Predatory publishers threaten to consume public research funds and undermine national academic systems - the case of Brazil

An unintended consequence of the open access movement, predatory publishers have appeared in many countries, offering authors a quick and easy route to publication in exchange for a fee and usually without any apparent peer review or quality control. Using a large database of publications, Marcelo S. Perlin, Takeyoshi Imasato and Denis Borenstein analyse the extent of this problem throughout the entire Brazilian academic system. While predatory publications remain a small proportion of the overall literature, this proportion has grown exponentially in recent years, with both early-career and established scholars found to have authored papers published in predatory venues. The inclusion of predatory publications in national journal quality rankings has been a key factor in this increase

The Prevalence of Errors in Machine Learning Experiments

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments

A New Approach for Heparin Standardization: Combination of Scanning UV Spectroscopy, Nuclear Magnetic Resonance and Principal Component Analysis

The year 2007 was marked by widespread adverse clinical responses to heparin use, leading to a global recall of potentially affected heparin batches in 2008. Several analytical methods have since been developed to detect impurities in heparin preparations; however, many are costly and dependent on instrumentation with only limited accessibility. A method based on a simple UV-scanning assay, combined with principal component analysis (PCA), was developed to detect impurities, such as glycosaminoglycans, other complex polysaccharides and aromatic compounds, in heparin preparations. Results were confirmed by NMR spectroscopy. This approach provides an additional, sensitive tool to determine heparin purity and safety, even when NMR spectroscopy failed, requiring only standard laboratory equipment and computing facilities