Original Research Article
A place for Big Data: Close and
distant readings of accessions data
from the Arnold Arboretum
Big Data & Society
July–December 2016: 1–20
! The Author(s) 2016
DOI: 10.1177/2053951716661365
bds.sagepub.com
Yanni Alexander Loukissas
Abstract
Place is a key concept in environmental studies and criticism. However, it is often overlooked as a dimension of situatedness in social studies of information. Rather, situatedness has been defined primarily as embodiment or social context. This
paper explores place attachments in Big Data by adapting close and distant approaches for reading texts to examine the
accessions data of the Arnold Arboretum, a living collection of trees, vines and shrubs established by Harvard University in
1872 (The original interactive data visualizations can be found online: http://www.lifeanddeathofdata.org). Although it is an
early and unconventional example of the phenomenon, there are several reasons that the Arboretum is a useful site for
investigating the relationship between Big Data and place. First, the category of place is embedded in a range of data fields
used in the Arboretum’s records. Second, the Arboretum has long sought to be a place in which scientists and citizens alike
can encounter large collections of data firsthand. Third, the place has shaped fluctuations in the daily production of data
over the course of the Arboretum’s 144 year history. Furthermore, Arboretum data can help us see place in ways not
necessarily tied to geolocation. Each of these place attachments suggests a different way in which data can be environmental: by being about, in, from, or generative of place. Taken together, these attachments offer a model for examining
other data in relation to their environments. Moreover, the paper contends that rather than being detached from place, as
prevailing discourses suggest, Big Data bring together more and further reaching place attachments than data sets of
smaller sizes.
Keywords
Environmental data, place, situated knowledge, close reading, distant reading
Introduction
A key concept in environmental criticism, ‘place’ is
often overlooked as a dimension of situatedness in
social studies of information. In this paper, I reflect
on the place of Big Data through an analysis of accessions records from the Arnold Arboretum. Established
in 1872, and located on 281 acres within the Boston
neighbourhood of Jamaica Plain, the Arboretum is a
long-lived collection of trees, vines, and shrubs managed by Harvard University. Equal parts urban laboratory and ‘zoo for plants’, it is one of the most
comprehensive and well-documented collections of its
kind in the world1 (Figure 1).
Although seemingly modest in size – hosting around
15,000 living plants today and about 70,000 over the
course of its history – the Arboretum is an apt site for
investigating Big Data’s attachments to place for several
reasons. First, place itself is an important kind of data
for the Arboretum. Indeed, its collections are assembled
from sites of scientific and cultural significance around
the world. Second, the Arboretum has long sought to be
a place in which scientists and citizens alike can encounter large collections of data first hand, simply by walking
the landscape and discovering the variety of carefully
tagged plants. Third, when understood as a set of conditions for production, the place has shaped fluctuations
School of Literature, Media and Communication, Georgia Institute of
Technology, Atlanta, GA, USA
Corresponding author:
Yanni Alexander Loukissas, Program in Digital Media, School of
Literature, Media and Communication, Georgia Institute of Technology,
TSRB 85 5th Street NW, Room 318A, Atlanta, GA 30308, USA.
Email: yanni.loukissas@lmc.gatech.edu
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://
www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further
permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-accessat-sage).
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Big Data & Society
Figure 1. Map of the Arnold Arboretum. Courtesy of the Arnold Arboretum Archives. ß President and Fellows of Harvard College.
in botanical data over the course of the Arboretum’s
long history. Finally, when looked at abstractly, the
Arboretum’s data can help us see place in new ways,
which are not limited to aspects of geolocation. As I
will show, each of these attachments to place is a different way in which data are subject to environmental criticism. Moreover, the dimensions of place attachment
identified in this paper – and the means of identifying
them – suggest a place-based approach that might influence other studies of Big Data. If we are to illuminate
what is distinctive about Big Data as a cultural form, we
must attend to the relationships between data and place
that they manifest. Because of their size and scope, Big
Data have more and further reaching place attachments
than data at other scales.
Having said this, the accessions data of the Arnold
Arboretum do not conform to present-day definitions
of Big Data as high magnitude in a variety of dimensions: volume (terebytes or petabytes), velocity, variety,
scope, resolution, flexibility, and relations with other
data sets (Kitchin and Lauriault, 2014). However, this
litany of attributes accounts for only the most ambitious of contemporary practices with Big Data (Kitchin
and McArdle, 2016). My use of the term is more in line
with the work of boyd and Crawford, who characterise
Big Data as a phenomenon with not only technological
but also cultural and scholarly dimensions (boyd and
Crawford, 2012). I approach Big Data as an epistemological and performative shift in ways of doing
research, with a long history involving data sets that
Loukissas
were previously unmanageable. Seen in this way, we
might say that the Arnold Arboretum has been
making Big Data for over a century.
In the 19th and early 20th centuries, arboreta – as
well as libraries, museums, and zoos – held the Big
Data of their day. Institutions like the Arnold
Arboretum prefigured Big Data by drawing together
representative specimens from far and wide. The most
ambitious of these institutions sought to establish
themselves as comprehensive models of the world
(Battles, 2004). As with contemporary holders of Big
Data, these institutions continually outstripped strategies for managing all the records necessary to organise,
preserve, and study their contents. The Arboretum’s
historical data illustrates, better than most, a variety
of environmental issues in Big Data. At the
Arboretum, data are about place, in place, from
place, and even generative of place. Learning about
these long-standing forms of place attachment can
prompt us to challenge settled conceptions about the
relationship between data and place in contemporary
life.
