Postdoctoral research fellows are a driving force of the academic biomedical research enterprise. They typically have doctorate (i.e., PhD) or medical degrees (i.e., MD or DVM) which means they have completed a significant amount of education, training, and publishing (Wright and Vanderford, 2017; Igami et al., 2015; Ghaffarzadegan et al., 2015; Larson et al., 2019). Postdoctoral fellows aspiring to continue in academic research typically seek tenure-track faculty appointments (Larson et al., 2019). Despite recent trends of new doctoral degree holders seeking private sector positions (Heggeness et al., 2017), tenure-track academic faculty positions are highly coveted and competitive due to the increasing number of PhD graduates but stagnant number of tenure-track faculty positions (Ghaffarzadegan et al., 2015; Daniels, 2015). Obtaining such positions requires consistent access to opportunities for success such as attending doctoral programs at perceived prestigious institutions, publishing manuscripts at the undergraduate, graduate, and postdoctoral levels, and receiving extramural grant funding throughout one’s career (Clauset et al., 2015; Fernandes et al., 2020; Brechelmacher et al., 2015; van Dijk et al., 2014). University prestige has significant impacts on resources (Way et al., 2019), paper acceptance rates (Okike et al., 2016), citations (Crane, 1965), and awards (Schlagberger et al., 2016). There is also significant bias against women, LGBTQIA+, and other systematically marginalized groups at all levels, which results in underrepresentation in STEM fields and fewer R01 awards compared to white/men counterparts (Wapman et al., 2022; Safdar et al., 2021; Oliveira et al., 2019; Check Hayden, 2015). To combat these biases, the National Institutes of Health (NIH) developed funding mechanisms, such as the K99 MOSAIC and K12 IRACDA awards, to promote scientists from diverse backgrounds into tenure-track faculty positions. In addition, other non-federal grant mechanisms exist, such as the Gilliam Fellowships for Advanced Study from the Howard Hughes Medical Institute which began in 2004. The success of these programs at addressing bias or improving equity for systematically marginalized groups in the tenure-track ranks is currently unclear because the programs are either too new (MOSAIC) or data are not publicly available (HHMI Gilliam Fellowships).

Within the United States, one of the most lucrative award mechanisms to facilitate the transition from postdoctoral fellow to tenure-track faculty member in the biomedical sciences is the NIH Pathway to Independence Award (K99/R00) (Carlson et al., 2016). Launching in 2006, this award provides salary support for postdoctoral fellows for 2 years during the K99 phase and $250,000/yr for 3 years during the R00 phase (Report, 2024; Funding, 2017). The candidate benefits directly through funding, visibility, and protected time (Wright and Vanderford, 2017; Funding, 2017). Having a K99/R00 award also demonstrates to hiring institutions that the candidate has a track record of receiving extramural funding which is a requirement for future success in academic research careers (Hsu et al., 2021). Roughly 89% of K99 awardees received R00 funding on a rolling yearly basis, which indicates having obtained a faculty position although factors such as publications, presentations, interpersonal skills, choice of advisor, and others also contribute to securing faculty positions. Of those that secure faculty positions, many factors influence postdoctoral researchers in their decision of which position to choose including career-oriented factors like department, support, colleagues, and personal factors such as family and location. About 50% of R00 awardees subsequently attain R01 funding (Pickett, 2019) which is a higher success rate than other NIH career development grants such as K01, K08, and K23 (Carlson et al., 2016; Conte and Omary, 2018), although having any K-award tends to increase future funding success (Nikaj and Lund, 2019). The specific and cumulative factors that influence future NIH funding success for R00 awardees has not been examined.

