-

National Aeronautics and

 Space Administration 

NASA 

RESEARCHER 

DEMOGRAPHICS 

2023 REPORT 

OFFICE OF 

THE CHIEF 

SCIENTIST 

ABSTRACT 

NASA is committed to supporting a research environment that is 

fair and equitable. In order to promote opportunity for everyone, 

NASA is collecting data on the demographic makeup of its 

research community with the aim of using this data to support 

decision making in future programs and projects. This report 

summarizes the demographic data we have collected for the 

Science Mission Directorate since 2016. 

www.nasa.gov 
NASA RESEARCHER DEMOGRAPHICS

 

A INTRODUCTION 

This will be NASA’s first report on the demographics of its researcher funded community. NASA 

has been collecting demographic information since 2016. We are compiling this report in order to 

(a) inform the community at large as to the existing demographics of our researchers; (b) provide

input to NASA policy planners and leaders on how NASA can continue to support and encourage

the diversity of the researcher community; and (c) delineate the number and types of institutions

NASA is funding, including minority serving institutions (MSIs).

In this first report, we are focusing on the Science Mission Directorate (SMD), which funds 

the majority of NASA research. Here, data are broken down for Astrophysics, Earth Science, 

Heliophysics, and Planetary Science. Biological and Physical Sciences (BPS) is not included as 

it recently moved from ESDMD/SOMD, so its data are mixed in ESDMD/SOMD and SMD and 

requires further analysis. Future reports will include ARMD, ESDMD/SOMD, and STMD, as well as 

reviewer panel and student demographics. 

A INTRODUCTION 2 
NASA RESEARCHER DEMOGRAPHICS

 

SMD Researcher Demographics: Data We Collect Currently 

NASA collects voluntary demographic information through the NSPIRES web portal. This portal is 

used by researchers (responding to NASA NRAs or Announcements of Opportunity), mail-in and 

panel reviewers, and students (typically applying for Fellowship opportunities). Anyone who logs 

onto the NSPIRES portal for the first time is asked to answer questions on gender, race, ethnicity, 

disability, education, year of highest degree, career sector, and career type. All responses are 

voluntary and there is always an option to say “prefer not to answer”, PNA. On subsequent logins, users can update their demographic information any time they wish, for example when they 

change jobs. The information that is being collected was cleared with OMB and complies with all 

applicable statutes and regulations.

Questions asked (PNA = Prefer Not to Answer): 

• Gender: Male/Female/Other (added in 2019)/ PNA

• Ethnicity: Hispanic or Latino/Not Hispanic or Latino/PNA

• Race: Asian/American Indian or Alaska Native/White/Black or African American/Native

Hawaiian or Other Pacific Islander/Other/PNA

• Disability: Hearing/Visual/Mobility-Orthopedic impairment/Other/None/PNA

Career information (added in 2019): 

• Year of highest degree: /PNA

• Highest degree earned: Bachelors/Masters/Doctorate/Other/PNA

• Career Classifcation Sector: Academia/Government/For-Profit/

Nonprofit/Other/ PNA

• Career Type: Primarily Research/Primarily Teaching/Sciencerelated/Engineering-Technology related/Further Training or

Education/Other/PNA

This report covers the years 2014–2020, although the 2020 data are 

incomplete. Data was extracted from NSPIRES in February 2021 and not 

every solicitation was updated for 2020 at that time. 

The Science Mission Directorate is the only Directorate covered initially, 

however all NASA research will be covered in future reports. 

A INTRODUCTION 3 
NASA RESEARCHER DEMOGRAPHICS

 

Gender, race, ethnicity, and disability status were collected starting in 2016, and the years 2014 and 

2015 were “back-casted” from the initial 2016 data. Career stage information was collected starting 

in 2019. 

Race and ethnicity are combined into a single variable according to guidance from the Equal 

Employment Opportunity Commission (EEOC). In the single variable, the category “Hispanic” 

includes all individuals who chose “Hispanic or Latino,” regardless of race. The categories 

American Indian Alaska Native (AIAN), Asian, Black or African American, Native Hawaiian Other 

Pacific Islander, White, Multiracial, and Race Not listed all consist of individuals who selected “Not 

Hispanic or Latino” and one race category. Further, the group “Multiracial” consists of individuals 

who chose more than one minority race. Individuals who selected White and one minority race 

are categorized as the minority race. The following acronyms were used: AIAN = American Indian or 

Alaska Native, NHOPI = Native Hawaiian or Other Pacific Islander, Black = Black or African 

American, Hispanic = Hispanic or Latino. 

For career stage (“age” of proposer based on year of terminal degree), we use these definitions for 

early, mid, and late career: 

• Early Career: < 10 years since final degree

• Mid Career: 10 – 19 years since final degree

• Late Career: 20+ years since final degree

We present data on individuals who submitted proposals by demographic group. An individual can 

submit more than one proposal in a particular time frame. For the data presented in this report on 

submissions, the data is individual-level rather than proposal level. To identify an individual, we use 

an identifier corresponding to their NSPIRES account.1 For the “All Years” category, each individual 

is only counted once, regardless of whether they are found in multiple years. 

We also present data on proposal success rates. Proposal success rates are defined as the 

percentage of proposals that were funded. There are duplicate records because a person would be 

counted more than once if that person was involved with multiple teams – for example, the same 

person could be a PI on one submission and Co-I on another one. On a given team, the same 

person would not be counted more than once. 

We applied data suppression rules to ensure confidentiality and the protection of subjects’ data. If 

there are 10 or less unique people in a cell, we don’t report a value for that category, we show 

1

 In very rare instances (<1% for PIs), the same person has created more than one account. If we look at the full proposer 

team, we see that there are 1% confirmed cases where the same person has created more than one account but there is the 

possibility of a couple of percentage points more of suspected cases. We are investigating this further and will conduct an 

analysis of the multiple accounts to be able to assess the impact, if any, this will have on the results we are reporting. 

A INTRODUCTION 4 
NASA RESEARCHER DEMOGRAPHICS

 

“NR” (Not Reportable). Further, we do not report on the number of selections and submissions 

and we do not calculate a “success” rate (selections/submissions) when the total submissions (the 

denominator) for a particular group is less than 50; or the number of unique people selected is 10 

or less. In that case we show “NR.” 

Note: due to the presence of the “NR” categories, pie charts showing the percentage of individuals 

submitting proposals do not always add to 100 percent. 

In this report, the analysis mostly focuses on a combined dataset of PIs and Co-Is. The Co-I data 

does not include the following roles - Co-Investigator/Science PI, Co-Investigator/Institutional PI, 

Co- Investigator/Co-PI, and Deputy PI. Statistics on PIs and Co-Is separately are available in our 

dashboard which will be made publicly available in the future. 

This analysis does not include Astrophysics division guest investigator/observer solicitations 

that are peer-reviewed externally. NSPIRES is not used for Guest Observer/Investigator (GO/GI) 

programs in Astrophysics that are managed outside of HQ (e.g., Hubble or JWST proposals which 

are managed by the Space Telescope Science Institute (STScI)). 

In this report, we use the term ROSES to refer to Supporting Research and Technology (SR&T). 

Note: cross divisional programs are included in roll-up but not presented separately. 

As a note, other teams at NASA are also doing analysis of the demographic data. This analysis has 

used different data filtering and different demographic groupings. 