Data and place
There is a long history of scholarship on the place of
information within discourses on cyberspace (Kalay
and Marx, 2001), cities (Mitchell, 1995), networking
(Graham, 1998), interaction (Dourish, 2006), and
development (Irani et al., 2010). However, discussions
of Big Data often downplay the significance of place.
Meanwhile, popular media depict Big Data as increasingly commonplace: a ubiquitous tool for governments
(Morozov, 2014), science (Anderson, 2008), and business management (Lohr, 2012). In scholarship, Big
Data and place are sometimes treated as incompatible
concepts. An influential article by Dalton and Thatcher
argues that Big Data distracts from attention to place.
‘Relying solely on ‘‘Big Data’’ methods’, they write,
‘can obscure concepts of place and place-making
because places are necessarily situated and partial’
(Dalton and Thatcher, 2014: 6). Rather, I understand
Big Data as situated and partial because of their attachments to distributed places.
Although place has been an important a topic of
interest in the social sciences (Gieryn, 2000), my readings of data in this paper are influenced by literary and
cultural studies. Buell, a leading voice for ecocriticism,
draws together many conceptions of place in his book,
The Future of Environmental Criticism (Buell, 2009).
Perhaps the most succinct of these is offered by
Agnew, who writes of places as ‘discrete if ‘‘elastic’’
areas in which settings for the constitution of social
relations are located and with which people can identify’ (Agnew, 2013: 263). Buell also expounds on the
3
multiple dimensions of place attachment in texts,
including temporal and imagined conceptions of
place. My development of the notion of place attachment for social studies of information builds on these
important precedents but is grounded in readings of Big
Data manifest at the Arnold Arboretum. In this article,
I define place as a framework with both social and spatial dimensions, in which data are created, displayed,
and/or managed, and which, reciprocally, is shaped by
those practices. Indeed, data are not simply site-specific
tools; they have the power to reconfigure place.
My reflections on the relationship between data and
place at the Arboretum only serve to refine existing
scholarship on the grounding of data within social studies of information and science, technology, and society
(STS). Scholars of information have examined how
the meaning and significance of the term ‘data’ has
evolved over the past few centuries (Day, 2014;
Drucker, 2011; Gitelman, 2013, 2014) as well as how
it differs in use across academic and professional
domains (Borgman, 2015; Star and Griesemer, 1989).
Borgman traces data to its earliest use in theology in
1646, when it was applied as a plural of the term datum.
It was not until the late 18th century, writes Borgman,
that data was used to describe the results of empirical
observations of the kind associated with scientific practice at the Arboretum.
Meanwhile, scholars in STS have developed empirical accounts of how data are situated in specific scientific contexts (Bowker and Star, 1999; Latour, 1987).
This scholarship has largely sought to complicate a
widely held, but simplistic perspective: that data are
universal, invariable, and altogether immaterial.
Latour deftly captures this purified conception of
data in the concept of ‘inscription’. In a frequently
referenced
paper
entitled
‘Visualisation
and
Cognition: Drawing Things Together’, Latour (1990)
explains inscriptions as things created for the production of scientific arguments. As he writes, ‘you have to
invent objects, which have the properties of being
mobile but also immutable, presentable, readable and
combinable with one another’ (Latour, 1990: 7).
Many scholars have challenged this instrumentalised
definition by exposing ways in which data practices,
and data themselves, vary from one context to the
next. Research on the diversity of data has been conducted in studies of laboratories (Cetina, 1999; Keller,
2003; Latour and Woolgar, 1979), museums (Star and
Griesemer, 1989), healthcare (Bowker and Star, 1999),
climate debates (Edwards, 2010), and space exploration
(Vertesi and Dourish, 2011).
Today, in the varied work practices at the Arnold
Arboretum, data are used as scientific evidence but also
simply as a tool for landscape management. I rely on a
grounded approach to data, with the aim of studying
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the term as it is used in multiple ways in practice.
Aligned with this thinking, Borgman suggests that
understanding data means asking, ‘when are data?’
(Borgman, 2015) She writes, ‘entities become data only
when someone uses them as evidence of a phenomenon,
and the same entities can be evidence of multiple phenomena’. (Borgman, 2015: 28) In other words, data
must be performed. Moreover, data are more that
merely representational. In common parlance, the term
data can be used to mean secondary, digital representations of objects that hold scientific and cultural import.
However, my findings support the view that data are
part of an ontological ‘looping effect’ whereby they
help to shape the practices and institutions that create
them (Hacking, 1991; Kitchin and Lauriault, 2014).
Finally, I have found that prior scholarship in information studies and STS scrutinises data primarily
through case studies of discrete technological moments
or controversies; these studies provide an event-based
reading of data. In contrast, this paper contributes to
the development of an emergent place-based perspective (Galison and Thompson, 1999; Kirsch, 2011;
Livingstone, 2003). Though the concept of place
has been important to environmental criticism, it has
been largely overlooked in discourses on situatedness
(Buell, 2009). Rather, situatedness is defined primarily
as embodiment or social context (Haraway, 1988;
Suchman, 2007).