Individuals from marginalized communities are typically at a disadvantage when applying for research grants such as R01s. For example, men typically experience 2–3% greater funding success rates; however, in 2019 women had an advantage in funding success rates for NIH research project grants (21% vs 20%) (Chaudhary et al., 2021 ). Men also receive an average of $35,000–$45,000 more NIH funding per grant than women or other gender identities (Oliveira et al., 2019; Chaudhary et al., 2021; Mayes et al., 2018), however, some report no difference (Pohlhaus et al., 2011). Women have been reported to have a disadvantage in transitioning from K-awards to R-awards from the NIH (Nguyen et al., 2023). Racial and ethnic minorities are also at a disadvantage in receiving R01 funding compared to white counterparts (Ginther, 2022; Ginther et al., 2011). The institution itself also influences R01 funding success. Here, we explore the conversion of K99 to R00 awards by year, gender identity, and institutions (other demographic data are not publicly available) to demonstrate the flow of K99/R00 awardees from postdoc to faculty positions. We also determine whether these influence future NIH funding success for K99/R00 awardees. These findings have the potential to influence how and where career development awards are made, how potential candidates are supported, and how grant reviewing practices may be changed to be more equitable and inclusive.

It has previously been shown that a minority of institutions populate the faculty ranks of the bulk of other institutions along prestige hierarchies across fields and disciplines (Clauset et al., 2015; Wapman et al., 2022). These findings are in-line with the Matthew effect whereby success early on increases the chance of success in the future (Merton, 1968; Bol et al., 2018). Our analyses focus on the K99/R00 career development award. We demonstrate there are significant disadvantages associated with mobility, gender identity, type of R00 institution, and whether faculty candidates move from the K99 institution or stay at the same institution. In the context of the Matthew effect, there are significant advantages for receiving the K99 award at a highly funded institution or for being classified as a man or having mobility in the choice of an R00 institution. Scientists are often the most mobile at the earliest stages of their careers whether by choice or necessity (Vaccario et al., 2021). Academic mobility, especially in early stage investigators has been described as ‘coerced movement from job to job’ and has also been termed ‘forced mobility’ (Ackers, 2008). International postdoctoral fellows may have a cultural reasons for moving to the United States based in perceived bias regarding the United States’ scientific training (Cantwell, 2011). Our analysis also demonstrates that K99 to R00 faculty flow generally follows similar hierarchies with some deviation for specific institutions. We find that a minority of private and public institutions with the largest NIH funding portfolios produce the majority of K99 awardees, and there are a handful of private and public institutions with the largest NIH funding portfolios that recruit the most R00 awardees. Many K99 awardees from institutions with the largest NIH funding portfolios move to public institutions and institutions with smaller NIH funding portfolios for the R00 phase of the award. These also happen to be less concentrated on the East and West coast. This supports the universal core-periphery structure of hiring that Wapman et al. conceptualized but suggests that prestige hierarchies in academic biomedical science align with NIH funding portfolio size (Wapman et al., 2022). Despite this, there are also K99/R00 awardees that move up the prestige hierarchy to public and private institutions with the largest NIH funding portfolios and some individuals that stay within the same category for both K99 and R00 award phases. There are stark differences in future major NIH grant funding depending on several factors including NIH funding portfolio size of R00 institution, gender identity, and whether the awardee moved or stayed at the K99 institution.

Notably, whether the K99 institution was private or public or the size of the NIH funding portfolio did not impact future funding success except when considering whether the candidate eventually moved for the R00 phase of the award. One key component of activating the R00 award is to demonstrate independence from the candidate’s postdoctoral advisor because the candidate will need to establish independence from the postdoctoral advisor to be competitive to receive future NIH funding. Moving to a new institution clearly establishes independence; however, there are many pressures to stay at the K99 institution including familiarity with the institution, location, and family. K99/R00 awardees that stay at the same institution have an increase in the median time to future major NIH funding (either R01, DP2, or R35 MIRA) and have a reduced overall chance to receive any major NIH award. This may be due to reduced start-up packages for internal candidates, which could leave them less well suited to perform the necessary research to be competitive for R01 funding. Candidates that remain at the same institution may also have additional duties in service work that drain their time. They may have more work on committees given the knowledge, experience, familiarity, and institutional history. Alternatively, this may be an internal bias of candidates within the institution or of grant reviewers who do not view the candidate as truly independent from their postdoctoral advisor. There are also cultural biases that largely reward white and Asian male academics by perceiving them to be more productive and de-valuing work done by non-white and non-Asian men (Blair-Loy, 2023; Blair-Loy et al., 2023). These data bring to light several factors that affect the scientific human experience. Moving institutions throughout one’s academic career can cause significant hardship on anyone. First-generation scientists and those in systematically marginalized groups may lack support systems that those from non-marginalized groups have. Familial/friendships ties play important roles in the happiness, support, and productivity in the lives of scientists. Compounding the burden of first-generation scientists and systemically marginalized scientists with the burden of moving away from supports offered by family and friends may have a significant negative impact on those scientists. The second is that obtaining K99 funding and staying at the same institution comes with inherent biases within the system designed to support scientific talent. How these two factors are at odds with each other is of significant note to the wider scientific community. Ameliorating these disparities to increase equity should be of future focus.