A INTRODUCTION 5 
NASA RESEARCHER DEMOGRAPHICS

-

B SMD SUMMARY DATA 

ROSES Demographics Rollup 2014–2020 

ཀྵ Gender ཀྵ Career Stage 

Male Female Non Male/ 

Female PNA 

Submissions 67% 22% NR 12% 

Success Rate 26% 25% NR 23% 

Early Mid Late PNA 

Submissions 30% 25% 30% 14% 

Success Rate 25% 23% 22% 26% 

ཀྵ Degree Type ཀྵ Declared Disability 

Bachelors Doctorate Masters Other PNA 

Submissions 2% 80% 5% <1% 13% 

Success Rate 24% 23% 22% NR 26% 

Disability None PNA 

Submissions 3% 81% 17% 

Success Rate 24% 26% 24% 

ཀྵ Career Sector 

Academia For Profit Governm. Nonprofit Other PNA 

Submissions 56% 4% 16% 8% 2% 14% 

Success Rate 23% 22% 24% 24% 25% 25% 

ཀྵ Career Type 

Education Engineer. Research Science Teaching Other PNA 

Submissions <1% 7% 66% 6% 5% 1% 14% 

Success Rate NR 23% 24% 24% 18% 21% 25% 

ཀྵ Race/National Origin (RNO) 

AIAN Asian Black Hispanic Multi 

Racial NHOPI PNA Race not 

Listed White 

Submissions <1% 13% <1% 4% <1% <1% 20% <1% 61% 

Success Rate 25% 24% 21% 23% NR NR 24% 24% 26% 

The gender data includes SMD ROSES PIs and Co-Is for all SMD divisions (Astrophysics, Earth 

Science, Heliophysics, Planetary, and Cross-Divisional) for 2014–2020 combined. Females made 

up 22% of individuals submitting proposals while males made up 67% and 12% chose not to 

answer. (Figure 1(a) and (b)) 

B SMD SUMMARY DATA 6 
NASA RESEARCHER DEMOGRAPHICS

Looking at the proposal-level success rates (percentage of all proposals that received funding), 

for all SMD all years combined the rates were comparable for females (25%) and males (26%), and 

slightly lower for the “prefer not to answer” category (23%). For the “non male/female” category, 

the numbers were not large enough to reach the thresholds for reporting. Combining this data with 

future pulls can potentially give us a bigger size for reporting. 

About 61% of the individuals submitting research proposals come from those who self-identified as 

White, 13% from Asian, <1% from American Indian or Alaska Native, <1% from Black or African 

American, 4% Hispanic, <1% Multiracial, <1% NHOPI, <1% Race not listed, but that 20% of 

respondents chose not to provide their race/ethnicity. (See Figure 2a) Success rates for all reported 

categories were 26% White, 25% AIAN, 24% Asian, 24% PNA, 24% Race not listed, 23% Hispanic, 

and 21% Black or African American. (See Figure 2b.) 

As can be seen, over 80% of NASA researchers report having no disability while 3% report some 

disability, and 17% selected “prefer not to answer.” Proposal success rates by disability status 

are close, with the “no disability” category (26%) having a very slight lead over the individuals with 

declared disabilities (24%) and the prefer not to answer category (24%). 

For the remaining analysis of the career related variables, data is presented for 2019 and 2020 

combined, as these items were only added to the survey starting in 2019.

The majority (80%) of individuals submitting research proposals had a doctorate, with the next 

highest category the PNA at 13%. Overall success rates were similar across those of known degree 

type – 24% Bachelors, 23% Doctorate, 22% Masters, and slightly higher for the PNA category 

– 26%.

Not surprisingly, most of the individuals submitting research proposals come from academic 

institutions. Fifty six percent of individuals submitting ROSES proposals come from academia. The 

second biggest sector (16%) is government, which would include NASA centers and JPL. About 

14% responded “prefer not to answer.” Success rates were similar across career sectors. 

Overall, 30% of individuals submitting were Early Career, 25% were Mid-Career, 30% were Late 

Career, and 14% chose not to answer. Success rates were similar across all career categories, and 

the PNA category had the highest success rate. 

Sixty-six percent of individuals submitting are researchers, as opposed to engineers or educators. 

Fourteen percent responded PNA to the career type question. Success rates were generally similar 

across the career types, with teaching being a bit on the lower side (18%). 

B SMD SUMMARY DATA 7 
NASA RESEARCHER DEMOGRAPHICS

30% 

25% 

20% 

15% 

10% 

5% 

0% 

25% AIAN 24% Asian 21% Black 23% Hispanic 24% PNA 24% Race Not Listed 26% White 

50%

40%

30%

20%

10%

0%

26%

Male

25%

Female

23%

PNA

FIGURE 1 

a. Gender of individuals who

submitted proposals

67%

Male

22%

Female

12%

PNA

b. Success rate

by gender

FIGURE 2 

a. RNO of individuals who

submitted proposals

4% Hispanic 

61% 

White 

13% 

Asian 20% 

PNA 

<1% for each of AIAN, 

Black, Multi-Racial,

 NHOPI, and Race

 Not Listed 

b. Success rate

by RNO

B SMD SUMMARY DATA 8 
NASA RESEARCHER DEMOGRAPHICS

30%

25%

20%

15%

10%

5%

0%

24%

Disability

26%

None

24%

PNA

FIGURE 3 

a. Declared Disability for individuals b. Declared Disability

submitting proposals Success Rate

81%

None

17%

PNA

3% Disability

FIGURE 4 

a. Degree Type for individuals b. Proposal Success Rates by

submitting proposals Degree Type 

13% PNA 2% 

Bachelors <1% 

Other

?80% 

Doctorate

5% Masters

30%

25%

20%

15%

10%

5%

0%

26% PNA

24% Bachelors23% Doctorate22% Masters

Other

B SMD SUMMARY DATA 9 
NASA RESEARCHER DEMOGRAPHICS

25%

20%

15%

10%

5%

0%

25% PNA 

23% Academia 

22% For Profit

24% Government 

25% Other 

24% Non Profit

FIGURE 5 

a. Career Sector for individuals

submitting proposals 

b. Proposal Success Rates by

Career Sector 

2% 

Other 

16% 56%

Government Academia

8% 

Non Profit 

14% 

PNA 

4% For Profit 

FIGURE 6 

a. Career Stage for individuals

submitting proposals

b. Career Stage Success Rate

0%

5%

10%

15%

20%

25%

30%

25% Early

23% Mid

26% PNA

22% Late

30% 

Early

25% 

Mid

30% 

Late

14% 

PNA

B SMD SUMMARY DATA 10 
NASA RESEARCHER DEMOGRAPHICS

5% 

Teaching

6% 

Science

<1% Education

66% 

Research

14% 

PNA

1% Other 7% Engineer

23% Engineer24% Research24% Science18% Teaching21% Other25% PNA

25%

20%

15%

10%

5%

0

FIGURE 7

a. Career Type for individuals

submitting proposals

b. Career Type Success Rate

B SMD SUMMARY DATA 11 
NASA RESEARCHER DEMOGRAPHICS

-

AO Demographics Rollup 2014–2020 

ཀྵ Gender ཀྵ Career Stage 

Male Female Non Male/ 

Female PNA 

Submissions 69% 21% NR 9% 

Success Rate 26% 28% NR 25% 

Early Mid Late PNA 

Submissions 21% 25% 35% 20% 

Success Rate 29% 28% 28% 29% 

ཀྵ Degree Type ཀྵ Declared Disability 

Bachelors Doctorate Masters Other PNA 

Submissions NR 81% NR NR 18% 

Success Rate NR 28% NR NR 29% 

Disability None PNA 

Submissions 3% 81% 16% 

Success Rate 29% 27% 24% 

ཀྵ Career Sector 

Academia For Proft Governm. Nonproft Other PNA 

Submissions 45% 2% 18% 13% 3% 20% 

Success Rate 27% NR 29% 30% NR 29% 

ཀྵ Career Type 

Education Engineer. Research Science Teaching Other PNA 

Submissions NR 2% 68% 5% 3% NR 20% 

Success Rate NR NR 29% 25% NR NR 28% 

ཀྵ Race/National Origin (RNO) 

AIAN Asian Black Hispanic Multi 

Racial NHOPI PNA Race not 

Listed White 

Submissions NR 8% NR 3% NR NR 20% <1% 67% 

Success Rate NR 29% NR 20% NR NR 24% NR 27% 

B SMD SUMMARY DATA 12 
NASA RESEARCHER DEMOGRAPHICS

Discussion (AOs): 2014–2020 Combined: AOs are not released every year necessarily, so there is 

less data compared to ROSES which are released every year. 

