50,614 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
How are topics born? Understanding the research dynamics preceding the emergence of new areas
The ability to promptly recognise new research trends is strategic for many stake- holders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the âparentsâ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Climate Change Research in View of Bibliometrics
This bibliometric study of a large publication set dealing with research on
climate change aims at mapping the relevant literature from a bibliometric
perspective and presents a multitude of quantitative data: (1) The growth of
the overall publication output as well as (2) of some major subfields, (3) the
contributing journals and countries as well as their citation impact, and (4) a
title word analysis aiming to illustrate the time evolution and relative
importance of specific research topics. The study is based on 222,060 papers
published between 1980 and 2014. The total number of papers shows a strong
increase with a doubling every 5-6 years. Continental biomass related research
is the major subfield, closely followed by climate modeling. Research dealing
with adaptation, mitigation, risks, and vulnerability of global warming is
comparatively small, but their share of papers increased exponentially since
2005. Research on vulnerability and on adaptation published the largest
proportion of very important papers. Research on climate change is
quantitatively dominated by the USA, followed by the UK, Germany, and Canada.
The citation-based indicators exhibit consistently that the UK has produced the
largest proportion of high impact papers compared to the other countries
(having published more than 10,000 papers). The title word analysis shows that
the term climate change comes forward with time. Furthermore, the term impact
arises and points to research dealing with the various effects of climate
change. Finally, the term model and related terms prominently appear
independent of time, indicating the high relevance of climate modeling.Comment: 40 pages, 6 figures, and 4 table
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