Additional disadvantages to future funding success of K99/R00 awardees is whether the candidate is a man or a woman and whether the candidate is from an institution with a large NIH funding portfolio. Each of these factors can prolong the median time to future funding by about half a year and reduces the overall chance of securing future funding by ~5.5%. These cumulative disadvantages may contribute to lower likelihood of receiving promotion and tenure and may contribute to the gender disparities in faculty ranks and in leadership positions. The United States has a more even gender balance for tenure-track positions than some other countries including Japan and South Korea (Xu et al., 2018); however, it is apparent that women are at a disadvantage compared with men despite this. There may also be additional disadvantages for K99/R00 awardees who identify as transgender, gender non-binary, or non-conforming, or for individuals from systematically marginalized groups, or those with disabilities. There have been recommendations to promote a sustainable biomedical research enterprise (Pickett et al., 2015). Future work is needed to examine how intersectional inequalities impact scientific success within the academic workforce.

We can speculate why K99 awards most often go to individuals at institutions with the largest NIH funding portfolios; however, data on individual K99/R00 applications that were not funded are not publicly available. Our data show that 772 scientists with funded K99 awards did not get R00 awards and 302 of these were from 2019 or earlier. Those made from 2020 onward may still have the possibility to convert to the R00 phase, even after the 2-year K99 period with a 1-year no-cost extension. The factors that prevented the other 302 K99 awardees from 2019 and earlier unable to convert their K99–R00 grants is cause for concern within our greater academic community. Possible explanations include leaving the biomedical workforce, accepting tenure-track positions or other positions abroad, or by simply not successfully securing a tenable tenure-track offer. Of the funded K99/R00 awardees, we can make the assumption that candidates at institutions with large biomedical research portfolios and built-in support to write competitive grant applications that may be lacking at institutions with smaller research enterprises. Institutions with the largest NIH funding portfolios may facilitate greater access to making scientific discoveries. Movement of postdocs from these institutions to independent faculty positions at institutions with the smaller NIH funding portfolios, may increase the effectiveness of the faculty at those institutions. We must also consider that senior faculty themselves review and score K99/R00 award applications, and by doing so, determine which candidates ultimately receive these awards. A contrary perspective is that K99/R00 awardees at institutions perceived as more prestigious receive their awards in part because of the name and reputation of their institution. Thus, the interpretation of our data is that the system of reviewing and scoring grants has inherent bias toward less prestigious institutions which is currently being reviewed at the NIH. We can extend this speculation of bias to explain why men who receive R00 awards and move to institutions with the largest NIH funding portfolios are the most successful at securing subsequent major NIH awards in contrast to women, people who stay at the same institution for the R00 phase, and individuals at institutions with smaller NIH funding portfolios.

Of note, no K99 award has been made to a candidate at an HBCU, and only two awardees activated R00 awards at HBCUs. Most HBCUs specialize in undergraduate education; however, several perform biomedical research. Thus, we used a different dataset of faculty institutions and doctoral degree institutions to examine how faculty hiring practices differ between institution types, in particular, HBCUs. In agreement with others, we also found that self-hiring is common ( Altbach et al., 2015). The self-hiring rate generally between 10% and 20% most institutions including HBCUs. One stark difference between HBCUs and all other US research institutions is that HBCUs appear to be the only institution type that hires faculty with doctoral degrees from other HBCUs. For example, of the 938 biomedical faculty members at Harvard, only 1 has a doctoral degree from an HBCU. Some institutions have lower rates of self-hiring. For example, Princeton faculty have degrees primarily from other Ivy league institutions but doctoral degrees from Princeton only represent the 14th most common for biomedical sciences. In agreement with the known and described systemic inequalities within the academic system we have highlighted in this article, self-hiring within HBCUs may occurs to enable survival of these institutions. Although many HBCUs focus primarily on undergraduate education, several have large biomedical research enterprises, and it is shocking that no K99 awardees have ever been from HBCUs. The K99 MOSAIC program, established in 2020 may increase equity; however, an effort to fund career development awards specifically at HBCUs may also increase equity in this area.

