Traditional co-citation analysis has not taken the proximity of co-cited references into account. As long as two references are cited by the same article, they are retreated equally regardless the distance between where citations appear in the article. Little is known about what additional insights into citation and co-citation behaviours one might gain from studying distributions of co-citation in terms of such proximity. How are citations distributed in an article? What insights does the proximity of co-citation provide? In this article, the proximity of a pair of co-cited reference is defined as the nearest instance of the co-citation relation in text. We investigate the proximity of co-citation in full text of scientific publications at four levels, namely, the sentence level, the paragraph level, the section level, and the article level. We conducted four studies of co-citation patterns in the full text of articles published in 22 open access journals from BioMed Central. First, we compared the distributions of co-citation instances at four proximity levels in journal articles to the traditional article-level co-citation counts. Second, we studied the distributions of co-citations of various proximities across organizational sections in articles. Third, the distribution of co-citation proximity in different co-citation frequency groups is investigated. Fourth, we identified the occurrences of co-citations at different proximity levels with reference to the corresponding traditional co-citation network. The results show that (1) the majority of co-citations are loosely coupled at the article level, (2) a higher proportion of sentence-level co-citations is found in high co-citation frequencies than in low co-citation frequencies, (3) tightly coupled sentence-level co-citations not only preserve the essential structure of the corresponding traditional co-citation network but also form a much smaller subset of the entire co-citation instances typically considered by traditional co-citation analysis. Implications for improving our understanding of underlying factors concerning co-citations and developing more efficient co-citation analysis methods are discussed.
Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy.
We introduce a new visual analytic approach to the study of scientific discoveries and knowledge diffusion. Our approach enhances
contemporary co-citation network analysis by enabling analysts to identify co-citation clusters of cited references intuitively,
synthesize thematic contexts in which these clusters are cited, and trace how research focus evolves over time. The new approach
integrates and streamlines a few previously isolated techniques such as spectral clustering and feature selection algorithms.
The integrative procedure is expected to empower and strengthen analytical and sense making capabilities of scientists, learners,
and researchers to understand the dynamics of the evolution of scientific domains in a wide range of scientific fields, science
studies, and science policy evaluation and planning. We demonstrate the potential of our approach through a visual analysis
of the evolution of astronomical research associated with the Sloan Digital Sky Survey (SDSS) using bibliographic data between
1994 and 2008. In addition, we also demonstrate that the approach can be consistently applied to a set of heterogeneous data
sources such as e-prints on arXiv, publications on ADS, and NSF awards related to the same topic of SDSS.
Large-scale scientific projects have become a major impetus of scientific advances. But few studies have specifically analyzed how those projects bolster scientific research. We address this question from a scientometrics perspective. By analyzing the bibliographic records of papers relevant to the Sloan Digital Sky Survey (SDSS), we found that the SDSS helped scientists from many countries further develop their own research; investigators initially formed large research groups to tackle key problems, while later papers involved fewer authors; and the number of research topics increased but the diversity of topics remains stable. Furthermore, the entropy analysis method has proven valuable in terms of analyzing patterns of research topics at a macroscopic level.