For the period 2014–2020 combined, 21% of individuals submitting AO proposals identified as 

female, 69% as male, and 9% selected prefer not to answer. Overall, the proposal success rates 

for males and females are comparable, with females having a slight lead (28%) over males (26%) 

and the prefer not to answer category (25%).

Overall, 67% of individuals submitting proposals identified as white, 20% selected “prefer not 

to answer,” 8% were Asian, 3% were Hispanic, and <1% were race not listed. The remaining 

categories were too small to report. Overall proposal success rates were 29% Asian, 27% white, 

24% prefer not to answer, and 20% Hispanic. 

Overall, 3% of individuals submitting a proposal declared a disability, 81% of individuals responded 

none, and 16% selected prefer not to answer. Overall proposal success rates were similar between 

individuals with declared disabilities (29%) and those with no disability (27%), with the declared 

disability category having a slight lead over those stating no disability. The success rate for the 

“prefer not to answer” category was lower at 24%. 

With respect to career stage, 35% of individuals submitting were late career (the largest category), 

25% were Mid Career, 21% early career, and 20% PNA. Proposal success rates were similar across 

career stages. 

25% PNA

0

5%

10%

15%

20%

25%

30%

25% PNA26% Male28% Female

21% 

Female

69% 

Male

9% 

PNA

FIGURE 8 

a. Gender for individuals

submitting proposals

b. Proposal Success Rates

by Gender 

B SMD SUMMARY DATA 13 
NASA RESEARCHER DEMOGRAPHICS

24% PNA20% Hispanic29% Asian

27% White

30

25

20

15

10

5

0

FIGURE 9 

a. RNO for individuals

submitting proposals

b. Proposal Success Rates

by RNO 

3% 

Hispanic

20% 

PNA

67% 

White

8% 

Asian

FIGURE 10 

a. Declared Disability for individuals

submitting proposals 

b. Proposal Success Rates by

Declared Disability 

81% 

None

16% 

PNA

3% 

Disability 30%

25%

20%

15%

10%

5%

0

24% PNA27% None29% Disability

B SMD SUMMARY DATA 14 
NASA RESEARCHER DEMOGRAPHICS

30%

25%

20%

15%

10%

5%

0

29% PNA28% Doctorate

29% Government 27% Academia 29% PNA 30% Non-profit

30%

25%

20%

15%

10%

5%

0

FIGURE 11 

a. Degree Type for individuals

submitting proposals

b. Proposal Success Rates by

Degree Type 

81% 

Doctorate

18% 

PNA

FIGURE 12 

a. Career Sector for individuals

submitting proposals 

b. Proposal Success Rates by

Career Sector 

3% 

Other 

45% 

Academia 

18% 

Government 

13% 

Non-profit 

20% 

PNA 

2% 

For Profit 

B SMD SUMMARY DATA 15 
NASA RESEARCHER DEMOGRAPHICS 28% Mid 29% Early

29% PNA28% Late

30%

25%

20%

15%

10%

5%

0

FIGURE 13

a. Career Stage for individuals

submitting proposals 

b. Proposal Success Rates by

Career Stage 

20% 21%

Early

25% 

35% Mid

Late

PNA

FIGURE 14 

a. Career Type for individuals

submitting proposals

b. Proposal Success Rates by

Career Type 

3% 2% 

68% 

Research

20% 

PNA

Teaching Engineering

5% 

Science

30%

25%

20%

15%

10%

5%

0

25% Science29% Research28% PNA

B SMD SUMMARY DATA 16 
NASA RESEARCHER DEMOGRAPHICS

Diversity Index (%) 

30%

25%

20%

15%

10%

30% 29%

21% 23%

18%

2014 2015 2016 2017 2018 2019 2020

50%

40%

30%

20%

10%

38% 39% 40% 41% 41%

35%

37%

2014 2015 2016 2017 2018 2019 2020

Diversity Index (DI) 

Here we show the Diversity Index trend for SMD applicants over the years 2014–2020. The Diversity 

Index (DI) is based on a formula from the Census Bureau and ranges from 0 to 1, with higher values 

representing more diverse populations. The Census Bureau defines the DI (in percentage form) as 

“the chance that two people chosen at random will be from different racial and ethnic groups.” 2 For 

example, there was a 41% chance that two individuals chosen randomly from the ROSES applicant 

pool in 2020 will be from different RNO categories. As can be seen, the pool of NASA researchers 

has been slowly increasing in diversity since 2014. 

Diversity Index, AO 

35%

Diversity Index, ROSES

2 https://www.census.gov/library/stories/2021/08/2020-united-states-population-more-racially-ethnically-diverse-than-2010.html 

B SMD SUMMARY DATA 17 
NASA RESEARCHER DEMOGRAPHICS

C YEARLY TREND DATA 

SMD Demographics for gender, RNO, and disability data by year 

Note: when examining changes in the demographic categories over time, we focus on 2016 and 

later as there is a significantly higher presence of the PNA category in earlier years (due to the fact 

that the questions were only asked starting in 2016). Although the PNA category is in a smaller 

proportion in later years, it is still present and one needs to keep that in mind when interpreting 

the data. In particular, we observe changes for declared categories (e.g. declared male, declared 

female). We don’t know if those changes are occurring for the whole population (e.g. all females), as 

that depends on the dynamic in the PNA category. 

1. Gender summary

ROSES: If we don’t look at 2020 (an anomalous year), the data for males is oscillating up and 

down. Including the 2020 data point there seems to be a downward trend, but given it was such an 

anomalous year we have to wait until later years to be able to be certain of decreasing tendencies. 

There is growth over time in the female category. By year, the proposal success rates for the male 

and female categories are comparable. 

AOs: For the AOs, there are only 3 data points from 2016 and later (2016, 2017, and 2019). We see 

a definite decrease in the percentage of males (75% to 70% to 64%), and a significant increase in 

the percentage of females (17% to 22% to 28%). By year, the proposal success rates for the male 

and female categories are comparable. 

2. RNO summary

ROSES: The percentage of whites is roughly constant over time with some oscillation around 

the values. 

AOs: The percentage of whites is decreasing (72% to 67% to 66%). For Asians, there is a possibility 

of an increasing trend but we would need to see more data, as there are only 3 data points. There 

is a slow increase in the prefer not to answer category. Success rates are not shown by individual 

year due to too many categories being suppressed. 

3. Disability summary

ROSES: The disability categories are roughly constant with some fluctuations over time. There is 

too little data to break down results for any one particular disability. 

C YEARLY TREND DATA 18 
NASA RESEARCHER DEMOGRAPHICS

2014 2015 2016 2017 2018 2019 2020 

Response 

■ Male

■ Female

■ Prefer not to answer

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Success Rate for Proposals 

40% 

30% 

20% 

10% 

0% 

35% 34%

31% 33%

30% 34%

27% 28%

20%

17%

14%

● Male

■ Female

◆ Prefer not to answer

✤ Non Male/Female

Response Submission % for Individuals 

● ● ● ●

■ ■ ■

■

◆ ◆ ◆ ◆

■

75% 

●

◆

75% 

70% 70% 

64% 

13% 

17% 17% 22% 

28% 

12% 13% 7% 8% 9% 

2014 2015 2016 2017 2018 2019 2020 

80% 

60% 

40% 

20% 

0% 

AOs: For the AOs, there is a slight decrease in the “none” category, a slight increase in the prefer 

not to answer category and the disability category remains roughly the same. However there are 

only 3 data points so it is difficult to tell the trend. 

