24,538 research outputs found
The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication
The scientific community of researchers in a research specialty is an
important unit of analysis for understanding the field specific shaping of
scientific communication practices. These scientific communities are, however,
a challenging unit of analysis to capture and compare because they overlap,
have fuzzy boundaries, and evolve over time. We describe a network analytic
approach that reveals the complexities of these communities through examination
of their publication networks in combination with insights from ethnographic
field studies. We suggest that the structures revealed indicate overlapping
sub- communities within a research specialty and we provide evidence that they
differ in disciplinary orientation and research practices. By mapping the
community structures of scientific fields we aim to increase confidence about
the domain of validity of ethnographic observations as well as of collaborative
patterns extracted from publication networks thereby enabling the systematic
study of field differences. The network analytic methods presented include
methods to optimize the delineation of a bibliographic data set in order to
adequately represent a research specialty, and methods to extract community
structures from this data. We demonstrate the application of these methods in a
case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS
Contextualization of topics - browsing through terms, authors, journals and cluster allocations
This paper builds on an innovative Information Retrieval tool, Ariadne. The
tool has been developed as an interactive network visualization and browsing
tool for large-scale bibliographic databases. It basically allows to gain
insights into a topic by contextualizing a search query (Koopman et al., 2015).
In this paper, we apply the Ariadne tool to a far smaller dataset of 111,616
documents in astronomy and astrophysics. Labeled as the Berlin dataset, this
data have been used by several research teams to apply and later compare
different clustering algorithms. The quest for this team effort is how to
delineate topics. This paper contributes to this challenge in two different
ways. First, we produce one of the different cluster solution and second, we
use Ariadne (the method behind it, and the interface - called LittleAriadne) to
display cluster solutions of the different group members. By providing a tool
that allows the visual inspection of the similarity of article clusters
produced by different algorithms, we present a complementary approach to other
possible means of comparison. More particular, we discuss how we can - with
LittleAriadne - browse through the network of topical terms, authors, journals
and cluster solutions in the Berlin dataset and compare cluster solutions as
well as see their context.Comment: proceedings of the ISSI 2015 conference (accepted
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