Scientometric Study of Doctoral Dissertations in Biochemistry in the University of Kerala, India

Biochemistry is the most fascinating subject as it deals with the chemical language of life. The ultimate goal of biochemistry is to describe the phenomena that distinguish living from non-living in the language of chemistry and physics. Researchers in biochemistry use specific techniques native to biochemistry, but increasingly combine these with techniques and ideas from genetics, molecular biology and biophysics. In India at present around 75,000 students are enrolled in research and nearly 11,000 are awarded PhDs every year, of which 50 percent are from science and technology disciplines. Theses and dissertations reflect the scholarly communication process. Scientometrics and citation characteristics of dissertations like the subject fields of dissertations, the number of citations and their distribution by type of source, years, and by number of authors etc., have been studied with a view to identify the basic features of the scholarly communication process in different fields of study. The purpose of the present study is to determine the bibliometric characteristics of the biochemistry research in the university of Kerala, India including subject distribution, bibliographic forms of cited documents, most cited journals, collaboration in authorship, etc. A total of 168 doctoral dissertations awarded between 1966 and 2007 at the Department of Biochemistry of University of Kerala were used as a source.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.