Scientopia Fri, 29 May 2015 17:41:55 +0000 en-US hourly 1 Updating the Glossary Fri, 29 May 2015 17:41:55 +0000 I maintain a blog Glossary page which is supposed to be a handy reference for newcomers to the blog. It is necessary because I am lazy and often use shorthand when I am writing blog posts. My commenters frequently do as well. I was just adding RAP to the list when I thought I should maybe solicit feedback from you.

So, any suggestions for the Glossary, Dear Reader?

Anything which stumped you when you first started reading? Or which stumps you now?

What jargon should I add?

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My Week In Review Fri, 29 May 2015 15:51:00 +0000 I have been treating inpatients this week and dealing with a particularly annoying virus that someone gave me last week. I am exhausted and sore from coughing.

Neither of these stopped the calendar. I still gave Grand Rounds on Wednesday, discussing the past and current recommendations for screening urinalysis in children in the US. I have my presentation on SlideShare, and I'm embedding it below. Enjoy the flow!

Have a good weekend. I look forward to thinking about something besides my own mucus next week!

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Young Scientists Caught between Scylla and Charybdis Fri, 29 May 2015 13:48:38 +0000 Scylla and Charybdis has always been one of my favorite expressions.  The original rock and a hard place.

I am writing a grant with a young colleague. She brings one set of strengths to the table and I another. We've been talking about this project for about a year, and finally came up with a mechanism, design and Spec Aims that will, shall we say, blow something out of the water. We hope.

She is a glorified postdoc in another lab. She would like PI status, but the med school where she is has something of a problem: you have to demonstrate that you can get funding and be a PI before they will let you do it. Hard to do. Her main mentor/advisor there has agreed that the joint project she and I are working on is hers (even though I am the PI) and that if we get scored she can have the status.

Please note: I am doing much of the heavy lifting for the project, and abundantly aware of the ethical pitfalls in this collaboration. She is also saavy enough to understand what PI status without a TT position means. Finally, she has family/personal limitations and I do believe she is doing an excellent balancing job.

Anyway, we had hammered out a good, but not quite finished set of SA's.  She was sending me one last version to tweak, before moving on to finish up the rest of the proposal. I looked at them and for a minute thought she had sent a much earlier version, because one of the two aims was entirely different, and not something that NIH would be the least bit interested in. There were two new paras, and the writing was awkward. My thought: WTF?

Rather than editing, I said "lets talk about this". We did and it scnturned out that she showed the Specific Aims to her mentor at new place (who is not NIH-science, but NSF-science). He hated them. Said the writing was dreadful. Said the SA's didn't make sense. So she rewrote along the lines of his suggestion. What a horrible place for her. We talked for about 3 minutes and she got it. One of the reasons, beyond my excitement about the specific project, that I want to work with this woman, is that she is very very good. My first response to her was to make sure she understood that I cared about the work and that I was giving this the best I could. For getting funded, I felt the most important thing was to be honest and explain that I thought her other mentor was mistaken here, and why. I said if the mentor wants to write out specific comments, of course we can take those into account. We are now back on track. The grant will go in for the June 21rst deadline.


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R01-equivalent PIs: 1985-2014 Thu, 28 May 2015 15:12:46 +0000 I recently posted data on the number of unique NIH PIs for all mechanisms listed in the NIH RePORT database.

I have now analyzed data for R01-equivalent grants (primarily R01s but also R23, R29, and R37 (MERIT) awards) as shown below:

R01 PI plot

This shows curves for all PIs (including multiple PIs) and for Contact PIs only. These curves clearly reveal the impact of the NIH budget "doubling" from FY1998 to 2003) and the subsequent decline due to the worse-than-flat NIH budget over the past 12 years (with the exception of the ARRA) funding.

The correction for multiple PIs is significant (although, of course, being PI on a multiple PI grant likely provides fewer resources than being the sole PI on an award of the same size). The 3564 New (Type 1) R01 grants in FY2014, 771 had multiple PIs.

