10.6092/issn.2038-3460/17168
We presently witness a profound transformation of the configuration of biomedical practices, as characterized by an increasingly collective dimension, and by a growing reliance on disruptive technologies that generate large amounts of data. We also witness a proliferation of biomedical databases, often freely accessible on the Web, which can be easily analyzed thanks to network analysis software. In this position paper we discuss how science and technology studies (S&amp;TS) may cope with these developments. In particular, we examine a number of shortcomings of the notion of networks, namely those concerning: (a) the relation between agency and structural analysis; (b) the distinction between network clusters and collectives; (c) the (ac)counting strategies that fuel the networking approach; and (d) the privileged status ascribed to textual documents. This will lead us to reframe the question of the relations between S&amp;TS and biomedical scientists, as big data offer an interesting opportunity for developing new modes of cooperation between the social and the life sciences, while avoiding the dichotomies – between the social and the cognitive, or between texts and practices – that S&amp;TS has successfully managed to discard.
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Michel Callon
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Big Data and the Collective Turn in Biomedicine. How Should We Analyze Post-genomic Practices?
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2014-07-14
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Michel Callon
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10.6092/issn.2038-3460/17168
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2038-3460
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actor-network theory
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42
Big Data and the Collective Turn in Biomedicine. How Should We Analyze Post-genomic Practices?
https://tecnoscienza.unibo.it/article/download/17168/16069
5
                                                                               Lecture

Big Data and the Collective Turn in
Biomedicine
How Should We Analyze Post-genomic Practices?

        Alberto                    Pascale         Vololona               Michel
       Cambrosio                   Bourret        Rabeharisoa             Callon
       McGill University         Aix-Marseille     Mines-ParisTech     Mines-ParisTech
                                Université, UMR
                                   SESSTIM
                                           	
  
                                           	
  
                                           	
  
       Abstract: We presently witness a profound transformation of the configu-
       ration of biomedical practices, as characterized by an increasingly collective
       dimension, and by a growing reliance on disruptive technologies that gener-
       ate large amounts of data. We also witness a proliferation of biomedical da-
       tabases, often freely accessible on the Web, which can be easily analyzed
       thanks to network analysis software. In this position paper we discuss how
       science and technology studies (S&TS) may cope with these developments. In
       particular, we examine a number of shortcomings of the notion of networks,
       namely those concerning: (a) the relation between agency and structural
       analysis; (b) the distinction between network clusters and collectives; (c) the
       (ac)counting strategies that fuel the networking approach; and (d) the privi-
       leged status ascribed to textual documents. This will lead us to reframe the
       question of the relations between S&TS and biomedical scientists, as big data
       offer an interesting opportunity for developing new modes of cooperation
       between the social and the life sciences, while avoiding the dichotomies – be-
       tween the social and the cognitive, or between texts and practices – that
       S&TS has successfully managed to discard.

       Keywords: big data; network analysis; post-genomic medicine; bio-clinical
       collectives; actor-network theory.

       Corresponding author: Alberto Cambrosio, Department of Social Studies
       of Medicine, McGill University, 3647 Peel St., Montreal (QC) Canada H3A
       1X1 – Email: alberto.cambrosio@mcgill.ca




	
  
TECNOSCIENZA
Italian Journal of Science & Technology Studies
5 (1) pp. 11-42 - ISSN 2038-3460
www.tecnoscienza.net                              2014
12                                                             Tecnoscienza - 5 (1)

1. Introduction

    This is a position paper. It discusses how science and technology stud-
ies (S&TS), confronted with recent changes in the configuration of bio-
medical practices – in particular their increasingly collective dimension,
and their reliance on disruptive technologies, such as microarrays and
next-generation sequencing, that generate large amounts of data – may
cope with these developments. Big data represent a (multifaceted) source
of information for both S&TS scholars and health care practitioners,
while also being the outcome of activities predicated upon the involve-
ment of a large number of heterogeneous actors. As such, they are em-
bedded in biomedical practices and have become key elements of
knowledge production, especially in domains such as genomics, where
they engender distinctive forms of evidence.
    The dual nature of big data – they act as sources of information while
also being the outcome of activities that are constitutive of biomedical
practices – is not something new. Scientific texts (articles, books, reports)
partake in scientific knowledge production, while simultaneously acting
as a data repository for the natural scientists who produce and use them.
As sources of evidence, they are also of use to social scientists who en-
gage, for instance, in scientometric analyses of the socio-cognitive struc-
ture of science, or to historians of ideas investigating the dynamics of a
given domain. S&TS scholars have successfully learned how to tame this
multi-dimensional nature of scientific texts by displaying the links they
entertain with scientific practices, without falling into the dichotomy be-
tween the social and cognitive dimensions of science. Big data, however,
raise a novel and difficult challenge, for two main reasons. First, because
we have only limited evidence concerning their actual use as part of re-
search practices (but see, e.g., Leonelli 2012, 2013, 2014, and Edwards
2010 for noteworthy exceptions), which in turn leaves social scientists
wondering how they should understand and use them. And second, be-
cause the “big” in big data refers not simply to the sheer quantity of data
available, but also to their instability, heterogeneity, and proliferation into
different domains. In other words, we presently face a dual task: on the
one end, we need to better understand the research activities that rely on
the production and analysis of big data, and, on the other hand, we need
to figure out how science studies scholars can embed big data, and the
configurations they generate, into their own practices, and what are the
consequences of doing so. In particular, we should be wary of solutions
that may end up reintroducing the dichotomies – between the social and
the cognitive, or between texts and practices – that S&TS has successfully
managed to dispense with. The present text explores a few of the issues
and problems involved in such an endeavor.
    Big data are everywhere, and thus the issues discussed in this text are
not confined to S&TS. Rather, big data represent a more general chal-
lenge for the social sciences because they raise the following conundrums:
Cambrosio et al.                                                           13
How should we, as social scientists, use them in our own investigations
while taking into account the fact that they also partake in the activities of
the actors we investigate, and cannot therefore be considered as unprob-
lematic evidence? How can we revisit, in the light of the growing im-
portance of big data, the traditional tension between qualitative and
quantitative approaches, or between local ethnographies and cross-
sectional studies? We are particularly interested in those situations in
which both the social scientists and the actors they investigate attribute a
strategic role to the notion of network, often generically defined. We will
center this paper on the theoretical and visualization issues engendered
by this notion. In a first section, we will briefly discuss the development
of big data in the oncology domain, showing that they have become part
and parcel of recent developments in this advanced biomedical domain.
This will lead us, in a subsequent section, to examine how the notion of
network plays a strategic role in this context. While this notion has, of
course, enjoyed a staggering success within S&TS, we will focus on its
shortcomings, and in particular on four thorny issues, namely: (a) the re-
lation between agency and structural analysis; (b) the distinction between
network clusters and collectives; (c) the (ac)counting strategies that fuel
the networking approach; and (d) the privileged status ascribed to textual
documents. We will explore how these shortcomings can be overcome, at
least tentatively. In turn, this will lead us to reframe the question of the
relations between the subjects and objects of observations, i.e., between
S&TS and biomedical scientists. Big data, as it turns out, may offer an in-
teresting opportunity for developing new modes of cooperation between
social and life scientists.


2. 21st Century Biomedicine: Clinical Wards, Wet Labs,
and Bytes

    In his address to the 2011 meeting of the American Society of Clinical
Oncology – with nearly 35,000 members, most likely the single largest
professional organization in its domain – the Society’s president, George
Sledge, warned fellow oncologists about the upcoming “tsunami” of ge-
nomic information that was likely to result from a sharp decrease in the
cost of sequencing tumors. He added: “When data are that cheap, every
patient’s cancer will be informative for tumor biology […] and things will
get very, very complicated” (cited in Goldberg 2011). That same year,
and along similar lines, in a promotional video for the European Multi-
disciplinary Cancer Congress, entitled: “Bench, bedside, ‘bytes’ and
back” (also referred to as the three Bs), noted clinical researcher Anne-
Lise Børreson Dale explained: “You start with the bed, you have the pa-
tients, and then you go to the bench, and then because we create so many
[…] huge amounts of data, you have bytes, as in gigabytes, and then you
14                                                                                                                                                                                                                                 Tecnoscienza - 5 (1)