We do not know how many unfunded K99 applications may have originated from HBCUs. A freedom of information act request in 2018 by Dr. Pickett (request # 47950) for fiscal years 2007–2017 for K99/R00 awardees found that the successful award rate for white K99 applicants was 31.0% which was higher than both Asian (26.7%) and Black (16.2%) applicants. Total successful R00 transition rates were also higher among white K99 applicants (77%) compared to Asian (76.1%) and Black (60.0%) awardees (United States, 2018). Although data on specific applicants are not available to determine trajectories from specific institution types, these data demonstrate that while improvements in equity to male and female applicants have improved; there is still a significant racial equity gap.

These analyses exclude those that received a K99 but were either unable to transition to an R00 award for various reasons including receiving a faculty position in the United States without needing the R00, receiving a faculty position outside of the United States, pursuing another line of work, or other unknown reasons. Furthermore, these analyses are limited to academic institutions in the United States and do not examine academic centers in other countries. When interpreting these data, it is important to consider that each data point corresponds to an individual with unique motivations for choice of postdoctoral research advisor, institution, and laboratory as well as autonomy in whether to pursue a faculty position and where that may be. Complex consideration of start-up package, institutional clout, and individual motivations factor into this decision. In addition, the choice to give K99/R00 awards and subsequent NIH awards lies mostly with grant reviewers who are generally mid- to senior-career faculty that may have different levels of conscious or unconscious bias for or against various factors that contribute to the overall score given. This dataset does not account for the motivation behind these complex decisions, and an understanding of these motivating factors would be enlightening.

It is important to note the limitations of this study. The present study is focuses on K99/R00 awardees and is limited to faculty flows within the United States. While similar practices may exist in other countries, there are likely significant differences also. Another limitation due to our focus on K99/R00 awardees, is that scientists without K99/R00 awards are excluded from our analyses. Furthermore, the public data on K99/R00 awardees do not provide demographic details including self-identified gender, race, ethnicity, age, and degree type. The gender identity used here was assigned as a binary man or woman gender based on the first name of the candidate. If these demographic data were available, a critical analysis of bias and how these impact outcomes could be made. For our analysis of K99 awardees, we do not have information on the doctoral degree granting institution, and for our exploration of where faculty members at HBCUs received their doctoral degrees, we do not have information on their postdoctoral research experience. If we had these details, we could make a more complete examination of flow from doctoral degree institution to postdoctoral institution to faculty institution and then to determine outcomes based on each of these. Future research should aim to complete a more comprehensive assessment of faculty flows and outcomes.

Here, we demonstrate the complex nature of faculty hiring within the NIH grant system that exists today in the biomedical sciences and related fields. The K99/R00 award mechanism undoubtedly increases awardees’ chance of securing a faculty position. As one of the most coveted NIH grants for postdocs, we have examined the flow of K99 awardees to R00 institutions and how these flows impact future NIH award funding. Many factors besides those considered and quantified here contribute to where K99 awardees choose to begin their faculty careers, and the K99 award itself does not guarantee securing a faculty position. Despite this, we have identified factors that pose significant disadvantages to future funding success for K99/R00 awardees which likely influence funding success more broadly. Future work must examine the role of ethnic and racial bias in these domains. As the K99 MOSAIC program becomes more established, a comparison of this program and the K99 grant mechanism explored here may reveal whether the MOSAIC strategy is effective at promoting equity for underrepresented minorities in the biomedical faculty ranks. By quantifying and understanding these factors, grant reviewers, faculty hiring committees, department chairs, and funding bodies may be able to more equitably award and administer grants and evaluate faculty candidates. Improvements to the faculty hiring process can be achieved centrally through the NIH creating a more equitable grant funding system or within the many diverse faculty searches (Bhalla, 2019; Schmidt et al., 2021). There is only one NIH and many searches, so systemic change will likely lead to more impactful change; however, both options could be implemented simultaneously for the optimal benefit of the scientific community.

