Distractions and amusements, with a sandwich and coffee.
Martin Krzywinski is a staff scientist at Canada’s Michael Smith Genome Sciences Centre.
Naomi Altman is a Professor of Statistics at The Pennsylvania State University.
Paul Blainey is an Assistant Professor of Biological Engineering at MIT and Core Member of the Broad Institute.
Danilo Bzdok is an Assistant Professor at the Department of Psychiatry, RWTH Aachen University, Germany, and a Visiting Professor at INRIA/Neurospin Saclay in France.
Kiranmoy Das is a faculty member at the Indian Statistical Institute in Kolkata, India.
Luca Greco is an Assistant Professor of Statistics at the University of Sannio in Benevento, Italy.
Jasleen Grewal is a graduate student in the Jones lab at Canada's Michael Smith Genome Sciences Centre.
Anthony Kulesa is a graduate student in the Department of Biological Engineering at MIT.
Jake Lever is a Postdoctoral Research Fellow in Bioengineering at Stanford University in Stanford, California, USA.
Geroge Luta Associate Professor of Biostatistics at the Georgetown University in Washington, DC, USA.
Jorge López Puga is a Professor of Research Methodology at UCAM Universidad Católica de Murcia.
Byran Smucker is an Associate Professor of Statistics at Miami University in Oxford, OH, USA.
Bernhard Voelkl is a Postdoctoral Research Fellow in the Division of Animal Welfare at the Veterinary Public Health Institute, University of Bern, Bern, Switzerland
Hanno Würbel is a Professor in the Division of Animal Welfare at the Veterinary Public Health Institute, University of Bern, Bern, Switzerland
We demand rigidly defined areas of doubt and uncertainty! —D. Adams
A popular notion about experiments is that it's good to keep variability in subjects low to limit the influence of confounding factors. This is called standardization.
Unfortunately, although standardization increases power, it can induce unrealistically low variability and lead to results that do not generalize to the population of interest. And, in fact, may be irreproducible.
Not paying attention to these details and thinking (or hoping) that standardization is always good is the "standardization fallacy". In this column, we look at how standardization can be balanced with heterogenization to avoid this thorny issue.
Voelkl, B., Würbel, H., Krzywinski, M. & Altman, N. (2021) Points of significance: Standardization fallacy. Nature Methods 18:5–6.
Clear, concise, legible and compelling.
Making a scientific graphical abstract? Refer to my practical design guidelines and redesign examples to improve organization, design and clarity of your graphical abstracts.
An in-depth look at my process of reacting to a bad figure — how I design a poster and tell data stories.
Building on the method I used to analyze the 2008, 2012 and 2016 U.S. Presidential and Vice Presidential debates, I explore word usagein the 2020 Debates between Donald Trump and Joe Biden.
We are celebrating the publication of our 50th column!
To all our coauthors — thank you and see you in the next column!
When modelling epidemics, some uncertainties matter more than others.
Public health policy is always hampered by uncertainty. During a novel outbreak, nearly everything will be uncertain: the mode of transmission, the duration and population variability of latency, infection and protective immunity and, critically, whether the outbreak will fade out or turn into a major epidemic.
The uncertainty may be structural (which model?), parametric (what is `R_0`?), and/or operational (how well do masks work?).
This month, we continue our exploration of epidemiological models and look at how uncertainty affects forecasts of disease dynamics and optimization of intervention strategies.
We show how the impact of the uncertainty on any choice in strategy can be expressed using the Expected Value of Perfect Information (EVPI), which is the potential improvement in outcomes that could be obtained if the uncertainty is resolved before making a decision on the intervention strategy. In other words, by how much could we potentially increase effectiveness of our choice (e.g. lowering total disease burden) if we knew which model best reflects reality?
This column has an interactive supplemental component (download code) that allows you to explore the impact of uncertainty in `R_0` and immunity duration on timing and size of epidemic waves and the total burden of the outbreak and calculate EVPI for various outbreak models and scenarios.
Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Uncertainty and the management of epidemics. Nature Methods 17.
Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Modeling infectious epidemics. Nature Methods 17:455–456.
Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: The SEIRS model for infectious disease dynamics. Nature Methods 17:557–558.
Our design on the cover of Nature Genetics's August 2020 issue is “Dichotomy of Chromatin in Color” . Thanks to Dr. Andy Mungall for suggesting this terrific title.
The cover design accompanies our report in the issue Gagliardi, A., Porter, V.L., Zong, Z. et al. (2020) Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes. Nature Genetics 52:800–810.
Clear, concise, legible and compelling.
The PDF template is a poster about making posters. It provides design, typography and data visualiation tips with minimum fuss. Follow its advice until you have developed enough design sobriety and experience to know when to go your own way.