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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Scientific impact is a difficult thing to measure. Nevertheless, a variety of indexes and metrics have been created that attempt to quantify it. Many of these measures are based on citations: the number of times a scientific paper is referenced by another scientific paper. Unfortunately, it is not entirely clear what these citation-based metrics actually reflect, nor is it clear
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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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In my last two posts, I talked about my experience with errors in science. Errors are part of life, and they are part of science. Without errors we can’t learn. However, if we hide away our data, our analysis and, if relevant, our code, errors will not be noticed. They and the data and results they generate will become part
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Scientifically Sound 