Late last Friday, the Puerto Rico Department of Health finally released monthly death count data for the time period following Hurricane Maria: BREAKING: the Puerto Rico Health Department has buckled under pressure and released the number of deaths for each month, through May of 2018. In September 2017, when Hurricane Maria made landfall, there was a notable spike, followed by an even larger one in October. pic.twitter.com/3Irw1eUOTC — David Begnaud (@DavidBegnaud) June 1, 2018 The news came three days after the publication of our paper describing a survey conducted to better understand what happened after the hurricane.
The success of a data analysis depends critically on the audience. But why? A lot has to do with whether the audience trusts the analysis as well as the person presenting the analysis. Almost al presentations are incomplete because for any analysis of reasonable size, some details must be omitted for the sake of clarity. A good presentation will have a structured narrative that will guide the presenter in choosing what should be included and what should be omitted.
All data arise within a particular context and often as a result of a specific question being asked. That is all well and good until we attempt to use that same data to answer a different question within a different context. When you match an existing dataset with a new question, you have to ask if the original context in which the data were collected is compatible with the new question and the new context.
Johns Hopkins is a pretty amazing place to do computational genomics right now. My colleagues are really impressive, for example five of our faculty are part of the Chan Zuckerberg Initiative and we have faculty across a range of departments including Biostatistics, Computer Science, Biology, Biomedical Engineering, Human Genetics. A number of my colleagues are activitely looking for postdocs and in an effort to make the postdoc job market a little less opaque I’m listing this non-comprehensive list of opportunities I know about here.
The sharing of data is one of the key principles of reproducible research (the other one being code sharing). Using the data and code a researcher has used to generate a finding, other researchers can reproduce those findings and examine the process that lead to them. Reproducibility is critical for transparency, so that others can verify the process, and for speeding up knowledge transfer. But recent events have gotten me thinking more about the data sharing aspect of reproducibility and whether it is tenable in the long run.
Software has for while now played a weird an uncomfortable role in the academic statistics world. When I first started out (circa 2000), I think developing software was considered “nice”, but for the most part, was not considered valuable as an academic contribution in the statistical universe. People were generally happy to use the software and extol its virtues, but when it came to evaluating a person’s scholarship, software usually ranked somewhere near the bottom of the list, after papers, grants, and maybe even JSM contributed talks.
I recently finished reading Steve Coll’s book Directorate S, which is a chronicle of the U.S. war in Afghanistan post 9-11. It’s a good book, and one line stuck out for me as I thought it had relevance for data analysis. In one chapter, Coll writes about Lieutenant Colonel John Loftis, who helped run a training program for U.S. military officials who were preparing to go serve in Afghanistan. In reference to Afghan society, he says, “Everything over there is about relationships.
Back in February, I gave a talk at the Walter and Eliza Hall Research Institute in Melbourne titled “Lessons in Disaster: What Can We Learn from Data Analysis Failures?” This talk was quite different from talks that I usually give on computing or environmental health and I’m guessing it probably showed. It was nevertheless a fun talk and I got to talk about space-related stuff. If you want to hear some discussion of the development of this talk, you can listen to Episode 53 of Not So Standard Deviations.
Several times over the last few weeks my hatred of Doodle polls has come up in meetings. I think the polling technology is great, but I’m still frustrated by the polls. Someone asked what I’d rather have happen and I said: “set the meeting, then let me know when it is, if I can come I will. if i’m not there then i’m happy for you to decide without me”
Defining success in data analysis has eluded me for quite some time now. About two years ago I tried to explore this question in my Dean’s Lecture, but ultimately I think I missed the mark. In that talk I tried to identify standards (I called them “aesthetics”) by which we could universally evaluate the quality of a data analysis and tried to make an analogy with music theory. It was a fun talk, in part because I got to play the end of Charles Ives’ Second Symphony.