AO Mission Gender Data 

AO Mission PIs and Co-Is (by year): 

C YEARLY TREND DATA 19 
NASA RESEARCHER DEMOGRAPHICS

Response Success Rate for Pr oposals 

0% 

10% 

20% 

30% 

40% 

26% 25% 24% 

26% 27% 26% 

20% 21% 

24% 

27% 26% 

23% 

26% 27% 

25% 26%25%

21% 22%22%

■ Female

■ Pr efer not to answer

■ Non Male/Femal e 27% Male Female Pr efer not to answer Non Male/Female Gender Male Female Pr efer not to answer Non Male/Female Gender Male Female Pr efer not to answer Non Male/Female Gender Male Female Pr efer not to answer Non Male/Female Gender Male Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

Male

Female

Pr efer not to answer

Non Male/Female Gender 

■ Male 2014 2015 2016 2017 2018 2019 2020 

 

Response Submission % for Individuals 

66% ● 69% 67% 63% ● 71% ●

70% ● ●

● 70% ●

Male 

Female 

26% 

22% 21% 23% 24% 

20% 20% 

■

17% ■ 8% 9% 8% PNA 14% ■ ■ ■ ■ ■

7% 7% 0% 

2014 2015 2016 2017 2018 2019 2020 

80% ● Male

◆ 70% Female

■ Prefer not to answer 60% 

❍ Non Male/Female Gender

50% 

40% 

30% 

20% 

10% 

ROSES Gender Data 

ROSES PI and Co-I data (by year) 

C YEARLY TREND DATA 20 
NASA RESEARCHER DEMOGRAPHICS

 

 

 

ROSES Race and Ethnicity Data 

AO Race and Ethnicity Data 

C YEARLY TREND DATA 21 
NASA RESEARCHER DEMOGRAPHICS

3%

Response 

■ None 80% 

Submission % for Individuals 

■ Prefer not to answer 70%

■ Disability

60% 

50% 

40% 

30% 

20% 

10% 

0% 

79% 81% 81% 

■ ■ ■ ■ ■ 83%

78% 

16% ■ ■ 13% ■16% ■16% 18% ■

3% 3% 2% 4% ■ ■ ■ ■

2014 2015 2016 2017 2018 2019 2020 

SMD AO Disability Data Disability Disability 

Disability Disability 

None 

Pr efer not to answer 

Disability 

None 

Pr efer not to answer 

Disability 

None 

Pr efer not to answer 

Disability 

None 

Pr efer not to answer Pr efer not to answer Pr efer not to answer Pr efer not to answer 

None None None 

C YEARLY TREND DATA 22 
NASA RESEARCHER DEMOGRAPHICS

3%

Submission % for Individuals 

Response 

■ None 80% 

■ Prefer not to answer 70%

■ Disability

60% 

50% 

40% 

30% 

20% 

10% 

0% 

82% ■ ■ ■

84% 

■

85% 

■

85% 72% ■ 84%

■ 77%

25% 

■

15% ■ 13% ■ 13% 13% 12% 19% ■ ■ ■ ■

3% 3% 3% 3% 3% 3% 3% 

■ ■ ■ ■ ■ ■ ■

2014 2015 2016 2017 2018 2019 2020 

Disability 

None

Pr efer not to answer

Disability 

None

Pr efer not to answer

Disability 

None

Pr efer not to answer

Disability 

None

Pr efer not to answer

Disability 

None

Pr efer not to answer

Disability 

None

Pr efer not to answer

Disability 

None

Pr efer not to answer 

Success Rate for Pr oposals 

2014 2015 2016 2017 2018 2019 2020 

22% 

25% 26% 25% 24% 25% 

21% 

19% 

27% 26% 28% 27% 26% 27% 27% 

21% 

25% 25% 

19% 

21% 22% 

Response 

■ 40% None

■ Prefer not to answer

30% ■ Disability

20% 

10% 

0% 

SMD ROSES Disability Data 

C YEARLY TREND DATA 23 
NASA RESEARCHER DEMOGRAPHICS

DISCUSSION BY DIVISION AND YEAR

We report on the percentage of individuals submitting proposals by demographic category and 

division over time. Caution should be taken in interpreting the results as the presence of the 

PNA category can influence the results. As noted earlier, we are observing changes for declared 

categories (e.g. declared male, declared female). Due to the presence of the PNA, we don’t know 

if those changes are occurring for the whole population (e.g., all females), as that depends on the 

dynamic in the PNA category.

Summary: We observe changes over time for many categories, but the changes are, if they exist, 

for the most part very gradual. In several instances we also noticed that 2020 (COVID year) had the 

biggest drop or jump (e.g., 4 or 5 percentage points, or in the case of females in Astrophysics, a 

7-percentage point jump). It is becoming clear that COVID had an effect but to understand how and

the full extent we will need to analyze the next few years of data.

C YEARLY TREND DATA 24 
NASA RESEARCHER DEMOGRAPHICS

80%

71% 73% Male 70%

70% 67% 75% 74% 67%

60%

50%

40%

30% Female 27%

20%

10% 8% 6% PNA 

17% 17% 17% 19%

22%

20%

15%

12%

9% 9% 6%

0%

2014 2015 2016 2017 2018 2019 2020

0%

10%

20%

30%

40%

50%

29%23%25%36%32%34%27%27%19%29%23%21%31%29%20%25%27%16%19%

Male Female PNA

2014 2015 2016 2017 2018 2019 2020

ROSES Submissions data by Gender, Division and Year 

a. Submissions data by gender (2014–2020) Astrophysics

b. Success rate by gender (2014–2020) Astrophysics

C YEARLY TREND DATA 25 
NASA RESEARCHER DEMOGRAPHICS

2014 2015 2016 2017 2018 2019 2020

80%

72% Male 68%

70% 67% 67%

71%

70%

60%

60%

50%

40%

Female 

30% 28% 25%

20%

10%

0%

PNA 

23% 24% 23%

19% 20%

17% 14% 6% 8% 7% 6% 6%

Male Female PNA

2014 2015 2016 2017 2018 2019 2020

0%

10%

20%

30%

40%

50%

29%27%18%32%31%25%31%28%35%

28%

30%26%29%29%27%30%33%

31%

21%24%

21%

a. Submission % for individuals (2014–2020) Earth Science

b. Success rate for proposals (2014–2020) Earth Science

C YEARLY TREND DATA 26 
NASA RESEARCHER DEMOGRAPHICS

80%

Male 

69% 69% 69% 70%

70%

71% 69% 67% 60%

50%

40%

30% Female 

21%

17% 16% 18% 18%

16% 15% 12%

11% 10%

19% 20%

20%

10%

10% PNA 11%

0%

2014 2015 2016 2017 2018 2019 2020

0%

10%

20%

30%

40%

50%

20%26%24%19%15%16%25%26%22%

31%

27%26%27%29%34%32%30%

33%

36%41%

36%

Male Female PNA

2014 2015 2016 2017 2018 2019 2020

a. Submission % for individuals (2014–2020) Heliophysics

b. Success rate for proposals (2014–2020) Heliophysics

C YEARLY TREND DATA 27 
NASA RESEARCHER DEMOGRAPHICS

80%

Male

70% 67% 69% 68%

63% 70%

67% 60% 63%

50%

40%

Female 

30% 24% 23% 25% 25% 22%

20%

10%

0%

2014 2015 2016 2017 2018 2019 2020

PNA 

19% 21%

18% 15% 8% 8%

9% 9%

7%

Male Female PNA

0%

10%

20%

30%

40%

50%

23%26%20%19%20%16%23%23%20%

23%

23%20%24%27%23%17%19%

16%

19%18%

21%

2014 2015 2016 2017 2018 2019 2020

a. Submission % for individuals (2014–2020) Planetary Science

b. Success rates for proposals (2014–2020) Planetary Science

C YEARLY TREND DATA 28 
NASA RESEARCHER DEMOGRAPHICS

White Asian Hispanic 

80%

70%

60%

50%

40%

30%

20%

10%

0%

Astrophysics Earth Science Heliophysics Planetary Science 

2014 2015 2016 2017 2018 2019 2020 

 

 

 

PNA

Black

RNL

PNA

Black

RNL

PNA

Black

RNL

PNA

Black

RNL

PNA

Black

RNL

PNA

Black

RNL

PNA

Black

RNL

ROSES Submissions by Division, RNO 

a. Submission % for individuals (2014–2020)