go back to the bench to find out what is the right treatment for that pa-
tient, and then you go to the patient again […] it’s like a spiral that goes
up […] every patient is sort of an experiment for the next who will be
coming in”1. These two quotations are far from uncommon. They illus-
trate recent themes and trends in oncology, namely the rise of translation-
al research, closely combining biological and clinical investigations; the
search for personalized treatments, whose horizon lies in the singulariza-
tion (Callon 2012) of patients; and, finally, the premises upon which the
previous two items are predicated, namely the availability of large sets of
data, whose proliferation, accumulation and heterogeneity raises major
interpretative challenges.
    We will return in subsequent sections to the collective dimension of
contemporary biomedicine, in particular translational research, as exem-
plified, for instance by large-scale genomic consortia – e.g. the “Breast
Cancer Linkage Consortium” that mobilized approximately 100 centres2,
or the “Autism Genome Project” that mobilized “120 scientists from
more than 50 institutions across 19 countries” (Szatmari et al. 2007) – or,
perhaps more mundanely, the staging of large-scale, national and interna-
tional clinical trials (Keating and Cambrosio 2012a), although we should
hasten to add that, as we will see, the term “collective” does not refer
simply to number and size. For now, let us examine the issue of big data
that is related to, but not identical to the former topic. The generation
and mining of large data sets is by no means an uncontroversial activity.
For instance, MIT biologist Michael Yaffe (2013) recently claimed that
while “the sequencing of human tumours [has] produced important data
sets for the cancer biology community […] these studies have revealed
very little new biology”, further complaining that scientists were “addict-
ed to the large amounts of data that can be relatively easily obtained [by
genome sequencing], even though these data seem unlikely, on their own,
to unveil new cancer treatment options or result in the ultimate goal of a
cancer cure” (Yaffe 2013, 1). The important point, as far as we are con-
cerned, is of course not whether Yaffe’s criticism is warranted. Rather,
our claim is that arguments both in favour and against the turn to big da-
ta confirm the fact that it has come to occupy a central place in contem-
porary biomedicine. The relevant issue, thus, is to examine what it
involves in terms of rearranging the flow of biomedical practices.
    This paper is part of a special issue entitled: From Bench to Bed and
Back. The synecdoche in the title refers to translational research, as char-
acterized by close relations between laboratory research (bench) and clin-
ical work (bed). The “back” adverb marks a rejection of the
unidirectional model of translation, as both the clinic and the laboratory
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
1
  Video retrieved on Feb 5, 2014 from: http://ecancer.org/conference/101-emcc-
2011/video/891/bench--bedside-----bytes----and-back--a-virtuous-cycle-of-
knowledge--1-5.php
2
  See http://www.humgen.nl/lab-devilee/bclchome.htm
Cambrosio et al.                                                                                                                                                                                                                   15
can be the starting point of a successful translation. We go further and
argue that rather than a relation or interface between two poles, transla-
tional research corresponds to a new, emerging site, characterized by the
presence of distinctive activities. As argued in the previously quoted
statement by Børreson Dale, in addition to benches and beds this site in-
cludes a third element, “bytes”, or, in other words, a new kind of data
and a new kind of practice, bioinformatics, needed to make sense of
them. Bioinformatics is the “new kid on the block” of biomedical re-
search3, and, as described elsewhere (Keating and Cambrosio 2012b) has
entertained somewhat controversial relations with the older data-
processing specialty, biostatistics. For our present purpose the main issue
is that by introducing bioinformatics, the rules of the game have changed.
For bioinformatics cannot be reduced to the computerization of biology;
rather, it involves a rearrangement of biological practices, a redefinition
of what counts as valuable biomedical work (Yaffe’s aforementioned crit-
icism is a symptom of this process), and it shapes the kind of knowledge
emerging from the translational research domain. As a bioinformatician
put it: “We’re not bioinformaticians who dabble in breast cancer”. In-
stead, he and the members of his lab are “focused on understanding the
disease”4. Understanding means reframing it, using the “new quantitative
methods – the methods of the New Biology”5.
    Let us take as an example the development of a gene expression signa-
ture to predict clinical outcome in breast cancer (Finak et al. 2008). The
researchers collected breast cancer tissue from 73 patients, used painstak-
ing laboratory methods (laser capture micro-dissection) to pre-process
the samples, and analyzed them with genomic tools in order to develop a
candidate signature. For the subsequent stage, however, which involved
the validation of the signature with independent samples, they no longer
used local biological samples but, rather, resorted to publicly available
data sets downloaded from institutions located in Amsterdam, Oxford,
Rotterdam, and Uppsala. The development of the signature, in other
words, was made possible by a hybrid approach that combined a “wet
lab” analysis of local biospecimens with virtual testing using data sets
available for download from the Internet. This is by no means an excep-
tional situation. If we take, for instance, MINDACT, a very large (several
thousand patients), multi-center European breast cancer clinical trial test-
ing another genomic signature, we find two parallel flows of material and
data. Participating centres will ship different kinds of biological material
(frozen and fixed tissue, RNA, and serum/blood) to central bioreposito-
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
3
   On the emergence and development of bioinformatics see McMeekin et al.
(2002, 2004).
4
  Interview, February 14, 2011.
5
  Committee on a New Biology for the 21st Century: “Ensuring the United States
Leads the Coming Biology Revolution”, A New Biology for the 21st Century,
(Washington: National Academies Press, 2009).
16                                                                                    Tecnoscienza - 5 (1)

ries located at cancer institutes in Amsterdam and Milan, and at the bio-
tech company that commercializes the signature. A parallel, web-based
circuit will channel clinical and laboratory data from and to the partici-
pating centres, and store them in databanks located at the trial sponsor’s
secretariat, a Swiss bioinformatics institute, and the biotech company (in
each case, with different rules for access). As recently forecasted by a
leading French oncologist, the databases generated by the first generation
of biomarker-driven clinical trials should lead the production of algo-
rithms propelling the design of a second generation of trials, which will in
turn generate databases, and so on (André n.d.). In the meantime, this
kind of data is becoming increasingly available, as shown, for instance, by
the recent announcement that the International Cancer Genome Consor-
tium has made publicly available data from thousands of cancer genomes.
    Bioinfomatics is not confined to the handling of data produced by the
new genomic technologies: it is constitutive of them. Let us take the ex-
ample of gene expression profiling (GEP) mentioned in the previous par-
agraph. One of the key technologies of post-genomic oncology, gene
expression profiling, has generated new entities, such as multi-gene “sig-
natures”, that have simultaneously been developed in clinical, laboratory
and commercial biotech settings (Kohli-Laven et al. 2011). Figure 1 re-
printed from an article that analyses the development of this field (Coin-
tet et al. 2012) uses a modified version of a scientometric technique called
“co-citation analysis”.


                                                                                   2005



                                                         basic methods                    bioinformatics and
                                         biostatistics   bioinformatics/ 2000             biostatistics
                                                         biostatistics
                             bioinformatics
                                              1995



                      1990



                                                                                           other tumors



         early techniques

                                       proofs of principle
                                       model organisms                          breast cancer

                                                 proofs of principle
                                                 blood and solid cancers




                                                                                                               	
  
Fig. 1 – Co-citation analysis of the development of gene expression profiling: see
        text for explanations. Source: modified version of Figure 2 in Cointet et
        al. (2012).
Cambrosio et al.                                                            17


    Briefly, two articles are co-cited if they appear together in the list of
bibliographic references of another article. Networks of highly co-cited
articles display key contributions to a field, and can be equated to its cog-
nitive substructure. After downloading over 16,000 GEP references from
the biomedical database PubMed, the authors used the software platform
CorTexT (www.cortetxt.net) to generate a map of the most frequently co-
cited references. Each node of the network corresponds to a reference
(labeled by the first author’s name and journal abbreviation), the size of
the node being proportional to the number of citations. The network is
arranged chronologically, with time flowing from left to right. Rather than
a professional historical narrative, it provides an account of the develop-
ment of the field as perceived by the authors of articles at a given point in
time (in the present case, during the 1990-2010 time window). Using dif-
ferent time windows, the resulting map would be different, as actors will
redefine the foundations of their domain: the “historicity” of chronologi-
cal sequences, in other words, will be displaced by the “historiality” of
science reshaping its past (Rheinberger 1997). Clusters of closely associat-
ed references organize themselves into specific subdomains that are au-
tomatically detected by a clustering algorithm and color-coded
accordingly. For further clarification we have added to the original map a
number of tags identifying the nature of the activities of each cluster.
    Here is a quick summary of the most relevant features of the map
(readers can refer to the original article for more information). The oldest
references correspond to the basic molecular biology techniques that are
held to provide a basis for the subsequent development of GEP. They
lead to two clusters of “proof of principle” articles, i.e. demonstrations
that GEP did actually work: this was done first with non-medical model
organisms, and then with human tumor specimens, thus entering the clin-
ical domain. At approximately the same time we notice a cluster of arti-
cles corresponding to biostatistical and bioinformatic methods, in
particular heat maps and hierarchical clustering techniques (Wilkinson
and Friendly 2009), which are needed to analyze the large data sets pro-
duced by GEP. In the case of GEP as with other recent biomedical tech-
niques, there is no such thing as “raw data”, strictly speaking, as the data
generated by the instruments are already highly processed, while mean-
ingful (i.e., interpretable) results necessitate further statistical and visual
manipulations (Cambrosio and Keating 2000). Hence the mutually con-
stitutive relation entertained by wet-lab and data analysis tools. In the
most recent period we see the deployment of GEP in the oncology do-
main, with a strong presence of breast cancer as a distinctive cluster, con-
current with the further development of robust bioinformatic and
biostatistical methods. Interestingly enough, references included in this
latter cluster refer back to two founding articles, one in biostatistics (on
false discovery rates) and one in bioinformatics (on the R language), cor-
18                                                            Tecnoscienza - 5 (1)

