalwork they must do
for us”(p. 348). Good definitions are productive. They underpin our paradigms, theories and
JD
78,6
1338
The author is grateful to Alyn Gamble, Wendy Gogel, Adam Kriesberg, Kris Markman, Colin
Rhinesmith and Mitch Wade for their willingness to debate the possibility of data as assemblage and
their encouragement as the author conducted this research. The author also thanks the peer reviewers
for their invaluable feedback that guided the author’s revisions.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 15 August 2021
Revised 30 January 2022
Accepted 8 February 2022
Journal of Documentation
Vol. 78 No. 6, 2022
pp. 1338-1352
© Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-08-2021-0159
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