Selected Data and Sources Relevant to Research Enterprise Sustainability

Mar 28 2015 Published by under Uncategorized

I participated in the follow-up meeting to the Alberts et al. paper that was summarized in a recent PNAS paper. This summary noted that "...most were surprised to learn that the percentage of NIH grant-holders with independent R01 funding who are under the age of 36 has fallen sixfold (from 18% to about 3%) over the past three decades." This statement is probably accurate, but I was disappointed that many participants were not familiar with many important facts and trends that have affected the biomedical enterprise over the past two decades.

What information is important for individuals to know in order to participate in discussions about potential corrections to the present system. In addition to the demographic data noted above, below are some slides that I have used in presentations on the topic of the sustainability of the biomedical research enterprise (some of which are derived from posts here or from my columns at ASBMB Today.

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Which of these are most important? What are other important data sets or data sources that should be included in such presentations?

18 responses so far

  • Neuro-conservative says:

    Why must every BSD above the age of 65 be a total and complete empty-suit figurehead? And how much of this phenomenon is a cohort effect vs age effect? (ie, are we going to be that clueless at that age?)
    /vent

    • datahound says:

      I think we all can be trapped in our own experience unless we work to avoid it.

      There was much discussion at the workshop about how much individuals had enjoyed their (usually relatively short) postdoctoral experience and how exciting it was to be given the freedom and the opportunity to establish their independent careers. My impression was that there was a strong sentiment that it would be good to move back toward such a system, but a lack of appreciation about how the evolution of the system over the intervening decades had made that very difficult.

      • Neuro-conservative says:

        Holy crap - a bunch of septuagenarians reminiscing about their training experiences in the late 1960's? How did you not stab your own eyes out with a pencil?

      • drugmonkey says:

        The enjoyed it because they were damn near guaranteed to get a job just by showing up.

    • drugmonkey says:

      Yes, yes we are, N-c.

  • Philapodia says:

    Would working this data up into an opinion (or research since there are data) piece for PNAS/C/N/S be a viable way to get the broader (and older) community better acquainted with the current problems in the system? ASBMB is a good platform, but there are many scientists who aren't members and will not ever see it or your or DM's blog. This may spur a more constructive conversation, especially if DH acts as senior author.

  • Established PI says:

    Wow, a lot to digest here. Could you help clarify the R funded investigator pool dynamics slide? I'm not quite sure what the numbers next to the red and green arrows mean.

    I find the Ph.D. data slides rather disturbing. I have seen other presentations supposedly showing that unemployment and underemployment among life sciences Ph.D.s is remarkably low, yet your slide shows 10% as out of the labor force or part-time. I am curious how this compares to other fields (say, chemistry) where there has not been a dramatic increase in Ph.D.s.

    The steep increase in Ph.D.s seems to outstrip the growth in PIs over the same period, even taking the lag into account. I had always thought that some of the growth in the number of PIs receiving grants occurred in clinical departments where there are few or no graduate students, which would mean that the average number of students per lab in degree-granting departments has gone up. Are there any data addressing this? I guess the PhD data could be more meaningful if seen side-by-side with data on growth of biomedical faculty.

  • AcademicLurker says:

    Wow, that basic biomedical PhD production line is grim. I'm curious, what accounts for the rise from the late 80s to mid 90s? I thought that the NIH funding situation in the early 90s wasn't all that great.

    • datahound says:

      Given the lag time, PhD production relates to events 5 or more years previously. Thus, the increase is likely due to events due in the early 1980s. Here, the NIH budget increases were reasonable. Another factor that drives graduate school improvement is a downturn in the economy. The recession in the early 80s may have played a role.

  • Aaron Goldstrohm says:

    Datahound,
    Thank you for your efforts to illuminate these important issues with data and facts. This information is very useful as we work to change and improve graduate and postdoctoral training.

  • Dave says:

    For me the institutional aspect of this whole story is not emphasized enough. The most striking figure is the 'Activities after PhD training' graph. It is absolutely amazing the decline in TT jobs, but I'm surprised there isn't a bigger jump in the 'non-TT' line. It doesn't make sense. Are you lacking some data here due to the difficulties in counting soft-money faculty?

    • thorazine says:

      I can't totally resolve the colors in that graph (colorblind) but I think, in this case, that may fit with the artist's intent, if you see what I mean. Is the 5-6 year cohort 5-6 years post PhD? Would it look similar with a 10-11 year cohort?

      What is "other"? Publishing? Non-science-related employment? How are "industry" and "government" defined - does "industry" just mean pharma/biotech and is "government" all governmental employment (from NIH PI to NYPD)?

      • Dave says:

        Basically, no other career path has been able to compensate for the rapid decline in TT jobs. That's how I see it anyway.

      • datahound says:

        thorazine: Sorry about the color issue. Here is a link to a presentation from economist Paula Stephan (the source of these data) where the curves are added one at a time. http://www2.gsu.edu/~ecopes/Georgetown%20Slides.pdf

        I hope this helps.

        I do not know the details of the categorization that she used, but the categories are likely quite broad rather than narrow.

  • Year2_PI says:

    These people-based numbers are MUCH more useful than rates based on applications. Is it correct to estimate that there are currently around 80K competing for R01s but only 25K who are funded? What is shocking to me is that the portion of "active PIs" (applying) who are funded was still only around half back in 2002! This paints a dismal picture.

    Some of those unfunded applicants are brand new PIs like me and some are maybe end of career. Where are the rest of those 55K+ unfunded applicants? NTT research positions? maybe NSF funded labs taking a stab at NIH? the big questions to me is how many T/TT faculty are actively applying for grants but have no funds to do science?

    • datahound says:

      Yes, I believe you are correct that there are many more people applying (at least once) than there are folks who are funded.

      The applicant pool is very broad, new PIs, MDs taking a shot at research, professors from less research intensive institutions, etc. Unfortunately, I do not know of any analysis of such data.

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