(by datahound) Sep 23 2016

I want to add my voice to those thanking Drugmonkey today. I started reading Drugmonkey when I was Director of NIGMS at NIH. While the blog could certainly be a bit strident, the author was generally more well informed about NIH policies, practices, and realities than almost anyone that I had met either inside or outside NIH. Over time, I started to comment on Drugmonkey posts when I thought I could add something to the discussion. This experience led me to start reading other blogs and this, in turn, let me to work with the excellent communications staff at NIGMS to launch the NIGMS Feedback Loop just in time for the ARRA funding chaos. Drugmonkey was very encouraging of this effort and we became frequent cross-commenters.

After I left NIGMS, I remained interested in blogging and was delighted when Drugmonkey and his/her colleagues at Scientopia invited me to join. After some time for consideration, Datahound was born. As this post indicates, Datahound is still alive and well although I am spending much of my blogging time at my newest blog Sciencehound. I greatly appreciate the personal, if mostly indirect, mentoring I have received from Drugmonkey as well as the service Drugmonkey provides to the scientific community in general (especially early career folks). Thanks!

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Datahound's New Littermate...Sciencehound

(by datahound) Aug 04 2016

It has been a bit over 2 years since Datahound was born. I have greatly enjoyed being brought into the Scientopia team and sharing data that I have gathered and analyses that I have done with my readers and plan to continue to do this going forward as my time permits. Today, I want to share some exciting news. Datahound has a new littermate...Sciencehound. I have started this new blog to share my thoughts about the communities of science similar to those I have shared here but also extending more deeply into scientific publishing as part of my new role as Editor-in-Chief at Science and the Science family of journals. Come on over and join the discussion!

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R01 Size Growth and the Modular Cap-2016 Edition-Part 1

(by datahound) May 27 2016

Two years ago, I posted an analysis of the distributions of the sizes of R01 awards from NIH and the relationship of this distribution to the cap of $250,000 direct costs on applications submitted with modular budgets.

The recent activity with regard to compensation for post-doctoral fellows got me thinking about this topic again. Here, I update the analysis with data from fiscal year 2015. The distribution of R01 annual total cost sizes (for a total of 22588 new (Type 1), competing renewal (Type 2) and non-competing renewal (Type 5) awards) is shown below:

Total Cost Dist Figure-2015_1

Unlike the corresponding plot from fiscal year 2003, this distribution is decidedly not Gaussian. It is bimodal with peaks near $330K and $380K and has a broad tail extending from approximately $420K to $800K. The bimodal nature was subtly present in the distribution for fiscal year 2013 but is now much more pronounced. The tail had grown substantially from fiscal year 2003 to fiscal 2013 and appears to be approximately the same size in the fiscal year 2015.

This distribution can be analyzed further since RePORTER now includes information about direct costs in addition to total costs. The distribution of annual direct costs is shown below:

Total-Direct Cost DIst Fig

Examination of the direct cost data reveals several important points. First, the bimodal distribution is present just at and below the modular cap of $250K. Thus, it is not the modular cap that is leading directly to this split distribution, but rather some other factor as will be discussed below. Second, the median for the overall distribution is exactly at the modular cap level. Thus, it appears that half of the funded R01s were submitted with modular budgets and the other half were not.

What is responsible for the bimodal distribution? One possibility is that is has to do with the number of years for which the grants have been in existence. Examining the distributions of the years of support for the first peak (from $190K up to but not including $230K) and the second peak (from $230K to $250K) reveals no difference with a correlation coefficient of 0.9986.

A second possibility is that the bimodal nature is due to different policies from different institutes and centers with regard to cutting awarded grant budgets. The number of grants in the second peak ((from $230K to $250K) versus the number of grants in the first peak ((from $190K to $230K) for each institute and center is shown below:

Peak1_2 figure

This plot clearly shows that differences in institute behavior is responsible. Some institutes such as NHLBI (HL), NIAID(AI), NIMH (MH) and NIDCR (DE) have many more grants in the peak with larger grant sizes, consistent with the fact that these institutes have tended not to cut grant budgets when the grants are first awarded. Other institutes such as NIGMS (GM), NCI (CA), NINDS (NS) and NIDDK (DK) have more grants in the peak with smaller grant sizes, consistent with the fact that these institutes have tended to make such budget cuts.