responding to the hybrid (and, as previously mentioned, somewhat con-
troversial) nature of this emergent domain.
    Mimicking, at a far smaller scale, the collaborative dynamics we saw in
the GEP domain between clinical and bioinformatics researchers, the
Cointet et al. (2012) article exemplifies a collaborative endeavor between
social scientists and informatics specialists, in the present case the devel-
opers of the CorTexT platform. This is why, to cite our own (admittedly
anecdotal) evidence, while more traditional social science audiences often
experience difficulties in understanding the network slides we present at
talks and conferences, natural scientists can readily relate to them, in par-
ticular when, as part of our fieldwork, we ask them to comment on the
maps corresponding to their activities (Bourret et al. 2006). We can now
apply Yaffe’s aforementioned critical questions to ourselves: are S&TS
analysts also becoming addicted to big data? To what extent does the
motley of newly available data sources contribute to a renewal of the
S&TS research agenda?


3. Problematizing Network Analysis

    At this point readers will have noticed that we are entering reflexivi-
ty’s territory, as the techniques used to produce a map like the one dis-
played by Figure 1 overlap with those used in the bioinformatics
references displayed on the map. While social network analysis has been
around for long time, network analysis has been recently transformed by
an inflow of mathematical and modeling approaches originating from the
physical and life sciences (Watts 2004). Supported by a staggering in-
crease in computer power, these new approaches have found a privileged
domain of application in the scientometric analysis of the scientific litera-
ture, in particular co-authorship patterns (e.g. Newman 2004), thanks to a
parallel development, namely the increasing availability on the Internet of
large databases of scientific publications such as Medline (and its search
engine PubMed freely accessible since 1997), Web of Science, Scopus, and
Google Scholar. Traditionally, social network analysis examined social ties
between a relatively small number of actors, often derived from ad hoc
procedures such as interviewing selected actors about their connections
or resorting to sampling (Scott 2000). Large-scale bibliographic databases
now allow, at the click of a mouse, to obtain information about relational
patterns, such as co-authorship, between millions of actors. But these new
possibilities come at a price. The fact that a reflexivity loop seems to exist
at the level of tools does not necessarily imply that a similar loop should
necessarily obtain in terms of conceptual framing. Put otherwise: the fact
that scientists can easily relate to maps created by network sociologists
can be a positive aspect, but also a symptom of looming problems.
    The large databases, the search engines that have been developed to
Cambrosio et al.                                                                                                                                                                                                                   19
exploit them, and the data-mining, text-mining, and network-analysis
tools that S&TS scholars use to process the resulting data, do indeed give
access to unprecedented amounts of information and lead to stunning
visuals (Lima 2011). We should not forget, however, that they have not
been conceived primarily for sociological analysis. As they emerge from
the physical and life sciences – sometimes transiting through the newly es-
tablished specialty of “information science” (Börner 2010) – they come
with built-in epistemological assumptions and models that are seamlessly
carried over into the social sciences when they are recycled for use by
S&TS scholars. Faced with the sterile alternative of either embracing the-
se new approaches without too many qualms because of their striking ef-
fectiveness, or of rejecting them for fear of contamination, we prefer a
third alternative, namely to explore the issue of the adequacy between
these newly available tools and S&TS research agendas.
    The notion of network has provided a key heuristic tool for develop-
ing a research program that rejects both technological and sociological
determinism, and can thus be put to fruitful use for the analysis of bio-
medical activities, but this notion is now a victim of its own success. We
find it everywhere, within and outside biomedicine, as the term is used
for every purpose, from the mundane to the specialized. The expansion of
its semantic field, in parallel with the steady increase in the offer of af-
fordable data-mining software and network visualization tools, has result-
ed in the development of a “network lingo” and of standardized
interpretations that are indistinctly applied to substantive, methodological
and conceptual issues. To further complicate the situation, the adoption
and deployment of network analysis tools have by and large taken place
within quantitative domains such as scientometrics and, most recently, in-
formation science and informetrics, whose development, in spite of their
focus on scientific and technical activities, has only occasionally intersect-
ed with conceptual developments in S&TS. Only rarely have these quan-
titative approaches been interfaced with ethnographic methods (for
exceptions see Velden and Lagoze 2013; Navon and Shwed 2012; Bourret
et al. 2006; Cambrosio et al. 2004), but, most often, their production
within self-contained professional circles of information specialists has re-
sulted in the offer of tools in search of possible uses (for a recent exam-
ple, see Skupin et al. 2013)6.
    As argued by Michel Callon (2001), thick ethnographic descriptions
of individual field sites are ill suited to deal with large-scale collaborative
endeavors such as the ones discussed in the previous section. The alterna-
tive of reducing such endeavors to a few quantitative indicators is equally
unsatisfactory, insofar as it destroys for all practical purposes the very
phenomena under investigation. The newly available network analysis
tools, in combination with more traditional fieldwork methods, seem to
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
6
 For earlier examples see the special issue of “Proceedings of the National
Academy of Sciences” on “Mapping knowledge domains” (2004; 101, suppl 1).
20                                                            Tecnoscienza - 5 (1)

