Computer code is used to analyse data in research studies across many fields including epidemiology, biomedical science, computational biology and physics. Many findings now depend on such analyses. What role do journals play in ensuring transparency and reproducibility of computer code used to generate research findings? How might this fit in with our efforts, as scientists, to reduce errors in
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T-tests and analysis of variance (ANOVA) are common statistical tests in physiology and biomedical science. While the SAMPL guidelines for reporting statistical analyses and methods in published literature state authors should “describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to verify the reported results”, such recommendations are rarely implemented. Simply stating
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In our previous post we learned how to verify what Python virtual environments were installed on our machine and what Python packages they contained. We also learned how to delete unwanted environments. In this post we are going to learn how to share our virtual environment with others. This is incredibly useful in this day and age of research reproducibility.
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I assume that, if you are reading this post, you are familiar with research reproducibility and the issues that surround it. Nevertheless, you might not be familiar with the difficulties early career researchers face in this era of open science. A recent paper published in PLOS Biology by Allen & Meller entitled Open science challenges, benefits and tips in early
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Academics and scientists, especially those in administrative positions looking to rate their peers/staff/candidates, have traditionally used impact factors as an indicator of quality and influence. Only the best, most trustworthy and innovative research gets published in journals with high impact factors. At least, that is what they would like us to believe… What is an impact factor? The impact factor
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The pressure to publish and current incentives that reward highly-cited discoveries leads to research findings that are not reproducible and inadvertently results in the natural selection of bad science. It is difficult to encourage scientists to take effort in conducting reproducible and rigorous research without better incentives. What kinds of metrics and incentives might reward scientists for conducting sound science?
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Scientifically Sound 