9,081 research outputs found
Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity
Using a large database (~ 215 000 records) of relevant articles, we
empirically study the "complex systems" field and its claims to find universal
principles applying to systems in general. The study of references shared by
the papers allows us to obtain a global point of view on the structure of this
highly interdisciplinary field. We show that its overall coherence does not
arise from a universal theory but instead from computational techniques and
fruitful adaptations of the idea of self-organization to specific systems. We
also find that communication between different disciplines goes through
specific "trading zones", ie sub-communities that create an interface around
specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and
Technology (2012) 10.1002/asi.2264
Modeling and Analysis of Scholar Mobility on Scientific Landscape
Scientific literature till date can be thought of as a partially revealed
landscape, where scholars continue to unveil hidden knowledge by exploring
novel research topics. How do scholars explore the scientific landscape , i.e.,
choose research topics to work on? We propose an agent-based model of topic
mobility behavior where scholars migrate across research topics on the space of
science following different strategies, seeking different utilities. We use
this model to study whether strategies widely used in current scientific
community can provide a balance between individual scientific success and the
efficiency and diversity of the whole academic society. Through extensive
simulations, we provide insights into the roles of different strategies, such
as choosing topics according to research potential or the popularity. Our model
provides a conceptual framework and a computational approach to analyze
scholars' behavior and its impact on scientific production. We also discuss how
such an agent-based modeling approach can be integrated with big real-world
scholarly data.Comment: To appear in BigScholar, WWW 201
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
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
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