Enhancing the Effectiveness of Team Science
The past half-century has witnessed a dramatic increase in the scale and complexity of scientific research. The growing scale of science has been accompanied by a shift toward collaborative research, referred to as “team science.” Scientific research is increasingly conducted by small teams and larger groups rather than individual investigators, but the challenges of collaboration can slow these teams’ progress in achieving their scientific goals. How does a team-based approach work, and how can universities and research institutions support teams? Enhancing the Effectiveness of Team Science synthesizes and integrates the available research to provide guidance on assembling the science team; leadership, education and professional development for science teams and groups. It also examines institutional and organizational structures and policies to support science teams and identifies areas where further research is needed to help science teams and groups achieve their scientific and translational goals. This report offers major public policy recommendations for science research agencies and policymakers, as well as recommendations for individual scientists, disciplinary associations, and research universities. Enhancing the Effectiveness of Team Science will be of interest to university research administrators, team science leaders, science faculty, and graduate and postdoctoral students.
I am flipping through yet another National Academy report this week. They know what hooks me. This time: What Research Says About Effective Instruction in Undergraduate Science and Engineering (2015). http://www.nap.edu/catalog/18687/reaching-students-what-research-says-about-effective-instruction-in-undergraduate
Lots of ideas for little changes to my class in here…
I mean, not exactly what I will do, but lots of inspiration.
A set of slides from a talk by Matthew Salgnik crossed my inbox recently, titled “Open and Reproducible Research: Goals, Obstacles, and Solutions”. Good stuff! I liked the *bonus points* in the Data-is-available section:
bonus points for releasing extra variables that are not need to reproduce specific analysis.
This gets at what I think is really the point of reproducible research. To make it faster and easier to make new knowledge.
There is a proposal to drop some questions from the American Community Survey (ACS), and I was planning to use one of them in a project I’m trying to get started. I hope they keep it.
“I know there’s a lot of angst in the community right now,” Treat says. “But I think there’s a lack of understanding that the survey is under attack. So I encourage everybody to respond to the notice. The more responses we get, the better understanding there will be about the value of collecting this information.”
Interesting questionnaire on research culture from CACM (article | questions) . Would be fun to have a school of public health version…
I read a short book about science and society last weekend, Unscientific America by Chris Mooney and Sheril Kirshenbaum. It’s a quick read, and the context is very much the 2008 elections, so you should browse it sooner than later. There are some good ideas, but the focus on web campaigns of 2008 are going to make them sound even more dated in a year.
The book argues strongly for the meaningful popularization of scientific ideas. I love the popularizers of science, and was very influenced by books like Surely You’re Joking, Mr. Feynman and Gödel, Escher, Bach when I was a youth. The modern history sections in Unscientific America trace these popularizations to Carl Sagan’s book/television series Cosmos. I should check that out.
Tom Paulson, the global health journalist behind the NPR blog Humanosphere, has been taking on some very non-transparent (opaque?) rules from the Pacific Health Summit here in Seattle. Fortunately, he took a break to laud the transparency with which the institute I’m working at operates. Maybe he thinks we can be an example for the summitteers, or at least a counter balance.
Paulson didn’t mention the aspect of IHME’s work which, as an ivory-tower inhabiting academic, I find most radically transparent, however. The journal Population Health Metrics, which IHME director Chris Murray is the co-editor-in-chief and big booster of, has a scarily open review process. It’s not just open publishing where everyone can read the papers, it’s so open that everyone can read the referee reports, and the responses to referees, and the whole chain of revisions that a paper goes through before being stamped peer-reviewed.
This is great for authors. As a referee, it makes me much more responsible for my actions, which takes longer, but is probably a good thing overall. I even put some PyMC code in a review once, to tell the authors how to do something the easy way. But now I’m not sure I want to go look at this correspondence after all.
The Global Health Metrics and Evaluation conference that I attended two weeks ago was scrupulously videotaped, and most sessions are now online. I had a really good time at the session Responsible data sharing and strengthening country capacity for analysis, which started with this unusual framing by moderator Elizabeth Pisani:
I got some good news for the weekend, an opinion piece that I wrote together with some of the other post-graduate fellows at IHME was published online as a Science e-letter. It is titled U.S. Health Care Reform: The Case for Accountability and it’s about the measuring the outputs, outcomes, and impacts of the reform, whatever shape they end up taking.
The part that I was especially interested in adding to the discussion appears in paragraphs 3 and 4, about what these some of these statistics look like currently:
Disparities in health outcomes in the U.S. are unacceptable. A healthy life expectancy at birth in the U.S. ranks behind 28 other developed countries (1). Sizable groups in the United States have mortality risks resembling those in sub-Saharan Africa (2), including urban blacks between the ages of 15 and 64 living in counties with high homicide rates.
On average, Asian women lived 21 years longer than high-risk urban black males in 2001 (2). Although life expectancy for most American women increased between 1983 and 1999, life expectancy for women in 180 counties in areas such as Appalachia, the Deep South, the southern Midwest, and Texas decreased by 1.3 years (3).
I made some figures to accompany this, which Science didn’t print, so I’ve included them for you here:
Probability of a 45 year-old male dying before age 65, 2001, from Murray et al., Eight Americas: Investigating mortality disparities across races, counties, and race-counties in the United States. PLoS Medicine 2006.
Female life expectancy in US counties, 1961-1999 from Ezzati et al., The reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States. PLoS Medicine 2008.