Astrophysics 24% <1% 19% 17% <1% 17% <1% 16% 14% 12% 

Earth Science 32% <1% 23% <1% <1% 16% 1% <1% 18% <1% <1% 17% 1% <1% 16% 1% <1% 14% 1% <1% 

Heliophysics 24% 23% 20% 1% 22% 21% 18% 20% 

Planetary Sci. 27% <1% 24% <1% <1% 18% <1% 1% 18% <1% <1% 18% <1% <1% 17% <1% <1% 15% 1% 

<1% for AIAN, Earth Science, 2016 

C YEARLY TREND DATA 29 
NASA RESEARCHER DEMOGRAPHICS

Asian Hispanic White PNA

50%

40%

30%

20%

10%

0%

2014 2015 2016 2017 2018 2019 2020

20%

34%29%

26%

34%

36%

31%

26%

29%28%

24%

23%

24%29%

25%

32%

27%31%

26%

28%

25%

22%

11%

17%

16%

Asian Hispanic White PNA

50%

40%

30%

20%

10%

0%

2014 2015 2016 2017 2018 2019 2020

25%18%30%

23%

29%

22%

34%

28%

26%31%32%

32%

25%30%30%

27%

25%22%31%

29%

25%

35%

34%

30%

17%

24%

23%

22%

ROSES Success Rates by Division, RNO and Year 

Success rates for proposals – Astrophysics 

Success rates for proposals – Earth Science 

C YEARLY TREND DATA 30 
NASA RESEARCHER DEMOGRAPHICS

Asian Hispanic White PNA

50%

40%

30%

20%

10%

0%

2014 2015 2016 2017 2018 2019 2020

16%

23%

22%

20%

18%

17%

24%

26%

22%

27%

32%

26%

27%

28%

31%

29%

33%

31%

29%

40%

35%

Asian Hispanic White PNA

50%

40%

30%

20%

10%

0%

2014 2015 2016 2017 2018 2019 2020

20%19%24%

20%

16%

18%

20%

17%

23%22%24%

21%

25%16%23%

20%

19%19%27%

23%

16%

17%

18%

16%

20%

17%

19%

19%

ROSES Success Rates by Division, RNO and Year 

Success rates for proposals – Heliophysics 

Success rates for proposals – Planetary Science 

C YEARLY TREND DATA 31 
NASA RESEARCHER DEMOGRAPHICS

SMD Division Disability Demographics—ROSES Submissions 

a. Submission % for individuals (2014–2020)

Disability 

Astrophysics Earth Science Heliophysics Planetary Science 

No Disability PNA

0%

20%

40%

60%

80%

100%

No Disability 

PNA 

Disability 

Astrophysics 

Earth Science 

0%

20%

40%

60%

80%

100%

No Disability 

PNA 

Disability 

Heliophysics 

0%

20%

40%

60%

80%

100%

No Disability 

PNA 

Disability 

0%

20%

40%

60%

80%

100%

No Disability 

PNA 

Disability 

Planetary Science 

2014 2015 2016 2017 2018 2019 2020 

C YEARLY TREND DATA 32 
NASA RESEARCHER DEMOGRAPHICS

Disability No Disability Prefer not to answer 

50% 

40% 

30% 

20% 

10% 

0% 

2014 2015 2016 2017 2018 2019 2020 

30%29%

21%

31%32%

28%

31%

30%

33%

35%

29%

27%

28%

29%

30%

31%

31%

33%

22%

21%

SMD Division Disability Demographics—ROSES Success Rates 

Success rates for proposals – Astrophysics 

2014 2015 2016 2017 2018 2019 2020 

0% 

10% 

20% 

30% 

40% 

50% 

Disability 

28%

No Disability Prefer not to answer 

24%

36%

33%

24%27%

24%

32%28%

23%

31%

30%

30%

26%

22%

16%

20%

Success rates for proposals – Earth Science 

C YEARLY TREND DATA 33 
NASA RESEARCHER DEMOGRAPHICS

Disability No Disability Prefer not to answer 

50% 

40% 

30% 

20% 

10% 

0% 

2014 2015 2016 2017 2018 2019 2020 

24%23%22%12%20%16%24%23%22%26%23%20%24%25%23%14%18%15%22%19%20%

SMD Division Disability Demographics - ROSES Success Rates 

Success rates for proposals – Heliophysics 

2014 2015 2016 2017 2018 2019 2020 

0% 

10% 

20% 

30% 

40% 

50% 

Disability 

22%

No Disability Prefer not to answer 

30%

21%

18%

18%

28%25%

22%

27%31%

26%

27%27%

34%

32%

32%

37%

38%

Success rates for proposals – Planetary Science 

C YEARLY TREND DATA 34 
NASA RESEARCHER DEMOGRAPHICS

30%

25% 

20% 

15% 

10% 

5% 

0% 

23% PNA 29% Male 26% Female 

 

D DATA BY DIVISION 

ROSES PI and Co-I researchers data appear on the following pages by Division. On the following 

pages we show the breakdown of PI and Co-I (combined) submissions and success rate data for 

ROSES by SMD Division (Astrophysics, Earth Science, Heliophysics, and Planetary Science). There 

is too little data to break down the AO data by separate divisions.

When comparing the demographics across different divisions, bear in mind that different subfelds 

can have different demographic profles. Future analysis will involve comparing demographics for 

individual divisions (e.g. Heliophysics, Planetary Science) to their respective populations, to the 

extent possible. 

SUBMISSIONS ALL YEARS COMBINED BY DIVISION 

ASTROPHYSICS 

a. Gender for Individuals

Submitting Proposals

b. Proposal Success Rates

by Gender 

19% 

Female 

69% 

Male 

12% 

PNA 

a. RNO for individuals

submitting proposals

b. Proposal Success Rates

by RNO 

<1% Black 

4% 

Hispanic 

<1% Race 

Not Listed 

19% 

PNA 

64% 

White 

10% 

Asian 

30% 

25% 

20% 

15% 

10% 

5% 

0%

D DATA BY DIVISION 35 28% Hispanic 25% PNA 19% Race not listed 29% White 25% Asian 
NASA RESEARCHER DEMOGRAPHICS

26% PNA 29% Male 

29% Female 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

30%

25% 

20% 

15% 

10% 

5% 

0% 

26% PNA 28% None 25% Disability 

 

ASTROPHYSICS (continued)

a. Declared Disability for individuals

submitting proposals 

b. Proposal Success Rates by

Declared Disability 

81% 

None 

17% 

PNA 

3% 

Disability 

23% 

Female 

66% 

Male 

11% 

PNA 

19% 

PNA 

58% 

White 

15% 

Asian 

<1% AIAN 

1% Black 

5% Hispanic 

<1% Race 

Not Listed 

EARTH SCIENCE 

a. Gender for Individuals

Submitting Proposals

b. Proposal Success Rates

by Gender 

a. RNO for individuals

submitting proposals

b. Proposal Success Rates

by RNO 

35% 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

28% PNA 

27% Hispanic 25% Asian 

31% Race not listed 

31% White20% Black 

D DATA BY DIVISION 36 
NASA RESEARCHER DEMOGRAPHICS

 

EARTH SCIENCE (continued)

a. Declared Disability for individuals

submitting proposals b. Proposal Success Rates by

Declared Disability 

2% Disability 

82% 

None 

16% 

PNA 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

28% PNA 

29% None 30% Disability 

13% 18%

Female 

69% 

Male 

PNA 

21% 

PNA 

58% 

White 

16% 

Asian <1% Black 

3% Hispanic 

<1% Race 

Not Listed 

25% PNA 

23% Hispanic 

25% Asian 

27% White 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

26% PNA 26% Male 27% Female 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

HELIOPHYSICS 

a. Gender for Individuals

Submitting Proposals

b. Proposal Success Rates

by Gender 

a. RNO for individuals

submitting proposals

b. Proposal Success Rates

by RNO 

D DATA BY DIVISION 37 
NASA RESEARCHER DEMOGRAPHICS

19% PNA 21% Male 

23% Female 

25% 

20% 

15% 

10% 

5% 

0% 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

26% PNA 

26% None 26% Disability 

 

