NOAA provides a map of potential flooding due to Hurricane Ian headed towards Florida. Red indicates greater than 9 feet of flooding above ground.
I talked about how data science has been used during the COVID-19 pandemic, spanning vaccine design, clinical trials, surveillance and policy advice, and highlighting the identification of risk factors for disease.
If you like this talk, you might be interested in the following courses available this academic year:
- Infectious Disease Modelling: Mathematical Techniques (September 2022)
- Infectious Disease Modelling: Applied Methods in R (January 2023)
- Pandemic Data Science (April 2023)
When someone fires a gun into the air, the bullet travels thousands of feet in elevation. Gravity pulls the bullet back down, and it accelerates fast enough to penetrate a human skull by the time it reaches ground-level. Acceleration and trajectory vary by type of gun and the shot angle. 1Point21 Interactive shows the variation and dangers with a visual explainer.
Stephen Sherry, PhD, is the new NCBI Director and NLM Associate Director for Scientific Data Resources
Posted by What's Newin
To teach, learn, and measure the process of analysis more concretely, Lucy D’Agostino McGowan, Roger D. Peng, and Stephanie C. Hicks explain their work in the Journal of Computational and Graphical Statistics:
The design principles for data analysis are qualities or characteristics that are relevant to the analysis and can be observed or measured. Driven by statistical thinking and design thinking, a data analyst can use these principles to guide the choice of which data analytic elements to use, such as code, code comments, data visualization, non-data visualization, narrative text, summary statistics, tables, and statistical models or computational algorithms (Breiman 2001), to build a data analysis. Briefly, the elements of an analysis are the individual basic components of the analysis that, when assembled together by the analyst, make up the entire analysis.
Randall Munroe provides another fine observation through xkcd.
I often wonder what our data and charts will look like a century or two from now. Will the conventions and aesthetics look silly and amateur or classic and vintage? Will what seems like a lot of detailed data now seem spotty and useless, or will we look back in disbelief that companies were allowed to track our activities? Will AI have taken over human cognition and make these questions obsolete, because we’re in a suspended dream state, our bodies used as energy to power super computers, unsure of what is real and what is simulated? Important questions.
Posted by cancer biologyin
Wildfire obviously damages the areas it comes in direct contact with, but wildfire smoke can stretch much farther. Based on research by Childs et al., Mira Rojanasakul, for The New York Times, shows how pollution from smoke spread between 2006 and 2020.
My kids’ rooms still have air filters from a few years ago, when a fire many miles away made the sky orange and our indoor environment smokey.