It is remarkable that this bimodal behavior has emerged from just a hint in the data from fiscal year 2013 to absolute clarity two years later. At NIH, each institute is given considerable autonomy to make and implement such policies.The clean separation of these two groups of institutes strongly suggests that the cap on modular budgets is distorting grant sizes because many applications are submitted at the modular cap level and then cut upon award by the funding institute or center. Furthermore, these data reveal some consequences of this where, for example, similar grants awarded by NINDS and NIMH could be funded at substantially different levels.

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The NIH Early Career Reviewer Program-Some Key Parameters

(by datahound) Feb 05 2016

Many have stated that serving on a study section can be an important step developing in an academic career. I did not serve until I was relatively well established, but I do vividly recall that the opportunity helped me understand the process better and improved my ability to craft better proposals. Moreover, it increased my faith that the peer review system was thoughtful and fair although certainly not perfect. In 2011, NIH announced a new program to allow early career scientists to serve on study sections. This program has two primary objectives. First, it allows early career investigators to observe and understand the review process directly with the goal of helping them enhance their abilities to write more competitive proposals. Second, it helps NIH review staff identify and screen potential reviewers for additional service.

To be eligible for the Early Career Reviewer Program, a individual must not have had substantial NIH review experience and have at least 2 years experience as an independent investigator with 2 recent publications as a senior author.

Following a recent discussion on Twitter about how many individuals who had applied to the program had actually served on a study section, I submitted a FOIA request for data regarding the program. I recently received the response. While the data are not comprehensive, they do allow an assessment of some of the key parameters.

(1)  The acceptance rate into the program appears to be around 64%. Specifically, from the period from June 2014 through December 2015, NIH received 1863 applications for the program and accepted 1189 individuals. The total number of applicants to date appears to be 4,534.

(2) The number of individuals who have actually served on a study section in this capacity is 1706 from the beginning of fiscal year 2012 when the program began through the first round of fiscal year 2016.

Graphically, the progression through the program is as follows:

ECReviewer plot

Three additional parameters are:

(3) The percentage of women accepted into the program is approximately 45% and a similar percentage of women have actually served.

(4) The pool of scientists accepted into the program is approximately 6% African American, 7% Hispanic, and 27% Asian.

(5) Approximately 13% of the individuals accepted into the program are from institutions eligible to apply for R15 (AREA) grants.

I do not know if NIH is planning an evaluation of this program. However, such an evaluation would seem to be straightforward and would help codify the value of study section experience to scientists developing their career and could also address the question of how well early career scientists perform compared to more established scientists in the review process. What are your thoughts?


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NIH High Risk Research Programs: Racial Composition of Applicant and Awardee Pools

(by datahound) Jan 17 2016

To continue my series of posts on the NIH HIgh Risk Research programs, I submitted a FOIA request to NIH for data regarding the race and ethnicity of the applicant pools for the four High Risk Research programs and recently received the results.

These data represent voluntarily self-identified race and ethnicity data that are collected when initiating an NIH Commons account (and used to be collected as part of an application). The categories listed are African American, American Indian, Asian, Multiple Races, Native Hawaiian, Unknown, White, and Withheld. While the data are presented by year (as I had requested), data are redacted for cells that represent fewer than 11 individuals. Since this limitation applies to many cells, I will focus on the data aggregated over all years of each program. It is important to note that these are not unique applicants, that is, applicants who apply to a given program multiple years will be counted multiple times.

The results for the African Americans, Asians, White, Unknown, and Withheld categories are shown below. The results for American Indians, Multiple Races, and Native Hawaiians are not included in this graph because of the redacted data.

Application pools

These data show that the percentage of White applicants ranged from 53% to 68% with the highest percentages for the Pioneer and Transformative R01 programs (68% and 63%) and lower percentages for the New Innovator (53%) and Early Independence (56%). This correlates with the likely career stage of the applicants with the Pioneer and Transformative R01 programs attracting established, more senior, investigators. The percentage of African American applicants ranged from 1.4% to 2.7% with the opposite trend (higher percentages for the New Innovator and Early Independence programs). The percentage of Asian applicants ranged from 16% for the Early Independence program to 30% for the New Innovator program with those for the Pioneer (18%) and Transformative R01 (20%) at intermediate values.

