Good Mentoring

July 1, 2021

One of the recurring discussions / rants in academic circles is the crediting of good mentoring to the Professor. I’m not going to tag the stimulus of the day because it generalizes and because I have no idea what motivates any particular person on any particular day.

There does seem to be a common theme. A Professor, usually female and usually more-junior, is upset that her considerable efforts to mentor students or postdocs does not seem to get as much credit as they should. This is typically contextualized by oblique or specific reference that some other professors do not put in the effort to “mentor” their trainees as well and this is not penalized. Furthermore, there is usually some recognition that a Professor’s time is limited and that the shoddiness of the mentoring of those other peers lets them work on what “really counts”, i.e., papers and grants, to an advantage over the self-identified good mentor.

Still with me?

There is a further contribution of an accusation, implicit or explicit, that those other peer Professors are not just advantaged by not spending time on “mentoring” but also advantaged by doing anti-mentoring bad things to their trainees to drive them to high scientific/academic output which further advantages the bad mentor colleagues against our intrepid hero Good Mentor.

Over on Twitter I’ve been pursuing one of my usual conundrums as I try to understand the nature of any possible fixes we might put in place with regard to “good” and “bad” academic mentoring, i.e., the role of career outcome in influencing how the mentee and evaluating bodies might view the quality of mentoring practices. My point is that I’ve seen a lot of situations where the same PI is viewed as providing both a terrible and a good-to-excellent mentoring environment by different trainees. And the views often align with whether the trainee is satisfied or dissatisfied with their career outcomes, and align less well with any particular behaviors of the PI.

Here, I want to take up the nature of tradeoffs any given Professor might have, in the context of trying to mentor more than one academic trainee, yes concurrently, but also in series.

My assertion is that “good mentoring” takes time, it takes the expenditure of grant and other funds and it takes the expenditure of social capital, in the sense of opportunities. In the case of most of the Professoriate, these are all limited resources.

Let us suppose we have two graduate students nearing completion, in candidacy and up against a program requirement for, e.g., three published papers. Getting to the three published papers, assuming all else equal between the two trainees, can be greatly affected by PI throw down. Provision of assistance with drafting the manuscript, versus terse, delayed “markup” activities? Insisting the paper needs to get into a certain high JIF journal, versus a strategy of hitting solid society journals. Putting research dollars into the resources, capital or personnel, that are going to speed progress to the end, versus expecting the trainee to just put in more hours themselves.

A PI should be “fair”, right? Treat everyone exactly the same, right? Well…it is never that simple. Research programs have a tendency not to go to plan. Projects can appear to level themselves up or down after each experiment. Peer review demands vary *tremendously* and not only by journal JIF.

Let us suppose we have two postdocs nearing a push for an Assistant Professor job. This is where the opportunities can come into play. Suggesting a fill-in for a conference presentation. Proposing conference symposia and deciding which trainee’s story to include. Choosing which project to talk about when the PI is herself invited. Pushing collaborations. Manuscript review participation with a note to the Associate Editor. Sure, it could be “fair”. But this is a game of competitive excellence and tiny incremental improvements to the odds of landing a faculty position. Is it “good mentoring” if taking a Harrison Bergeron approach means you never seem to land any postdocs from the laboratory in the plummiest of positions? When a focal throwdown on one would mean they have a good chance but divide-and-conquer fails to advance anyone?

More importantly, the PIs themselves have demands on their own careers. “Aha”, you cry, “this selfishness is what I’m ON about.”. Well yes…..but how good is the mentoring if the PI doesn’t get tenure while the grad student is in year 3? Personal experience on that one, folks. “not good” is the answer. Perhaps more subtly, how is the mentoring going to be for the next grad student who enters the laboratory when the PI has generated “fair” publishing prior trainees but not the glamourous publications needed to land that next grant? How much better is it for a postdoc entering the job market when the PI has already placed several Assistant Professors before them?

Or, less catastrophically, what if the PI has expended all of the grant money on the prior student’s projects which the student constructed and just happens to be highly expensive (“my mentor supports my research (1-5”))? Is that good mentoring? Well yeah, for the lucky trainee but it isn’t fair in a serial sense, is it?

Another common theme in the “good mentor” debate is extending “freedom” to the trainee. Freedom to work on “their ideas”. This is a tough one. A PI’s best advice on how to successfully advance the science career is going to be colored in may cases by practicality of making reasonable and predictable forward progress. I recently remarked that the primary goal of a thesis-proposal committee is to say “gee that’s nice, now pick one quarter of what you’ve proposed and do that for your dissertation/defense“. Free range scientists often have much, much larger ideas than can fit into a single unit of productivity. This is going to almost inevitably mean the PI is reining in the “freedom” of the trainee. Also see: finite resources of the laboratory. Another common PI mentoring pinch point on “freedom” has to do with getting manuscripts actually published. The PI has tremendous experience in what it takes to get a paper into a particular journals. They may feel it necessary to push the trainee to do / not do specific experiments that will assist with this. They may feel it necessary to edit the hell out of the trainees’ soaring rhetoric which goes on for three times the length of the usual Intro or Discussion material. …..this does come at a cost to creativity and novelty.

