New approaches to using scientific data - statistics, data mining and related technologies in research and research training

This paper surveys technological changes that affect the collection, organization, analysis and presentation of data. It considers changes or improvements that ought to influence the research process and direct the use of technology. It explores implications for graduate research training. The insights of Evidence-Based Medicine are widely relevant across many different research areas. Its insights provide a helpful context within which to discuss the use of technological change to improve the research process. Systematic data-based overview has to date received inadequate attention, both in research and in research training. Sharing of research data once results are published would both assist systematic overview and allow further scrutiny where published analyses seem deficient. Deficiencies in data collection and published data analysis are surprisingly common. Technologies that offer new perspectives on data collection and analysis include data warehousing, data mining, new approaches to data visualization and a variety of computing technologies that are in the tradition of knowledge engineering and machine learning. There is a large overlap of interest with statistics. Statistics is itself changing dramatically as a result of the interplay between theoretical development and the power of new computational tools. I comment briefly on other developing mathematical science application areas - notably molecular biology. The internet offers new possibilities for cooperation across institutional boundaries, for exchange of information between researchers, and for dissemination of research results. Research training ought to equip students both to use their research skills in areas different from those in which they have been immediately trained, and to respond to the challenge of steadily more demanding standards. There should be an increased emphasis on training to work cooperatively

A two-phase model for smoothly joining disparate growth phases in the macropodid Thylogale billardierii

Generally, sigmoid curves are used to describe the growth of animals over their lifetime. However, because growth rates often differ over an animal's lifetime a single curve may not accurately capture the growth. Broken-stick models constrained to pass through a common point have been proposed to describe the different growth phases, but these are often unsatisfactory because essentially there are still two functions that describe the lifetime growth. To provide a single, converged model to age animals with disparate growth phases we developed a smoothly joining two-phase nonlinear function (SJ2P), tailored to provide a more accurate description of lifetime growth of the macropod, the Tasmanian pademelon Thylogale billardierii. The model consists of the Verhulst logistic function, which describes pouch-phase growth--joining smoothly to the Brody function, which describes post-pouch growth. Results from the model demonstrate that male pademelons grew faster and bigger than females. Our approach provides a practical means of ageing wild pademelons for life history studies but given the high variability of the data used to parametrise the second growth phase of the model, the accuracy of ageing of post-weaned animals is low: accuracy might be improved with collection of longitudinal growth data. This study provides a unique, first robust method that can be used to characterise growth over the lifespan of pademelons. The development of this method is relevant to collecting age-specific vital rates from commonly used wildlife management practices to provide crucial insights into the demographic behaviour of animal populations.Financial support was provided by the Tasmanian Community Forest Agreement: Alternatives to 1080 Programme. In-kind support was provided by the University of Tasmania

Technology- enabled advance in the worlds of statistics, machine learning and data mining

​Advances in digital computing continue to have large effects on all aspects of life and society, including science. These advances are possible because we have computer languages that translate directly into computational steps that can be implemented in computer hardware. Here, I draw attention to changes that are affecting the theory and practice of data analysis, with a focus on methodologies that feature in expositions of data mining and machine learning. The R language and system is playing an increasingly important role in making the new abilities readily accessible at the scientific workbench

Statistical Learning from a Regression Perspective

Abstracts not available for BookReview

Generalized Additive Models: An Introduction with R

Abstracts not available for BookReview

Modern Multivariate Statistical Techniques: Regression, Classification and Manifold Learning

Abstracts not available for BookReview