How do the applicant pools compared with the awardee pools? Because of the relatively low number of awardees for these programs, data about the race and ethnicities of these individuals were inferred by examining each awardee individually. It is important to note the difference in methodology between self-reported race and ethnicity for the applicant pool and inferred race (based on available biographical information, appearance, and name) for the awardee pool.

With this important disclaimer, I estimated the numbers of African American, Asian (primarily Chinese, Indian, and Japanese), and Other awardees in each program. These were compared with the corresponding numbers from the applicant pools. The ratios of Awardees to Applicants for these three racial groups for each program are shown below. The numbers of applicants in each group is also shown since these bear on the interpretation of the results.


For the Pioneer program, the ratio of awardees to applicants is 8/64 = .125. This ratio is higher than the ratio for Other (non-African American, non-Asian) at 107/3053 = 0.035. This difference is statistically significant with a p value of 0.002. Similarly, the ratio for Asians of 40/693 = 0.058 is significantly larger than that for Other with a p value of 0.009. The difference between the ratios for African Americans and Asians is not statistically significant (p = 0.054).

These results suggest that the success rates for African Americans and Asians are higher than that for Other applicants. Interpretation of these observations must be done with considerable caution. First, the number of African American applicants and awardees is quite small, an average of less than 1 awardee per year. While the result is statistically significant, it is not very robust to small changes in the number of awardees. Second, there is considerable selection bias in this program, based on my direct experience trying to encourage individuals to apply. Some are not aware of the program (or the timeline) while others feel that they are so unlikely to succeed that they are reluctant to apply even when encouraged. This self-selection could apply somewhat differently to different racial groups although I have no data that bear of this.

For the New Innovator program, the observed ratio of awardees to applicants is lowest for African Americans, slightly higher for Asians, and highest for Other, but these differences are not statistically significant. The numbers of applicants in all three groups are relatively large so that these findings are more robust than those for the Pioneer program.

For the Early Independence program, there appear to have been no African American awardees. However, there have been only 10 self-identified African American applicants (this value was not redacted in the materials I received) and the difference between the ratio of 0/10 is not statistically significantly different from 59/381 for Other (p value 0.37). The ratio for Asians is slightly higher than that for Other, but this difference is also not statistically significant.

The analysis for the Transformative R01 program is slightly more complicated because this program allows multiple principal investigators (PIs). I have included all PIs in my analysis of the awardees and believe this is also true for the applicant data that I received from NIH (although I am working to confirm this). With those caveats (and the disclaimer above about methodology), the ratio of awardees to applicants is 2/75 for African Americans. This ratio is slightly lower than the ratio of 154/4099 for Other, but this difference is not statistically significant (p = 1.00). The ratio for Asian applicants is also not statistically different from the other ratios.

As a final note, there is some overlap between the awardees in the Pioneer, New Innovator, and Transformative R01 programs with awardees from one program going on to receive additional awards in the same or other programs. Indeed, both African American Transformative R01 recipients had previously received Pioneer awards. This overlap will be the subject of a future post.

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Gender Balance in NIH High Risk Research Programs-Overall Award Pool versus Applicant Pool

(by datahound) Dec 10 2015

I recently posted analysis regarding the gender distribution between the awardees and the applicant pools for the four NIH High Risk Research (Pioneer, New Innovator, Early Independence, and Transformative R01) programs for 2015. Data regarding the awardees can be gleaned from NIH RePORTER or the NIH Common Fund website. However, data regarding the applicants are not available except through Freedom on Information Act (FOIA) requests. This is how I obtained the data for the 2015 applicants. I subsequently submitted a request for data regarding the applicants pools for all years since each award program was created. While I intended to get data for each year separately, I was not sufficiently clear in my request and I instead received aggregate data over all years. I have clarified my request and am awaiting a response. In the meantime, I want to share the analysis of the aggregate data.

Here are the data:

Program                     Applicants (Male)   Applicants (Female)       Applicants

.                                                                                                         (Gender not given)

Pioneer                              2051 (75.4%)           666 (24.6%)                    53

New Innovator               3498 (67.2%)         1706 (32.8%)                   120

Early Independence        233 (59.9%)            156 (40.1%)                    62

Transformative R01      3245 (78.4%)            894 (21.6%)                  160


A glance at these data reveals a couple of points. First, the gender balance of the applicant pool follows a pattern that could be anticipated based on the career stage mix with the closest balance for the Early Independence ("Skip the postdoc") program, followed by the New Innovator Award program, the Pioneer program, and, lastly, the Transformative R01 program. Second, while the number of investigators with genders withheld or unknown is less that 4% for three of the programs, it is 13.7% for the Early Independence program. This introduces some uncertainly in the analysis as will be discussed later.