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The NIH Cull and the K99/R00 cohorts Thu, 28 May 2015 15:00:48 +0000 DataHound posted two key analyses on the state of the NIH-funded extramural work force. In the first one he presents the number of unique PIs from 1985-2014. It looks to me, roughly, that there are about 18% fewer PIs than the peak and approximately 10% fewer PIs if we ignore the ARRA interval.

Most of the real drop (i.e., not postARRA) occurred between FY2011 and FY2012 but there has been a downward trend from 2012 to 2014 so this looks to be the new reality.

The Cull is in full view now.

Has it seemed like grants are getting funded slightly more easily lately? If so, you can thank the Cull. (No doubt the pressure is more about applications than funded awards to unique PIs. But if the applications are seeing similar drops, this explains the feeling of relief, if you have it.)

The second post at DataHound presents several graphs on the K99/R00 awardees by original award year.

Transition to the R00 phase did not vary much up through the 2010 cohort and the cohorts are on the same trajectory, given the time function. Importantly the 2007-2009 cohorts follow the exact same trajectory, 2010 cohorts have a little bit of drop-off at the far end, due to less time since original award. Six years after the K99 award is the hard ceiling on transition to R00 in the first three cohorts and 2010 K99ers aren't quite there yet.

Where the K99 awardee cohorts are not on the same trajectory is the transition to R01. DataHound's plots show a clear plateau for the 2007-2010 cohorts. The 2007 awardees topped out at about 58% transitioning to R01 funding and subsequent cohort success rates are lower, year over year. Success in gaining an R01 for the 2009 cohort is about 70% that of the 2008 grouup and about half that of the 2007 cohort. The 2010 cohort is at least 20% less-successful than the 2009 K99 awardees.

It is pretty clear the Cull described in the first linked post is falling harder on the K99/R00 awardees than on the general pool of NIH-funded PIs. Depending on whether you take the ARRA high water mark for unique PIs or something lower that adheres to the normal trend, the Cull is only about a 10-20% as of FY2014.


All this talk about getting more new scientists over the hump to faculty level career status. All this whinging and moaning about eating our seed corn. All the handwringing over ESIs.

And the program that is the crown jewel in doing something about transition is....not working.

If history is any guide, it would have taken official NIHdom about 15 years to "suddenly realize" this is the case and to try something new.

Thank goodness for DataHound. I anticipate he has accelerated this process by posting these two key analyses.

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The Number of NIH PIs 1985-2014: The Effect of Multiple PIs Thu, 28 May 2015 14:58:03 +0000 I recently posted a somewhat startling curve showing the total number of NIH contact PIs for all mechanisms in the NIH RePORT database. This showed a drop in the total number of PIs from FY2010 to the present.

As I lay awake thinking about this curve and what might mean, I thought it might change somewhat if I included all PIs instead of just Contact PIs. Recall that the NIH multiple PI policy only went into effect in around 2005.

I was able to examine this point relatively quickly. The results are shown below:

NIH PI Plot wNonContact


This shows that the inclusion of all PIs decreases the magnitude of the drop since FY2010.

Some other interesting statistics about non-Contact PIs are:

Total Contact PIs:  216,521

Total PIs listed as other than Contact PI:  11,504

PIs who have never been Contact PI:  2,873


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Ray Bradbury quotes for the morning Thu, 28 May 2015 13:53:27 +0000
This quote about writing could also be about science. Its not easy. There are lots of caveats, bumps and compromises. But...
Love. Fall in love and stay in love. Write only what you love, and love what you write. The key word is love. You have to get up in the morning and write something you love, something to live for.
This pairs nicely with another Bradbury quote:
“The first thing you learn in life is you're a fool. The last thing you learn in life is you're the same fool. Sometimes I think I understand everything. Then I regain consciousness”
Ray Bradbury, Dandelion Wine

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Analysis of Subsequent Years of K99-R00 Program Thu, 28 May 2015 12:34:07 +0000 I had previous done some analysis of the NIH K99-R00 program for the first two cohorts.  I wrote R scripts to assemble information about the R00 and R01 (as well as DP1 and DP2) awards subsequently obtained by K99 recipients and to analyze these results. I included precise grant start and end times rather than simply fiscal years as I had done in my initial analysis.

