Stanford Center for Reproducible Neuroscience: A new preprint has been posted to the ArXiv that has very important implications and should be required reading for all fMRI researchers. Anders Eklund, Tom Nichols, and Hans Knutson applied task fMRI analyses to a large number of resting fMRI datasets, in order to identify the empirical corrected “familywise” Type I error rates observed under the null hypothesis for both voxel-wise and cluster-wise inference. What they found is shocking: While voxel-wise error rates were valid, nearly all cluster-based parametric methods (except for FSL’s FLAME 1) have greatly inflated familywise Type I error rates. This inflation was worst for analyses using lower cluster-forming thresholds (e.g. p=0.01) compared to higher thresholds, but even with higher thresholds there was serious inflation. This should be a sobering wake-up call for fMRI researchers, as it suggests that the methods used in a large number of previous publications suffer from exceedingly high false positive rates (sometimes greater than 50%).
The first part of the STM innovations seminar focused on the problems of reproducibility in science. For some years now, there have been voices of concern noting that when previously reported results are tested, the data very often doesn’t come out the same way. During the seminar, Andrew Hufton of Scientific Data went so far as to state that progress in the pharmaceutical sciences is being held back by lack of reliability in the basic literature.
Although shoulder questionnaires validated for Brazil do exist, none of them are aimed at populations with rheumatic disease. We believe that the Oxford Shoulder Score (OSS) may be useful in this population. The objective of this study was to translate the OSS, adapt it to Brazilian culture and test its reproducibility.
Independently verifying research can help science regain its credibility, argues Laurie Zoloth. His paper: "Why Most Published Research Findings Are False", was published in August 2005, in PLOS Medicine. It became one of the journal’s most-cited articles. While climate sceptics, anti-vaccination campaigners and the rest of the pseudo-science community have dined out on this paper, arguably it has been a shot in the arm for science.
The Reproducibility Project: Cancer Biology aims to get a better, quantitative estimate of the reproducibility of important work and to understand the challenges such efforts present. Begun in 2013, the project is run jointly by the Center for Open Science (COS) in Charlottesville, Virginia, and Science Exchange in Palo Alto, California.
According to a 2013 report from the American Association for the Advancement of Science, $115 billion is spent annually in the United States on life science research. Fifty percent of this total is spent on preclinical research, half of which—$28 billion—is not reproducible.