If the “freedom” pays off, without undue cost to the trainee or PI or other lab members…fantastic! “Good mentoring!”

If that “freedom” does not pay off- grad student without a defendable project or publishable data, significant expenditure of laboratory resources wasted for no return – well this is “Bad mentoring!”

Different outcome means the quality of the same behaviors on the part of the PI is evaluated as polar opposites.

Asked and Answered

June 2, 2021

A tweet in response to a question I asked

said that perhaps a grad student’s job is to learn to answer questions and a postdoc’s job is to learn to ask questions.

I thought about that for half a second and concluded that this is backwards, for me. I think that I started into grad school thinking I knew how to ask scientific questions. I then spent the entirety of my time in grad school, right up until my committee signed off on my dissertation, learning the hard way that this was not so. I concluded that the main part of my graduate school training was learning how (not) to ask scientifically tractable questions.

In my postdoctoral training, I think that I learned how to answer questions. Not in the limited sense of “conduct this experiment, analyze the data and conclude a result”. Answering a question in the much broader sense of deploying available resources to address a scientifically tractable question and to bring this to an “answer” that was publishable in the scientific record.

I believe my career as a PI simply extends upon this, in the sense that my job is to secure the available resources in a broader sense and that “tractable” now includes the ability to direct the hands of more people. And the questions may no longer be my questions, but rather the questions of those who are in my laboratory. But it’s all just answering questions.

A quick google search turns up this definition of prescriptive: “relating to the imposition or enforcement of a rule or method.” Another one brings up this definition, and refinement, for descriptive: “describing or classifying in an objective and nonjudgmental way….. describing accents, forms, structures, and usage without making value judgments.

We have tread this duality a time or two on this blog. Back in the salad days of science blogging, it led to many a blog war.

In our typical fights, I or PP would offer comments describing the state of the grant-funded, academic biomedical science career as we see it. This would usually be in the course of offering what we saw as some of the best strategies and approaches for the individual who is operating within this professional sphere. Right now, as is, as it is found. Etc. For them to succeed.

Inevitably, despite all evidence, someone would come along and get all red about such comments as if we were prescribing, instead of describing, whatever specific or general reality we were describing.

Pick your issue. I don’t like writing a million grants to get the barest hope of winning one. I think this is a stupid way for the NIH to behave and a huge waste of time and taxpayer resources. So when I tell jr and not so jr faculty to submit a ton of grants this is not an endorsement of the NIH system as I see it. It is advice to help the individual to succeed despite the problems with the system. I tee off on Glam all the time….but would never tell a new PI not to seek JIF points wherever possible. There are many things I say about how NIH grant review should go, that might seem to contrast with my actual reviewer behavior for anyone who has been on study section with YHN. (For those who are wondering, this has mostly to do with my overarching belief that NIH grant review should be fair. Even if one objects to some of the structural aspects of review, one should not blow it all up at the expense of the applications that are in front of a given reviewer.) The fact that I bang on about first and senior authorship strategy for respective career stages doesn’t mean that I believe that chronic middle-author contributions shouldn’t be better recognized.

I can walk and chew gum.

Twitter has erupted in the past few days. There are many who are very angered by a piece published in Nature Communications by AlShebli et al which can be summarized by this sentence in the Abstract “We also find that increasing the proportion of female mentors is associated not only with a reduction in post-mentorship impact of female protégés, but also a reduction in the gain of female mentors.” This was recently followed, in grand old rump sniffing (demi)Glam Mag tradition by an article by Sterling et al. in PNAS. The key Abstract sentence for this one was “we find women earn less than men, net of human capital factors like engineering degree and grade point average, and that the influence of gender on starting salaries is associated with self-efficacy“. In context, “self-efficacy” means “self-confidence“.

For the most part, these articles are descriptive. The authors of the first analyze citation metrics, i.e. “We analyze 215 million scientists and 222 million papers taken from the Microsoft Academic Graph (MAG) dataset42, which contains detailed records of scientific publications and their citation network”. The authors of the second conducted a survey investigation: “To assess individual beliefs about one’s technical ability we measure ESE, a five-item validated measure on a five-point scale (0 = “not confident” to 4 = “extremely confident,” alpha = 0.87; SI Appendix, section S1). Participants were asked, “How confident are you in your ability to do each of the following at this time?”:”

Quite naturally, the problem comes in where the descriptive is blurred with the prescriptive. First, because it can appear as if any suggestion of optimized behavior within the constraints of the reality that is being described, is in fact a defense of that reality. Intentional or unintentional. Second, because prescribing a course of action that accords with the reality that is being described, almost inevitably contributes to perpetuation of the system that is being described. Each of thse articles is a mixed bag, of course. A key sentence or two can be all the evidence that is needed to launch a thousand outraged tweets. I once famously described the NSF (in contrast to the NIH) as being a grant funding system designed for amateur scientists. You can imagine how many people failed to note the “designed for” and accused me of calling what I saw as the victims of this old fashioned, un-updated approach “amateurs”. It did not go well then.

