The intro may be trigger-y for some.

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The takeaway message from the report of Ginther and colleagues (2011) on Race, Ethnicity and NIH Research Awards can be summed up by this passage from the end of the article:

Applications from black and Asian investigators were significantly less likely to receive R01 funding compared with whites for grants submitted once or twice. For grants submitted three or more times, we found no significant difference in award probability between blacks and whites; however, Asians remained almost 4 percentage points less likely to receive an R01 award (P < .05). Together, these data indicate that black and Asian investigators are less likely to be awarded an R01 on the first or second attempt, blacks and Hispanics are less likely to resubmit a revised application, and black investigators that do resubmit have to do so more often to receive an award.

Recall that these data reflect applications received for Fiscal Years 2000 to 2006.

Interestingly, we were just discussing the most recent funding data from the NIH with a particular focus on the triaged applications. A comment on the Rock Talk blog of the OER at NIH was key.

I received a table of data covering A0 R01s received between FY 2010 and FY2012 (ARRA funds and solicited applications were excluded). Overall at NIH, 2.3% of new R01s that were “not scored” as A0s were funded as A1s (range at different ICs was 0.0% to 8.4%), and 8.7% of renewals that were unscored as A0s were funded as A1s (range 0.0% to 25.7%).

I noted the following for a key distinction between new and competing-continuation applications.

The mean and selected ICs I checked tell the same tale, i.e., that Type 2 apps have a much better shot at getting funded after triage on the A0. NIDA is actually pretty extreme from what I can tell- 2.8% versus 15.2%. So if there is a difference in the A1 resubmission rate for Type 1 and Type 2 (and I bet Type 2 apps that get triaged on A0 are much more likely to be amended and resubmitted) apps, the above analysis doesn’t move the relative disadvantage around all that much. However for NIAAA the Type 1 and Type 2 numbers are closer- 4.7% versus 9.8%. So for NIAAA supplicants, a halving of the resubmission rate for Type 1 might bring the odds for Type 1 and Type 2 much closer.

So look. If you were going to try to really screw over some category of investigators you would make sure they were more likely to be triaged and then make it really unlikely that a triaged application could be revised into the fundable range. You could stoke this by giving an extra boost to triaged applications that had already been funded for a prior interval….because your process has already screened your target population to decrease representation in the first place. It’s a feed-forward acceleration.

What else could you do? Oh yes. About those revisions, poorer chances on the first 1-2 attempts and the need for Asian and black PIs to submit more often to get funded. Hey I know, you could prevent everybody from submitting too many revised versions of the grant! That would provide another amplification of the screening procedure.

So yeah. The NIH halved the number of permitted revisions to previously unfunded applications for those submitted after January 25, 2009.

Think we’re ever going to see an extension of the Ginther analysis to applications submitted from FY2007 onward? I mean, we’re seeing evidence in this time of pronounced budgetary grimness that the NIH is slipping on its rather overt efforts to keep early stage investigator success rates similar to experienced investigators’ and to keep women’s success rates similar to mens’.

The odds are good that the plight of African-American and possibly even Asian/Asian-American applicants to the NIH has gotten even worse than it was for Fiscal Years 2000-2006.

NIH Blames the Victim

January 16, 2014

Just look at this text from RFA-RM-13-017:

The overarching goal of the Diversity Program Consortium is to enhance the diversity of well-trained biomedical research scientists who can successfully compete for NIH research funding and/or otherwise contribute to the NIH-funded workforce. The BUILD and NRMN initiatives are not intended to support replication or expansion of existing programs at applicant institutions (for example, simply increasing the number of participants in current NIH-funded research training or mentoring programs would not be responsive to this funding announcement).

The three forgoing major initiatives share one thing in common: Make the black PIs better in the future.

The disparity we’ve been talking about? That is clearly all the fault of the current black PIs….they just aren’t up to snuff.

Specifics? also revealing

 

Goals for the NRMN include the following:

  • Working with the Diversity Program Consortium to establish core competencies and hallmarks of success at each stage of biomedical research careers (i.e., undergraduate, graduate, postdoctoral, early career faculty).

  • Developing standards and metrics for effective face-to-face and online mentoring.

  • Connecting students, postdoctoral fellows, and faculty in the biomedical research workforce with experienced mentors, including those with NIH funding, both in person and through online networks.

  • Developing innovative strategies for mentoring and testing efficacy of these approaches.

  • Active outreach is expected to be required to draw mentees into the network who otherwise would have limited access to research mentors.

  • Developing innovative and novel methods to teach effective mentoring skills and providing training to individuals who participate as mentors in the NRMN.

  • Providing professional development activities (grant writing seminars, mock study sections, etc.) and biomedical research career “survival” strategies, and/or facilitating participation in existing development opportunities outside the NRMN.

  • Enhancing mentee access to information and perceptions about biomedical research careers and funding opportunities at the NIH and increasing understanding of the requirements and strategies for success in biomedical careers through mentorship.

  • Creating effective networking opportunities for students, postdoctoral fellows, and early career faculty from diverse backgrounds with the larger biomedical research community.

  • Enhancing ability of mentees to attain NIH funding.

To my eye, only one of these comes even slightly close to recognizing that there are biases in the NIH system that work unfairly against underrepresented PIs.

Namnezia has initiated an interesting conversation on the criteria for awarding a PhD in the sciences. A commenter over there alleged a set of rules that is nearly impossible for me to believe is true. RX claims:

No official requirements for my PhD program, it’s up to the PI.
My lab is crazy. Here’s the requirement: total first author impact factor: 30, total pages of paper: 20. The first graduate of my lab got 1 Neuron and 1 Nature Neuroscience paper. All the rest graduates tend to follow this pattern.

This is one reason it shouldn’t be left up to the PI, there is a reason doctoral committees and doctoral program rules exist.

Go Play at the Take it to the Bridge blog.

Bora Zivkovic has been a skeevy, predatory harasser of women. He was accused in online public and confessed. Subsequent revelations from other women who were similarly preyed upon follow a similar narrative. So even if Bora’s original confession admitted only to one incident, well, nobody believes that and nor should anyone.

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Some low normal trying to get some free content written for his science-blog type of site seems to miss this point.

Here is a kindly reminder from @DNLee5 of The Urban Scientist blog.

Hmmm, can’t find Danielle Lee’s original post anymore so go over to dristorm’s pad and read the text of Danielle’s response too.

I hope this commenter was being facetious.

With paylines around 5-percentile, the only way to have a shot at having a proposal approved is to quite simply fake data.

and I hope this other commenter was just wising off in frustration.

Certainly in my field the proportion of cheaters at the top venues seems to have increased the harder it is to get in. In fact, in one specific venue that shall remain nameless in my estimation over half of the papers contain some fake data.

Don’t get me wrong. I am concerned about cheating in science. I am convinced that the contingencies that affect the careers of individuals scientists is a significant motivating factor in data fraud. I am not naive.

but for today, I wish to object to this normalization behavior. It is not normal to cheat in science. Data faking is NOT standard old stuff that everybody is doing.

“Everybody does it.”

This is one of the standard defenses of the cheater pants. It is the easy justification we have seen time and time again in the revelations of performance-enhancing drug use in professional sports. It is the excuse of the data faker as well.

Consequently it is imperative that we do not leave the impression of normalcy unchallenged.

It is not the norm. Faking is not endemic to science. It may be more common than we would like. It may be more common than we estimate. But it is not normal.

Despite claims, it is not necessary. I have more than one grant score that was better than the 5th percentile and I didn’t have to fake any data to get those. So that first claim is wrong for sure. It is not required to fake data.