Things to consider before applying for a K99/R00

It officially looks like I’ll be awarded a K99/R00 (!!). The application process was a long, overwhelming slog — only possible with the generous support of mentors, colleagues, friends, and strangers.

Here, I will try to pay it forward by sharing some thoughts and advice. There are plenty of good blog posts about applying for K99’s, so I’ll try to avoid repeating those. Instead, I’m going to focus on things I didn’t know before and/or didn’t read elsewhere. It will be based on (1) insight from others who applied, (2) advice from mentors of successfully funded applicants, and (3) my interpretation from reading about 20 K-award summary statements and applications (both funded and unfunded).

If there’s enough interest in the topic, I might write about the writing process itself, but here I’m going to focus on things to do before you apply. The tl;dr is (1) consider non-K99 options, (2) apply early in your postdoc, (3) give yourself more time than you think you’ll need, (4) be strategic about your target institution, and (5) avoid easy critiques.

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Collaboration network from 2010 to 2019

I have been trying to wrap my head around working with temporal networks — not just simple edge activation that changes over time but also evolving node attributes and nodes that may appear and disappear at random. What better way than to work with a small concrete example I’m already very familiar with?

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My Collaboration Network

My Twitter timeline is blowing up with #NetSci2018 tweets and awesome visualizations this week, so I was inspired to see if I can quickly make my own “gratuitous collaboration graph” (as Dan would say).

Hover over each node to see the name of the paper (red), co-author (blue), or other project (green for data and orange for software).

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Looking at opioid-related mortality, by race, 1979 to 2016

Our paper (with Monica Alexander and Magali Barbieri) is out now (in published ahead-of-print form). Monica has a great, short Twitter thread on the findings so if you don’t want to read the whole thing, check that out. The publisher’s PDF is here.

After submitting our paper, the NCHS released the 2016 multiple cause of death files — so a couple months ago we were curious to see how (or if) our results would change when adding the 2016 data.

[NOTE (2/25/2019): Since this post, the 2017 data have also been released. I did a similar analysis comparing our original paper with the additional two years of data. See this post here or our Github repo.]

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tldr; San Diego weather is better than Boston weather

I am taking a break from a crazy couple months of writing and coding by… writing code. Just a quick post comparing weather in Boston (where I am) to weather in San Diego (where I’m from).

While the New York Times may have made the original, most data viz people will recognize the plot above from Tufte’s classic, Visual Display of Quantitative Information. It presents a ton of data in a clear, concise, and appealing way. The background bars show the record high and low daily temperature, the mid-ground bars show the “normal” (though as far as I can tell, normal is never clearly defined) high and low temperature, and the foreground shows the high and low for that year. In addition, we have annotations for days that met or made the record. The original plot even had a subplot for daily precipitation.

Here is a similar plot for Boston:

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