offer a partial response to this predicament, provided they avoid the limi-
tations of traditional social network analysis. These limitations include an
exclusive focus on human actors, and the assumption of the existence of a
unified social space within which social ties can be properly measured
and described. In a subsequent English version of the 2001 paper, Callon
(2006) revisited this issue by postulating that network analysis tools
should avoid two pitfalls. First, the aforementioned assumption that ac-
tors’ interactions take place within a unified space; this assumption belies
the existence of a multiplicity of regimes of engagement deployed in dif-
ferent, more or less overlapping spaces (Boltanski and Thévenot 2006;
Moreira 2012). A second pitfall lies in the a priori categorization of enti-
ties according to a number of pre-set, analyst-defined attributes. In con-
trast with this approach stands a focus on the emergent categories
generated by the relational ties that human and non-human entities estab-
lish between each other. By taking into account the heterogeneity of net-
works (both in the sense of consisting of different entities and of
corresponding to different regimes of engagement) social scientists can
enter in a reflexive relation with the entities they analyze. Such a reflexive
relation can itself be of different kinds. It has a substantive dimension, as
actor-generated categories and, more generally, the framing they produce,
will often question the analyst’s assumptions about the proper categories
that constitute the world, and his/her epistemological privilege to define
them. It also has a methodological dimension, because of the aforemen-
tioned, increasing overlap between the network analysis tools developed
by natural scientists and those used in the social sciences.
    Still, while one should not mistake the co-authorship “network” gen-
erated by a few clicks on the Internet for the “network” of actor-network
theory (ANT) (Latour 2011), the new tools offer interesting opportunities
for the empirical exploration of new techno-scientific configurations, us-
ing the conceptual avenues opened-up by ANT. It should be noted, in
this respect, that the founders of ANT were among the pioneers of map-
ping approaches, in particular co-word analysis (Callon et al. 1986). The-
se initial attempts have been criticized for their alleged reductionism with
regards to the issue of agency, and for lending themselves to structural in-
terpretations. In the meantime, several versions of ANT have been devel-
oped that are not always mutually compatible. On the one hand, in
response to the aforementioned criticism, there have been attempts to re-
visit the processes previously analyzed solely in terms of networks by us-
ing notions such as regimes and assemblages, or collectives and
arrangements. From this perspective, visualization tools can become
problematic, and do in fact partake of the emergence of new regimes of
innovation that S&TS should investigate rather than adopt blindly (Cal-
lon 2012; Rabeharisoa et al. 2014). On the other hand, and in spite of
their acknowledged limitations and shortcomings, navigational practices
that are made possible by the availability of large databases and software
tools initially devised to investigate complex systems, are seen as creating
Cambrosio et al.                                                          21
the conditions of possibility for a new kind of generalized social theory,
one that could dispense with the opposition between individuals and ag-
gregates (Latour 2011; Latour et al. 2012).
    In the present paper we adopt a position closer to the first alternative
in order to explore some of the problems raised by the new visualization
tools and to discuss, using examples from recent studies of biomedical
practices, how we can partly address them. These problems fall in at least
four different categories:
•     As previously mentioned, while network analysis algorithms are in
      principle well adapted to the kind of relational sociology embraced,
      among others, by ANT, they tend to reify the notion of network and
      to convey structural or strategic interpretations of specific network
      configurations. Typical examples include analyses in terms of struc-
      tural holes, obligatory passage points, centrality, etc. The issue thus
      becomes: Is it possible, and if so how, to interpret maps without re-
      sorting to a vocabulary that is derived from structural and strategic
      analysis? A major obstacle, in this respect, is that ‘structure’ is em-
      bedded into the very production of maps; for instance, the algo-
      rithms used to position nodes rely on structural properties, such as
      symmetry, structural equivalence of points, centrality and ‘between-
      ness’ of nodes. Put otherwise: does network analysis allow us to
      make inferences about the dynamics of a given domain without re-
      ducing it to changes in the morphology of the network? Or should
      we rather opt for a hybrid approach, whereby networks will no
      longer represent the ultimate analytical horizon, but a tool to better
      investigate assemblages, or, to use a term that avoids mechanical im-
      plications and reintroduces agency, agencements (Callon 2013; see
      also Rheinberger 2009 for the case of biomedicine)? While shifting
      the conceptual and substantive focus from networks to agencements,
      such a move would still leave room for networks, as they add flexi-
      bility, dynamics, but also some amount of ordering to agencements.
•     In order to make sense of a network, as already hinted in the case of
      Figure 1, analysts (or the algorithms that replace them) trace bound-
      aries around clusters of closely connected nodes. The sociological
      relevance of these (formally defined) clusters is itself open to inter-
      pretation, as they do not necessarily correspond to taken-for-granted
      groups or institutions: in fact, if and when they do (which is proba-
      bly more often the case with homogeneous social networks than
      with heterogeneous ones), the heuristic interest of tracing a network
      decreases correspondingly, as it transmutes from being an investiga-
      tional tool able to produce surprises to a redundant illustration of
      well-known arrangements. If they do not, we then face the issue of
      the collective agency of the heterogeneous clusters displayed on
      maps. When adopting a structural interpretation, this issue is most
      often swept under the carpet. A closely related issue, similarly over-
      looked by structural interpretations, has to do with situations in
22                                                           Tecnoscienza - 5 (1)

     which the transformation of the entities making up a heterogeneous
     collective are not the consequence but, rather, the cause of the dy-
     namics of these collectives. Here again, the path forward may neces-
     sitate a shift in focus from networks per se to the processes involved
     in producing specific agencements that account for the heterogene-
     ous and distributed nature of collective agency.
•    As already mentioned, network analysis, because of its figurational
     dimension, can be seen as a healthy alternative to the statistical re-
     ductionism of quantitative indicators. It also partakes, however, of
     the quantitative domain, as networks are firmly embedded in a met-
     rological infrastructure. The point is not to contrast qualitative with
     quantitative analysis, as in the longstanding conflict within profes-
     sional sociology, but to signal that the modality of action that under-
     lies networks is to “add up”, to be “counted in”. Other modalities
     are possible, such as qualifying links instead of accumulating them.
     The “adding up” strategy, as exemplified most obviously by citation
     counts, is embedded in a number of databases whose goal is precise-
     ly to make things (ac)countable in this specific way. The seamless
     production of networks derived from these databases brackets the
     very infrastructure that makes those data, and their relational nature,
     available and witnessable. From this point of view, networks have no
     epistemological privilege, as they are one among possible forms of
     interpretation and enactment of ‘the social’. How, then, to integrate
     this aspect in our analysis? The maps we produce bear the invisible
     traces of the strategies deployed by data providers: how can we
     make them visible and, most importantly, take them into considera-
     tion when interpreting our results?
•    Most often than not, the components of a network are obtained by
     analyzing bibliographic databases (articles, patents, etc.), reposito-
     ries of full-text articles, blogs, and other textual documents. While,
     given its focus on the materiality of practices, non-textual elements,
     in combination with textual ones, play a key role in ANT analyses,
     only the latter, or at least entities mediated through inscriptions, end
     up in the maps. How, then, to convey the heterogeneity of networks
     when we can only produce and access them via textual inscriptions?

   In what follows we revisit these issues – the reductionist understand-
ing of agency resulting from strategic/structural interpretations of net-
works, their limited capacity to account for the dynamics of collectives,
their actuarial nature that privileges quantity over content, and their ex-
clusive reliance on texts. We focus on the first two elements using a few
concrete examples.
Cambrosio et al.                                                                                                                                                                                                                   23
4. Network Dynamics

     Both from a methodological and theoretical point of view, accounting
for network dynamics has been one of the major stumbling blocks of this
kind of analysis. Change has mostly been interpreted as structural change.
A notion such as ‘obligatory passage point’ equates a given position with-
in a network with processes of circulation, displacement or movement.
Dynamics is thus reduced to the distribution of points and their relations
in a (virtual) space. The agency of the entities represented in a network is
mechanically conflated with their structural/strategic positioning, and
since the capacity to act strategically and reflexively is generally ascribed
solely to humans, it is not surprising that social network analysis still oc-
cupies center-stage. Methodologically speaking, attempts to account for
dynamical processes often rely on the structural comparison of the ‘same’
network at different times, pointing to the elements that are held respon-
sible for the observed changes. Algorithms can be used to identify the en-
tities (actors or groups thereof) that are at the origin of structural
transformations.
     A possible, although not entirely satisfactory way out of this predica-
ment is to opt for interpretations focusing on events, i.e. to ‘play’ with the
content of maps7 by taking into account the heterogeneous roots of a
network’s dynamics. A structural reading, when comparing maps corre-
sponding to different periods (say: t1 and t2), focuses on networks char-
acterized by the presence of the same kind or category of entities, e.g.,
academic researchers, clinicians, biotech or pharmaceutical companies,
either individually or as members of homogenous subdomains. To ac-
count for change, analysts will for instance point to the role of biotech
companies that while only playing a marginal role at t1, have become key
intermediaries between public and large private organizations at t2. This
kind of account is characterized by the presence of a strong and sophisti-
cated human agency: observers easily acknowledge the key role of biotech
companies (or, rather, the entrepreneurial skills of their managers), but
are less keen to attribute a similar role to cells and molecules. A non-
structural reading will opt for a different approach: to account for the dif-
ference between t1 and t2 we should consider the role of entities that
were absent from the original t1 and t2 maps, i.e. produce complemen-
tary maps that include cells, instruments, molecules or diseases. In other
words, the passage from a homogeneous network at t1 to a homogeneous
t2 network can in fact be accounted for by the presence of a number of
heterogeneous entities that did not appear on the initial maps: the emer-
gence (or disappearance) of connections between two groups of research-
ers is not reducible to the sole agency of other researchers; it involves the

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
7
 As the very notion of a ‘map’ lends itself to structural interpretations, we should
opt for a term with different undertones.
24                                                                                                                                                                                                                                                                                                                                                                            Tecnoscienza - 5 (1)

simultaneous agency of biomedical entities such as mutations, antibodies,
or cells.