I contend that all data can be studied through a local
lens, in terms of their place attachments. Even Big Data
are connected to ‘local knowledge’, grounded in and
inseparable from their social, material, and spatial conditions (Geertz, 1985). Although data are reliably transferred across global communication networks;
everywhere, they remain marked by local artefacts:
traces of the conditions and values that are particular
to their origins. Accepting this claim necessitates a significant shift in our expectations of digital data, given
that the digital was invented to be independent of any
substrate (Hayles, 2008). Indeed, all data – not just
those created at arboreta and other sites for documenting nature – can be read through their attachments to
environments. However, data do not speak for themselves. Reading is a means of enacting data, which is
also locally situated. In this paper, I use close and distant readings to not only discover but also produce,
salient connections between Big Data and place.
Close and distant readings
Examining place attachments in data requires adopting
appropriate methods. In this paper, I make use of a
combination of techniques, which I will refer to as
close readings and distant readings. These are complimentary ways of interpreting accessions records from
Big Data & Society
the Arboretum: one up-close, the other from a distance.
This hybrid model of analysis owes much to developments in ‘close’ and ‘distant’ reading as methods of
interrogating texts in literary and cultural studies
(Jänicke et al., 2015). When used as a method of analysis for literary texts, Culler explains that close readings attend to ‘how meaning is produced or conveyed’
(2010: 22). Meanwhile, distant reading aims, paradoxically, not to read. Instead, the later technique, pioneered in literature by Moretti, aims to ‘generate an
abstract view by shifting from observing textual content
to visualizing global features of a single or of multiple
text(s)’ (Jaenicke and Franzini, 2015: 2). Moretti uses
traditional methods of graphical display, such as maps,
graphs, and trees to illuminate large-scale narrative and
geographic patterns in texts. Both close and distant
readings reveal not just what is in a data set, but how
that data might be enacted.
Through close and distant readings, I treat data as
texts: cultural expressions subject to interpretive and
speculative examination. However, accessions data
resemble indices more than prose. As such, they require
a great deal more context to decipher. Additionally,
both techniques suggest their own relationship to
place. The terms close and distant seem to describe a
spatial relationship between the analyst and the data.
However, my distance from the Arboretum data is not
so simply summarised. All my readings of the
Arboretum rely on the interpretations of Arboretum
staff members, who use their own local knowledge to
identify place attachments in the data that are not
immediately apparent. Rather, the difference between
close and distant reading techniques, applied to data,
hinges on the pervasiveness of the features being investigated. Close readings focus on isolated features in a
data set; distant readings illuminate features common
throughout.
Creating both kinds of readings for this paper relied
on a prolonged ethnographic engagement with the
Arnold Arboretum. During the period of 2012 to
2014, I lived and worked in close proximity to the
Arboretum. I conducted nine semi-structured interviews with researchers, administrators, and technologists at the institution and did archival work at their
library. But more importantly, I was a participant
observer in both formal and informal engagements,
including: a course on landscape architecture, a series
of outings to map a ‘wild’ portion of the Arboretum,
and an intensive two-day workshop that brought
together Arboretum staff with STS scholars (http://
stsdesignworkshop.tumblr.com). Over the course of
the final year of this engagement, I worked with
Arboretum staff to develop close and distant reading
techniques appropriate for looking at their data.
Beyond the findings about place attachments in Big
Loukissas
Data, this approach furthers the development of interpretive digital methods and their adaptation from traditional humanities subjects to the study of other forms
of data.
Reading accessions data as texts
Seeing data as texts accessible to traditions of hermeneutic inquiry means reading them within an interpretive
context. I argue that it would be difficult to understand
these records without considering the way they are culturally and materially situated in place. Indeed, accessions records have a long history of development and use
at the Arboretum. For one thing, they were not always
recognisable as data. The Arboretum has weathered
many successive regimes of documentation. Thus, each
organism has germinated within a social and technological setting, its care and curation managed through
the instruments and information structures deployed
during its lifetime. These place-based practices, and the
documents they produce, register what is valued about
individual organisms at the Arboretum and, in turn,
how those values change over time (Figure 2).
Today, plants collected from around the world and
across time are held together at the Arboretum by a
custom digital record system called BG-Base, a database system developed specifically for this collection.
Each entry in the Arboretum’s data set includes an
accession number, an extensive list of scientific,
common, and abbreviated names, redundant ways of
identifying the time of accession, the form and mechanism of reception, individuals associated with the
plant, various descriptions of the place the accession
hails from, its condition in the wild, and an additional
catch-all category. A list of fields used by the
Arboretum includes the following:
ACC_NUM, HABIT, HABIT_FULL, NAME_NUM,
NAME, ABBREV_NAME, COMMON_NAME_
PRIMARY, GENUS, FAMILY, FAMILY_COM
MON_NAME_PRIMARY, APG_ORDER, LIN_
NUM, ACC_DT, ACC_YR, RECD_HOW, RECD_
NOTES,
PROV_TYPE,
PROV_TYPE_FULL,
PSOURCE_LABEL_ONE_LINE,
COLLECTOR,
COLL_ID, COLLECTED_WITH, COUNTRY_
FULL, SUB_CNT1, SUB_CNT2, SUB_CNT3,
LOCALITY,
LAT_DEGREE,
LAT_MINUTE,
LAT_SECOND,
LAT_DIR,
LONG_DEGREE,
LONG_MINUTE, LONG_SECOND, LONG_DIR,
ALTITUDE, ALTITUDE_UNIT, DESCRIPTION,
COLLECTION_MISC
If found within a library, museum, or archive, many of
these fields would be incorporated into metadata: the
information necessary to catalogue a book or other
5
object, such as details of their contents, context, quality, structure, and accessibility. At the Arboretum, this
locally defined selection of fields is known simply as
‘accessions data’. However, accessions data are
shaped by many of the same local forces that affect
metadata (Edwards et al., 2011; Mayernik et al.,
2011). Furthermore, as with metadata, each accession
record exists as part of a local constellation of information, including the details of the associated plant’s
phenology, genetic characteristics, transpiration rate,
and growth habit. Even the specimen itself is a kind
of data (Gnoli, 2012). This entire ‘data assemblage’ is
necessary to make plants real and present in the contemporary ecological, scientific, and public life of the
Arboretum (Kitchin and Lauriault, 2014) (Figure 3).