HELIOPHYSICS (continued)

a. Declared Disability for individuals

submitting proposals 

b. Proposal Success Rates by

Declared Disability 

3% Disability 

79% 

None 

18% 

PNA 

12% 21%

Female 

67% 

Male 

PNA 

21% 

63% PNA

White 

11% 

Asian 

<1% AIAN 

<1% Black 

4% Hispanic 

<1% Race 

Not Listed 

PLANETARY SCIENCE 

a. Gender for Individuals

Submitting Proposals

b. Proposal Success Rates

by Gender 

a. RNO for individuals

submitting proposals

b. Proposal Success Rates

by RNO 

25%

20% 

15% 

10% 

5%

0% 

19% PNA 

19% Hispanic 

20% Asian 

23% White

23% Black 

17% RNL 

D DATA BY DIVISION 38 
NASA RESEARCHER DEMOGRAPHICS

 

 

PLANETARY SCIENCE (continued)

a. Declared Disability for individuals

submitting proposals 

b. Proposal Success Rates by

Declared Disability 

3% Disability 

80% 

None 

17% 

PNA 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

20% PNA 

22% None 21% Disability 

Here we report on ROSES success rates (2014–2020) by division. Looking individually at each 

division, there are only slight differences in the success rates for those self-identifying as male or 

female, and persons with self-reported disabilities are successful at a similar rate as those who report 

no disabilities. Should we know who is in the prefer not to answer category, this could play a role.

For Astrophysics, the success rates (all years combined) were: Asian – 25%, Hispanic – 28%, Prefer 

Not to Answer – 25%, race not listed – 19%, and White – 29%. It is difficult to make an assessment 

of the “race not listed” category as it is unclear who is in this category. Examining the data for the 

most recent 2 years, we do not observe noticeable differences: 18% Asian, 20% Hispanic, PNA 

19%, 20% White. 

In Earth Science, the success rates (all years combined) were: Asian – 25%, Black – 20%, Hispanic 

– 27%, PNA – 28%, race not listed – 31%, White – 31%. In the last 2 years, the success rates were

Asian – 22%, Black – 21%, Hispanic- 30%, PNA – 28%, race not listed – 31%, white – 30%. The

success rate for Black individuals is lower overall than for other races/ ethnicities in Earth Science,

requiring further investigation.

For Heliophysics, success rates (all years combined) were: Asian – 25%, Hispanic – 23%, PNA – 

25%, white – 27%. In the last 2 years, the success rates were Asian – 29%, Hispanic – 29%, PNA 

– 32%, and white – 34%. There are too few Black individuals in Heliophysics to allow reporting, so

their success rate is unknown at this time.

For Planetary Science, success rates (all years combined) are Asian – 20%, Black – 23%, Hispanic –

19%, PNA – 19%, race not listed – 17%, and white – 23%. In last 2 years, success rates were 

similar across all groups, although the Black category could not be reported: Asian – 17%, Hispanic 

– 17%, PNA – 17%, White – 18%.

D DATA BY DIVISION 39 
NASA RESEARCHER DEMOGRAPHICS

E TYPES OF INSTITUTIONS 

Types of Institutions, including Minority Serving Institutions (MSIs)3

: 

For ROSES, additional analysis of educational institutions was conducted for 2014-2019 based on 

the name of the submitting institutions. As the name of submitting institution is a required field, there 

are no responses of prefer not to answer. The data presented here is for proposals rather than 

individuals. 

Minority serving institutions (MSIs) were identified using Carnegie classification data from 2014– 

present. The following types of institutions were classified as MSIs: 

• Tribal Colleges and Universities (TCUs)

• Hispanic Serving Institutions (HSIs)

• Asian American and Native American Pacific Islander Serving Institutions (AANAPISIs)

• Historically Black Colleges and Universities (HBCUs)

• Native American Serving Nontribal Institutions (NASNTIs)

• Predominantly Black Institutions (PBIs)

• Alaskan Native and Native Hawaiian Serving Institutions (ANNHSIs)

Overall, 11% of the PI proposals submitted from educational institutions come from MSIs, and 11% 

of selections fall in this category. Thus, PIs from MSIs appear to be selected at a similar rate that they 

submit. As a note, some of the MSIs are from R1 (Very high research activity) institutions. Future work 

will involve further classification of MSIs using the Department of Education criteria, as the Carnegie 

classifications are only updated every 3 years.

We also examined the percentage of proposals associated with R1 (Very high research activity), R2 

(High research activity), R3 (Doctoral/Professional universities) institutions, and Non R1, R2, R3: Does 

not belong to R1, R2, R3. The classification was based on the Carnegie Classifications from 2010 to 

Present. Looking at SMD PIs, 81% of proposals came from R1 institutions (versus 84% of selections) 

and 14% from R2 institutions (versus 12% of selections), with the remainder coming from R3 and Non 

R1, R2 or R3. Note: this analysis only looks at proposals submitted by educational institutions. 

Comparisons to the Physical Sciences Field 

Here we compare our NASA grant applicant demographic data for SMD to the demographics of 

the broader physical sciences field. Finding an appropriate comparison group is very important for 

the data NASA provides. However, each survey is designed to satisfy the needs of the survey, so 

there are no completely comparable datasets so we have to do the best we can and find a common 

denominator to compare to. Additionally, some fields of research (e.g., heliophysics and planetary 

science) span multiple university research curricula, and aren’t easily isolated. 

3 Thank you to the SMD Data Analytics team for providing the numbers in the section “Types of Institutions, including Minority 

Serving Institutions (MSIs).” 

E TYPES OF INSTITUTIONS AND COMPARABLE WORK FORCE 40 
NASA RESEARCHER DEMOGRAPHICS

We obtained the public use data file for the NSF National Survey of College Graduates for 2019 (the 

most recent year available). This survey includes those who 1) live in the United States, 2) have at 

minimum a Bachelor’s degree, and 3) are aged less than 76 years old.4 Using these data, we carried 

out an analysis of the gender and race/ethnic profile for employed physical scientists (excluding 

Chemistry) in 2019. We applied survey weights to account for the survey’s complex sampling design. 

Also, we provide 90% Confidence Intervals (the standard for NSF’s National Center for Science and 

Engineering Statistics).5

In the NSF data, employed physical scientists include individuals with bachelor’s, master’s, and 

doctorate degrees; as well as individuals of different generations. The distribution of AO and ROSES 

grant applicants over degree levels is not the same as in the NSF data. For example, 30% of employed 

physical scientists in the NSF data have a doctorate; this compares to 99% of AO grant applicants 

(2019)6

. In addition, 67% of those in the NSF data were less than 20 years from highest degree vs about 

57% for AO grant applicants. Due to this fact, a direct comparison is not appropriate, and we carried 

out separate analysis by degree type and career stage (years from highest degree). As the degrees 

present in our applicant pool are predominately doctorates, we focus on doctorates here. 

Females 

Doctorates Only: Percentage of Female ROSES and AO Grant Applicants and estimated 

percentage of Females in the Physical Sciences in Each Career Stage Category 2019. 

20+ years 

<20 years 

0 10 20 30 40 

Physical Sciences-90% CIs Physical Sciences AO-Co-I 

AO-PI+Co-I ROSES-PI+Co-I ROSES-PI 

ROSES-Co-I 

Percentage of Females 

• 20+ years since PhD: In the NSF data, females accounted for 6% [3%–10%] of these

individuals; the percentage of Female ROSES and AO applicants ranged from 15% to 19%.

• <20 years since PhD: In the NSF data, females made up 32% [25%–40%] of these

individuals. The percentage of females for AO Co-I was 43%.

4 https://www.nsf.gov/statistics/srvygrads/#tabs-1&sd 

5

 Confidence Intervals were calculated using a logit transform which guarantees the CIs are always between 0 and 1.

