The narrative version of your scientific life

At xBio I heard a great talk by Gordon Mitchell, the Comroe lecture. The lecture, about respiratory plasticity and apnea was great. It was not my area, so I was also listening on a more meta-level. What Mitchell did was give the history of his scientific development. Being able to do this is a good skill, and I tweeted so.

Enter, the inestimable Dr. Becca who said “this is how I approached my TT job talks”.  So we’re writing this together – a double dose of advice. It’s good for talks, but it is also incredibly important for any pre-tenure mentoring meetings, and a near necessity for tenure documents.

Doc Becca:

I arranged to practice my TT job talk in front of some folks from my post-doc department, and as was customary for my talks until that day, at the bottom of the title slide I had “Dr Becca, advisor’s lab, post-doc Institution.” Before I even got a chance to begin speaking, one of the newer TT profs in the audience said, “Take your advisor’s name off this slide. This talk is about YOU.” This was some of the best advice I ever got in preparation for my interviews. This WAS about me. It’s the Me Show! It is not about me representing my mentor anymore. That tiny change helped me frame the whole talk to be about my journey, not just my data. I took my audience through the thought processes that led from my graduate work to my post-doc, and then how I used my post-doc work to carve a niche for myself in my field. And I guess I did a pretty convincing job, because I got offers from both departments that interviewed me. When search committees interview job candidates, they’re looking for someone who knows who they are and what they’re going to do with their careers- not just someone who’s done some stuff in some fancy labs. Your job talk is your narrative, just like your tenure packet statements will be your narrative in another 6 years’ time. Do you know what your story is, where it’s going?

Back to Potnia:

At my MRU there is a letter that goes in with your CV for your tenure or promotion package. Its called the Dean’s letter cause the chair signs it  and addresses it to the Dean (who may or may not ever read it. There is a school wide committee who certainly will). The chair may sign it, but by and large the candidate writes it.

This letter is the narrative of what you have done. It needs to include ideas and data and references to papers you have published. For an assoc prof, its at least 5 single spaced pages, and for full its nearer to 10. Crafting this letter is a skill, and one that you can’t start too sooWhat does it look like? It is the story, the narrative. It fills in the gaps in the CV.

To wit:

As a postdoc, I was  in the world’s foremost bunny hopping lab. When I moved to MRU, I  started a new program in chinchilla hopping. The first 2 papers I published developed the chinchilla model, and demonstrated the ways  in  which chinchillas were different from bunnies, and the interesting biomechanical and ecological consequences of chinchilla hopping. This led me to do field work on chinchillas in South American and my paper XYZ is an ecological study of chinchilla hopping in their natural setting, something that was unknown until I did this work. While I was there, the chinchillas all  started losing one leg. I first studied one legged hopping in collaboration with Dr. Jose BigWhig, an expert in chinchilla bacterial 8leg disease. Working with Dr. BigWhig, we published papers X3, X4 and X5. I was  responsible for all the biomechanics, but also learned a great deal about bacterial leg disease. When a similar outbreak occurred in North American bunnies, I was able to document that this was the same bug as infected the chinchillas. In fact, it is likely that I was responsible for the transfer of this bug because the outbreak occurred shortly after my return from South America. In my papers Y1 and Y2  I did both the biomechanics and the bacterial disease analyses for the North American bunnies.

This para shows: 1) independnce from postdoc advisor, 2) growth of abilities 3) collaboration and 4) new program.   If this was for real, there would be references to students who worked on the projects, papers presented at meetings, grants etc. Its woven into a timeline of the history and intellectual development.

 

 

More Old Fartes telling Young ‘uns what they should be doing (and a short diatribe on the necessity of calculus for premeds & bio majors).

This op-ed in the New Scientist has its heart in the right place. It’s telling mathematicians not to work for the NSA. Other mathematicians want to cut all ties to the NSA. They want math departments to cut ties to the NSA. The NSA hires more mathematicians, they say, then any place else in the world.

I wonder how many young math PhDs have their only job offer at the NSA? Or how many take a job there, opening up an academic position for someone else?

When I was in grad school (probably before you were born) I shared a house with two math PhD students. Their job search strategy was a lot different than mine. They picked where they wanted to go  (NYC as it turned out). There were multiple jobs at every Ivy League school, every major Inst of Technology. These weren’t tenure track – they were “instructors”, aka glorified postdocs. They were there to do research and teach calculus to the unwashed masses of bio and chem majors. In my day these jobs were even  called Ast. Prof jobs.

I have a friend in the math dept and she (yes, she) suggests that things are not much different. (But feel free to slap me upside the head and say its not this way). Today they tend to be “fellows” or even “postdocs”. Math depts teach a lot of a calculus to non-majors (aside on calculus to follow) – this is their bread and butter. There are usually not a lot of math majors, relative to all the various Bio majors and even Chemistry.The crunch for these guys comes after the fellowship, or postdoc. My sense is that this is now much closer to what life sciences PhDs perceive.

