6,518 research outputs found
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
Dynamic Animations of Journal Maps: Indicators of Structural Changes and Interdisciplinary Developments
The dynamic analysis of structural change in the organization of the sciences
requires methodologically the integration of multivariate and time-series
analysis. Structural change--e.g., interdisciplinary development--is often an
objective of government interventions. Recent developments in multi-dimensional
scaling (MDS) enable us to distinguish the stress originating in each
time-slice from the stress originating from the sequencing of time-slices, and
thus to locally optimize the trade-offs between these two sources of variance
in the animation. Furthermore, visualization programs like Pajek and Visone
allow us to show not only the positions of the nodes, but also their relational
attributes like betweenness centrality. Betweenness centrality in the vector
space can be considered as an indicator of interdisciplinarity. Using this
indicator, the dynamics of the citation impact environments of the journals
Cognitive Science, Social Networks, and Nanotechnology are animated and
assessed in terms of interdisciplinarity among the disciplines involved
Tweeting biomedicine: an analysis of tweets and citations in the biomedical literature
Data collected by social media platforms have recently been introduced as a
new source for indicators to help measure the impact of scholarly research in
ways that are complementary to traditional citation-based indicators. Data
generated from social media activities related to scholarly content can be used
to reflect broad types of impact. This paper aims to provide systematic
evidence regarding how often Twitter is used to diffuse journal articles in the
biomedical and life sciences. The analysis is based on a set of 1.4 million
documents covered by both PubMed and Web of Science (WoS) and published between
2010 and 2012. The number of tweets containing links to these documents was
analyzed to evaluate the degree to which certain journals, disciplines, and
specialties were represented on Twitter. It is shown that, with less than 10%
of PubMed articles mentioned on Twitter, its uptake is low in general. The
relationship between tweets and WoS citations was examined for each document at
the level of journals and specialties. The results show that tweeting behavior
varies between journals and specialties and correlations between tweets and
citations are low, implying that impact metrics based on tweets are different
from those based on citations. A framework utilizing the coverage of articles
and the correlation between Twitter mentions and citations is proposed to
facilitate the evaluation of novel social-media based metrics and to shed light
on the question in how far the number of tweets is a valid metric to measure
research impact.Comment: 22 pages, 4 figures, 5 table
On the relationship between interdisciplinarity and scientific impact
This paper analyzes the effect of interdisciplinarity on the scientific
impact of individual papers. Using all the papers published in Web of Science
in 2000, we define the degree of interdisciplinarity of a given paper as the
percentage of its cited references made to journals of other disciplines. We
show that, although for all disciplines combined there is no clear correlation
between the level of interdisciplinarity of papers and their citation rates,
there are nonetheless some disciplines in which a higher level of
interdisciplinarity is related to a higher citation rates. For other
disciplines, citations decline as interdisciplinarity grows. One characteristic
is visible in all disciplines: highly disciplinary and highly interdisciplinary
papers have a low scientific impact. This suggests that there might be an
optimum of interdisciplinarity beyond which the research is too dispersed to
find its niche and under which it is too mainstream to have high impact.
Finally, the relationship between interdisciplinarity and scientific impact is
highly determined by the citation characteristics of the disciplines involved:
papers citing citation intensive disciplines are more likely to be cited by
those disciplines and, hence, obtain higher citation scores than papers citing
non citation intensive disciplines.Comment: 10 pages, 3 figures, 1 table. Forthcoming in JASIS
Citations: Indicators of Quality? The Impact Fallacy
We argue that citation is a composed indicator: short-term citations can be
considered as currency at the research front, whereas long-term citations can
contribute to the codification of knowledge claims into concept symbols.
Knowledge claims at the research front are more likely to be transitory and are
therefore problematic as indicators of quality. Citation impact studies focus
on short-term citation, and therefore tend to measure not epistemic quality,
but involvement in current discourses in which contributions are positioned by
referencing. We explore this argument using three case studies: (1) citations
of the journal Soziale Welt as an example of a venue that tends not to publish
papers at a research front, unlike, for example, JACS; (2) Robert Merton as a
concept symbol across theories of citation; and (3) the Multi-RPYS
("Multi-Referenced Publication Year Spectroscopy") of the journals
Scientometrics, Gene, and Soziale Welt. We show empirically that the
measurement of "quality" in terms of citations can further be qualified:
short-term citation currency at the research front can be distinguished from
longer-term processes of incorporation and codification of knowledge claims
into bodies of knowledge. The recently introduced Multi-RPYS can be used to
distinguish between short-term and long-term impacts.Comment: accepted for publication in Frontiers in Research Metrics and
Analysis; doi: 10.3389/frma.2016.0000
Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling
The animation of network visualizations poses technical and theoretical
challenges. Rather stable patterns are required before the mental map enables a
user to make inferences over time. In order to enhance stability, we developed
an extension of stress-minimization with developments over time. This dynamic
layouter is no longer based on linear interpolation between independent static
visualizations, but change over time is used as a parameter in the
optimization. Because of our focus on structural change versus stability the
attention is shifted from the relational graph to the latent eigenvectors of
matrices. The approach is illustrated with animations for the journal citation
environments of Social Networks, the (co-)author networks in the carrying
community of this journal, and the topical development using relations among
its title words. Our results are also compared with animations based on
PajekToSVGAnim and SoNIA
- …