The first set of authors’ suggestions are being interpreted as saying that nobody should train with female PIs because it will be terrible for their careers, broadly writ. The war against the second set of authors is just getting fully engaged, but I suspect it will fall mostly along the lines of the descriptive being conflated with the prescriptive, i.e., that it is okay to screw over the less-overconfident person.

You will see these issues being argued and conflated and parsed in the Twitter storm. As you are well aware, Dear Reader, I believe such imprecise and loaded and miscommunicated and angry discussion is the key to working through all of the issues. People do some of their best work when they are mad as all get out.


We’ve been through these arguments before. Frequently, in my recollection. And I would say that the most angry disputes come around because of people who are not so good at distinguishing the prescriptive from the descriptive. And who are very, very keen to first kill the messenger.

A certain someone has taken it upon himself to lampoon certain types of solicitations issued by a lab head for postdocs and occasionally for graduate students, when they appear on Twitter. The triggering material included in such solicitations are terms such as “independent”, “energetic”, “brilliant”, “highly motivated”, “creative” and the like. Sometimes the trigger for this certain someone is merely a comment that the applicant should be experienced in some particular scientific technique. Seemingly inoffensive and very traditional, right? I mean, every lab head wants the lab to be as successful as possible and that means that they want good rather than bad employees.


Whoops. But we’re talking about trainees, right? Graduate students and postdoctoral trainees.

They are supposed to be getting something from the lab, not the other way around. Correct? So this over emphasis on how the PI only wants to hire the most talented, rather than the most needy, individuals pulls back the curtain to reveal the seamy truth.

“Trainees” in biomedical science are in large part the workforce. Which is obtained for less money due to the “training” misdirection.


This is one that set me off recently, thanks to our beloved aforementioned trollerpants. Chit chat amongst the Professor class that they “need” a postdoc now. Or general announcements that they will be soon looking to “hire a graduate student” in their new appointment, whee! but…”need”.

And of course coupling this to the above focus on the very best, most motivated, well trained, energetic self-starting individuals?

The notion of actually competing for the best of the available postdocs raises an ugly head.

You will be entirely unsurprised that I couple all of this to my views on labor in academic research labs and, in particular, the way we go along deluding ourselves that we are not part of any sort of labor market. I couple this to my thinking about ways to make academic careers slightly less hellish on the factors which are usually rubbing points.

Thinking more about the labor aspects of what is now academic “training” lets us think, I believe, more creatively about making things better for all of us.

No, it does not magically invent more Professor jobs. It does not restore State level commitment of funds to public Universities and thereby relieve the pressure for extramural funds. It does not make the NIH budget double overnight and therefore reduce pressure for the grant seekers.

But creating stable, long term job categories for those who are now some thin rebranding of “postdoc” could advance us. Creating stable career jobs to do the pure work part of the graduate student job could advance us.

Yes, this means we will “train” fewer graduate students and replace that labor with technicians. Who will be more or less expected to journey through their career as a career. Benefits. Increasing salaries with experience and longevity.

It’s gonna cost.

That brings me around to grant review. It always comes back to grant review.

One of the reasons NIH put the modular budget in place is to get reviewers to stop with the ticky tack over costs. Costs that vary all across the country from place to place. Costs that a certain species of reviewer just could not get through their head would vary. Costs that a certain species of reviewer delighted in using to spike a grant because those outrageous cage costs at Big U were higher than they were paying at their LessBig U.

And salary.

A certain species of reviewer is very concerned about salaries paid, if they can just get their beady little eyes on the information. A related species is very concerned about how many individuals are being paid off the grant, if they can just get their eyes on that information.

It is very hard to get their eyes on contributions by graduate students or postdocs who are on a fellowship or Program paid stipend. It is inevitably that they get their eyes on technician salaries when looking at an itemized budget.

I have recently received a grant review comment that clearly I was paying my technical staff too much, coupled with an obviously grudging admission that the person had long experience as a technician.

I have related more than once on these pages that over time I have generally relied more on tech labor than on the “trainee” scam. This, as our second President of the USA John Adams famously remarked about his refusal to use enslaved labor, costs me. It costs my grants and therefore I get less productivity per dollar compared with someone who is willing to fully exploit cut-rate labor under guise of “trainee” job categories. I do not turn my techs over willy-nilly every several years to reset salaries, either. And the way things work in these here United States, people get paid more over time. Those with more experience get paid more than those with less, even if the lesser experienced person could do the same job.

So when my peers who review my grants say that the merit of my proposal is diminished because I make these labor choices in my lab, and suggest that what I should be doing is exploiting the heck out of labor by using less experienced and cheaper techs…..

I get a little shouty. and bloggy.