                                                                                                                                                                                        TokyoM etrop.I.M ed.Sci.
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               U.Lund                     U.Freiburg                                                                                                                                                                                                                                                                          E
                                                                                                                                                                       Chin.Acad.M ed.Sci./Tianjin                                                        U.Washington/Seattle                                                                                                                        H.SickChildren/Toronto

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                                     M axDelbruckCtrM ol.M ed./Berlin                                                                                                                                                                                                                                                               I.ClinicalIm m unol./G iessen
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                                                                                                                                                     I.m m unology/M oscow                                                                                                                                            Stockholm U. Q ueenVictoriaH/Sussex                       LondonH.M ed.Coll.
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                                                                                                                                                                                                  BO EHRING ER




Fig. 2 – Map of laboratories producing monoclonal antibodies targeting different
        categories of cells: see text for explanations. Source: modified version of
        Figure 7 in Cambrosio et al. (2004)



   The following example is taken from a paper (Cambrosio et al. 2004)
that, in the wake of ethnographic fieldwork on the emergence and circu-
Cambrosio et al.                                                           25
lation of a new kind of reagents known as monoclonal antibodies, at-
tempted to visualize the regulatory infrastructure that resulted in their
generalized use. This infrastructure emerge from the establishment of
equivalences between individual antibodies produced by different labora-
tories around the world: antibodies that were held to be the same, in spite
of their different institutional or geographical origin, were assigned a
same CD (cluster designation) number and could be used interchangea-
bly. In the present case, the authors used an ad hoc database of substances
and laboratories that they found on the Web, rather than a bibliographic
database such as Medline. Figure 2 considers two kinds of entities: indi-
vidual laboratories or companies (round red nodes), and the general cate-
gory of cells (T-cells, B-cells, etc.) targeted by antibodies (square orange
nodes: their size is proportional to the number of antibodies available for
that category). A structural interpretation will focus on the positioning of
the laboratories vis-à-vis these general cell categories, as the latter corre-
spond to specific biomedical domains (of varying importance as shown by
the size of the nodes). The organizations at the center of the map (includ-
ing all major commercial companies in that field) position themselves
strategically, in order to ensure their presence throughout the spectrum of
biomedical activities, whereas organizations at the periphery of the map,
while aiming to profit from the scientific and/or commercial opportuni-
ties offered by this new technology, have adopted a specialization or
niche strategy. The original article included maps corresponding to dif-
ferent points in time, thus arguably allowing readers to follow the evolu-
tion of these strategies.
    Figure 3, in contrast, disaggregates, so to speak, the previous figure by
including the same institutions (square orange nodes) and the specific CD
antibodies they had developed (round red nodes): the size of the nodes
corresponds to the number of antibodies produced by a given organiza-
tion or included in the same CD. Figure 3 can no doubt also be interpret-
ed structurally (e.g., large vs. specialized producers of widely used vs.
esoteric CD antibodies), but a non-structural interpretation will insist on
the evolution of the links between researchers and entities in this rapidly
unfolding domain. For instance, it appears that some CDs are very ro-
bust, as their existence is supported by several laboratories, whereas oth-
ers are weak, as their existence is ensured by the presence of only one
laboratory. Moreover, maps from different periods (not shown here: see
original article) document the emergence of novel categories of cells in
conjunction with the proliferation of antibodies targeting them, or the
transformation (splitting, redefinition, etc.) of individual CDs.
    Admittedly, the alternative illustrated by this example still conveys as-
pects and elements of a structural interpretation, only alleviating its worst
shortcomings. This is due in large part to the limits of the database that
only listed a limited number of different entities. Moreover, the database
did not provide indications about the informational content of the anti-
bodies, i.e. the domains, tests or diseases for which they were deemed to
26                                                                                                                                                                                                                                                                                                                                                                 Tecnoscienza - 5 (1)

be relevant. Combining data from different databases could circumvent
this difficulty, an approach exemplified in practice (but with a quite dif-
ferent intent) by Boyack et al. (2004) who in the case of melanoma re-
search analyzed a data set consisting of papers from Medline, genes
derived from the Entrez Gene database, and proteins from the UniProt
database. Similarly, but using different techniques and with a different
perspective, Mogoutov et al. (2008) explored the development of micro-
arrays by combining data derived from Web of Science articles, with those
from the CRISP database of research grants awarded by the US National
Institutes of Health (NIH), and patents from the US Patent office and the
Derwent Innovation Index.



                                                                                                                                                                BC C ancerR C /Vancouver
                                                                                                                                                                                                                       C D 45RU0.R ennes

                                                                                                                               SEIKAG AKU                C D 62L
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                                                                                                                                                                                                                             D N AX                                                                   C D 72
                                                                                             C D 104                                                      C D 15                                                                    CD4
                                                      IC R F/O xford                                                                                                     C D 49b
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                                          U .H eidelberg                                                                                   C D 47                                                                                                                                                                              O N C O M EM BR AN E
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                                                                                                           C D 65                                                                                                                                                                                     C D 38                   U .G öttingen
                                                                                                                                                                                                           BEC TO N D IC KIN SO N
                                                  C D 87                                 C D 158a                                C D 28                  C D 11a        C D 1a
                                                                       H .Princesa/M adrid                                                                                                                                                                                                                                                  C D 53
                                  Tech.U .M unich                                                                                                                                                                                                        C D 23
                                                                                                                                                                                                                                         C D 8a                                    C D 11c
                                                                                                                                                                                                C zech.Acad.Sci./Prague                                                                                 H .C linic/Barcelona                    C D 46
                                                       U .M innesota
                                                                                                                                                                                                                                                                                           C D 86                      C D 48
                            TorontoH .                                                                                                                                                                                                                               C D 29                                                                         C D 59
                                                                       U .M arseileI                  C D 147                                                                                                                                                                      C D 39
                                                  C D 109                                                                                                                                                                                                                                                              H .deC linicas/BuenosAires
                                                                                                                                                                         U .N ijm egen                                                                      C D 31                                                                                      C D 81
                             C olum biaU ./N ew York                      C D 30                                                                                                                C D 15s
                                                                                                                                                                                                                                                                                           C D 14
                       U .Sheffield                                                                               C D 33                FreeU ./Berlin
                                                              C D 63
                    C D 49c                                                              C D 95            U .Louisvile                                                                                                                                                                                       I.N azionaleTum ori/MCilan
                                                                                                                                                                                                                                                                                                                                       D 139
                                                    U .M ichigan/AnnArbor                                                                                                                                                                    U .Birm ingham                                                                                            Westm eadH .
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                                                                 IN SER M U 291/M ontpellier                                                                                          C D 50
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            C D 151                                                                                                         C D 25
                                                                                                                                                                                   C ancerR es.C tr./M oscow                                                           C D w 136                                  C D 74
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                    C D 142                                                                                                                                             G SF/M unich                                                              C D 49f
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                                                                                                                            C D 127                                                                                                                                                                                               C D w 75
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                                                                                                                                                                                                              I.M ed.Vet.Sci./Adelaide                                                                                                     O TSU KAAM ER IC A
                                                                                          Brigham &Wom en'sH ./BostonAlb.EinsteinC oll.M ed./N ew York                                                                                                                                                                         U .Kentucky/Lexington
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                                                                                                                                                                         Sa ita m a M ed . Sc h oo




Fig. 3 – Map of laboratories and equivalent categories of monoclonal antibodies.
    Source: modified version of Figure 12 in Cambrosio et al. (2004).
Cambrosio et al.                                                              27
    Readers may wonder why a combination of data from different data-
bases is at all necessary, since one could extract those heterogeneous act-
ants from articles alone. But there are two main reasons for pursuing this
strategy. First of all, text-mining article databases for these different kinds
of entities runs into a number of technical problems (such as identifying
the nature of those entities) that can at least in part be obviated by the
combination approach. Second, and most importantly, each database cor-
responds to different regimes of engagement: the modality of engagement
of the ‘same’ gene in a patent vs. an article or a grant proposal will vary in
significant ways. The analytical strategy, then, amounts to diversifying the
‘entry points’: one can start with a set of human actors, as identified by
fieldwork, or, alternatively, with a variety of bio-clinical entities that can
be found in publications, but also in specialized databases devoted to
genes and mutations, biomarkers and tests, or microarray experiments.
Information can also be retrieved from websites, such as medical blogs or
patient organization websites. Other (but expensive) opportunities to di-
versify entry points are offered by databases such as RECAP
(http://www.recap.com/) that provide information about commercial
deals in the biopharmaceutical domain. Multiple maps may destabilize
conventional readings, generate a feeling of analytical strangeness, and
record unexpected events, in a way similar to how new objects, accounts,
and relations redefine and displace the boundaries of emerging domains.
    We mentioned these examples as possible, uncertain avenues for fur-
ther investigation, as they have so far not been exploited in the perspec-
tive we are advocating here (but see the next section for steps in this
direction). This is partly due to the fact that laborious technical bridges
need to be established between the different databases; these calculations
and manipulations stand in contrast with the seamless association of het-
erogeneous entities that underlies translations and mediations between
different regimes of engagement, as captured by (multi-site) fieldwork. In
the biomedical translational research domain, a promising development is
the establishment of the CinicalTrials.gov database by the NIH. The crea-
tion of this database is itself part of policy initiatives aiming at regulating
the controversial domain of clinical research, marred by accusations of
conflicts of interest, publication bias, etc. Unsurprisingly, the database it-
self has run into trouble, due to criticism about its incomplete coverage,
failure to include relevant information, and lack of standardization, which
in turn has led to additional policy initiatives (compulsory registration of
trials if results are to be published, etc.) (Zarin et al. 2011). In spite of all
these problems that complicate its appropriation for our own purposes,
the database offers the advantage of assembling in a single virtual space
entities such as clinical researchers, molecules (drugs), the institutions
performing the trial, public organizations (oncology networks), commer-
cial organizations (pharmaceutical and biotech companies), diseases,
technologies, and publications. Bridges with other databases with a dif-
ferent take on those ‘same’ entities can then be built. Other databases,
28                                                                    Tecnoscienza - 5 (1)

such as Orphanet on rare diseases similarly offer opportunities for the
kind of heterogeneous analysis we advocate.