All data generated or analyzed during this study are included in the manuscript and supporting files.

1. 1. Solis C

   (2023) Zenodo

   chsolis/K99toR00SankeyNetwork2007-2022: K99 to R00 Sankey Network App v1.0.

   https://doi.org/10.5281/zenodo.10005359

1. 1. Wapman KH
   2. Zhang S
   3. Clauset A
   4. Larremore D

   (2022) Zenodo

   Quantifying hierarchy and dynamics in U.S. faculty hiring and retention.

   https://doi.org/10.5281/zenodo.6941651

1. 1. Ackers LI

   (2008) Internationalisation, mobility and metrics: a new form of indirect discrimination?

   Minerva 46:411–435.

   https://doi.org/10.1007/s11024-008-9110-2

   • Google Scholar
2. 1. Altbach PG
   2. Yudkevich M
   3. Rumbley LE

   (2015) Academic inbreeding: local challenge, global problem

   Asia Pacific Education Review 16:317–330.

   https://doi.org/10.1007/s12564-015-9391-8

   • Google Scholar
3. 1. Bhalla N

   (2019) Strategies to improve equity in faculty hiring

   Molecular Biology of the Cell 30:2744–2749.

   https://doi.org/10.1091/mbc.E19-08-0476

   • PubMed
   • Google Scholar
4. 1. Blair-Loy M

   (2023) On authorship and gender equity

   Science 380:352.

   https://doi.org/10.1126/science.adh2719

   • Google Scholar
5. 1. Blair-Loy M
   2. Reynders S
   3. Cech EA

   (2023) Productivity metrics and hiring rubrics are warped by cultural schemas of merit

   Trends in Microbiology 31:556–558.

   https://doi.org/10.1016/j.tim.2023.03.004

   • PubMed
   • Google Scholar
6. 1. Bol T
   2. de Vaan M
   3. van de Rijt A

   (2018) The Matthew effect in science funding

   PNAS 115:4887–4890.

   https://doi.org/10.1073/pnas.1719557115

   • PubMed
   • Google Scholar
7. Book

   1. Brechelmacher A
   2. Park E
   3. Ates G
   4. Campbell DFJ

   (2015) The rocky road to tenure – career paths in academia

   In: Fumasoli T, Goastellec G, Kehm B, editors. Academic Work and Careers in Europe: Trends, Challenges, Perspectives. Springer International Publishing. pp. 13–40.

   https://doi.org/10.1007/978-3-319-10720-2_2

   • Google Scholar
8. 1. Cantwell B

   (2011) Transnational mobility and international academic employment: gatekeeping in an academic competition arena

   Minerva 49:425–445.

   https://doi.org/10.1007/s11024-011-9181-3

   • Google Scholar
9. 1. Carlson DE
   2. Wang WC
   3. Scott JD

   (2016) Initial outcomes for the NHLBI K99/R00 pathway to independence program in relation to long-standing career development programs: Implications for trainees, mentors, and institutions

   Circulation Research 119:904–908.

   https://doi.org/10.1161/CIRCRESAHA.116.309238

   • PubMed
   • Google Scholar
10. 1. Chaudhary AMD
    2. Naveed S
    3. Safdar B
    4. Saboor S
    5. Zeshan M
    6. Khosa F

    (2021) Gender differences in research project grants and r01 grants at the national institutes of health

    Cureus 13:e14930.

    https://doi.org/10.7759/cureus.14930

    • PubMed
    • Google Scholar
11. 1. Check Hayden E

    (2015) Racial bias continues to haunt NIH grants

    Nature 527:286–287.

    https://doi.org/10.1038/527286a

    • PubMed
    • Google Scholar
12. 1. Clauset A
    2. Arbesman S
    3. Larremore DB