As mentioned above, documentation practices at the
Arboretum long predate contemporary notions of data.
Today, records are available in multiple formats simultaneously: on maps, in ledgers, on index cards, and only
recently, in digital forms. It was not until the summer of
1985 that the Arboretum started converting its accessions data from index cards crowded in a vertical file to
digital data stored in BG-Base (Figure 4).
These digitised data afford new opportunities for
access and analysis. Even so, some staff members continue to use older formats exclusively, for they do not
yet trust the process of digitisation. Regardless of their
format, what counts as data at the Arboretum is a
matter of context. Del Tredici explains, ‘the data, in
and of itself, is only valuable [for] somebody who
understands its significance’.2 To further his point,
Del Tredici likens the ‘raw data’ to seeds. When a
seed will not germinate, there are innumerable possible
reasons. ‘Unless you know how to interpret the behaviour of the seed, it is just non-data’.3 In the sections that
follow, I examine the role of place as a form of context
necessary for interpreting Arboretum data by looking
at three significant place attachments: when place is a
form of data, when data are encountered in place, and
when place shapes data.
Close readings of place in
accessions data
Understanding data in their environmental context
means being attentive to the category of place. Big
data, exemplified here by the accessions records of
the Arnold Arboretum, exhibits place attachments
that are more complex and distributed than might be
expected. The scale and diversity of a collection
has significant implications for its ties to a variety
of environmental conditions and conceptions. In the
three examples that follow, close readings of data
related to individual specimens reveal diverse place
attachments.
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Big Data & Society
Figure 2. Early map of the Arboretum. Image by the author.
Place as data: The case of Prunus Sargentii
PROV_TYPE, PROV_TYPE_FULL, PSOURCE_
LABEL_ONE_LINE, COUNTRY_FULL, SUB_
CNT1, SUB_CNT2, SUB_CNT3, LOCALITY,
LAT_DEGREE, LAT_MINUTE, LAT_SECOND,
LAT_DIR, LONG_DEGREE, LONG_MINUTE,
LONG_SECOND,
LONG_DIR,
ALTITUDE,
ALTITUDE_UNIT.
The subset of fields listed above all contribute to the
characterisation of place in Arboretum accessions data.
In order to understand the origin of a single specimen
using these data, it is necessary to take account of multiple fields and how they might interact. A special
cherry tree (Prunus sargentii) accessioned to the
Arboretum on a leap day in 1940 provides an
example of this process. The history of the cherry tree
is catalogued in BG-Base under the specimen
number 130–140. The provenance of the plant
Loukissas
7
Figure 3. Early ledger containing accessions data. Image by the author.
(PSOURCE_LABEL_ONE_LINE) is attributed to the
institution’s founding director, Charles Sprague
Sargent, at the address of the Arboretum itself:
‘125 The Arborway, Jamaica Plain, MA’. Part of
the tree’s Latin name, sargentii, honours this parentage.4 Meanwhile, the tree’s country of origin
(COUNTRY_FULL) is listed as ‘Japan’. Sargent
might have acquired the plant during an expedition to
Asia. However, this would seem to be in conflict with
other known conditions. Sargent died in 1927, 13 years
before the listed accession date. Moreover, for reasons
that will be explained later in the paper, wild plants
from abroad had not been taken in at the Arboretum
since the mid 1920s.
Sargent could not have transported the plant from
Japan to the Arboretum on the date of accession. This
inconsistency is an artefact of the way that origins are
documented today at the long-lived institution. Staff at
the Arboretum know that a few select fields – date,
place of origin, provenance – do not tell the whole
story. One has to look to another field, the provenance
type (PROV_TYPE) of the cherry tree, to learn that it
is a ‘cultivated plant of known (indirect) wild origin’ or
‘z’ for short. In other words, specimen number 130–140
grew from a cutting taken off a specimen collected in
the wild. Provenance type is a classification of disputed
value, for it a social distinction, rather than a biological
one. Wild plants and their cuttings are genetically
identical, and in this case the ‘z’, helps to clarify that
the cherry tree in question was grown from a cutting of
one of Sargent’s original specimens – probably number
16760, unearthed from its native Japanese soil in 1892.
This example illustrates some of the complexities of
place as presented within the Arboretum’s data. Data
about place is not simply contained in a field. This form
of place attachment must be understood through a
matrix of values and coordinated through local knowledge about the history of data collection practices at
the Arboretum.
