This article presents an exploratory analysis of publication delays in the science field. Publication delay is defined as
the time period between submission and publication of an article for a scientific journal. We obtained a first indication
that these delays are longer with regard to journals in the fields of mathematics and technical sciences than they are in
other fields of science. We suggest the use of data on publication delays in the analysis of the effects of electronic publishing
on reference/citation patterns. A preliminary analysis on a small sample suggests that—under rather strict assumptions—the
cited half-life of references may be reduced with a factor of about 2 if publication delays decrease radically.
This study analyzed 2443 papers published in 2006 by European Union authors on pain-related research. Five EU countries (the
UK, Germany, Italy, the Netherlands and France) each published > 200 papers while three countries (Cyprus, Malta and Estonia)
published none; socio-economic indicators were related to each country’s productivity. The 2443 papers were published in 592
journals and Cephalalgia, Pain and European Journal of Pain were the most prolific. Publications were also analyzed for intra- versus inter-EU/non-EU collaborations and subdisciplines
profiles in Clinical Medicine and the Life Sciences for the World, USA, EU and the top-four EU countries were compared.
Most studies of scholarly influence within disciplines using citation data do not investigate the extent of an individual’s
influence; does it extend over a number of years with a sequence of publications or is it confined to a short period and a
small number of publications? Using bibliographic data from a series of quadrennial reports into developments in UK geography,
this paper finds that few authors are cited on more than one occasion.
universities (Shin, in press).
However, the previous literature published in international journals does not adequately address the social forces shaping U-I-G relations development and innovation diffusion in Asia. In order to investigate the TH or U
media technologies (Baez et al. 2010 ) may reduce the onus of using the scientific literature. Indeed, in other knowledge-intensive work such as software development, enhanced search capability leads to greater component reuse (Banker et al. 1993
Authors:Robert K. Abercrombie, Akaninyene W. Udoeyop, and Bob G. Schlicher
conference literature), the subsequent critical discoveries (evident via original scientific, conference literature and patents), and the transitioning through the various TRLs ultimately to commercial application.
Using statistical method, the author analyzed the citation rate of articles published in Chinese Science Bulletin (CSB) between 1995 and 1999 in Science Citation Index Expanded (SCIE) databases. Results indicated that: 1. Majority of authors who published in CSB were Chinese; 2. The articles were
basically cited by the authors themselves in the first year after publication; 3. The peak of total citation rate appeared
in the third year after publication and the peak of non-self-citation rate was further delayed. There are relatively high
self-citation rates of articles from CSB and most of these citations are from Chinese scientific journals. This indicates
that our citation environment is limited to a closed circle. The author, therefore, proposed a strategy for changing the current
conditions of Chinese scientific journals to raise their influence.
The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received.
The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations
accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications
within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that
it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features
using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and
their statistical analysis provides greater insight into citation behavior.
Authors:S. Sangam, Liang Liming, and Gireesh Ganjihal
The present paper describes the application of growth models as suggested by Egghe and Ravichadra Rao (Scientometrics 25:5–46,
1992). The scope of the paper is limited to study the growth and dynamics of Indian and Chinese publications in the field
of liquid crystals research (1997–2006).