                                                               	
  




Fig. 4 – Security streams: see text for explanations. Source: Chavalarias et al.
       (2011).

    Before closing this section, we would like to briefly introduce a recent
attempt to tackle the issue of dynamics. The example below is taken from
a report on food security based on the analysis of around 20,000 press ar-
ticles published between 2004 and 2011 and listed in the database Factiva
(Chavalarias et al. 2011). A somewhat similar approach, albeit with far
more primitive tools, was introduced 20 years earlier by the developers of
cop-word analysis (Callon et al. 1991), and applied to the biomedical do-
main shortly afterwards (Cambrosio et al. 1993). The authors of the 2011
article divided the corpus into 20 subsets, text-mined them, and pro-
duced for each of them a semantic network that included clusters of
closely associated terms, each corresponding in principle to a topic. In-
stead of analyzing individual maps separately, they produced a single map
with streams of clusters, according to the following principle: clusters
from a given point in time are linked to previous or subsequent clusters
through a stream if they have terms in common. As shown in Figure 4, a
stream can split, merge, grow, emerge, decay etc. In spite of a common
designation – food security – the domain in 2005 bears little resemblance
to the domain in 2011, as new entities have emerged and redefined how
Cambrosio et al.                                                          29
this issue is problematized. Stream analysis amounts to observing the digi-
tal traces left by evolving associations in a dynamic landscape, whereby
innovation derives from the emergence of new “concerned” entities (Cal-
lon and Rabeharisoa 2008), rather than from relational shifts between a
predefined list of entities describing a stable state of the world. Parallel
instances of “overflows” (Callon 2002) can be associated with these dy-
namic streams, as indicated on Figure 4.


5. Clusters and Collectives

    As previously suggested, the most common interpretation of network
maps hinges, first, on the identification of clusters of closely connected
entities, and, subsequently, on the analysis of the relations each of these
subsets entertains with the others. The tracing of cluster boundaries used
to be done manually, by visual inspection of the maps, but cluster detec-
tion algorithms, some of which include a fuzzy approach whereby a node
can belong to more than one cluster, now increasingly perform this task.
Insofar as these algorithms are based on purely structural calculations,
they do not necessarily lead to sociologically meaningful units, if by the
latter we refer to collective forms of organization and their associated
practices, programs, and bodies of knowledge; in short, agencements
characterized by coordinated (if not homogeneous) ways of problematiz-
ing issues. From this point of view, visual inspection, whereby one could
deploy his or her sociological imagination, might at first appear as a better
alternative, were it not for the following two counter-arguments. First,
clustering algorithms are not inflexible tools: one can vary their parame-
ters depending on whether one wants to emphasize, for instance, continu-
ities or discontinuities between clusters, thus playing with variable
boundaries. Far from dictating their will, clustering algorithms can thus
be used as interactive tools for exploring the associations deployed on a
map, the latter becoming an experimental device that can be used to ex-
plore alternative configurations in connection to working hypotheses and
fieldwork observations. Second, it is far from obvious that the analyst’s
presuppositions about the proper constitution of the world (which can
moreover vary from observer to observer) should have priority over the
surprises generated by unexpected network configurations, especially
when elicited by interactive tools. Here too maps can function as devices
for exploring the variable geometry of the world, rather than as final
statements about its ontology. The relevant components of social ontolo-
gy are in any event open to debate, as shown by the not always mutually
consistent attempts to capture them through different notions, such as
“communities of practice” (Wenger 1998), “epistemic communities”
(Akrich 2010), “collaborative communities” (Adler et al. 2008), and the
like.
30                                                                                                                                                                                                                                 Tecnoscienza - 5 (1)

    For maps to play an optimal role in this respect we can resort to a
trick similar to the one discussed in the previous section, namely to pro-
duce a number of maps displaying different categories of actants, i.e. hu-
man actors such as researchers and clinicians, infrastructural components
such as journals, techniques and models, and notions or concepts. Adding
or subtracting some of these components in different combinations could
lead to more dense or fragmented situations, helping analysts to put for-
ward hypotheses about the elements that lead to new associations or re-
sult in disjunctions. Could, for instance, the densification of a network
following the introduction of conceptual components or, alternatively, of
certain kinds of tools and techniques be used to differentiate between ep-
istemic communities and communities of practice? While, for a variety of
reasons, this seems unlikely, we mention this possibility as a thought ex-
periment to illustrate the kind of analytical approaches we would like to
deploy. Actual examples of these approaches do not fully correspond to
an ideal translation into practice of this research agenda, but are still
worth examining.
    Navon and Shwed (2012) analyzed 1400 articles to investigate how a
genetic mutation (a microdeletion) transformed biomedical understand-
ings of several rare clinical syndromes, unifying a set of previously inde-
pendent clinical entities on the basis of molecular analysis. The
microdeletion, in other words, was a key actant in “foster[ing] enduring
ties between several small, previously disjunct fields of medical research,
creating a densely connected literature that brought together an otherwise
incoherent set of patients, expertise and clinical observations” (Navon
and Shwed 2012, 1640). Their demonstration relies on generating net-
works derived from citation links between three decades of papers, iden-
tifying research communities interested in the older conditions with the
help of a modularity algorithm, and showing how the microdeletion pro-
gressively unified them, turning a previously invisible collection of condi-
tions into a visible field of coordinated knowledge production. They tell
this story by using a set of four maps corresponding to four distinct peri-
ods during the last 30 years of the 20th century, and a set of two maps de-
picting the situation at the beginning of the 21st century.8 They describe
their approach as a way to overcome the limitations of our existing social
scientific toolkit that is unable to grapple with non-human entities, such
as genetic mutations, that are presently reconfiguring the biomedical
field.
    Navon and Shwed’s (2012) article, which, it should be added, relies
on concurrent fieldwork, is a fine-grained investigation of a specific bio-
medical domain. The reconfiguration of biomedical work by new bio-
clinical entities can be observed at a higher level of aggregation, where
one can examine how translational research has emerged as a distinctive
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
8
 Given the number of figures, we refer readers to the original publication instead
of reprinting them.
Cambrosio et al.                                                                                                                                                                                                                   31
site of biomedical activities that cannot be conflated with fundamental
and clinical research. These categories are not a priori categories defined
by the analyst: they can be derived by the self-organizational properties of
maps9, for instance by observing how journals organize themselves into
distinct clusters by spinning a dense web of inter-citations; these clusters
can subsequently be qualified by an algorithm that distributes them along
a translational continuum on the basis of the terms that appear in the ti-
tles of the journals’ articles. Cambrosio et al. (2006) have adopted this
approach to examine a large set of oncology journals: while early periods
were characterized by the presence of two major clusters that correspond
to activities taking place in either the laboratory or in the clinic, at the
turn of the century a third, in-between cluster has become apparent. To
further investigate the nature of that cluster, the authors text-mined the
titles and abstracts of the articles published by a number of journals in
each of the three clusters. Figure 5 shows the resulting map, with the
translational space associated with a number of recent bio-clinical enti-
ties, such as oncogenes and mutations. Here again, this interpretation was
supported by extensive, concurrent fieldwork.
                     	
