Scanning the horizon: towards transparent and reproducible neuroimaging research

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions

Longitudinal dispersion coefficients within turbulent and transitional pipe flow

The longitudinal dispersion coefficient is used to describe the change in characteristics of a solute cloud, as it travels along the longitudinal axis of a pipe. Taylor (1954) proposed a now classical expression to predict the longitudinal dispersion coefficient within turbulent pipe flow. However, experimental work has shown significant deviation from his prediction for Re < 20,000. This chapter presents experimental results from tracer studies conducted within the range 2,000 < Re < 50,000, from which longitudinal dispersion coefficients have been determined. Initial results are also presented for a numerical model that aims to predict the longitudinal dispersion coefficient over the same range of Reynolds numbers