How does these results compare with the Awardee pools?

Program                        Awardees (Male)        Awardees (Female)

Pioneer                              110 (71.0%)                   45 (29.0%)

New Innovator               263 (66.0%)                 135 (34.0%)

Early Independence         54 (75.0%)                   18 (25.0%)

Transformative R01       164 (83.2%)                   33 (16.8%)


Let's compare the applicant pool with the awardee pool program by program. For the Pioneer program, women correspond to 24.6% of the applicant pool and 29.0% of the awardee pool. This difference is not statistically significant with a p value 0f 0.18.

For the New Innovator program, women correspond to 32.8% of the applicant pool and 34.0% of the awardee pool. This small difference is not statistically significant with a p value of 0.62.

For the Early Independence program, women make up 40.1% of the applicant pool but only 25% of the awardee pool. This difference is statistically significant with a p value of 0.0049. Recall that 62 of the Early Independence applicants had genders that were unknown or withheld. If we assume that these applicants were split with the same proportions as the remainder of the pool (60% male, 40% female), the p value is decreased slightly to 0.0040. If we assume that the applicants of unknown gender were 50% male, 50% female, the p value is increased t0 0.0090. Only if we assume that all or nearly all of the applicants of unknown gender were male (and there is not reason to think that this is true), does the p value go above 0.05 (p value = 0.078 if all are assumed male). Thus, it appears very likely that there is a statistically significant decrease in the proportion of women in the Early Independence awardee pool compared with the applicant pool. This observation supports my proposal that NIH should investigate this program carefully to try to understand the source(s) of this disparity.

For the Transformative R01, women make up 21.6% of the applicant pool and 16.8% of the awardee pool. This difference is not statistically significant with a p value of 0.092. However, it is noteworthy that both the applicant pool and the awardee pool are strongly dominated by male principal investigators. This may reflect the relative seniority of applicants to this program or other factors about the program structure or management. This will require more analysis.

These results are summarized in the figure below:

High Risk Programs-Gender

I await the data broken down by year to see if there are meaningful trends over time. In the meanwhile, these data do provide the scientific community and the NIH some food for thought.

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Nelson Diversity Surveys: A Rich Data Source regarding Women and Minorities in Science

(by datahound) Dec 03 2015

The diversity (or relative lack thereof) across university campuses is a central issue in academia. Many institutions have ongoing diversity programs include several high-profile institutions (e.g. Yale, NYU, Brown) that have recently announced new initiatives. The diversity of the faculty represents a crucial subset of this issue and the diversity of the science and engineering faculty is an important component of this.

The development and implementation of programs intended to increase diversity depends, in part, on the availability of data to provide baselines and to monitor progress. The university (as opposed to medical center) based community is blessed with the Nelson Diversity Surveys, robust data sets regarding the representation of women and minorities in university science and engineering departments, generated through the efforts of Professor Donna Nelson and her colleagues. These surveys include essentially complete information regarding the number of faculty members in the top (in terms of federal funding) 50-100 departments in  Chemistry, Biological Sciences, Astronomy/Astrophysics, Physics, Mathematics/Statistics, Computer Science, Psychology, Political Science, Sociology, Economics, Chemical Engineering, Electrical Engineering, Civil Engineering, Mechanical Engineering, and Earth Sciences, broken out by rank, gender (Male, Female) and race/ethnicity (White (non-Hispanic), Black, Hispanic, Asian, and Native American). Importantly, these data are not samples, but complete snapshots taken in 2002, 2007, and, more recently 2012) obtained by polling and relentlessly following up with each department. The reports including data for 2002 and 2007 are freely available and the 2012 data should soon be available once the data validation and initial analysis are complete. I first became aware of these data when I was at NIGMS and we subsequently had Professor Nelson speak at one of our Advisory County meetings.