    (2015) Systematic inequality and hierarchy in faculty hiring networks

    Science Advances 1:e1400005.

    https://doi.org/10.1126/sciadv.1400005

    • PubMed
    • Google Scholar
13. 1. Conte ML
    2. Omary MB

    (2018) NIH Career Development Awards: conversion to research grants and regional distribution

    The Journal of Clinical Investigation 128:5187–5190.

    https://doi.org/10.1172/JCI123875

    • PubMed
    • Google Scholar
14. 1. Crane D

    (1965)

    Scientists at major and minor universities: a study of productivity and recognition

    American Sociological Review 30:699–714.

    • PubMed
    • Google Scholar
15. 1. Daniels RJ

    (2015) A generation at risk: young investigators and the future of the biomedical workforce

    PNAS 112:313–318.

    https://doi.org/10.1073/pnas.1418761112

    • PubMed
    • Google Scholar
16. 1. Fernandes JD
    2. Sarabipour S
    3. Smith CT
    4. Niemi NM
    5. Jadavji NM
    6. Kozik AJ
    7. Holehouse AS
    8. Pejaver V
    9. Symmons O
    10. Bisson Filho AW
    11. Haage A

    (2020) A survey-based analysis of the academic job market

    eLife 9:e54097.

    https://doi.org/10.7554/eLife.54097

    • PubMed
    • Google Scholar
17. Software

    1. Funding G

    (2017) A history of new and early stage investigator policies and data

    Grants.Nih.Gov.

    https://grants.nih.gov/policy/early-investigators/history.htm
18. 1. Ghaffarzadegan N
    2. Hawley J
    3. Larson R
    4. Xue Y

    (2015) A note on PhD population growth in biomedical sciences

    Systems Research and Behavioral Science 23:402–405.

    https://doi.org/10.1002/sres.2324

    • PubMed
    • Google Scholar
19. 1. Ginther DK
    2. Schaffer WT
    3. Schnell J
    4. Masimore B
    5. Liu F
    6. Haak LL
    7. Kington R

    (2011) Race, ethnicity, and NIH research awards

    Science 333:1015–1019.

    https://doi.org/10.1126/science.1196783

    • PubMed
    • Google Scholar
20. 1. Ginther DK

    (2022) Reflections on race, ethnicity, and NIH research awards

    Molecular Biology of the Cell 33:ae1.

    https://doi.org/10.1091/mbc.E21-08-0403

    • PubMed
    • Google Scholar
21. 1. Heggeness ML
    2. Gunsalus KTW
    3. Pacas J
    4. McDowell G

    (2017) The new face of US science

    Nature 541:21–23.

    https://doi.org/10.1038/541021a

    • PubMed
    • Google Scholar
22. 1. Hsu NS
    2. Rezai-Zadeh KP
    3. Tennekoon MS
    4. Korn SJ

    (2021) Myths and facts about getting an academic faculty position in neuroscience

    Science Advances 7:eabj2604.

    https://doi.org/10.1126/sciadv.abj2604

    • PubMed
    • Google Scholar
23. 1. Igami M
    2. Nagaoka S
    3. Walsh JP

    (2015) Contribution of postdoctoral fellows to fast-moving and competitive scientific research

    The Journal of Technology Transfer 40:723–741.

    https://doi.org/10.1007/s10961-014-9366-7

    • Google Scholar
24. 1. Larson LR
    2. Duffy LN
    3. Fernandez M
    4. Sturts J
    5. Gray J
    6. Powell GM

    (2019) Getting started on the tenure track: Challenges and strategies for success

    SCHOLE 34:36–51.

    https://doi.org/10.1080/1937156X.2019.1589804

    • Google Scholar
25. 1. Lockhart JW
    2. King MM
    3. Munsch C

    (2023) Name-based demographic inference and the unequal distribution of misrecognition

    Nature Human Behaviour 7:1084–1095.

    https://doi.org/10.1038/s41562-023-01587-9

    • PubMed
    • Google Scholar
26. 1. Martinez LR
    2. Boucaud DW
    3. Casadevall A
    4. August A