Place of data: The case of Torreya Grandis
As the previous example illustrates, the Arboretum is
an aggregated landscape stitched together from plants
once residing in other places. Most of these specimens
hail from an ecological zone defined by close proximity
to the latitude of Boston, stretching across England,
Greece, South Korea, China, and Japan. When encountered at the Arboretum, each of these plants stands with
its data. A thin plastic card embossed with a subset of
accession details usually hangs from its trunk or
branches. The cards contain fields that are relevant
for Arboretum staff, researchers, and visitors: scientific
name, accession number, plant family, accession date,
propagation material (e.g., seed ‘SD’ or scion ‘SC’),
location, common name, source/collection data.
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Figure 4. Card catalogue containing accessions data. Image by the author.
Together the plants and their tags make the Arboretum
into a full-scale scientific map, organised using
the Bentham and Hooker taxonomy, a system that
dates to the late 19th century. The Arboretum
landscape is itself a place for encounters with data
(Figure 5).
In order to understand this second form of place
attachment, let us revisit a tour of the Arboretum
Loukissas
9
Figure 5. Arboretum tag diagram. Courtesy of the Arnold Arboretum Archives. ß President and Fellows of Harvard College.
grounds that occurred in late June of 2013. During a
workshop that I co-organised, a group of visitors were
guided by Del Tredici through the Explorer’s Garden,
an area of the Arboretum nestled in a microclimate
beneath the summit of Bussey Hill. Del Tredici stopped
to comment on his relationship to the living collections.
‘I’ve got a lot of direct connection to a lot of these
plants. That little plant, Torreya grandis, I collected
in China in 1989. So a lot of these are like my offspring’.5 Del Tredici explains that he found the seeds
of the Torreya grandis at a market in China. Fleshy and
green, they struck him as unusual examples of edible
seeds produced by a conifer. But beyond what is interesting about the plant itself, this quote provides a compelling starting point for understanding what is and is
not included in the data landscape of the Arboretum
(Figure 6).
The acquisition date of the Torreya grandis and
Del Tredici’s association with it are duly noted on
the Torreya’s tag. Also pressed into the tag’s smooth
surface, ‘pinales’, registers the plant’s bemusing status
as a conifer. However, there is no hint of the oddness of
this ordering. Moreover, several features of the plant’s
local significance are not included on the tag, which
serves only to position the Torreya grandis within a scientific landscape. Tags do not explain how plants like
the Torreya are literally and figuratively torn up by the
roots and relocated to a new ecological and cultural
context. Let’s explore a few of these absences.
Del Tredici is identified on the tag as a ‘collector’,
not as a ‘progenitor’, or ‘breeder’, as his statement
would suggest – this, despite the fact that he is responsible for the reproduction of the plant in the Boston
region. The term ‘collector’ speaks of the scientistand-specimen relationship between Del Tredici and
the plant, rather than the more nurturing association
between Del Tredici the horticulturalist and the organism he has cultivated. The latter is more in line with his
own intimate way of identifying with the Torreya
grandis as an ‘offspring’. Furthermore, there are few
traces of the fruitful intersections between the living
collections and the local communities in Boston that
surround the site. Do not look to data for connections
between dandelions (Taraxacum officinale) and the elderly Greek women who collect them in the early
summer to make horta vrasta (boiled greens), or associations between the ‘tree of heaven’ (Ailanthus altissima) and the devout Dominicans who discover starlit
sites for their Santeria rituals in the groves of the
Arboretum’s Bussey Brook Meadow. Such details,
though important to the local meaning of the
Arboretum’s plants, are not part of the way data as
tags interact with the place.
I introduce the example of the Torreya grandis to call
attention to the placement of data, but also their limits
as tools for understanding the places they reside in.
Data do not capture the full lives of Arboretum
plants. This understanding reinforces prior studies of
data that show institutionalised categories to be connected to specific social groups (Star and Griesemer,
1989). While useful for establishing shared references
among the Arboretum’s staff and its visitors, data
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Figure 6. Peter Del Tredici in the Arboretum. Image from a workshop hosted by the author.
categories sit beside, but do not account for, all the
varied place-based meanings that Arboretum plants
embody.
Data of place: The case of Tsuga Caroliniana
So far, my close readings have revealed how place
appears in data and how data appears in place. It is
also important to understand how a place affects data’s
production. For this last point, let us consider the hemlock, which is a local tree that has been in rapid decline
all over the eastern United States due to the non-native
insect, the hemlock woolly adelgid. In the late 1990s, a
large, unaccessioned stand of hemlocks in the
Arboretum fell victim to the pest. A note in the accession record for one Carolina Hemlock reads ‘plants
producing very heavy seed crop, heavily infested with
woolly adelgid’.6 Although these trees had been residents on the institution’s grounds for decades, they
were only accessioned into the collection in order for
the infestation to be tracked and treated with imidacloprid, a powerful insecticide. Originally intended as a
backdrop for species of scientific significance, the hemlocks were never intended to be an official part of the
collection. The blighted hemlock accessions made 1998
a peak year of expansion for the Arboretum, but only
from the perspective of data (Figure 7).
This example illustrates that even seemingly straightforward fields like ‘date’ can have a complex relationship to place. For each entry in BG-Base, what the
accession date means is dependent on context. It
might mean when a seed was planted, when a seedling
arrived on site, or simply – as in the case of these hemlocks – when an existing plant was annexed to the collection. But beyond the curious and local significance of
their accession dates, the hemlocks are interesting
because they raise deeper questions about the role
that data perform.