For every reviewer who is dumb enough to actually write this in a critique, there are ten that are thinking it. They are taking a less positive cant on my proposal as a consequence. And possibly looking for other ways to express their disapproval.

I myself have occasionally fallen into to the “too many staff for the work described” review space. I’ve done it super rarely, so I think I’m probably on solid ground. The only cases I can recall were really, really egregious. But I need to watch myself, as do you. How often are you thinking that a major grant will receive the supplemental help of undergraduate “interns”? grand students or postdocs on “their own” fellowships? How many times have you questioned the role of a staff scientist when surely a postdoc would do?

First Generation

April 24, 2020

It can be difficult to be the first person in your family to do something when it comes to careers and training for them. There are always going to be aspects of that career, or training, that are opaque, obscure or intentionally concealed. Many of these things give an advantage, significantly so in many cases, to those who are aware of them.

Academia is our main concern around here and just about every aspect is easier if you know more about how things work, and especially “how things really work” in several cases where the latter is in contradiction to the surface level information.

In a prior brief post I mentioned that I am, more or less, in the family business, i.e., that of public funded education and academics. I’ve probably blogged or commented several related aspects but suffice it to say, academic careers were not strange to me at any part of my life that I can remember. “Dean is a four letter word” is a concept that was drilled into me since childhood. I’ve known about “undergraduate summer research experiences” since before I left elementary school. I knew about tenure and the difficulties associated with not being “amenable to the senior faculty” (yeah that’s a quote from an actual someone’s initial tenure denial after going up early). I got this sort of vague indoctrination into what it meant to be a “good” professor at a Primarily Undergraduate Instruction institution, as compared with various forms of phone-it-in-deadwood….in certain points of view, of course. I have known if you want to be a Professor you had to go to graduate school to get a PhD since approximately forever.

Despite all of this, there is a metric efftonne of stuff about my career as it unfolded that I had not the foggiest clue about until I was upon it or, most likely, after the fact. I attended a smaller college for undergraduate studies and the research opportunities were limited. My department faculty was all emeritizing at the time and were not doing much research at all. They were not trying to groom us for doctoral studies in any specific way. One relatively new professor took me under his wing, got me some summer research-project funding one year and tried to kind of help me along, but it was not super aggressive in terms of telling me all the ins and outs of career planning.

I have found many aspects of my career opaque, some of which is my own fault of course. I just blundered forward on the immediately observable rules in front of me at the time. “Apply for grad school, this Prof stuff seems like something I would like to do forever“. Apply for these graduate fellowships at the same time, sure why not? Financial aid was familiar from the college application/choice process so…”this is basically the same, I think?“. Which graduate programs? “Well, I want to live here, here or here and not there….I think these programs are somehow ranked in the top 25 of my discipline so..looks good!“.

I knew less than Jon Snow. Somewhere in the process of reviewing graduate school materials I realized they were going to pay me a stipend. Or maybe it was when I started looking for what I thought of initially as “financial aid” to cover tuition and maybe living expenses in part. At some point I connected the dots. I am certain I never cottoned on to this from my family experience. I was missing that piece. I don’t think any of my college professors ever told me this directly (and most of their experiences were decades out of date). I didn’t really think that hard about how graduate training disciplines came with important differences in how graduate support worked. Nobody explained any of this.

I didn’t know what I didn’t know and I didn’t know how to find out until….oh, around my second postdoc I started to get a clue.

I cannot imagine how hard all of this is for someone who does not have any family members who have completed a four year undergraduate education. I can’t imagine how random it would be for such a person to really grasp, as they are being educated at a research university even, all of these critical facts. I have gone out of my way with every undergraduate who has sought a research experience in my laboratory to point out that graduate school pays a salary. I think it is absolutely critical, if we are to do even the most basic of recruiting efforts with people who are underrepresented in academics and in science.

The world of academia, particularly the one I inhabit, has been much better in recent years about paying attention to first generation students. From undergraduate admissions, retention, support and assistance to the provision of graduate slots and fellowships, through to postdoctoral and faculty funding opportunities, we are treating first generation as special. Explictly or implicitly as if they are an underrepresented minority group.

Now yes, many such individuals are already within some other category of under-representation. Which then makes us ask who is leftover, and of course it generally means less-privileged white folks in these here United States of America. I grew up in a super majority white part of the country where the best hope for a really smart kid from one of the local deeply rooted families was to join the military and gtfo of that place. I am not kidding. Despite the presence of a local undergraduate institution and we, the families of the Professors of said institution, there is no friggin way the local yokel family kid who was really fricken smart (and I went to school with them, there were several) was going to end up where I ended up. Ok, ok, I know that statistically many of you, Dear Readers, did come from similar backgrounds, but the hit rate is really low. I get it. I believe in it. I work for it. We should focus on first generation people as if they are an underrepresented minority.


This gets us deep into the Oppression Olympics about who is most deserving and who needs to come second at the gravy train. There are no good answers and I am sure we all struggle with our own perspectives and biases.

