2,257 research outputs found
Quantifying the interdisciplinarity of scientific journals and fields
There is an overall perception of increased interdisciplinarity in science,
but this is difficult to confirm quantitatively owing to the lack of adequate
methods to evaluate subjective phenomena. This is no different from the
difficulties in establishing quantitative relationships in human and social
sciences. In this paper we quantified the interdisciplinarity of scientific
journals and science fields by using an entropy measurement based on the
diversity of the subject categories of journals citing a specific journal. The
methodology consisted in building citation networks using the Journal Citation
Reports database, in which the nodes were journals and edges were established
based on citations among journals. The overall network for the 11-year period
(1999-2009) studied was small-world and scale free with regard to the
in-strength. Upon visualizing the network topology an overall structure of the
various science fields could be inferred, especially their interconnections. We
confirmed quantitatively that science fields are becoming increasingly
interdisciplinary, with the degree of interdisplinarity (i.e. entropy)
correlating strongly with the in-strength of journals and with the impact
factor.Comment: 23 pages, 6 figure
Using text analysis to quantify the similarity and evolution of scientific disciplines
We use an information-theoretic measure of linguistic similarity to
investigate the organization and evolution of scientific fields. An analysis of
almost 20M papers from the past three decades reveals that the linguistic
similarity is related but different from experts and citation-based
classifications, leading to an improved view on the organization of science. A
temporal analysis of the similarity of fields shows that some fields (e.g.,
computer science) are becoming increasingly central, but that on average the
similarity between pairs has not changed in the last decades. This suggests
that tendencies of convergence (e.g., multi-disciplinarity) and divergence
(e.g., specialization) of disciplines are in balance.Comment: 9 pages, 4 figure
Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --
Journals were central to Eugene Garfield's research interests. Among other
things, journals are considered as units of analysis for bibliographic
databases such as the Web of Science (WoS) and Scopus. In addition to
disciplinary classifications of journals, journal citation patterns span
networks across boundaries to variable extents. Using betweenness centrality
(BC) and diversity, we elaborate on the question of how to distinguish and rank
journals in terms of interdisciplinarity. Interdisciplinarity, however, is
difficult to operationalize in the absence of an operational definition of
disciplines, the diversity of a unit of analysis is sample-dependent. BC can be
considered as a measure of multi-disciplinarity. Diversity of co-citation in a
citing document has been considered as an indicator of knowledge integration,
but an author can also generate trans-disciplinary--that is,
non-disciplined--variation by citing sources from other disciplines. Diversity
in the bibliographic coupling among citing documents can analogously be
considered as diffusion of knowledge across disciplines. Because the citation
networks in the cited direction reflect both structure and variation, diversity
in this direction is perhaps the best available measure of interdisciplinarity
at the journal level. Furthermore, diversity is based on a summation and can
therefore be decomposed, differences among (sub)sets can be tested for
statistical significance. In an appendix, a general-purpose routine for
measuring diversity in networks is provided
Quantifying the consistency of scientific databases
Science is a social process with far-reaching impact on our modern society.
In the recent years, for the first time we are able to scientifically study the
science itself. This is enabled by massive amounts of data on scientific
publications that is increasingly becoming available. The data is contained in
several databases such as Web of Science or PubMed, maintained by various
public and private entities. Unfortunately, these databases are not always
consistent, which considerably hinders this study. Relying on the powerful
framework of complex networks, we conduct a systematic analysis of the
consistency among six major scientific databases. We found that identifying a
single "best" database is far from easy. Nevertheless, our results indicate
appreciable differences in mutual consistency of different databases, which we
interpret as recipes for future bibliometric studies.Comment: 20 pages, 5 figures, 4 table
Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
Researchers' networks have been subject to active modeling and analysis.
Earlier literature mostly focused on citation or co-authorship networks
reconstructed from annotated scientific publication databases, which have
several limitations. Recently, general-purpose web search engines have also
been utilized to collect information about social networks. Here we
reconstructed, using web search engines, a network representing the relatedness
of researchers to their peers as well as to various research topics.
Relatedness between researchers and research topics was characterized by
visibility boost-increase of a researcher's visibility by focusing on a
particular topic. It was observed that researchers who had high visibility
boosts by the same research topic tended to be close to each other in their
network. We calculated correlations between visibility boosts by research
topics and researchers' interdisciplinarity at individual level (diversity of
topics related to the researcher) and at social level (his/her centrality in
the researchers' network). We found that visibility boosts by certain research
topics were positively correlated with researchers' individual-level
interdisciplinarity despite their negative correlations with the general
popularity of researchers. It was also found that visibility boosts by
network-related topics had positive correlations with researchers' social-level
interdisciplinarity. Research topics' correlations with researchers'
individual- and social-level interdisciplinarities were found to be nearly
independent from each other. These findings suggest that the notion of
"interdisciplinarity" of a researcher should be understood as a
multi-dimensional concept that should be evaluated using multiple assessment
means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On
Indicating interdisciplinarity: A multidimensional framework to characterize Interdisciplinary Knowledge Flow (IKF)
This study contributes to the recent discussions on indicating
interdisciplinarity, i.e., going beyond mere metrics of interdisciplinarity. We
propose a multi-dimensional and contextual framework to improve the granularity
and usability of the existing methodology for quantifying the interdisciplinary
knowledge flow (IKF) in which scientific disciplines import and export
knowledge from/to other disciplines. To characterize the knowledge exchange
between disciplines, we recognize three dimensions under this framework,
namely, broadness, intensity, and heterogeneity. We show that each dimension
covers a different aspect of IKF, especially between disciplines with the
largest volume of IKF, and can assist in uncovering different types of
interdisciplinarity. We apply this framework in two use cases, one at the level
of disciplines and one at the level of journals, to show how it can offer a
more holistic and detailed viewpoint on the interdisciplinarity of scientific
entities than plain citation counts. We further compare our proposed framework,
an indicating process, with established indicators and discuss how such
information tools on interdisciplinarity can assist science policy practices
such as performance-based research funding systems and panel-based peer review
processes
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