Controversy still surrounds the decision to make the
feral stand of hemlocks part of the collection. Del
Tredici sees the trees as invaluable for studying the
infestation process. ‘It was only by accessioning the
plants that we could track their decline over time or
the insecticidal treatment of those plants we decided
to treat’.7 Meanwhile, the current Arboretum director
William Friedman looks on the hemlocks of questionable provenance as inherently undesirable, for they lack
essential data about their origins that would make them
reliable subjects of scientific study. Why not replace
them with trees of actual research significance?
Such disagreements highlight the tensions between
competing realities at the Arboretum; it is a living
place, but also a repository for data. Hence, data may
be looked upon as ‘just good-enough’ tools (see the
article on data as ‘just good-enough’ in this issue) to
support direct work with the collection: organising
plants, notes, and relationships among them in a convenient manner. But without reliable data, the emergent
form of the collection can disappear altogether, its contents scattered in an ontological wild.
Loukissas
11
Figure 7. A hemlock tree at the Arboretum. Image by the author.
Coexisting concerns about the necessity of data and
their inherent instability over time reinforce a lesson
from STS that holds across shifts in technology: data
must be part of a knowledge ecology (Edwards, 2010).
The metaphor to environmental processes is apt.
Arboretum scientists, specimens, and data infrastructures are all necessary to generate, verify, and sustain
what the place knows. It is the place – of which data are
only a part – that holds knowledge about the
Arboretum hemlocks, their deadly infestation and its
implications for similar trees across the Northeast. But
at the Arboretum, the knowledge ecology is more than a
metaphor. Data are necessary components of the functioning biological system created and maintained at the
Arboretum. They transcend their roles as representations by directly supporting the reality they describe.
12
Distant readings of place in
accessions data
Through close readings of the Arnold Arboretum’s
accessions records, the previous section demonstrates
that there are numerous ways in which data can be
entangled with place: when place is a kind of data,
when place is the site of encounters with data, and
when place is the site of data’s production. Each of
these place attachments can be exposed through close
readings of accessions data for individual plants:
Prunus Sargentii, Torreya grandis, and Tsuga caroliniana. However, looking at the accessions to the
Arboretum all-together, through distant reading techniques, can reveal alternative conceptions of place.
I use the term distant reading to describe the experience of looking at the whole Arboretum through the
data of it parts. Rather than being a god’s eye view,
characterised by Haraway as one that seems to come
‘from nowhere, from simplicity’ (Haraway, 1988: 589),
a distant reading is a situated but wide-ranging perspective. It offers views of data, rather than views through
data. Creating distant readings requires a critical sensibility towards data, including attention to what might
be occluded as well as what other vantage points are
possible. This approach compliments prior work in
geography on the critical studies of landscape representation (Barnes and Duncan, 2013; Cosgrove, 2008), as
well as the development of critical practices in mapping
(Crampton, 2011; Kitchin et al., 2013).
A distant reading as more like a panorama than a
map. Although it is not uncommon to hear the term
panorama used today to describe graphical displays of
data, few acknowledge that, unlike maps, panoramas
are situated ways of seeing places. As far back as the
18th century, the term was used to describe pictorial
representations of landscapes as seen by an observer
positioned at a single strategic point. Moreover, panoramas have long been understood as mediated. Like
distant readings, they are enacted through technological means. The historian, Schivelbusch (1986),
uses the term panoramic to evoke the once unfamiliar
view across an expansive landscape afforded by the
speed of the passenger train.8 Just as the rapid pace
of the passenger locomotive offered new vistas across
broad stretches of space, the distant readings included
here reveal perspectives at previously incomprehensible
scales. But distant readings are not narrowly defined
technical tools (Hall, 2008). Rather, they generate alternative experiences of data and the places they depict.
In the distant reading presented below (Figure 8.
Best seen in colour), the Arboretum is portrayed as
an agglomeration, a pattern, and a system in flux.
Here, data are enlisted to construct a new sense of
place. Because of their scale and heterogeneity, large
Big Data & Society
data sets offer opportunities for new experiences of
place that – like Schivelbusch’s locomotive panorama
– are different from anything seen before. In the portion
of the paper that follows, I will introduce a series of
distant readings. None of these readings are neutral or
inevitable. Rather, they help us reimagine the
Arboretum as a place with origins, structures, and
dynamics that are not constrained by their geography.
Instead, they depict landscapes of temporality.
Place as history
Figure 8 portrays the Arboretum as an aggregate place
developed over time. Beginning in 1872 and ending in
2012 (when this set of records was made available for
use), the distant reading portrays a temporal graph of
plant specimens. The image is a kind of timeline: structured by yearly accessions, much like trees record environmental patterns in their annual growth rings.
Months and days index accumulated specimens, each
denoted by a dot. This two-dimensional view can
be enhanced by a series of section cuts through daily
accessions (see Figure 9). In the original interactive version of this visualisation (accessible here: http://www.
lifeanddeathofdata.org), the section, which portrays the
number of accessions on each day of the selected year,
can be produced for any year along the timeline.
Such distant readings can be used to call attention to
variations in the data by linking them to colour, size,
and other visual cues. For instance, Figure 8 displays
changes in provenance type (a category mentioned earlier) across the history of the collection. Here, a green
dot represents a plant collected in the wild, a yellow dot
signifies a cutting from a wild plant, a black dot indicates a cultivated plant, and a grey dot stands in for a
plant from an unknown origin (far more common than
one might expect). Fluctuations across these provenance-related colours illustrate shifts in the make up
of the Arboretum, between collections of scientific
importance (mostly collected from the wild), and selections in the service of horticulture (mostly from other
cultivated collections).