To give an example of the richness of these data, one Table (for the top 50 chemistry departments in 2007) is shown below. Note that the numbers presented as decimals represent women faculty members.Chemistry-Nelson-07Report-RotatedSome overall statistics for this subset of the Nelson Diversity Survey can be derived. Overall, these chemistry faculty were 13.7% women, 1.6% Black, 2.2% Hispanic, 10.0% Asian, and 0.2% Native American. These data can be compared with other population statistics. For example, the percentages of Ph.D.s awarded in Chemistry from 1995-2006 (also provided in the report) were 32.4% for women, 3.5% for Blacks, 3.4% for Hispanics, 12.8% for Asians, and 0.6% for Native Americans. Thus, the data reveal (not at all surprisingly) that the representations of all of these groups within the faculty is lower than they are in the recent Ph.D. population.

The availability of data broken down by department makes it possible to examine these averages in much more detail. The departments ranged in size from 18 to 56 faculty with a median of 33. Below is a histogram of the number of departments versus the percentage of women in each department. For comparison, a Poisson distribution with a mean of lambda = 13 (the distribution that would be expected if recruitment of women faculty into a given department were random with the same overall rate).


The fit to the Poisson distribution is approximate, but with substantial probability density moved out of the center toward departments with lower or higher percentages of women. Being cautious not to over-interpret this small data set (although it is certain possible to examine other fields to see if similar trends are observed), this supports the hypothesis that departments that already have a higher percentage of women tend to recruit more women (keeping in mind the low overall percentages for chemistry departments in general).

Below are histograms depicting the number of departments with a given percentage of faculty in each minority racial/ethnic group.


62% or more of the departments did not have a single Black faculty member. Similarly, 50% of the departments did not have a single Hispanic faculty member. The distribution for the percentage of Asian faculty members resembles that for women with an approximately Poisson distribution with some probably density moved out of the center. Only 3 departments identified have even one Native American faculty member. 13 of the 50 departments did not have a single Black, Hispanic, or Native American faculty member.

This quick pass through a small subset of the Nelson Diversity Surveys reveals some of the potential for exploring details and developing and testing hypotheses. Expanding this across different fields and, most interestingly, across time with the release of the 2012 data, should provide insights to help focus discussions of diversity and implementation strategies for improving diversity for the benefit of all.


Update:  Dr. Nelson informed me that the 2012 results will be released at the American Chemical Society meeting in March.

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NIH "High Risk" Programs-Part 3-New Innovator Award Program

(by datahound) Oct 26 2015

I have previously discussed the history of the NIH Director's Pioneer Award program. The daughter program of the Pioneer program is the NIH Director's New Innovator Award program. This program was created in 2007 when Congress was working on an appropriations package in February for the fiscal year that started the previous October. I was involved in the conception of this program and I recall quite clearly how I was sitting in a meeting when someone from the NIH Director Elias Zerhouni's office came in and asked if I could spare a few minutes to speak with Dr. Zerhouni and the NIH Budget Director. Needless to say, I said that I could. In the course of the budget negotiations, Congressional members and staff had heard Dr. Zerhouni's strong concern about the plight of young investigators and had been pleased with the initial progress with the Pioneer program. They wanted to know if NIH could establish a "junior Pioneer" program if they provided some additional funding. I was asked what I thought of the idea and if we could set up such a program. Again, needless to say, I indicated that I thought we could do that. The bill was passed with $40M for this new program in the budget for the NIH Common Fund.

This was mid-February and this program has to be set up from scratch and operationalized to get these funds out the door by the end of the fiscal year (September 30, 2007). After the meeting, I walked back to NIGMS and found Judith Greenberg and others involved with the Pioneer program (which was now being administered through NIGMS) and told them that I had so much faith in them that I had committed us to build this new program on this very short timeline. I am not sure they were entirely thrilled with me but, as always, they rose to the occasion and got right to work on the many tasks that needed to be tackled.

The first key question was how to target the program to young investigators since basing eligibility on age was clearly not legal. We came up with the idea of using time since an individual had received their "terminal degree" (typically PhD or MD). But, what was the appropriate time period? After some discussion and a little analysis, we settled on 10 years (with some exceptions for clinical training or time off for family responsibilities). This decision became the basis for the subsequent NIH definition of an "early stage investigator (ESI)".

