For better or worse, our mind plays tricks on us: we are prone to cognitive biases and logical fallacies. These biases don’t impact how do the laundry or prepare supper, although I may convince myself that I always intended to make a galette despite having the cookbook open at the soufflé page. Unfortunately, these same biases and fallacies can wreck
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Recently, I reviewed a number of research proposals in which some applied qualitative or mixed (i.e. quantitative and qualitative) methods to answer health questions. I had my “reviewer hat” on as I assessed the proposals for research quality. After 3-4 proposals, it occurred to me that while assessing quantitative research proposals for research quality and reproducible practices was straightforward, doing
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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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