The distribution of green, yellow, black, and grey
dots faintly demarcates three eras of collecting at the
Arboretum identified by curator of living collections
Michael Dosmann.9 In the late 19th and early 20th
centuries, Sargent engaged in a global project of scientific fieldwork to collect distantly related species from
around the world as evidence to support Darwin’s
theory of evolution. However, in the 1920s, the
United States Department of Agriculture discovered
that the Arboretum was inadvertently collecting invasive bugs along with its imported plants. Sargent lost a
legal battle with the government and wild collecting
decreased substantially thereafter.
Loukissas
Figure 8. Linear timeline of Arboretum accessions. Image generated from JavaScript code by the author and Krystelle Denis.
13
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Big Data & Society
Figure 9. Section cut through linear timeline of Arboretum accessions. Image generated from JavaScript code by the author and
Krystelle Denis.
The ensuing middle years of the 20th century are
sometimes known at the Arboretum as the Wyman
era, after a prominent horticulturist. During this time,
the Arboretum halted its foreign expeditions and relocated its scientific research to Harvard’s Cambridge
campus. The ensuing research centred on the herbarium, a much larger collection made up entirely of
dried plants (Figure 10). Dosmann explains that the
expansive grounds in Jamaica Plain became a ‘showcase
garden’, a place to display the horticultural trends of the
day. During this period, says Dosmann, ‘if you did want
to go and collect anything, you went to a nursery’10.
It was not until the early 1970s, during a re-evaluation of the mission of the collection associated with its
centennial, that the Arboretum re-initiated its expedition work abroad. The renewal of overseas fieldwork
built on expanded relationships with institutions in
Asia and a focus on emergent and imperative questions
around global climate change. The distant reading
registers some aspects of these long-term temporal
shifts, highlighting in particular the relationship
between the two defining arms of the Arboretum, scholarship and horticulture, and the ways in which their
relationship changed over time.
This visually oriented reading of the Arboretum as
data is unlike a photograph or geographic map of the
place. It illuminates a landscape shaped over time by
otherwise invisible ecological, organisational, and even
political forces. However, this particular use of the
method is but one way of looking. The data supports
many alternative portrayals of place.
Alternative histories
A radial version of the same timeline pushes the metaphor to embedded arboreal processes – the forming of
rings in a tree (see Figure 11). Moreover, new patterns
are illuminated by the density gradient from centre to
periphery. The three eras of collecting become more
prominent as the sparse accessions in early years are
compressed into a smaller space. Moreover, subtle
lines of accessions running through significant dates
of the year are accentuated. They appear as concentrated rays within the circular geometry. Finally, the
radial organisation suggests an entirely different kind
of temporality: one that has an origin at some fixed
point and then expands indefinitely into the future.
This radial image can be read against the linear one,
which presents time as being infinite in two directions.
The accessions depicted in linear form seem sparse in
comparison. In the linear version, one can more clearly
see increased collection occurring over the years, albeit
Loukissas
15
Figure 10. Herbarium specimen. Image courtesy of the Herbarium of the Arnold Arboretum of Harvard University. Cambridge,
Massachusetts, USA.
with a narrowing in the 1940s. Moreover, practices
seem to change dramatically across seasons in the
second half of the 20th century, transitioning from
winter accessioning to accessioning year-round.
However, other patterns are not present in the linear
timeline. The dispersion of accessions in the early years
makes it more difficult to note the intensity of wild
collecting during the first period of exploration, and
its symmetry with the period after the 1970s, when
the Arboretum began to collect externally again. At a
16
Big Data & Society
Figure 11. Radial timeline of the Arnold Arboretum. Image generated from Java code by the author.
more detailed level, a substantial gap in collecting on
Christmas day appears clearly in the radial version but
disappears into the fringe of the linear image. This gap
could be made more prominent by simply reordering
the arrangement of months, but what other patterns
would be shifted out of view?
Both the radial and linear versions obscure the exact
number of accessions that have come in per day. A 3D
approach as demonstrated in Figure 12 can help to
make the number of accessions more evident. Rather
than being arranged solely by date, the 3D image highlights every accessioned plant at the Arboretum and
exposes the rate of accumulation along a new z-axis.
The resulting form is a cone. Moments of rapid growth
in the collection appear as narrow segments of the cone,
whereas periods of slower development flatten it out.
While evocative in its shape, the 3D visualisation is
more difficult to read overall; In fact, most of the patterns exposed by the previous images are compromised
when portrayed in 3D. Graphics overlap from opposite
sides of the cone, the visible circumference of the yearly
rings is narrowed, and daily accessions are difficult to
align with month and year markers.
Such examples of distant reading are both interpretive and speculative. They present the Arboretum as
multiple. Each version of the place offers its own
experience of the substantial collections brought
together over a long history.
Histories out of place
The distant readings depicted above suggest different
ways of making sense of the Arboretum as a whole.
But distant readings can contain telling details as well.
Loukissas
17
Figure 12. 3D Timeline of Arboretum accessions. Image generated from Java code by the author.