The second key question was how large the awards should be. The NIH Budget Director John Bartrum had very cleverly required that this awards be made with a new mechanism, the DP2. This mechanism is quite unusual in that it allows NIH to commit funds for multiple years from the same fiscal year. This was done to avoid adding to the commitment base with these awards. Normally, when an institute funds a multi-year award in one fiscal year, this commits the institute to funding out years of the award in subsequent fiscal years. Since NIH does not know the appropriation level for the next fiscal years, this can be a real challenge. Making lots of commitments and then receiving a poor appropriation the next year can limit the number of new and competing grants substantially, leading to low success rates and other issues. With the new DP2 mechanism, NIH could fund each entire award out of one fiscal year without taking on any new commitments. The decision was made that the awards be $1.5 M direct costs over 5 years (but paid to the institution in the first year). Thus, with $40M and an estimated average grant size of $1.5M direct plus ~$1.M indirect) = $2.5M, we expected to be able to make about 14-16 awards.

The next challenge was writing the Request for Applications and getting the word out about this program so that eligible individual would have some time to get their applications conceived and submitted. It is a testament to Judith Greenberg and her coworkers that the RFA was published on March 9, 2007, approximately 3 weeks after the bill was passed. Trust me, the NIH bureaucracy does not normally work that fast. We worked with our communication staff to publicize the funding opportunity through every reasonable channel we could think of since our fear was that eligible folks would not find out about the program in time to submit applications. Applications were due on May 22 with this short period dictated by the need to get the applications processed, reviewed, and funding decisions made by September 30. Our publicity approach was successful when we realized that we had 2153 applications in by the deadline. This, of course, created a new challenge with funds for 14 awards and 2153 application leading to a projected success rate of a whopping 0.6%.

We had taken the lessons that we had learned through the Pioneer program and made sure that all stages of the process emphasized that innovative research could be expected from scientists of all genders and from diverse backgrounds. The review process (again, set up on a short timescale) involved a 2 phase electronic review. No interviews were included (in distinction to the Pioneer award). We spent considerable time orienting each group of reviewers so that they understood the vision for the program in all aspects. When the reviews were completed, we has an outstanding set of highly ranked applications. I set to work soliciting funds from institutes to increase the number of awards. This was challenging since these awards cost about $2.5M a pop but the lack of out-commitments was a selling point. By September 30, we were able to make a total of 30 awards to some outstanding scientists including 12 women and 18 men in a range of fields. I had the privilege of personally calling these individuals to tell them that they would likely be receiving these awards (and getting some additional information to make sure that they were still eligible).

Based on the successful launch, funds were provided for the New Innovator program in subsequent years. After the program had been running for three years, we initiated an outside process evaluation, parallel to the one for the Pioneer program. This report contains considerable information about the program, including comparisons of the applicant, finalist, and awardee pools.

New Innovator Gender

There were no statistically significant differences between the compositions of the applicant and awardees pools with regard to either gender or race/ethnicity.

The New Innovator program continues to the present with 497 applicants and 41 awards in fiscal year 2015. The goal of enabling young scientists to get off to a running start in hugely important and others feel the same way. Bruce Alberts, when he was Editor of Science magazine (and a reviewer for the New Innovator program) called for increasing the number of awards to 500 per year. One of the most touching (but also distressing) experiences that I had with this program was receiving several emails from unsuccessful applicants telling me how much fun it had been to write an application about what they actually want to achieve as opposed to what they thought they could get funded to do. This seems to me to be a significant indictment of the current state of affairs. In addition, of course, it is essential that support is available to sustain these careers once they are launched.

6 responses so far

Gender Balance in NIH High Risk Research Programs-2015

(by datahound) Oct 23 2015

When the NIH High Risk Research Program awardees for 2015 were announced concerns were raised about the balance of women and men among the awardees. This portfolio includes four programs:  NIH Director's Pioneer Award, NIH Director's New Innovator Award, NIH Director's Early Independence Award, and NIH Transformative R01 program.  These gender distribution among the awardees is shown below:

Pioneer: 10 men, 3 women (23% women)

New Innovator:  28 men, 13 women (32% women)

Early Independence:  13 men, 3 women (19% women)

Transformative R01:  10 men, 3 women (23% women)


Overall, women hold approximately 27% of research project grants at NIH so that the percentages in the High Risk programs do tend to be low. However, as I noted in my previous post, it is difficult to interpret these percentages without knowledge of the pool of individuals who applied to these programs.

