Tracing the Evolution of Physics on the Backbone of Citation Networks

Many innovations are inspired by past ideas in a non-trivial way. Tracing these origins and identifying scientific branches is crucial for research inspirations. In this paper, we use citation relations to identify the descendant chart, i.e. the family tree of research papers. Unlike other spanning trees which focus on cost or distance minimization, we make use of the nature of citations and identify the most important parent for each publication, leading to a tree-like backbone of the citation network. Measures are introduced to validate the backbone as the descendant chart. We show that citation backbones can well characterize the hierarchical and fractal structure of scientific development, and lead to accurate classification of fields and sub-fields.Comment: 6 pages, 5 figure

Brief communication: Gender differences in publication and citation counts in librarianship and information science research

An analysis is presented of the publications by, and citations to, 57 male and 48 female academics in five departments of librarianship and information science. After taking account of differences in subject and differences in numbers of academics, it is shown that male academics publish significantly more papers on average than do female authors, but that there is no significant difference in the numbers of citations to published papers

Comment: Citation Statistics

We discuss the paper "Citation Statistics" by the Joint Committee on Quantitative Assessment of Research [arXiv:0910.3529]. In particular, we focus on a necessary feature of "good" measures for ranking scientific authors: that good measures must able to accurately distinguish between authors.Comment: Published in at http://dx.doi.org/10.1214/09-STS285B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Just how difficult can it be counting up R&D funding for emerging technologies (and is tech mining with proxy measures going to be any better?)

Decision makers considering policy or strategy related to the development of emerging technologies expect high quality data on the support for different technological options. A natural starting point would be R&D funding statistics. This paper explores the limitations of such aggregated data in relation to the substance and quantification of funding for emerging technologies. Using biotechnology as an illustrative case, we test the utility of a novel taxonomy to demonstrate the endemic weaknesses in the availability and quality of data from public and private sources. Using the same taxonomy, we consider the extent to which tech-mining presents an alternative, or potentially complementary, way to determine support for emerging technologies using proxy measures such as patents and scientific publications

Do peers see more in a paper than its authors?

Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances

Incidental or influential? – A decade of using text-mining for citation function classification.

This work looks in depth at several studies that have attempted to automate the process of citation importance classification based on the publications’ full text. We offer a comparison of their individual similarities, strengths and weaknesses. We analyse a range of features that have been previously used in this task. Our experimental results confirm that the number of in-text references are highly predictive of influence. Contrary to the work of Valenzuela et al. (2015), we find abstract similarity one of the most predictive features. Overall, we show that many of the features previously described in literature have been either reported as not particularly predictive, cannot be reproduced based on their existing descriptions or should not be used due to their reliance on external changing evidence. Additionally, we find significant variance in the results provided by the PDF extraction tools used in the pre-processing stages of citation extraction. This has a direct and significant impact on the classification features that rely on this extraction process. Consequently, we discuss challenges and potential improvements in the classification pipeline, provide a critical review of the performance of individual features and address the importance of constructing a large-scale gold-standard reference dataset

Talking Judges

What kinds of empirical questions about themselves and their colleagues on the bench are judges interested in asking? This was the topic of a recent conference at the Duke Law School. Our Essay reflects on the ways in which the judges at this conference and at a prior one talked about the empirical study of their community. To put it mildly, most of the judges were not fans of the empirical research. Our interest in this Essay is not, however, in responding to the judicial criticisms. Rather it is in drawing insights about how judges view themselves and their profession from how they discussed the research at the conference

Citation Analysis: A Comparison of Google Scholar, Scopus, and Web of Science

When faculty members are evaluated, they are judged in part by the impact and quality of their scholarly publications. While all academic institutions look to publication counts and venues as well as the subjective opinions of peers, many hiring, tenure, and promotion committees also rely on citation analysis to obtain a more objective assessment of an author’s work. Consequently, faculty members try to identify as many citations to their published works as possible to provide a comprehensive assessment of their publication impact on the scholarly and professional communities. The Institute for Scientific Information’s (ISI) citation databases, which are widely used as a starting point if not the only source for locating citations, have several limitations that may leave gaps in the coverage of citations to an author’s work. This paper presents a case study comparing citations found in Scopus and Google Scholar with those found in Web of Science (the portal used to search the three ISI citation databases) for items published by two Library and Information Science full-time faculty members. In addition, the paper presents a brief overview of a prototype system called CiteSearch, which analyzes combined data from multiple citation databases to produce citation-based quality evaluation measures