  




                                                                                                                                                                                                                                        	
  
Fig. 5 – Heterogeneous network of journals and bio-clinical entities in the oncology
        domain: see text for explanations. Source: Cambrosio et al. (2006).
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
9
  Methods have been recently developed for generating maps of biomedicine
based on self-organizing algorithms; see Skupin et al. (2013).
32                                                                                                                                                                                                                                 Tecnoscienza - 5 (1)


    As noted at the beginning of this paper, recent biomedical work, in
particular translational research, is characterized by a collective turn,
which situates it firmly within the scope of inter-laboratory arrangements.
This is one of the reasons why local ethnographic observations show their
limitations, and the resort to cartography has been suggested as an im-
portant complement for investigating contemporary biomedicine, even if
ethnography maintains its relevance, in particular for interpreting the
maps. As shown by work on French cancer genetics (Bourret 2005) and
psychiatric genetics (Rabeharisoa and Bourret 2009), this collective turn
is not to be confused with a mere increase in the number of authors co-
signing a paper. It is better captured by the notion of “new bio-clinical
collective”, rather than understood as a network, because it corresponds
to a configuration centered on a specific activity, namely the simultaneous
development of cancer genetics as a research field and as a domain of
clinical intervention. One must start with this activity in order to define
the collective. The human components of the collective include a variety
of healthcare professionals, whose direct or indirect collaborations and
interactions are a sine qua non for the development of this hybrid domain.
The non-human components include a number of emerging bio-clinical
entities, in particular different kinds of mutations, whose uncertain status
needs to be managed, re-adjusted, and stabilized as part of the emergence
of a “clinic of mutations” (Rabeharisoa and Bourret 2009). The focus of
the collective lies precisely in the necessarily temporary qualification of
these bio-clinical entities, which explains why the structure and nature of
the collective modifies itself on an ongoing basis in relation to the emerg-
ing entities that need to be domesticated and mastered for the activity to
continue. While the activities of the collective center on building more
robust bio-clinical entities, they also involve producing knowledge about
what should count as uncertain and unstable: the known unknown. In-
stead of a passage from local to extended networks, as typically described
by early ANT analysts, we face here a situation characterized by the pres-
ence of an open-ended list of problematic entities. This is why in order to
mobilize these entities they need to be often re-qualified and re-specified.
As a result, the collective evolves by incorporating new actors, technolo-
gies, entities, and by opening up new fields of investigation.
    As an attempt to capture at least a few elements of this dynamics,
Bourret et al. (2006) collected a comprehensive set of publications by
French cancer geneticists over more than three decades10, and divided
them into four periods as defined by major turning points in the history
of the field. These data were then used to produce two kinds of maps.
First, a set of more traditional co-authorship maps that displayed the pro-
gressive constitution of a social network, from an initially fragmented sit-
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
10
   The procedure involved combining references from Medline with those
obtained from individual CVs.
Cambrosio et al.                                                                                                                                                                                                                                                                                                                                           33
uation with a number of local, regional sites, to a fully integrated situation
defined by the presence of a single major component. For the second
kind of maps the authors opted for an approach displaying the relations
between researchers and the bio-clinical entities derived from text-mining
titles and abstracts. And here something interesting became visible. Fig-
ures 5 and 6 show improved versions (obtained using more sophisticated
text-mining software) of the maps used in the original publication. Figure
5 corresponds to the initial period (1970s and early 1980s) of French can-
cer genetics. As can be seen, the map is organized around a few key re-
searchers: although relations between these researchers are mediated by
non-human entities, the distribution of these entities espouses the polarity
defined by human actors. Figure 6, corresponding to the turn of the cen-
tury period, shows a reversal of this situation, as non-human entities, such
as mutations, exons, chromosomes, and cell lines, appear to play a key
role in organizing the map. The initial maps (not shown here) did not
correspond to a given field or specialty, but to the early activities of re-
searchers who subsequently converged on cancer genetics. In other
words, the maps do not display structural positions in a scientific field or
social world; rather, they follow the movement of researchers and bio-
clinical entities leading to the establishment of a collective, even when in-
dividual researchers might not conceive of themselves as members of that
collective.


                                                                                                    hamster papovavirus                                                                                                                             frezal j
                                                                                          genome                                                                                                                                   feingold j

                                                                                                                                                                                                                                                    briard ml
                                                                                                                                                       deletion
                                                                                                             feunteun j
                                                                                                                                                                                                                           bonaiti c
                                                                                                                                                                                     phenotype                                                demenais f
                                                                              simian virus                       simian virus 40
                                                                                                                                                                                                                                                                   hla                                                     favre a
                                                                          synthesis
                                                                    electron microscopy                                                                                                                                        markers                                                                                               thomas g
                                                                                                                       individuals                                                                                                                  existence
                                                                                                                                       burkitt's lymphoma patients                                                                                                    frequency
                                                                                                                                                                     proliferation
                                                                                                                                                                           france
                                                                                                                                                                                                                                                    syndrome
                                                                                                comparative study                                                                                 derived cell line

                                                               infection
                                              seigneurin jm                                                         rickinson ab
                                                                                                              rooney cm                  vuillaume m
                                                                                                                           lymphoblastoid cell lines                                                              burkitt's lymphoma line
                                                                                                                burkitt's lymphoma cell line                                                                                                               battey j                 locus
                                                                                                   antigen                                                          epstein-barr virus genome

                                lymph nodes                                                                                     lymphoid cell lines                                                                                               taub r        leder p                                         fellous m
                                                                                                 virus                                                       population                                                                                                                                                                 breast cancer patients
                                                      antibodies                                                           malignant cells
                                                                                                                                                                                                                                                                                                                        lidereau r
                                     infectious mononucleosis
                                                                                                                                                                                       bornkamm gw
                                                              de the g
                                         raji cells       nuclear antigen
             calender a                                                            epstein-barr virus nuclear antigen
                                                                                                                                                                                                                                                                                                   proto-oncogene
                                                                        early antigen                               epstein-barr virus                          lenoir g                                                                         oncogene         exon
                                                          nasopharyngeal carcinoma                                                                                                                                                                                                             mauchauffe m
                                                                                                                                                                                          cell lines
                                              indirect immunofluorescence                 reactivity                                                               burkitt's lymphoma                                                                                          chromosomal translocation                              polymorphism
                                                                                                                                                                                                       burkitt's lymphoma cells
                                                                                                                                                                                                                                                                                             larsen cj
                           epstein                                                                                                                                                               lymphocytes    variant translocation                                 breakpoint
                                                                                    monoclonal antibodies                                         philip t                                                                                                                                   mathieu-mahul d
                                                      africa                                                                                                                                                                                                          rearrangement                            human chromosomes
                                                                                                                                                                                                                        burkitt                 chromosome               q11                                                                localization
                                                                                                       favrot m                                                                                                                     translocation
                                                                                brunat-mentigny m                                                                                                                     immunoglobulin
                                                                                                                                                                                                                                                       q24
                                                                                                                                                                                                       lymphoma
                                              frappaz d                                                           favrot mc                          tumor cells                                                                                      bernheim a                                               leukemic cells
                                                                                                                                                                                                                                                      berger r
                                                       dore jf                                               distinction                                                                      bone marrow                                                                                                                                          capodano am
 freycon f                                                                                                                                            complete remission                                        arm of chromosome                   leukemia                                   sigaux f
                                                                                                                                                                                                                                                 cytogenetic studies                                                                            stahl a
                                                                                                                                      epstein-barr virus-associated
               surgery                                                                                                                                                                                                                                    daniel mt                      anomaly
                                                                                                                  malignant lymphoma                                                                                                     chromosome studies                    mitoses
                                                                                                                                                                           correlation                                brouet
                                                                                                                                                                                            burkitt-type                                                   valensi f
                                                                                                                                                                                                                      j                                              chromosomal abnormalities
                                                                                                    receptors
                                                                                                                                                                                                                                                             chromosome abnormalities
                                                                                                                                                                                                                                                    flandrin g                               erythroblasts
                          chemotherapy                                                                                                                                                                     relapse                     q32                                                                     short arm
                                                                   biopsy                                                                                                                                                                                        abnormalities

                                                                                                                                                                                                                                                                                    schaison g
                                                                                                                                                                                                                           malignancies                                        le coniat m
                                                                                                                                     breast cancer                                                                                                                        acute nonlymphoblastic leukemia
                                                                                 vennin p                                                                                                                                                                          vecchione d
                                                                                                prolactin                                                                                                                                                                            acute leukemias
                                                                           lefebvre j
                                                                                                    peyrat jp
                                                                                              bonneterre j
                                                                               djiane j                   human breast cancer
                                                                                           vandew alle b estradiol
                                                                                                              prolactin receptors



                                                                                                                                                                                                                                                                                                                                                                 	
  
                                                                                                      progesterone receptor
                                                                                           hecquet b                          endoplasmic reticulum
                                                                                                              demaille a




Fig. 6 – Heterogeneous network of early French cancer genetics. Humans: blue
        nodes; non-humans: orange nodes. Source: revised version of Figure 4 in
        Bourret et al. (2006).
34                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Tecnoscienza - 5 (1)



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                                                                                                                                                                                                                                                         puj o lp




Fig. 7 – Heterogeneous network of turn of the century French cancer genetics.
        Humans: blue nodes; non-humans: orange nodes. Source: revised version
        of Figure 9 in Bourret et al. (2006).