Its lovely that the old guys can tell the young guys not to take a job. Are any of them giving up their tenured (or even tenure track) jobs so that the new PhDs have an alternative to the NSA? What about the ones who don’t get tenure, willing to step down so they can have a job? Hmmm… not seeing a lot of hands here.

What does “exploration of your data” mean in this context? (part 2)

So when we last left our fearless data, we had done some basic, first step exploration.

One of the things we saw was that variation within a time point, within a species seemed to differ by time point and species. One exploratory step to look at this further is to generate a new measure of variation and plot that out. So for each smallest-level group (species at a time point), I calculated the Interquartile range.

The interquartile range is to the standard deviation, as the median is to the mean: it is a measure of variation that is not as biased by outliers as the sd. It is simply the value of the upper quartile minus the lower quartile. Visually, it is the box of the box plot, and it is the range in which half the data fall. Here is a boxplot for our SCLEN variable for young, blue species, values < 7am. The value for the IQrange here is 0.51. The standard dev is 0.44.

iq range

Now, lets plot the AMOUNT of variation vs. time, for old and young, by spp:

p2f2

The y-axis here is the IQrange. It shows us that the young red do have much more variation than the others. And the young seem to have, maybe, more variation than the adults. But pretty much its the red that is weird. Depending on your data you could do different things at this point: figure out why red is more variable (were the methods suspect, is it the species, or just variation). There might be more variation at 7 & 8 am than other times, and that might be worth testing. But I’d be pretty comfortable about going forward with other hyps about the data.

Now back to original data:

graph1

So the first hypothesis this suggests to me is that pre-dawn and post-dawn differ (call this time-of-day). I could test if 5am is different from 7am, and lump all that data. Or I could set it up in a nested fashion with hour nested within time-of-day. If I was doing this, I’d also add age and cross age with time-of-day. This plot also suggests a bigger effect of time-of-day in the older than younger animals. People often forget to test for effect size differences. In clinical work, that can be as important as an effect itself.

Another hypothesis that these data suggest is that the changes in pre-dawn are consistent, but that there is a pattern of change in the adult post-dawn. If all I was testing were multiple comparisons, I might end up testing if times are different from each other. If on the other hand I had seen this (but is it dominated by yellow in this plot?), I might craft a more specific hypothesis about the relative values in each hour in the post dawn time range.

I’ve not used up degrees of freedom by making this graph and examining. Instead I’ve honed in on what differences *might* exist. Even if pre-collection, I had hypothesized a pre-dawn/post-dawn difference, and a young-old difference, looking at these data can give me the shape of those differences, and suggest the specific differences for which I could test.

 

 

 

 

 

 

 

What does “exploration of your data” mean in this context? (part 1)

Yesterday on the tweets:

14h  Please remember that if using t-tests for post hoc paired comparisons, correct your t tests for multiple comparisons

13h or decide upon some (or one!) a priori tests, based on exploration of your data so you don’t do n*(n-1)/2 tests.

11h What does “exploration of your data” mean in this context?

11h GRAPH IT! Don’t just type in the data and hit GO on SPSS

 

What does exploring your data mean? Here are some real data, but the variable names (and question) have been changed to protect the innocent.

We measured blood levels of  hormone X hourly in sets of animals (remotely! in the wild!  because we can!). Measurements started at 1am, and ran through 11am. No measurement was taken at 6am (which was dawn). There were eight animals in each group. What were the groups? So glad you asked. There were four species (oh, of monkeys), let’s call them red, blue, yellow and green. The red and blue species live up in the trees and the yellow and green species live on the ground. Also, there were five adults, and five juveniles in each group. The numbers are not absolute values, but scaled to body size or total blood volume.

If you were clever, as Michael Hunsaker implied, you would have generated your hypotheses before starting. Things like: hormone X increases linearly over time. Or hormone X is always higher in adults than juveniles. Or tree dwellers have higher levels of X.

But what if someone gave you these data. Someone clueless who did not have specific hypotheses, but sorta kinda a feeling that hormone X is going to be different? (alternatively, you are the brilliant trainee and your clueless mentor just handed you these data and said: tell me what it means). These are situations for  (cue voice of Carl Sagan or Neil deGrasse Tyson depending on your generation)… Exploration .

What is your first exploratory step? Plot the data. The second? Plot it a different way. And? keep plotting till you have a sense of “the story” so you can generate some hypotheses. In these data there are obviously enough potential differences (10 time points * 2 ages * 4 species) to do far more comparisons than any sane person would want to do.

Here is the first and most simple plot I can think of. (but perhaps you could think of something different. that’s ok).

graph1

There are a large number of hypotheses from this graph. First – there is a difference in nighttime levels versus morning levels. Next, there is a difference in daytime levels between adults and young, but not in nighttime levels.

But, there are also some weird things you can see in these data. For example, what the heck is going on in the red species young? Let’s look at those data (I have “jittered these data so that the points don’t overlap. this means adding a small random noise on the x-axis):

graph2

Compare this to the previous plot. There is more variation in the young data in red.  Is this true of other species? Here is the green species:

graph 3

Now this shows a different, but interesting relationship. What is going on in the 7-11 hours in this animal? Less variable than Red, but different. Here’s blue:

graph 4

Yet another pattern. These different patterns may have to be tested separately. But certainly doing an all-possible-groups comparison would not have found these differences.