    (2018) Factors contributing to the success of nih-designated underrepresented minorities in academic and nonacademic research positions

    CBE Life Sciences Education 17:ar32.

    https://doi.org/10.1187/cbe.16-09-0287

    • PubMed
    • Google Scholar
27. 1. Mayes LM
    2. Wong CA
    3. Zimmer S
    4. Fernandez-Bustamante A
    5. Bartels K

    (2018) Gender differences in career development awards in United States’ anesthesiology and surgery departments, 2006-2016

    BMC Anesthesiology 18:95.

    https://doi.org/10.1186/s12871-018-0561-1

    • PubMed
    • Google Scholar
28. 1. Merton RK

    (1968)

    The Matthew effect in science. The reward and communication systems of science are considered

    Science 159:56–63.

    • PubMed
    • Google Scholar
29. 1. Nguyen M
    2. Panyadahundi A
    3. Olagun-Samuel C
    4. Chaudhry SI
    5. Desai MM
    6. Dardik A
    7. Boatright D

    (2023) Transition from mentored to independent NIH funding by gender and department

    JAMA 329:2189–2190.

    https://doi.org/10.1001/jama.2023.7693

    • PubMed
    • Google Scholar
30. 1. Nikaj S
    2. Lund PK

    (2019) The impact of individual mentored career development (k) awards on the research trajectories of early-career scientists

    Academic Medicine 94:708–714.

    https://doi.org/10.1097/ACM.0000000000002543

    • PubMed
    • Google Scholar
31. 1. Okike K
    2. Hug KT
    3. Kocher MS
    4. Leopold SS

    (2016) Single-blind vs double-blind peer review in the setting of author prestige

    JAMA 316:1315–1316.

    https://doi.org/10.1001/jama.2016.11014

    • PubMed
    • Google Scholar
32. 1. Oliveira DFM
    2. Ma Y
    3. Woodruff TK
    4. Uzzi B

    (2019) Comparison of national institutes of health grant amounts to first-time male and female principal investigators

    JAMA 321:898–900.

    https://doi.org/10.1001/jama.2018.21944

    • PubMed
    • Google Scholar
33. 1. Pickett CL
    2. Corb BW
    3. Matthews CR
    4. Sundquist WI
    5. Berg JM

    (2015) Toward a sustainable biomedical research enterprise: Finding consensus and implementing recommendations

    PNAS 112:10832–10836.

    https://doi.org/10.1073/pnas.1509901112

    • PubMed
    • Google Scholar
34. 1. Pickett CL

    (2019) The increasing importance of fellowships and career development awards in the careers of early-stage biomedical academic researchers

    PLOS ONE 14:e0223876.

    https://doi.org/10.1371/journal.pone.0223876

    • PubMed
    • Google Scholar
35. 1. Pinheiro DL
    2. Melkers J
    3. Newton S

    (2017) Take me where I want to go: Institutional prestige, advisor sponsorship, and academic career placement preferences

    PLOS ONE 12:e0176977.

    https://doi.org/10.1371/journal.pone.0176977

    • PubMed
    • Google Scholar
36. 1. Pohlhaus JR
    2. Jiang H
    3. Wagner RM
    4. Schaffer WT
    5. Pinn VW

    (2011) Sex differences in application, success, and funding rates for NIH extramural programs

    Academic Medicine 86:759–767.

    https://doi.org/10.1097/ACM.0b013e31821836ff

    • PubMed
    • Google Scholar
37. Software

    1. Report N

    (2024) Success rates

    Success Rates.

    https://report.nih.gov/funding/nih-budget-and-spending-data-past-fiscal-years/success-rates
38. 1. Safdar B
    2. Naveed S
    3. Chaudhary AMD
    4. Saboor S
    5. Zeshan M
    6. Khosa F

    (2021) Gender disparity in grants and awards at the national institute of health

    Cureus 13:e14644.

    https://doi.org/10.7759/cureus.14644

    • PubMed
    • Google Scholar
39. 1. Santamaría L
    2. Mihaljević H

    (2018) Comparison and benchmark of name-to-gender inference services

    PeerJ. Computer Science 4:e156.

