The new Meta-Research Section in PLOS Biology is not the only example of how PLOS strives to improve the scientific endeavor through innovative communication efforts. PLOS has always recognized that publication of studies that reproduce published work or null results, either confirming or refuting the original result, is essential for progress in research. In fact, the largest journal at PLOS, PLOS ONE, is one of only a handful of publications that actively encourage these types of submissions with The Missing Pieces Collection.

While experiments may be published even in a top scientific journal, other researchers who attempt to repeat the same experiments under the same conditions often find contradicting results. As a measure of this, a recent study attempted to reproduce psychology publications and successfully replicated only 39 out of 100 studies. It turns out that excluding sex in experimental design may have contributed to reproducibility issues. Furthermore, sex can also have a biological impact on our scientific understanding and influence how well early biological studies translate into advances in human medicine.

Experimental results that don’t hold up to replication have caused consternation among scientists for years, especially in the life and social sciences (SN: 1/24/15, p. 20). In 2015 several research groups examining the issue reported on the magnitude of the irreproducibility problem. The news was not good.

The finding that acute and chronic manipulations of the same neural circuit can produce different behavioural outcomes poses new questions about how best to analyse these circuits.

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.