Information about the applicant pool is not publicly available directly. However, I filed a FOIA request on October 8th and was pleased to receive the response yesterday (October 22nd, 2 weeks, record time for me...Thank you NIH staff and NIH FOIA office).

Below is the information that I received regarding the applicant pool gender composition:

Pioneer:  154 men, 49 women, 5 unknown/withheld (24% women among known)

New Innovator:  349 men, 138 women, 10 unknown/withheld (28% women among known)

Early Independence:  36 men, 26 women, 18 unknown/withheld (42% women among known)

Transformative R01:  248 men, 64 women, 17 unknown/withheld (21% women among known)


Note that gender information is not available for 18/80 = 22.5% of the applicants for the Early Independence Award. This may reflect that many of these applicants are new to NIH and have not provided this information. If we assume that all of the applicants with unknown gender are men, then the percentage of women is 33%. If we assume that all of these applicants are women, the percentage of women is 55%.


For the Pioneer program, the percentage of women awardees matches the percentage of women applicants. Based on the numbers, the p-value is 1.00, that is, there is no evidence that these distributions are different.

For the New Innovator program, the percentage of women awardees is slightly higher than the percentage of women in the applicant pool (32% versus 28%). The p-value is 0.72, indicating that the gender distribution of awardees is reasonably likely given the gender distribution of applicants.

For the Early Independence program, the percentage of women awardees is lower than the percentage in the applicant pool (19% versus 33-55%). Using the numbers for those with known gender, this mismatch has a p-value of 0.15. This is concerning as I will return to shortly.


For the Transformative R01 program, the percentage of women awardees is slightly higher than the percentage in the applicant pool (23% versus 21%). The p-value is 0.74.

Thus, for three of the programs, there is either no evidence of bias going from the applicant pool to the awardee pool. However, the percentages of women in the applicant pools are relatively low (21 to 28%). Particularly for the New Innovator program, the fact that only 28% of the applicants are women may reflect the pool of eligible faculty (although examining this will require additional data) or may reflect the likelihood that eligible women apply at the same frequency as do eligible men.


The most concerning data are for the Early Independence program. There is reasonably strong evidence for bias against women in moving from the applicant pool to the awardee pool (although knowledge of the magnitude of this effect is limited by the missing data for the applicant pool). There are at least two levels where this bias may be manifest. First, of course, is the review and selection process. But, one should keep in mind that this program requires considerable evidence of institutional support. Each institution is limited to two applicants and the application requires details about institutional support. Regardless of the sources, the NIH should examine this aspect of this program in short order to understand and try to correct any shortcomings of the process. This program has the potential to be particularly valuable for women since it is intended to shorten the time to independence, potentially better aligning the career path with biological clocks for those interested in having a family.

The data that I obtained allow one additional bit of analysis. The success rates for the program are as follows:

Pioneer:  13/208 = 6.3%

New Innovator:  41/497 = 8.2%

Early Independence:  16/80 = 20%

Transformative R01:  13/329 = 4.0%

While these success rates are low but the application processes, at least for the Pioneer and New Innovator awards, are relatively streamlined and, in my opinion, many additional scientists should consider applying to these programs. For the Early Independence program, the success rate is relatively high, but this reflects the limitation of two applicants per institution. This limitation presents another point of potential bias toward particular types of applicant.

The program are important in their own right and are flagships for NIH. It is essential that they be examine carefully to ensure as much as possible that they are serving their stated goals and are capturing the full range of outstanding scientific talent in the community.

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NIH "High Risk" Programs-Gender Issues-Part 1-Pioneer Program

(by datahound) Oct 12 2015

With the recent announcement of the "high-risk" research awards from the NIH, a discussion on Twitter began around the relatively low number of women awardees for these awards. I will return to this issue later but first I want to provide some background. For this post, I will focus on the first in this suite of what is now four programs.

The NIH Director's Pioneer Award (DP1) was initiated as part of the original NIH Roadmap for Medical Research. The award was intended to be an experiment driven by a frequent concern raised to Elias Zerhouni, then the NIH Director, that there were a number of highly innovative researchers in fields relevant to the NIH mission who were not applying to NIH because the relatively opacity and complexity of the NIH application process.