    It can thus be argued, almost paradoxically, that these maps allow one
to (pragmatically) distinguish networks from collectives, as the emergence
of the collective coincides with the activity of the emerging bio-clinical
entities that led to the ongoing readjustment of its internal connections. A
collective, thus, amounts not merely to a set of collaborative ties but to a
configuration where collaborative work takes place and has been reor-
ganized around these entities – in other words, what we have referred to
as an agencement. The developmental trajectory of the collective cuts
across the initial distinctions between different specialties (cytogenetics,
hematology, oncology or medical pediatrics), and reaches a stage where it
displays collective agency. The point is not to investigate how networks
relate to collectives, but to use network analysis to produce something
different from networks. To do so, we need to connect what we see on
the maps – the organization of the collective around a number of entities
– with what happens in the field, i.e. with the disparate, yet mutually con-
stitutive activities of the collective, including the production, qualifica-
tion, regulation and circulation of the new entities; in short, all that is
needed for these objects to achieve a clinical existence. It is worth repeat-
ing that for this to happen actants do not need to be directly acquainted
with each other, as long as they work on the same biomedical platforms
Cambrosio et al.                                                          35
(Keating and Cambrosio 2003) that establish transitive relations between,
for instance, mutations, diagnostic categories, drugs, and diseases. This
also means that in order to capture this dynamics we need to go beyond
texts, and take into account wet lab and clinical activities, the circulation
of material entities (test kits, samples etc.), and, most recently, the algo-
rithms and codes of bioinformatics.


6. Conclusion: Back to Reflexivity

    We presently witness a proliferation of data and databases, often
freely accessible on the Web, that can be easily searched and analyzed
thanks to a mounting offer of dedicated software platforms, including
network analysis software. S&TS scholars, even those with little under-
standing of quantitative approaches, can now easily perform
(semi)quantitative analyses. This is a positive development, but it raises
the issue of how S&TS analysts have come to accept these opportunities
without asking too many questions about the sociotechnical scripts em-
bedded in the databases they so happily use. Indeed, while S&TS scholars
have had much to say about infrastructure, in particular information in-
frastructure (Star and Ruhleder 1996; Star 1999; Bowker 2006), they have
so far not quite succeeded in reflexively incorporating these insights into
their own work with (rather than on) information databases. Those of us
who work on biomedicine consult almost daily the Medline database, and
yet we rarely investigate how it has established – thanks to its peculiar
structure, format, outreach and universal access – a network-like, world-
wide space that multiplies inter-textual relations and favors strategies
based on the accumulation of references, citations and co-authorship
links. Critical observers have focused their attention on a database like
Web of Science, holding it responsible for the rise of a whole industry of
citation counts and evaluations through controversial tools such as impact
factors. Fewer analysts have looked at how Medline and its search engine
PubMed, which are regularly reformatted in response to new information-
retrieval needs to whose emergence they contribute, have led to the con-
stitution of a collaborative space by multiplying socio-semantic networks.
The aforementioned debates and controversies surrounding the estab-
lishment of a database such as ClinicalTrials.gov provide a clear indication
of how much is at stake in developing this kind of initiatives.
    Databases are not restricted to bibliographic databases. Genomics, as
noted at the beginning of this text, is generating its own avalanche of big
data stored in a number of databases. In order to become “actionable”
(Nelson et al. 2013) these data need to be interpreted (Leonelli 2014),
and part of this interpretation process involves establishing connections
between the information provided by the articles and the bio-clinical data
stored in the genomics databases. Private companies have invested in this
market niche. For instance, Linguamatics (http://www.linguamatics.com)
36                                                             Tecnoscienza - 5 (1)

offers text-mining software and services to extract and combine infor-
mation from the life sciences literature, electronic medical records, clini-
cal pathology documents, clinical trial data and patents. Notice how text-
and data-mining tools allow researchers to navigate a seamless web of
heterogeneous documents, by the same token moving across the material
basis of specialties and disciplines. Nor is this circulation limited to tools:
it also involves conceptual transfers, as when information scientists bor-
row a molecular biology notion – DNA transcriptional bursting – to de-
sign and designate algorithms that track word and topic bursts in
documents (e.g., Mane and Börner 2004).
    As previously noted, in S&TS we are mostly on the receiving end of
these processes, both with respect to the tools used to investigate existing
databases, and to the establishment of the databases carrying the infor-
mation that is then retrieved and processed by those tools. We need to
investigate these processes in order to understand how these new ways of
producing, storing, interpreting, and disseminating data are reframing bi-
omedical activities and configurations. Work by Sabina Leonelli (2012,
2013, 2014) is particularly useful in this respect. But, as just noted, we al-
so need to find ways to reflexively integrate these analyses into our own
work with data- and text-mining tools, which in turn means re-
positioning ourselves both vis-à-vis network analysis approaches and the
conceptual and analytical scripts and frames they embed. The point is not
simply that we urgently require visualization tools that are better adapted
to our theoretical and conceptual framings. A discussion of the shortcom-
ings of existing tools should also lead us to re-examine some key aspects
of our conceptual and methodological approaches, especially when they
tend to mistake one-click network structures for more complex, rhizome-
like arrangements, or to replace agency with structure.
    The notion of network is, of course, central to this line of questioning,
especially when the actors we investigate reason in terms of networks and
extended circulation spaces. But this reflexivity loop, while offering new
opportunities for collaboration between S&TS and biomedical research-
ers, could lead to serious difficulties if insufficiently problematized. As far
as opportunities are concerned, we can think of jointly exploring the dy-
namics (and thus also the forms of agency) characterizing a given domain,
or the nature of collectives involved in specific endeavors. Biomedical col-
leagues are in a good position to replace the few, selective connections
displayed on a map with accounts that better correspond to what
Strathern (1999) calls the “proliferation of the social”, and at the same
time our position vis-à-vis their activities is no longer one of externality,
as a granting agency such as Genome Canada strongly supports the inte-
gration into biomedical projects of ancillary studies on Genomics and its
Ethical, Economic, Environmental and Social aspects (GE3LS). As for
difficulties, the main one, as we hope to have shown, lies in the notion of
network itself, which needs to be theoretically repositioned, because what
is relevant in this new context are collective agencies and processes of
Cambrosio et al.                                                              37
agencement, rather than bundles of relations. Such a requirement does
not simply express a theoretical preference; it also derives from a close
observation of the development of biomedicine in the last half-century
(Rheinberger 2009).
    As readers who have followed us so far will have realized, we only of-
fer partial solutions, mostly based on tinkering. In fact, we suggested the-
se temporary work-arounds more as a way of exemplifying our questions
than as a solution to the conundrums mentioned in this paper. One way
of weakening a too strong reliance on structural network interpretations
is to multiply the networks, by including different kinds of entities and
diversifying entry points. A more intriguing suggestion concerns illegible
maps: very often, maps produced in the early stages of a research project
do not seem to offer any interpretative handle, as nodes and ties either
form a dense, shapeless network, or seem to be randomly distributed. A
lot of algorithmic work is then deployed to make those maps legible, to
uncover network patterns that were not there at the outset. But what if
the lack of a network is indeed the relevant result, and what if instead of
using algorithms to turn illegible into legible maps we were to develop
tools to explore and account for that illegibility? The point is not to add
mess to mess, as some would wish (Law 2004), but to explore the work
needed to make maps readable as part of an experimental setting that in-
cludes other devices and forms of investigation, not necessarily only in-
terviews, observations, or other traditional forms of fieldwork, but also
membership, however temporary, in the collectives we investigate. Be-
yond what at first we mistook as illegibility we might discover the vanish-
ing points of collective agency.


Acknowledgments
    Research for this paper was made possible by grants from the Canadian Insti-
tutes for Health Research (MOP-93553) and the Fonds de recherche du Québec
– Société et culture (SE-164195). The roots of this paper go back to a September
2007 presentation at the meeting celebrating the 40th anniversary of the “Centre
de Sociologie de l’Innovation” (Mines-ParisTech). More recently, preliminary ver-
sions of this paper were presented at the “STS Italia Workshop” (Università di
Padova, 19 April 2013) and at the “4ème Congrès du Réseau international
francophone de la recherche qualitative” (Fribourg, Switzerland, 19-21 June
2013). Many thanks to our colleagues Peter Keating and Nicole Nelson for
numerous and ongoing discussions of the issues raised in this paper.



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