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I think it would be interesting to organize a pub outing for one of the nights during the conference. It can be hard to meet people at these big conferences (unless it’s through people you already know) so I thought I would cast a wider net by posting here. If you’re planning on attending and want to meet, let me know by e-mail or in comments. Feel free to suggest a good place to go if you’re familiar with the area.
Drinking not required. DNA smiley from Paul Rothemund, as usual.
Comment [2]
They don’t all go like this, but the vast majority of seminars I attend seem to follow this general outline.
1. Introduction of Esteemed Speaker by Local Professor with the largest overlap in research interests. Enumeration of every award Esteemed Speaker has ever garnered is standard issue, and if Local Professor and Esteemed Speaker know each other, humorous story from “well, not THAT long ago” is recounted, though chances are you probably had to be there (unless it involves breaking obscenely expensive equipment, in which case everyone has a good laugh).
2. Esteemed Speaker takes over, and begins with a bunch of overly broad introductory slides. Naive audience members might think cancer was about to be cured, or a theory of everything (or at least, everything the speaker is interested in) is near discovery.
3. (For experimental talks, which are the majority I see.) Very brief overview of experimental technique presented, to the point that you have no idea how anything is being measured. Sample prep usually not even mentioned, despite having been years-long labours of love for at least a few graduate students who may or may not have even lasted long enough to see the experiments carried out.
4. A handful of slides on results, culminating with a question, usually from Local Professor, which elicits the following response from Esteemed Speaker: “Well, it’s not QUITE that straightforward” often followed quickly by “We can talk about it after if you like.”
5. A conclusion with extremely specific results, with no cure for cancer or the unification of gravity and quantum mechanics in sight.
6. An acknowledgement slide of all the graduate students and postdocs who actually did the work presented, where the couple of students or postdocs who have since found gainful employment are highlighted. Students in the audience wonder what the other 90% of the former lab members are doing, and then get depressed thinking about their own futures. (Alternatively, this could have been at the very beginning of the talk.)
If nothing else, number 4 is a shoe in. Related to this, I am finding more and more that the kind of talk given in a group meeting environment is more interesting: there’s no grand-standing, there’s no “massaging” of the data, and there is usually good discussion about the real issues faced on data analysis and collection, or the development of simulations, or what have you. Basically all the stuff that is, in number 4 above, brushed over and relegated to a private discussion between Esteemed Speaker and Local Professor after the talk, despite the fact that this is where a lot of the interesting science is found.
I guess I could rephrase my displeasure this way: results are, to first order, presented as though everything fits into a nice little box. But everyone knows that’s rarely the case in research. Also worth reading, YoungFemaleScientist voiced her displeasure with seminars in a slightly different way here (though I don’t agree that most speakers are out to please the old white guys in their field).
Comment [2]
James Watson has written another memoir, this time entitled Avoid Boring People: Lessons from a Life in Science, which this Times article says “contains an inflammatory epilogue with eye-popping theories that will, undoubtedly, leave ethicists choking with disbelief”. I’m less excited about his thoughts on the intelligence of people from Africa or how to get a date, and more excited by his insight into how to be a successful scientist. That being said, the article does contain a lot on Watson’s day to day life, and I enjoyed reading all of it.
This section outlines something we all know, but don’t necessarily emphasize enough:
For Watson, the ability to socialise is a key skill, one he believes can help propel you far beyond your peers. “Gossip is a fact of life also among scientists. And if you are out of the loop of what’s new, you are working with one hand tied behind your back.”
Socialising is certainly an important aspect of any successful career in science (and out, for that matter), but it isn’t always easy to do at huge meetings like the March Meeting of the APS, where there are literally thousands of people in attendance. The smaller conferences I’ve attended (with 50-100 people) have been a great way to meet others, in my own field and out. Part of it is the (usually) more relaxed atmosphere, where it doesn’t matter if sessions run a little late, and everyone isn’t as rushed, often leading to longer time for questions after talks, which I’ve found are the best way to start talking to someone—everyone likes it when you’re interested in their science! On the opposite side, the audience is necessarily quite a bit smaller. Does anyone have any advice on how to make the most out of your conference trips, particularly the large national conferences where almost everyone (boring and interesting alike) can be found?
Update — for another take on the book itself, see this week’s Nature review by Huntington F. Willard, here (subscription required).
Comment [1]
Anybody have any suggestions for
a) How to get revved up to self-promote? Do you listen to the theme from Rocky? What works for you?
b) What’s the most important thing to get across in a cover letter?
You’d think there would be a 10 Simple Rules article on this, but somehow there isn’t.
Having just done a round of cover letters when applying for my PhD position, I’d also like to ask those who are a couple of steps ahead: What differentiated your post-doc and/or faculty cover letters with those from grad school? For faculty searches your teaching experience comes into play, but are there any other tips or tricks we should all know about?
Comment [2]
This month’s PLoS Biology has a correspondence from a number of scientists* on the problem of assessing the contributions of each author on a multi-author paper (open access, as per PLoS usual).
They go on to list a number of sensible options for determining the author sequence based on credit. The technique I assume most common is that of “SDC”, or “sequence-determines-credit”. The first author played the most important role, while the following authors’ contributions are monotonically decreasing. In the “EC” scheme (“equal-contribution”), the authors should be listed alphabetically. Another option, “FLAE”, for “first-last-author-emphasis”, can be used when the second most “important” author is the final name on the list. A fourth and final suggestion is that “PCI”, or “percent-contribution-indicated”, where each author is assigned a percentage of the total credit for the paper.
However, I was very surprised to learn that detailed quantitative calculations based on schemes similar to these are routinely used by evaluation committees, with the impact factor of the journal thrown in as a scaling factor. This seems overly silly to me, as we already know that the impact factor is a pretty poor judge of impact, and when listing things like the percent you’ve contributed to a paper, well, who knows what you’ve actually done.
While all the above author-list-schemes are reasonable enough, I think it makes more sense to simply list what exactly each author contributed to the paper, and the relative worth can be left unsaid. This is the path being taken by a number of journals, including most recently Science, and as Alex pointed out at the time, perhaps this will lead to papers without sprawling author lists who are included for very questionable reasons.
* Thankfully the authors let us know who did all the legwork, as the authors followed the SDC, or “sequence-determins-credit”, approach.
Comment [7]
PLoS Computational Biology seems like lists as much as I do. This time around in Ten Simple Rules for Selecting a Postdoctoral Position they’re offering advice to those soon to be finishing their PhDs. In fact, the majority of the list is good reading for those who are about to start a graduate degree as well, so click here to read the whole list, no subscription required.
Since I’m in the proccess of figuring out labs in which I’d like to apply for my own PhD, I’ve spent a lot of time thinking about the combinations of rules 5 (Choose a Project with Tangible Outcomes That Match Your Career Goals) and 10 (Learn to Recognize Opportunities). Taking on exciting new projects by definition means you are charting in unknown waters, and tangible outcomes are no longer guaranteed (and even for a lot of me-too science). The editorial notes,
[f]or a future in academia, the most tangible outcomes are publications, followed by more publications.
How does one successfully ballance tangible outcomes (publishing papers [that people will read]) with choosing a lab doing novel and interesting science, and what are some safeguards from overly-ambitious “opportunities” that take too long to produce those tangible results? I’ve seen a number of really excellent talks (most recently at the Frontiers in Biophysics retreat), only to realise by the end that the number of dead ends graduate students (over a few generations) followed on these projects numbered much larger than the successes.
I’ve started learning the lessons, but I don’t yet have the answers.
Comment [10]
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