There were a large number of nominees (the program initially involved nominations rather than applications) and most of the nominations were submitted shortly before the deadline catching the NIH slightly off guard (more of this later). The Pioneer application involves a 5 page essay (rather than the more standard R01-type application of 25 pages at the time) and 22 of the most highly rated applicants are interviewed in person in Bethesda. This program was initiated just as I was starting my position as Director of NIGMS and I was not involved in the program in the first year.

When the first Pioneer awards were announced in September of 2004, I was surprised and disappointed by the outcome. There were nine recipients, several of whom were relatively well established within the NIH community including, for example, Steve McKnight (who was already well recognized within NIH as an innovative and productive scientist although he has gone on to make some controversial statements about the scientific community) and Homme Hellinga (who was recognized as a rising star at the time although much of his research has turned out to be, at best, irreproducible). I was hoping to go back to my office to google the awardees because I had not heard of them or did not know much about them. In addition, all nine of the awardees were male and this, appropriately, raised concerns within the scientific community both outside and inside NIH.

After the next meeting of the Institute and Center directors, I was sharing my views with Raynard Kington, then Deputy Director of NIH. He listened carefully and told me that Dr. Zerhouni needed to hear such concerns and I dutifully went back to my office and composed a long email. A couple of days later, I walked into a meeting at which both Drs. Kington and Zerhouni were present. They called me over and asked if I/NIGMS would like to take over running the Pioneer program. I was delighted if a bit daunted by this opportunity and asked some of my key colleagues including former acting-NIGMS Director Judith Greenberg if she would be willing to help with this effort.

We had a bit of time to review the processes that were used the first year and made a number of small changes including removing a "leadership potential" criterion that was used the first year since it seemed to peripheral to the goals of the program and had the potential to introduce biases of various sorts, allowing self-nominations and later applications, recruiting a more diverse pool of reviewers (more on this later), reaching out more aggressively through many outlets about the Pioneer program, reminding applicants and reviewers at all stages that "pioneering" researchers are quite diverse in all dimensions including gender, race and ethnicity, field, and career stage.

We again received a large number of applications and the process worked fairly smoothly. The end result was 13 awardees in a wide range of fields and career stages including 7 men and 6 women. As one would expect given access to $500K per year for 5 years as well as a competitive selection process, these investigators have done quite well, some exceptionally so.

The process continued for several more years with relatively similar results. After a total of five years were complete (so that we would have a reasonable data set), we initiated a process evaluation. This was completed and released in 2010. This is quite a thorough report and I encourage interested readers to have a look in its entirety.

With regard to gender distributions of Pioneer applicants, interviewees, and awardees, the key findings were:

The percentage of female applicants ranged from 22% to 27% with a mean of 25%. This number increased the year after we took over the program, a reassuring results after the results of the first year.

The percentage of female interviewees was 27% and the percentage of female awardees was 29%. The differences between these percentages and the applicant pool were not significantly significant.

The percentage of female awardees at 29% was higher than the percentage of female R01 awardees over the same period (23%).

One striking and distress result from the first year was the percentage of women among the reviewers. These results are shown below:

Pioneer Evaluators

While it is important to keep in mind that gender makeup of a review groups often does not eliminate or even reduce unconscious gender bias (example), the results from the first year of the Pioneer program were quite worrisome. The NIH staff running the program did not anticipate the number of nominees (1331) and had to scramble to recruit enough reviewers on short notice. With that constraint, the result was 59 men and 4 women including only 1 woman on the interview committee.

As an aside, the first years of the Pioneer program were run before Grants.gov existed. A special system had to be built and this allowed collection of data about exactly when applications were submitted. The results for the first year that NIGMS ran the program are shown below:

Pioneer timing

This shows the number of nominations/applications as a function of the data from the opening of the submission site (3/1) to the closing date (4/1). This reveals that many applicants submitted within the last few days before the due date. In addition, the eventual awardees (shown with red bars) tended to submit late in the day including a few minutes before the deadline. I would never have some much faith in a website.

I will discuss some of the other programs in subsequent posts. For now, I welcome thoughts about this analysis of the Pioneer program including gender balance issues. I have submitted a FOIA request for information about the applicant pools for these programs for the current year so that I hope to have data to do some analysis beyond looking at the awardees.

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