I thought you might be interested in this article that we recently published in the Lancet HIV on “Definitions of Implementation Science in HIV/AIDS.” The full article can be accessed here: http://dx.doi.org/10.1016/S2352-3018(15)00061-2. In this article, we review the use of the term “implementation science” in the HIV scientific literature, then synthesize those existing definitions using network analysis, and finally offer a working definition.
In addition, we used D3 to create an interactive visualization of the network of definitions – available here: http://tinyurl.com/imp-scie-defs-hiv. Hovering over a node reveals the text of the definition; and you can click-and-drag the nodes if any are obscuring author names. Just a few tricks from my days taking your interactive data visualization class 🙂
Thanks for introducing me to interactive data visualization! I very much appreciated your course and I’m happy to have been able to apply your lessons in this article! Hope you’re having a great time with the babies!
Tag Archives: IDV4GH
Here is a little feature in Matplotlib that I never saw before: stacked bar plots with tables attached. Perhaps too ugly for my Iraq Mortality stacked bar charts, but definitely handy for exploratory work.
I learned about it because it doesn’t work in `mpld3`… just one more benefit of being part of an open-source project. It would be so cool to have a `mpld3` version with some interactivity included, since interactivity can address one pitfalls of the stacked bar chart, the challenge of comparing lengths with different baselines.
Some notes on them here: http://nbviewer.ipython.org/gist/aflaxman/c93489dd19cee2eabf00
A new issue of Stephen Few’s Visual Business Intelligence Newsletter is out. I love categorizing things like this. I have been thinking of interaction very differently than he has, but everything else seems sensible to me.
I have added some stylish HTML tooltips to mpld3, make something pretty with them. Demonstration here.
There is an exciting new project in pythonic interactive data visualization that I have my eye on: mpld3. It plays well with matplotlib-based pretty plotting packages, and has the beginnings of a plugin framework for adding custom interactivity.
I used it to mock up a Cartesian fish eye distortion plot, something I’ve wanted for DisMod-MR ever since I learned about it. (Sometimes the interactivity doesn’t work in that notebook, and requires reloading everything… cutting edge software has some rough edges.)
This may come in handy: http://gangerolf.blogspot.com/2012/09/norway-in-geojson.html
I know I have seen a nice one for USA somewhere as well.
IHME has recently worked with the World Bank to release a series of regional reports on relevant findings from the Global Burden of Disease 2010 Study. It is cool to see this work getting disseminated, and now even in non-English editions. This raises questions for data visualization translations, like should 1990 and 2010 be in reversed positions when accompanying right-to-left text?
I sometimes wish transitions in a data viz went more smoothly. Maybe this frame-per-second calculator can help with optimization:
I had two colleagues call my attention to a cool use of GBD 2010 estimates recently: the Economist observed World Hepatitis Day by calling attention to the deaths due to hepatitis as compared to the deaths due to HIV. It is very nice to see these numbers getting out into the world.
But there are a lot of metrics to use for this comparison, and a lot of ways to show them besides a four-colored map. Find a country of interest from their map, and then make a detailed comparison on the GBD-Compare tool: China, North Africa/Middle East, United States.