The results for the first K99 cohort (from fiscal year 2007) are shown below. This shows the number of investigators (out of 182 initial K99 awardees) who had K99 awards, R00 awards, or R01 (or DP1, or DP2) awards aligned with the start dates for the initial K99 award at time 0.

2007 K99 Cohort Plot

This shows that more than 90% of these K99 awardees transitioned to the R00 phase and that more than 100 of these PIs had obtained at least one R01 (or equivalent) award as shown previously but now with more precision about the timing of these awards.

With these scripts in hand, it was straightforward to analyze subsequent K99 cohorts. The results are shown below:


K99 Awards Plot


This graph reveals that the overall pattern for the K99 phase is remarkably consistent from year to year, with substantial transitions at the end of year 1, a steady decline and then a sharp drop at the end of year 2, and the remaining ~20% of PIs transitioning off the K99 by the end of year 3.

The results for the R00 phase are shown below:

R00 Award Plot


Again, the pattern is quite consistent. The fraction of K99 awardees who have transitioned to the R00 phase is approximately 50% at the end of year 2 (since the start of the K99 award) and peaks at between 80 and 90% in the middle of year 3. The curves are different for the FY2010, FY2011, and FY12 K99 cohorts since they have not yet had time to fully transition, but the curves look quite similar for the regions that overlap the other curves.

The final curve shows the transition to R01 awards (I also included DP1 (Pioneer) and DP2 (New Innovator) awards).

R01 Award Plot-2


Here, the curves are more different. For the first (FY2007) cohort, more than 50% of the K99 awardees have transitioned to R01 funding. More than 40% of the FY2008 cohort have transitioned, but comparison of the FY2007 and FY2008 curves suggests that this cohort is transitioning more slowly or will not achieve the same level of the FY2007 cohort. This trend continues with the FY2009 cohort. Of course, these attempted transitions to R01 funding are occurring over the period where the overall number of NIH supported PIs dropped (as revealed in my previous post). The FY2010 cohort showed an initial burst above the FY2008 and FY2009 curves but has slowed since then. It is too early to say much about the FY2011 and FY2012 cohorts.

The ability to analyze these data in kinetic detail with relative ease allowed some comparisons that were much harder to make in my previous analysis. I am impressed with the continuing development of R by a large open community (especially Hadley Wickham) that are making R an ever-more-powerful tool.

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FIFA Thu, 28 May 2015 02:09:11 +0000 Can someone explain why the U.S. is wasting a single penny going after corruption in futbol? Nobody gives a fig about soccer in this country. Let the UK or Germans or someone go after these people.

Meanwhile, we should be prosecuting the financial and banking greedholes who crashed the world economy.

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Analyzing NIH Data with R Thu, 28 May 2015 00:56:28 +0000 Most of the analysis of NIH data that I have done with NIH data has been done using Excel. While Excel does have some useful features, it has many limitations. My son who, as an actuary, does considerable data analysis for a living, urged me to migrate to a more powerful platform, R, for my analyses. He can be quite convincing and I have spent time over the past month developing some rudimentary R skills (in part through an on-line course). I am now fully convinced that he was right.

I downloaded all of the data used by NIH RePORTER (from NIH ExPORTER) and wrote R scripts to parse the data into a forms that could be easily analyzed by R. The full file has 1,907,841 grant records with readable contact PI numbers for fiscal years 1985 to 2014. These correspond to 216,521 unique contact PIs.

As an initial exercise with these data, I decided to plot the number of unique contact PIs as a function of fiscal years. The result is shown below:

Unique PI Plot-2


What I attempted as a test of my data analysis skills revealed a striking result. The number of unique contact PIs had grown almost linearly from 1985 to about 2009-2010 (the ARRA years) but subsequently dropped quite sharply from 2010 to 2014. This graph provide much clearer evidence for "the cull" than I anticipated.

Despite this bottom line, considerable work remains to be done to probe this further since this includes a wide variety of mechanisms. With the powerful file manipulation and analysis tools in R, this should be relatively straightforward.

Let the analysis begin!

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