One key issue is passing. A person from my home town who manages to make it into academia may be able to pass entirely. His or her colleagues will never know about their background unless they choose to share. This is countered by the “what about Obama’s kids???” cries about how visual distinction may hide a background that is advantaged and just like everyone in the majority.

Yep. Lots of nasty arguments to be had.

This sort of Oppression Olympics thinking affects our takes on any claims to first generation in academics in weird ways. I’ve seen, I think, people trying to claim that they are super underprivileged because they didn’t have a parent ever go to grad school. Now, Mummy and or Daddy may be four year college educated, possibly at a awesome-name college and the family may be rich as all get out due to success in some endeavor unrelated to anything PhD holders do. But the person has not been around life-long academic scientist types and so feels justified in identifying with hashtag-firstgen with a PhD addendum.

This angers some people.

I am not certain how I feel about it. I feel that many of us can be very much at sea about our careers despite a family steeped in higher education activity. Sometimes it is because the family experience was in a totally different discipline or our experience of it was when that family member was in a totally different job type than the one we are targeting. Sometimes it is because of the decades long gap and the changing nature of being an academic.

On the other hand, yeah, we have a problem in all of USian life right now where everyone in the upper-middle to frankly upper slice wants to define themselves as average. And to actually feel just a little bit under-privileged. Because of course we are always looking up at someone who has a little bit more than us, instead of the majority of this country which has less. It’s common. I get it. But it does also ring a little false to me when someone of apparent socio-economic privileges starts brandishing a “pity me” hashtag of first gen, just because they have two kids in their grad class with parents that published papers in the field they are in.

Lab Size, take 3

May 17, 2019

I was struck by a couple of tweets. The first seems to find it worthy of remark that staff on a grant are expensive.

and the second seems to outline what appears normal for one grant proposal in the eyes of this tweeter.

One postdoc, three grad students, an unspecified part time for an undergrad and similarly unspecified time for a tech. Lets say part-time is half and “some hours” equals a quarter. But the specifics here don’t really matter because “my technician” gives us a pretty good clue when it comes to lab size.

I return to a question I have asked this audience before. What, in you view, is a standard, average lab size? What size do you think would be about right for yourself? What are the bounds? What size is too small for you really to feel like you have “a lab” going? What size is getting up there into “whoa, that’s a big lab” territory?

The follow up question is, once you have this ideal lab size in mind, how does it fit with your heartfelt beliefs about how NIH grant moneys should be distributed. This tweep came up with a number that is significantly above the full-modular NIH limit of $250,000 per year. It is a number that would require traditional budgeting and, in some ICs, might get cuts even larger than their default 10%. It is a number that would fit a lot more comfortably into two R01 scope grants…in terms of the budget.

Can you square your ideas of fair NIH grant award with your ideas on normal average nothing-special lab size?

A recent twitt cued a thought.

Don’t ask your staff for a meeting without giving an indication of what it is about.

“Hey, I need to see you” can be very anxiety provoking.

“Come see me about the upcoming meeting Abstracts deadline” is not that hard to do.

“We need to talk about the way we’re doing this experiment” is duck soup.

Try to remember this when summoning your techs or trainees.

As mentioned in Science, a new report from the US Academies of Sciences, Engineering, and Medicine have deduced we have a problem with too many PhDs and not enough of the jobs that they want.

The report responds to many years of warning signs that the U.S. biomedical enterprise may be calcifying in ways that create barriers for the incoming generation of researchers. One of the biggest challenges is the gulf between the growing number of young scientists who are qualified for and interested in becoming academic researchers and the limited number of tenure-track research positions available. Many new Ph.D.s spend long periods in postdoctoral positions with low salaries, inadequate training, and little opportunity for independent research. Many postdocs pursue training experiences expecting that they will later secure an academic position, rather than pursuing a training experience that helps them compete for the range of independent careers available outside of academia, where the majority will be employed. As of 2016, for those researchers who do transition into independent research positions, the average age for securing their first major NIH independent grant is 43 years old, compared to 36 years old in 1980.

No mention (in the executive summary / PR blurb) that the age of first R01 has been essentially unchanged for a decade despite the NIH ESI policy and the invention of the K99 which is limited by years-since-PhD.

No mention of the reason that we have so many postdocs, which is the uncontrolled production of ever more PhDs.

On to the actionable bullet points that interest me.

Work with the National Institutes of Health to increase the number of individuals in staff scientist positions to provide more stable, non-faculty research opportunities for the next generation of researchers. Individuals on a staff scientist track should receive a salary and benefits commensurate with their experience and responsibilities.

This is a recommendation for research institutions but we all need to think about this. The NCI launched the R50 mechanism in 2016 and they have 49 of them on the books at the moment. I had some thoughts on why this is a good idea here and here. The question now, especially for those in the know with cancer research, is whether this R50 is being used to gain stability and independence for the needy awardee or whether it is just further larding up the labs of Very Important Cancer PIs.