It is time to do experiments again (they have gone well so far this week). More on this tomorrow.

 

Collecting data: if you have never missed a flight, you’re getting to the airport too early

I’ve missed a few flights. And I am sure that I still waste too much time at the airport. I try to work there, but mostly I appreciate a small bit of downtime. Being able to take downtime in the midst of chaos is a talent? skill? ability? that I have worked hard to, if not master, at least achieve threshold ability.

The equivalent for data is the project that didn’t work. The data that is still on the backup hard disk (if you’re young, on paper and notebooks if you’re not).  I used to feel mildly guilty about ignoring it in favor of the more exciting stuff. I used to worry much more about that stuff than I do today.

I’ve come to a better place (an analog, not homologue to downtime at the airport). I feel that as long as I am productive, as  long as my peeps are doing well and they are productive, as long as the  distress and discomfort to the animals is as low as I can get it, and they are not wasted, its OK not to use Every Bit Of Data.

Maybe when you are a first year asst prof you don’t have data you won’t have used or not want to use (but its not unreasonable that stuff from your thesis might not see the light of day).

But if you use all the data you collect, well, you are making every plane. You’ve not taken the risks you need to take. Need? yes. You need to push and grow intellectually. There are lots of different paths to get there, but risk-free ones are ultimately as rewarding, challenging (in a good way) and leading to success.

What one can learn from failed experiments is multifaceted. This includes: what doesn’t work, ways it might work, and an array of micro, low level, proximal solutions. Or solutions to proximal, low level and micro problems. It also includes ideas for New Things. Something lots of jr. faculty (but not Maria) worry about: where will I get my new ideas? From your mistakes. From the data you can’t publish. From dreams late at night (but also see this).

The balance of risk and surety is hard. But total risk-avoidance and making every plane is not a good thing.

 

 

 

 

Minding your time (or my father wins again) junior faculty edition

My father was an economist and we grew up with cost/benefit principles governing our lives. In general, irrationality was not well received while I grew up, which did lead to frustration and top-of-our-lungs fights when my sibs and I were teenagers. Sigh. I loved being a teenager, but I would not want to be in the thrall of my hormones again.

My URM has a program to bring first year med students into labs.  Its 10-12 weeks of free to the faculty labor of the intensely serious and quite often solemn type.

For choosing a trainee, I have quite simple criteria: what was your grade in Anatomy/Cell Biology/Biochemistry/Physiology (since I teach in one of these, I know)? The knowledge base is not particularly important. What matters to me: Can you work hard at something that really may not seem (to you) immediately relevant to your goal (of becoming a world famous neurosurgeon)? For undergrads, I want to know your grades in Chem/Physics. If you picked a  “soft major” like psych don’t bother applying (and dear readers, please do not bombard me with comments about the non-softness and importance of social science – I’m talking about people who want to go to medical school. They picked an ‘easy’ major for good grades. People who want to do psych do NOT come to my lab to do a research project).

One point to keep in mind: Getting summer help is NOT the same thing as finding a postdoc or even picking a grad student to do a rotation in your lab.

Two of my most beloved junior faculty are interviewing right now. They schedule an hour each with 6 or 7 students. One reason they think this is necessary is a problem with the application form. It is not much more than what is your experience and a CV. They feel they need more info. But seriously, IT IS NOT WORTH THE TIME. Six hours? That is an experiment. Or a set of serious data analyses, or doing all the extra bits of an NIH grant. Writing a lecture for class. Editing a student’s (argh) paper.

Repeat after me: I DO NOT HAVE TIME TO  WASTE. TIME IS MY MOST IMPORTANT RESOURCE.

This student will not make or break your research. You want a smart (hence the grades) and hardworking person who will be the bottom of the food chain of your lab (even if it is only you and the student) over the summer. They will help push your productivity. You will give them a good experience. You do not need to know what was their biggest challenge and how they met it.

Which brings me to the second point. What do you do with this student over the summer? How much time do you spend with them? It’s still cost/benefit (yes, Pops, I listened). If you end up putting more into them than you get out, it is NOT worth your time. Training a summer student is not something you have to do. I am not advocating treating them like … well the way we all got treated. But you are working in a factory.  The factory produces widgets. These widgets are called scientific papers. There are other things you must do besides produce widgets to keep your job. You do them. They are called teaching and service. When it is time to do them, you do them as well as possible, within the limits of the stopping curves. But, if your summer is devoted to research, then you are in the widget producing business. (this is URM territory, I recognize that this is different for SLACs, etc). There is a threshold productivity for non-widget production, but the ultimate judgement (for tenure) is widgets.

I have been told that this advice sounds very cold and hard. Not caring. Not empathetic. That’s right. There is lots of time for caring after you get tenure. But if you fill your time with things that do not produce widgets, you will never have time to do the other things in the future.