For example, what often gets
glossed over in these endeavors is that visualizations of scientific
prescribe how these developments should be known in the first place. Science maps are produced by particular
statistical algorithms that might have been chosen otherwise, calculations
performed on large amounts of ‘raw’ data stored in databases, and for this reason they are not simply ‘statistical
information presented visually’. The choice for a particular kind of visualization is
often connected to the specificities and meaning of the underlying dataset and
the software used to process the data. Several software packages have been specifically
designed for this purpose (the VOSViewer supported by CWTS being one of them).
These packages prescribe how the data should be handled. Different choices in
selection and processing of the data will lead to sometimes strikingly
different maps. Therefore, we will increasingly need systematic experiments and
studies with different forms of visual presentation (Tufte, 2006).
At the same time, a number of interfaces are built into the mapping
process, where an encounter takes place with a user who approaches these
visualizations as evidence.
But how do these users actually behave? To our knowledge hardly any
systematic research is done on how users (bibliometricians, computer
scientists, institute directors, policy makers and their staff, etc.) engage
with these visualizations, and which skills and strategies are needed to engage
with them. A
critical scrutiny is needed of the degree of ‘visual literacy’ (Pauwels, 2008)
demanded of users who want to critically work with and examine these
visualizations. The visualizations embody
formal choices that determine what can be visualized and what will remain
hidden. Furthermore, they are also shaped by the broader cultural and
historical context in which they are produced.
there is a
tendency to downplay the visuality of science maps, in favor of the integrity
of the underlying data and the sophistication of transformation algorithms.
However, visualizations are “becoming increasingly dependent upon technology,
while technology is increasingly becoming imaging and visualization technology”
(Pauwels 2008, 83). We expect that this interconnection between data selection,
data processing and data visualization will become much stronger in the near
future. These connections should therefore be systematically analyzed, while
the field develops and experiments with different forms of visual
As said, science
mapping projects do not simply measure and describe scientific developments –
they also have a normative potential. Suppose, in an hypothetical example, that the director of a research institute wants to
map the institute’s research landscape in terms of research topics and possible
applications, and wants to see how the landscape develops over the next five
years. This kind of mapping project, like any other description of reality, is
not only descriptive but also performative. In other words, the map that gets
created in response to this director’s question also shapes the reality it
attempts to represent. One possible consequence of this hypothetical mapping
project could be that the director decides on the basis of this visual analysis
to focus more on certain underdeveloped research strands, at the expense of or
in addition to others. The map that was meant to chart the terrain now becomes
embedded in management decision processes. As a result, it plays an active part
in a shift in the institute’s research agenda, an agenda that will be mapped in
five years’ time with the same analytical means that were originally merely
intended to describe the landscape.
A comparable example can actually be found in
Börner’s book: a map that shows all National Institute of Health (NIH) grant
awards from a single funding year. The project comes with a website, giving
access to a database and web-based interface. The clusters on the map
correspond to broader scientific topics covered in the grants, while the dots
correspond with individual grants clustered together by a shared topical focus.
Here, too, it
would be informative to analyze the potential role these maps play as policy instruments
(for instance, in accountability studies). This type of analysis will be all
the more urgent when bibliometric maps are increasingly used for the purposes of research
evaluation. The maps created on the basis of bibliometric data do not simply ‘visualize
what we know’. They actively shape bibliometric knowledge production, use and
dissemination in ways that require careful scrutiny.