Expand existing awards or create new competitive awards for postdoctoral researchers to advance their own independent research and support professional development toward an independent research career. By July 1, 2023, there should be a fivefold increase in the number of individual research fellowship awards and career development awards for postdoctoral researchers granted by NIH.

As we know the number of NIH fellowships has remained relatively fixed relative to the huge escalation of “postdocs” funded on research grant mechanisms. We really don’t know the degree to which independent fellowships simply annoint the chosen (population wise) versus aid the most worthy and deserving candidates to stand out. Will quintupling the F32s magically make more faculty slots available? I tend to think not.

As we know, if you really want to grease the skids to faculty appointment the route is the K99/R00 or basically anything that means the prospective hire ” comes with money”. Work on that, NIH. Quintuple the K99s, not the F32s. And hand out more R03 or R21 or invent up some other R-mechanism that prospective faculty can apply for in place of “mentored” K awards. I just had this brainstorm. R-mechs (any really) that get some cutesy acronym (like B-START) and can be applied for by basically any non-faculty person from anywhere. Catch is, it works like the R00 part of the K99/R00. Only awarded upon successful competition for a faculty job and the offer of a competitive startup.

Ensure that the duration of all R01 research grants supporting early-stage investigators is no less than five years to enable the establishment of resilient independent research programs.

Sure. And invent up some “next award” special treatment for current ESI. and then a third-award one. and so on.

Or, you know, fix the problem for everyone which is that too many mouths at the trough have ruined the cakewalk that experienced investigators had during the eighties.

Phase in a cap – three years suggested – on salary support for all postdoctoral researchers funded by NIH research project grants (RPGs). The phase-in should occur only after NIH undertakes a robust pilot study of sufficient size and duration to assess the feasibility of this policy and provide opportunities to revise it. The pilot study should be coupled to action on the previous recommendation for an increase in individual awards.

This one got the newbie faculty all het up on the twitters.


being examples if you are interested.

They are, of course, upset about two things.

First, “the person like me”. Which of course is what drives all of our anger about this whole garbage fire of a career situation that has developed. You can call it survivor guilt, self-love, arrogance, whatever. But it is perfectly reasonable that we don’t like the Man doing things that mean people just like us would have washed out. So people who were not super stars in 3 years of postdoc’ing are mad.

Second, there’s a hint of “don’t stop the gravy train just as I passed your damn insurmountable hurdle”. If you are newb faculty and read this and get all angree and start telling me how terrible I am… need to sit down an introspect a bit, friend. I can wait.

New faculty are almost universally against my suggestion that we all need to do our part and stop training graduate students. Less universally, but still frequently, against the idea that they should start structuring their career plans for a technician-heavy, trainee-light arrangement. With permanent career employees that do not get changed out for new ones every 3-5 years like leased Priuses either.

Our last little stupid poll confirmed that everyone things 3-5 concurrent postdocs is just peachy for even the newest lab and gee whillikers where are they to come from?

This new report will go nowhere, just like all the previous ones that reach essentially the same conclusion and make similar recommendations. Because it is all about the

1) Mouths at the trough.
2) Available slops.

We continue to breed more mouths PHDs.

And the American taxpayers, via their duly appointed representatives in Congress, show no interest in radically increasing the budget for slops science.

And even if Congress trebled or quintupled the NIH budget, all evidence suggests we’d just to the same thing all over again. Mint more PhDs like crazee and wonder in another 10-15 years why careers still suck.

As you know I am not a super big fan of NIH grant review sentiments which boil down to “tut, tut, Dr. Junior Faculty, let’s not get too big for your britches. Try this small starter award and see how you do with that before you get to play with the big kids.”

I believe things like size of grant and number of grants (and relatedly, overall total direct costs) should be taken on a case by case basis. And I believe that modern “junior” faculty are pretty old, phenomenally broadly experienced and generally pretty capable compared to junior faculty minted in, say, the early to mid eighties.

The question of the day, however, has more to do with lab size and specifically to do with the number of academic trainees.

Is there a limit to the number of grad students, postdocs or grad students plus postdocs that most junior faculty should be training?

My gut take is “heck yes”. I don’t know that I’ve ever had to act up this. I can’t recall a time when I ever had to judge a R-mechanism or F-mechanism where the PI or supervisor (respectively) was seemingly overburdened with trainees. But my gut says that this is possible. There would be times where I might raise an eyebrow about how many concurrent trainees a junior (or senior, but that’s another argument) PI might be proposing to have. Whether that be due to taking a look at the “training environment” for a F32/F31 application or in looking at relative commitment levels for a new Rproposal there are seemingly times that this might come up. Conceivably.

My gut feeling on this is guided by my own experience which, as we know, is wildly out of touch with y’all.

We have had one or two conversations about what people think of as a small, medium or large lab. My takeaway from these is that people think a 6-7 person lab is average, medium, normal and basically expected value.

To me this is “on the larger side”.

I have run anywhere from 0-4 concurrent academic trainees and when I am at 4 postdocs I definitely feel a bit stretched.

