Over at the BenchFly blog, three well-established PIs, who've each trained dozens of grad students and postdocs, share their thoughts on "the characteristics that make a truly great graduate student." Charles Craik at the University of California, San Francisco, says that "great students show a sense of urgency," which is "critical" in science. Michael Marletta at UC Berkeley says that "drive, determination, and confidence" factor most in his lab. And the Vanderbilt University School of Medicine's Lawrence Marnett values creativity and motivation in his trainees. "The really good ones would frequently surprise me at group meetings when I would suggest an experiment and they would say, 'Yeah, I did that already and here’s the result.' Our interactions, then, were more along the lines of colleagues," he tells the bloggers at BenchFly. When asked whether these qualities can be taught or if some students are inherently better prepared to succeed in the lab, the experts conceded that while motivation cannot be taught, scientific skills can always be sharpened. "It's not necessarily that that students are born great," Marnett says, "but the combination of creativity and hard work gives students a real edge." Craik adds that "if there is real passion for the work and an intense curiosity then a lot of the rest can be learned."
Great graduate students must tread the fine line between being confident enough to tackle projects independently and being overly bold and ambitious. Craik says that one of the greatest weaknesses he's seen in past trainees is "not listening and paying enough attention to the experts. It is fine to be bold and creative, but there is usually a very strong foundation to build off of and not taking advantage of that can be a waste of time." On the flip side, Marnett has witnessed students who are overly afraid of failure. It doesn't happen often, he says, "but I have occasionally seen fear of failure in people who are very smart, talented, and articulate … But for some reason they are afraid of not getting a good result or of wasting their time. That holds them back from just jumping in and doing a bunch of experiments, getting some data, and then deciding where to go next."