6 After removing the PNA category. 

E TYPES OF INSTITUTIONS AND COMPARABLE WORK FORCE 41 
NASA RESEARCHER DEMOGRAPHICS

Black 

Doctorates Only: Percentage of Black ROSES Applicants and estimated percentage of 

Black Individuals in the Physical Sciences for <20 years 2019 

<20 years 

0 .5 1 

Percentage of Blacks 

Physical Sciences-90% CIs ROSES-PI+Co-I 

ROSES-PI 

Physical Sciences 

• Black individuals made up about 0.3% [0.1%–0.8%] in the NSF data, whereas <1% of ROSES

Co-I and 1.1% of ROSES PI applicants in this category were Black or African American.

Hispanic 

Doctorates Only: Percentage of Hispanic ROSES and AO Applicants and estimated 

percentage of Hispanics in the Physical Sciences by Career Stage Category 2019 

20+ years 

<20 years 

0 5 10 15 

Percentage of Hispanics 

Physical Sciences-90% CIs Physical Sciences AO-Co-I 

AO-PI+Co-I ROSES-PI+Co-I ROSES-PI 

ROSES-Co-I 

• 20+ years since PhD: Hispanic individuals accounted for 2% [1–6%] of these individuals in

the NSF data; the proportion of Hispanic applicants ranged from 2 to 3%.

• <20 years since PhD: Hispanics made up 7% [4–13%] in the NSF data, while the percentage

of Hispanic applicants ranged from 5 to 6%.

E TYPES OF INSTITUTIONS AND COMPARABLE WORK FORCE 42 
NASA RESEARCHER DEMOGRAPHICS

20+ years 

<20 years 

0 10 20 30 40 

Asian 

Doctorates Only: Percentage of Asian ROSES and AO Grant Applicants and estimated 

percentage of Asians in the Physical Sciences in Each Career Stage Category 2019 

Percentage of Asians 

Physical Sciences-90% CIs Physical Sciences AO-Co-I 

AO-PI+Co-I ROSES-PI+Co-I ROSES-PI 

ROSES-Co-I 

• 20+ years since PhD: Asian individuals made up 9% [5–13%] in the NSF data; the

percentage of Asian applicants was 15% (ROSES-Co-I) and 20% (ROSES-PI)

• <20 years since PhD: Asians made up 25% [18–33%] in the NSF data, while for AO-Co-I

applicants the value was 12%.

White 

Doctorates Only: Percentage of White ROSES and AO Applicants and estimated percentage 

of Whites in the Physical Sciences by Career Stage Category 2019 

20+ years 

<20 years 

60 70 80 90 

Percentage of Whites 

100 

Physical Sciences-90% CIs Physical Sciences AO-Co-I 

AO-PI+Co-I AO-PI ROSES-PI+Co-I 

ROSES-PI ROSES-Co-I 

• 20+ years since PhD: White individuals made up 88% [82–92%] in the NSF data; the

percentage of White applicants was 76% for ROSES-PI and 81% for ROSES Co-I. For AO PI,

100% of applicants 20+years from PhD were white.

• <20 years since PhD: Whites made up 67% [59–74%] in the NSF data; AO Co-I applicants

were 80% white.

E TYPES OF INSTITUTIONS AND COMPARABLE WORK FORCE 43 
NASA RESEARCHER DEMOGRAPHICS

Limitations of this analysis: 

• The distribution of ROSES and AO applicants over institution types (academia, nonprofit, for

profit, etc.) differs from the distribution in the physical sciences population data.

• In addition, we cannot assume that the subfields within physical sciences at NASA SMD

(e.g. Planetary Science, Astrophysics, etc.) are in the same proportions as in the physical

sciences population data, as our categories do not directly align with the occupation codes

in the NSF data.

• The Hispanic and Black estimates in the NSF data reflect small samples and in several

cases the samples are too small to produce a coefficient of variation of <50% (indicating the

estimates are imprecise). Rather than aggregating the data, we show each category, but

caution should be taken when interpreting the results for Hispanic and Black individuals.

• Additionally, the “prefer not to answer,” “race not listed,” and “non male/female gender”

categories in the SMD data had to be removed (and percentages recalculated on those

remaining) as these categories are not present in the NSF data or for confidentiality as the

numbers were too small to report in SMD’s data.

E TYPES OF INSTITUTIONS AND COMPARABLE WORK FORCE 44 
NASA RESEARCHER DEMOGRAPHICS

 

F CONCLUSIONS 

What conclusions can we draw from the overall data? 

To fully understand the demographic distribution in our data, to assess the possible presence of 

under-representation, we need to start by comparing to the pool of applicants that NASA can draw 

from. Is the low percentage of certain minority groups observed in our data due to the absence 

of these groups in the pool or due to a lack of participation in our grant programs? We used the 

best available data to us for comparison – individuals employed in the physical sciences with 

doctorates from NSF’s 2019 National Survey of College Graduates. The conclusions are not clear 

cut—it is a mixture of certain minority groups having low presence in the pool, being somewhat 

underrepresented, and in other cases overrepresented compared to the NSF data. Specifically, 

Asians have a strong presence among ROSES PI and Co-I applicants 20+ years from PhD, as we 

have more Asians than the NSF comparison data points to and fewer Whites. Among applicants 

to AOs PI and Co-Is, comparison to the NSF data indicates fewer minority applicants in 2019 than 

indicated in the comparison data. For example, 100% of AO PI applicants 20+years from PhD in 

2019 (after removing the PNA category) declared themselves white, but the number of applicants 

is low, and adding or subtracting a few people could sway the findings. Thus, it is important to 

augment the analysis with additional years’ worth of data. Examining the AO PI PhD applicants for 

2014–2021, 88% of applicants who provided a race/ethnicity were white. It would be worthwhile 

to take a deeper look into the data to try and identify the reason why fewer minorities are applying 

to AOs. For example, the demographic distribution might not be evenly distributed over research 

institutions of different sizes, and larger institutes may be better equipped to support these complex 

grants. Comparisons of ROSES PI and Co-I and AO Co-I applicant gender data for 2019 to the NSF 

physical sciences population show similar or higher representation of women than indicated in the 

NSF comparison data.

Note that the comparison to the NSF data described above is for SMD as a whole. To fully 

understand the demographic distribution that we see for each division (Astrophysics, Earth 

Science, Heliophysics, or Planetary Science), we need to compare to the demographics of the 

particular sub-field. To do this, we will compare our data to workforce surveys such as the 2020 

Survey of the Planetary Science Workforce. Previous analysis comparing NASA’s proposer data7 

to workforce surveys has not accounted for both differences in degree type and experience 

levels between the proposer pull and the workforce data. NASA’s analysis in this area is based 

on the recognition that career stage and degree type have a strong influence on demographics. 

Analysis conducted by OCS of NSF’s 2019 National Survey of College Graduates found that among 

7 National Academies of Sciences, Engineering, and Medicine. 2022. Advancing Diversity, Equity, Inclusion, and Accessibility in 

the Leadership of Competed Space Missions. Washington, DC: The National Academies Press. https://doi.org/10.17226/26385. 

F CONCLUSIONS 45 
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employed physical scientists in 2019 (excluding Chemistry), 34% [29–39%] are women. However, 

among employed physical scientists with a PhD, an estimated 21% [17–26%] are women. It is 

necessary to account for career stage because data shows that the demographic distributions in the 

physical sciences have changed over time. For example, among individuals employed in the physical 

sciences with doctorates in 2019, an estimated 32% [25–40%] of those with <20 years from PhD are 

female, and an estimated 6% [3–10%] of those with 20+ years from PhD are female. Thus, we are 

requesting customized data pulls from organizations carrying out these surveys so we can carry out 

more targeted and accurate comparisons to the NASA proposer pool. 

Currently, in order to evaluate NASA’s grant demographics, one needs to make comparisons to 

different surveys where the demographic questions (in particular, disability) are often not asked in the 

same way. Thus, subjective judgement is involved in translating the questions from one survey to 

another. There is a need for synchronization of these surveys so that we can extract information that 

can be acted on with a higher level of reliability than we are doing now. NASA intends to work with 

NSF and AIP, and other relevant external organizations, to improve the timing and usefulness of 

these surveys. 