I have been doing this gig for some time now. When I was a wee newbie PI I thought that two concurrent trainees was pretty much good. Three was not something that I thought was sustainable.

Whatcha think, Dear Reader?

Can most junior PIs handle 5 or more concurrent academic trainees? Should they just take as many as possible?

*I solemnly swear this is not a troll to further complain about the training of too many PhDs.

PI seeks postdoc

March 23, 2018

Every PI wants only the most brilliant, creative and motivated trainees that will put in insane levels of effort to advance the lab agenda.

We know this because it is how they write their postdoc solicitation blurbs.

This is not what is consistently available.

I know this because a consistent backchannel theme of my dubious life online as science careers nerd features PIs complaining about their trainees.

My usual response is to point out that they became PI due to being much better than average. So of course most of their trainees aren’t going to be as good as they are*.



This question is for those who have ever entered a doctoral program in the sciences.

When did you realize that it was really, really important for you to publish first-author papers as a graduate student?

I recall that I really thought that the requirements and goals of grad school were to pass the first year exam (which was a research project presentation), pass the qualifying exam and write and defend a monolithic thesis describing a body of independently dreamed-up and designed research that I conducted myself.

I became aware of a bit of a debate about monolithic theses versus publications in the opinion of various faculty somewhere in my 2nd or 3rd years. So I knew of the idea that some Professors thought that three first-author publications stapled together with a cursory introduction and summary material was superior to the monolithic thesis.

I sided with the monolithic-thesis types and this, I think, let me continue to mislead myself about the importance of publications for my career. I also had career aspirations (right up until about six months before my first faculty appointment started to crystallize as reality) that did not necessarily require a strong publication record from graduate studies. Finally, I had the not-uncommon realization that I was going to have to do some postdoc work after graduate school, and the accompanying notion that postdoc work was when you really got steaming on publications, that let me off the hook.

So my answer would have to be that I didn’t really grasp how important first-author pubs in grad school would be until I was late-postdoc and looking to land a faculty gig (and grants). I had probably the first dawning realization midway through my first postdoc. I would have to say that I had no serious understanding of this throughout most of grad school. I had ZERO concept of this as a graduate school applicant and graduate school interviewee.

A new paper in PLoS ONE purports to report on the relationship between traditional graduate school selection factors and graduate school success.

Joshua D. Hall, Anna B. O’Connell, Jeanette G. Cook. Predictors of Student Productivity in Biomedical Graduate School Applications. 2017, PLoS ONE, Published: January 11, 2017 [Publisher Link]

The setup:

The cohort studied comprised 280 graduate students who entered the BBSP at UNC from 2008-2010; 195 had graduated with a PhD at the time of this study (July 2016), 45 were still enrolled, and 40 graduated with a Master’s degree or withdrew. The cohort included all of the BBSP students who matriculated from 2008-2010.

The major outcome measure:

Publications by each student during graduate school were quantified with a custom Python script that queried Pubmed ( using author searches for each student’s name paired with their research advisor’s name. The script returned XML attributes ( for all publications and generated the number of first-author publications and the total number of publications (including middle authorship) for each student/advisor pair.

For analysis they grouped the students into bins of 3+, 1-2 or 0 first author pubs with a ‘0+’ category for zero first-author pubs but at least one middle-author publication.

OMG! Nothing predicts graduate school performance (especially those evil, evil, biased – I mentioned evil, right? – standardized scores).

Yes, even people who score below the 50th percentile on quantitative or verbal GRE land first-author publications! (Apple-polishing GPA kids don’t seem to fare particularly well, either, plenty of first author publications earned by the 3.0-3.5 riff-raff.)

Oh bai the wai…prior research experience doesn’t predict anything either.

Guess what did predict first author publications? Recommendation scores. That’s right. The Good Old Boys/Girls Club of biased recommendations from undergraduate professors is predictive of the higher producing graduate students.

As the authors note in the Discussion, this analysis focused only on student characteristics. It could not account for the mentor lab, interaction of student characteristics with the mentor lab characteristics and the like.

I’ll let you Readers mull this one over for a bit but I was struck by one thing.

We may be talking at cross purposes when we discuss how application metrics are used to predict graduate student success because we do not have the same idea of success in mind.

This analysis suggests the primary measure of success of a graduate student is the degree to which they succeeded in being a good data-monkey who produces a lot of publishable stuff within the context of their given research laboratory. And by this measure, nothing is very predictive, going by the Hall et al analysis, except the recommendation letter of those who are trying to assess the whole package from their varied perspectives of “I know it when I see it*”.

Grad student publication number is, of course, related to who will go on to be a success as a creative independent scientist because of the very common belief that past performance predicts future performance. Those who exit grad school with zero pubs are facing an uphill battle to attain a faculty position. Those with 3+ first author pubs will generally be assumed to be more in the hunt as a potential future faculty member all along the postdoctoral arc.

Assuming all else equal.

This is another way we talk past each other about standardized scores, etc.