Indeed, we can learn more about the kind of place the
Arboretum is by inspecting components of the distant
readings close up. In particular, it is useful to pay attention to apparent anomalies or glitches in the images. I
have previously called these ‘data artefacts’ (Battles and
Loukissas, 2013). In most work with data visualisation,
such irregularities are cleaned up. Like various kinds of
data dirt, they appear to be simply out of place
(Douglas, 1978; Mody, 2001). But data artefacts speak
to the human history of their accumulation.
18
Consider, for example, the rays of clustered accessions so prominent in the radial version of the timeline.
A literal reading of these rays suggests that accessions
arrived en mass on certain days, particularly on the
15th of every month, on the first of the year, and on
the first of July. But Del Tredici suggests that the rays
are most likely technological artefacts. ‘If something
came in (during) August of 1942, I think BG-Base
would output that [by] default as August 15th’.11
Without a precisely recorded day of accession, BGBase places accessions squarely in the middle of the
month. The pattern is similar at the scale of the year.
Accessions appear unusually heavy on 1 July, the
beginning of the Arboretum’s fiscal year.
Deriving from various processes, such artefacts are
often entangled with the contingencies of a place. Those
mentioned above are technological artefacts, resulting
from the material conditions of data creation.
However, disciplinary artefacts might betray specialised
ordering systems, and vernacular artefacts might be the
result of dialects or local language uses. These various
kinds of artefacts can be extraordinarily subtle and difficult to tease out, but distant reading is an adept tool
for bringing such artefacts to the surface.
Data artefacts register not only local changes in
technology, personnel, and organisation but also
broader cultural rhythms and events. Look closely
and you can spot the Second World War as well as
Christmas (mentioned previously), as gaps between
denser periods of accessions. The first is manifest as a
bald swath in the middle of the 1940s. The second is
particularly noticeable in the radial timeline as a wedge
of space radiating down 25th December. Accessions
from particular regions are also affected by international relations. Del Tredici recounts, ‘when Nixon
went to China, I started to get small little exchanges of
seed packets and things like that’.12 Through data artefacts, we can see more than a collection of plants. Kyle
Port, the Arboretum’s plant records manager, notes
that artefacts betray the ‘personalities’ behind the
data.13 Together, these personalities contribute to the
sense of place generated through distant readings.
Conclusions
Social studies of information have much to learn from
methods currently emerging in environmental studies
and criticism. We should learn to see data as cultural
forms that are situated socially and materially, but also
in place. Moreover, related techniques from the digital
humanities can frame these data as texts, to be read up
close or from a distance. Reading the place attachments
in data can help us learn what is distinctive about Big
Data. I contend that because of their heterogeneous
nature, Big Data – exemplified by the historical
Big Data & Society
example of the Arnold Arboretum – bring together
more and further reaching attachments to place than
data sets of smaller sizes. Although the Arboretum does
not conform to contemporary expectations of Big Data
as petabyte-scale, it belongs to the long historical arc of
the present-day Big Data phenomenon. The Arboretum
has long contended with data sets verging on the
unmanageable and aspiring to the scale at which n
(the number of elements in the data set) ¼ all.
Each of the place attachments explored in this paper
suggests a different way in which data can be environmental: by being about, in, from, or even generative of
place. Taken together, these four ways of probing
data offer a model for how to read Big Data from an
environmental perspective. However, the approach
demonstrated in this paper should not be mistaken as
a formula for engaging with data anywhere. The
methods used here were developed in situ, with the particular place attachments of the Arboretum at hand.
Instead, this paper is meant as an example of environmental data studies. Such studies can unsettle conceptions of Big Data, by calling attention to their
origins as well as the experiences they create.
Following on this paper, scholars of environmental
data would do well to seek out other places and place
attachments.
Understanding the possible relationships between
data and place can help us challenge the wisdom of
Big Data’s centralised models of management. As this
paper has shown, thinking about data as mobile,
immutable, and generally detached from place can
obscure important ways in which data rely on local
knowledge and experience for meaningful interpretation and responsible use. When taken out of place,
data can come to be seen by unfamiliar audiences as
either the view from nowhere or nothing more than
data dirt.
Acknowledgements
This work was made possible by William (Ned) Friedman,
director of the Arnold Arboretum, and by the generous participation of many Arboretum staff members. In addition,
the paper has benefitted from feedback offered by the editors
and reviewers of the journal as well as numerous colleagues:
Matthew Battles, Kyle Perry, Katherine Diedrick, Lauren
Klein, Greg Zinman, and collaborators at metaLAB(at)
Harvard and in the digitalSTS community. Some of the visualisations used in this paper were designed and implemented in
collaboration with Krystelle Denis.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Loukissas
19
Funding
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Notes
1. From an interview by the author with Michael Dosmann,
2014.
2. From an interview by the author, with Peter Del Tredici
2014.
3. Ibid.
4. Note that the provenance is not a place of origin, but
rather the name and address of a collector.
5. From an interview by the author, with Peter Del Tredici
2014.
6. From a record in BG-Base, the Arnold Arboretums database of plant accessions.
7. From an interview by the author, with Peter Del Tredici
2014.
8. STS scholar Laura Forlano first connected the use of this
term to Schivelbusch’s work in a phone conversation with
the author.
9. From an interview by the author, with Michael Dosmann
2014.
10. From an interview by the author with Michael Dosmann,
2014.
11. From an interview by the author with Peter Del Tredici,
2014.
12. From an interview by the author with Peter Del Tredici,
2014.
13. From an interview by the author with Kyle Port, 2014.
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