    https://doi.org/10.7717/peerj-cs.156

    • PubMed
    • Google Scholar
40. 1. Schlagberger EM
    2. Bornmann L
    3. Bauer J

    (2016) At what institutions did Nobel laureates do their prize-winning work? An analysis of biographical information on Nobel laureates from 1994 to 2014

    Scientometrics 109:723–767.

    https://doi.org/10.1007/s11192-016-2059-2

    • PubMed
    • Google Scholar
41. 1. Schmidt R
    2. Curry S
    3. Hatch A

    (2021) Creating SPACE to evolve academic assessment

    eLife 10:e70929.

    https://doi.org/10.7554/eLife.70929

    • PubMed
    • Google Scholar
42. Software

    1. Solis C

    (2023) K99Tor00Sankeynetwork2007-2022, version swh:1:rev:5ae23b340dd04deefc880a68d3087f269664e62e

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:c561b5e1fd79a3eec938c4faa8f6bed89175606b;origin=https://github.com/chsolis/K99toR00SankeyNetwork2007-2022;visit=swh:1:snp:6aab9030d9841c370c9efce3428779ad8d9d0511;anchor=swh:1:rev:5ae23b340dd04deefc880a68d3087f269664e62e
43. Software

    1. Tyrrell DJ

    (2023) K99-R00-analysis: data analysis using googleVis package, version 868b7ba

    Github.

    https://github.com/dantyrr/K99-R00-analysis
44. Report

    1. United States

    (2018)

    Race and Ethnicity on K99 Applicants and K99/R00 Awardees Report No.: 273–18

    National Institutes of Health Office of Extramural Research.

    • Google Scholar
45. 1. Vaccario G
    2. Verginer L
    3. Schweitzer F

    (2021) Reproducing scientists’ mobility: a data-driven model

    Scientific Reports 11:10733.

    https://doi.org/10.1038/s41598-021-90281-9

    • PubMed
    • Google Scholar
46. 1. van Dijk D
    2. Manor O
    3. Carey LB

    (2014) Publication metrics and success on the academic job market

    Current Biology 24:R516–R517.

    https://doi.org/10.1016/j.cub.2014.04.039

    • PubMed
    • Google Scholar
47. 1. Wapman KH
    2. Zhang S
    3. Clauset A
    4. Larremore DB

    (2022) Quantifying hierarchy and dynamics in US faculty hiring and retention

    Nature 610:120–127.

    https://doi.org/10.1038/s41586-022-05222-x

    • PubMed
    • Google Scholar
48. 1. Way SF
    2. Morgan AC
    3. Larremore DB
    4. Clauset A

    (2019) Productivity, prominence, and the effects of academic environment

    PNAS 116:10729–10733.

    https://doi.org/10.1073/pnas.1817431116

    • PubMed
    • Google Scholar
49. 1. Wright CB
    2. Vanderford NL

    (2017) What faculty hiring committees want

    Nature Biotechnology 35:885–887.

    https://doi.org/10.1038/nbt.3962

    • PubMed
    • Google Scholar
50. 1. Xu H
    2. Gilliam RST
    3. Peddada SD
    4. Buchold GM
    5. Collins TRL

    (2018) Visualizing detailed postdoctoral employment trends using a new career outcome taxonomy

    Nature Biotechnology 36:197–202.

    https://doi.org/10.1038/nbt.4059

    • PubMed
    • Google Scholar

Author details

1. Nicole C Woitowich

   Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing - review and editing

   Contributed equally with

   Sarah R Hengel

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3449-2547

2. Sarah R Hengel

   Department of Biology, Tufts University, Medford, United States

   Contribution

   Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - review and editing

   Contributed equally with

   Nicole C Woitowich

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0408-8623

3. Christopher Solis

   Department of Health, Nutrition, and Food Sciences, Florida State University, Tallahassee, United States

   Contribution

   Data curation, Software, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Tauras P Vilgalys

   Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Joel Babdor

   Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Daniel J Tyrrell

   Division of Molecular and Cellular Pathology, Department of Pathology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   danieltyrrell@uabmc.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0811-6724

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