The choice of the PI who is trying to select a graduate student for their lab can assume “all else equal”. Approximately. Same lab, same basic environment. We don’t have this information from Hall et al. and I think it would be pretty difficult to do the study in a way that used same-lab as a covariate. Not impossible…you just are going to need a very large boat.

I think of it this way. Maybe there are some labs where everyone gets 3 or more first-author papers? Maybe there are some where it takes a very special individual indeed to get more than one in the course of graduate school? And without knowing if the student characteristics determine the host lab, we have to assume random (ish) assignment. Thus it could be the case that the better GREquant, for example, gives a slight advantage within lab but this is wiped out by the variability between-labs.

The choice of a selection committee for graduate programs can be less confident about all else being equal. They have to ask what sort of student can be successful across all of the lab environments in the program. Or successful in the majority of them. The Hall et al. data say that many types can be. But we are still asking a question of whether the training environment is such an overwhelming factor that almost nothing about the individual matters. This seems to be the message

If so, why are we bothering to select students at all? Why have them apply with any details other than the recommendation letters?

Maybe this is another place we are speaking at cross purposes. Some of us may still believe that the point of graduate school selection is to train the faculty (or insert any other specific career outcome if relevant) of tomorrow. Part of the goal, therefore, may be to select people on the basis of who we think would be best at that future role**, regardless of the variation in papers generated with first-author credit as a graduate student.

Is the Hall et al. paper based on a straw notion of “success”?

I think you’ve probably noticed, Dear Reader, that my opinion is that the career of grant-funded PI takes some personality characteristics that are not easily captured by the number of first-author pubs as a graduate student. Grit and resilience. Intrinsic motivation to git-er-done. Awareness of the structural, career-type aspects. At least a minimal amount of interpersonal skills.

What I am not often on about is the fact that I think that given approximately equal conditions, smarts matters. This is not saying that smarts is the only thing. If you are smart as all heck and you don’t have what it takes to be productive or to take a hit, you aren’t going to do well. It’s the flip side. If two people do have grit and resilience and motivation…the smarter person is going to have an easier time of it or achieve more for the same effort**. On average.

And this is a test that is not performed in the new paper. Figuring out how to compare outcomes within laboratory groups might be an advance on this question.

*When I write recommendation letters for undergrads who have worked with me I do not have access to their standardized scores or grades. I have my subjective impressions of their smarts and industry from their work in my lab to go by. That’s it. Maybe other people formally review a transcript and scores before writing a letter? I doubt it but I guess that is possible.

**Regarding that future role, again it may be a question of what is most important for success. Within our own lab, we are assuming that differential opportunity to get publications is not a thing. So since this part of the environment is fixed, we should be thinking about what is going to lead to enhanced success down the road, given conceivable other environments. From the standpoint of a Program, the same? or do we just feel as though the best success in our Program is enough to ensure the best success in any subsequent environment? The way we look at this may be part of what keeps us talking past each other about what graduate selection is for.

There is a lot of great advice of the usual sort floating around – talk to current grad students and postdocs about Department, Program and Lab culture. Median time to completion*. So I won’t repeat that.

But here’s one thing you may not hear about.

Ask the Program Director for the past two 5-year reviews of the Program. Yes, graduate training programs get peer reviewed on a periodic basis. Every 5 years in my limited experience.

Ask to see the review. Absent that ask for the top five most serious criticisms. In fact you should ask this latter question if anyone who interviews you to get a sense of how much the Program is integrated vs ad hoc.

Here’s another important question to ask the interviewing faculty: “Who are the most recent 5-10 faculty appointments to come from your Program alumni?” The key here is to ask it on the spot so they can’t look it up.

The most important thing here will not be the actual-factual answers. It will be how the faculty respond to your inquiries.

Good luck.

*Please tell me every prospect asks about the median time to PhD?

UPDATE: I meant this as a step to take after you are invited to interview or offered admission. A step for you to take to help decide which program to attend. Although I suppose even if you only get one offer it is helpful to know what to expect or watch out for.

If the lab head tells the trainees or techs that a specific experimental outcome* must be generated by them, this is scientific misconduct.

If the lab head says a specific experimental outcome is necessary to publish the paper, this may be very close to misconduct or it may be completely aboveboard, depending on context. The best context to set is a constant mantra that any outcome teaches us more about reality and that is the real goal.

*no we are not talking about assay validation and similar technical development stuff.

The very first rule of PI/mentorship is get your trainees first author publications.

This is the thing of biggest lasting career impact that you can determine almost with absolute control.

Yes, things happen but if you are not getting the vast majority of your trainees first author pubs you are screwing up as a mentor.

So. 2017 is about to start. Do you have a publication plan for all of your postdocs and later-stage graduate students?

Obviously I am in favor of active management of trainees’ publishing plans. I assume some favor a more hands-off approach?

“Let the postdoc figure it out” has an appeal. Makes them earn those pubs and sets them up for later hard times.

The problem is, if they fail to get a publication, or enough, their career takes a bad hit. So ability to grunt it out isn’t ever used.