The NSF data on the individuals employed in the physical sciences with doctorates suggests 

very low proportions of Black, AIAN, and other minority groups. The majority of grant applicants have 

a PhD which means that the population from which the applicants are coming from has very small 

percentages of Blacks, AIAN, and other minority groups. There is an obvious need to make graduate 

schools more closely mimic the demographics of the national population. Continuing partnerships 

with universities will help reach these goals. Additionally, the continued small proportions of Blacks 

and other minority groups among doctorates in the physical sciences means assessment of existing 

programs is called for, and potentially incorporating a different approach to reaching these groups to 

achieve higher participation in the sciences.

The ROSES disability categories are roughly constant with some fluctuations over time. For the AOs, 

there is a slight decrease in the “None” category, a slight increase in the “Prefer not to answer” 

category and the disability category remains roughly the same. However, there are only 

3 data points for the AOs so it is difficult to establish the trend. One has to be careful interpreting the 

disability data because the disabilities we collect are narrower than how disabilities are defined in 

OPM form SF2568. For example, the following disabilities are missing from the NSPIRES survey: 

depression, anxiety disorder or other psychiatric disorder, nervous system disorder, diabetes, 

autoimmune disorder, cancer, etc. Even though there is an option to choose “Other Disability”, given 

the strong emphasis of the question on certain disabilities it is quite possible that those with other 

disabilities would not have responded that they have a disability. This could mean some of the actual 

disabilities are underreported in this question. Thus, there is a need to redefine this question to

8 https://www.opm.gov/forms/pdf_fll/sf256.pdf

F CONCLUSIONS 46 
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include additional disabilities. This is the first step necessary to assess whether we have a lower 

percentage of individuals with declared disabilities compared to the pool or if differences are due to 

different definitions. 

Next, we examine proposal success rates by gender, race/ethnicity, and disability status. When 

interpreting the proposal success rates, bear in mind that in some cases, the “prefer not to answer” 

category—if large enough—can potentially sway the findings. Note: it is not possible to draw 

conclusions about the presence or absence of bias in the review process from the data presented in 

this report. A common statistical data fallacy is to confuse association with causation. Causation 

cannot be inferred from analysis involving associations between only two variables 

(e.g., gender and funding outcome). For example, suppose one observes a statistically significant 

difference in funding outcomes by gender. A causal interpretation of this result is that a certain 

gender or genders have a lower likelihood of getting funding because of their gender, which may 

lead one to think there is gender bias. However, to support a causal interpretation, one must first 

rule out alternative explanations and possible true causes such as differences in career stage, 

qualifications, institution type, program applied to, etc. Not accounting for confounders might lead 

to false positive or negative results and prompt actions that are not suitable to correct any problem. 

Future work will involve controlling for other variables that could also affect the probability of getting 

a grant funded through the building of relevant statistical models, already underway. Examples of 

these variables include experience, degree type, submission year, and institution type. 

ROSES: For 2014–2020 combined, we see only slight differences in proposal success rates for 

the male and female categories, as well as individuals with self-reported disabilities versus those 

who report no disabilities. For race/ethnicity, proposal success rates for ROSES were: AIAN – 25%, 

Asian – 24%, Black or African American – 21%, Hispanic – 23%, PNA – 24%, Race not listed - 24%, 

and White – 26%. Thus, there is a 5 percentage point difference between the proposal success 

rates for Black or African American and Whites. Looking at the most recent two years only (2019– 

2020) for ROSES, there are slight differences in the success rates. The success rates were: Asians 

– 22%, Black 24%, Hispanic – 23%, PNA – 23%, Race Not listed – 25%, and White – 24%. Thus,

the differences are decreasing and success rates have been improving to where we want them to be.

AOs: For 2014–2020 combined, we do not observe noticeable differences in proposal success 

rates by gender or disability status. For race/ethnicity, success rates were Asian – 29%, White – 

27%, Hispanic – 20% and PNA – 24% (data by year is sparse). Thus, there is a 9 percentage point 

difference in success rates between Asian and Hispanic.

Before drawing conclusions and making recommendations, additional analysis is needed to

pinpoint possible explanations for the observed gap. In order to understand the effect of RNO on 

the probability of having a grant funded, it is important to control for other factors that could influence 

F CONCLUSIONS 47 
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this. For example, we would want to look at differences in experience levels, whether there are any 

educational differences between the applicant groups; are PhDs more prevalent in certain applicant 

groups than others; the types of institutions; and other variables such as year of submission, division, 

mission size, team size, and so on. 

For the “non male/female” category, the numbers were not large enough to reach the thresholds for 

reporting. Combining this data with future data will give us a bigger size for reporting. There is a need to 

redefine our gender question to include multiple options, such as transgender. We have notified OMB of 

the need to change the category to reflect the trends in LGBT reporting, however, this will take time. 

NASA is already responding to and engaging in efforts to improve diversity and equity. The 

following paragraphs are from the 2022 NASA Equity Action Plan: 

• Conduct a barrier analysis of NASA grants and cooperative agreements. The Agency is

reviewing its grant and cooperative agreement process to (1) identify Historically Black

Colleges and universities and Minority-Serving Institutions and small/minority-owned

businesses that are eligible to compete for awards but are not submitting proposals and (2)

analyze barriers for those that did not apply or applied but did not receive awards. The study,

scheduled for completion by the end of 2024, will allow the Agency to identify and address

recurring barriers. It also is expected to promote an increase in the diversity of individuals and

institutions participating in NASA grant and cooperative agreement programs.

• Increase outreach and training to underserved communities. A key to increasing the

participation of small and minority businesses in NASA grants and cooperative agreements

lies in (1) making them aware of the opportunities and (2) providing the tools, resources,

training, and knowledge needed to partner with the Agency. To that end, NASA will

implement a series of six training sessions per year and increase our outreach workshops

and events to four per year. The Agency will evaluate the effectiveness of these efforts by

developing surveys to collect feedback from training and outreach participants. It also will

review data to determine if new and first-time participants are subsequently submitting

proposals, and how many of those proposals are being funded.

• Expand Dual Anonymous Peer Review of grant proposals. A key to increasing

participation of underserved communities in NASA grants is ensuring fairness in the

selection process. In 2021, NASA began expanding a Dual Anonymous Peer Review (DAPR)

process, supported by the National Academy of Science, in which names and identities

of reviewers and proposers are kept hidden in select competitions. The anonymous process

has been shown to increase fairness and reduce hidden biases. For instance,

in previous NASA Astrophysics Data Analysis Program solicitations, women constituted

26% of applicants but finished in the top two places in the selection panel rankings just

F CONCLUSIONS 48 
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16% of the time. When the DAPR process was implemented, women constituted 31% of 

applicants and were in the top two rankings 32% of the time. NASA plans to study lessons 

learned from the implementations and adopt the new process for all applicable Research 

Opportunities in Space and Earth Sciences (ROSES) by fiscal year 2024. The Agency plans 

to expand use of DAPR and other mechanisms even more by fiscal year 2026, helping to 

promote more representative selection rates for its award programs. 

Further, the Science Mission Directorate is also taking active steps in this direction, with its Bridge 

Program. The NASA Science Mission Directorate (SMD) Bridge Program (https://science.nasa.gov/

smd-bridge-program) is a new initiative to improve diversity, equity, inclusion, and accessibility 

within the NASA workforce and within the U.S. science and engineering community. It aims to 

increase engagement with Minority-Serving Institutions (MSIs), such as Historically Black Colleges 

and universities (HBCUs), Hispanic Serving Institutions (HSIs), Tribal Colleges and universities 

(TCUs), Community Colleges and Primarily undergraduate-Serving Institutions (PUIs), and other 

PhD-granting universities and NASA Centers.

It will include paid research and engineering studentships with the goal of enabling and supporting 

the transition of science and engineering students from undergraduate studies into graduate 

programs and employment by NASA. 

Future work from the OCS, besides adding additional years of data, will focus on review panel 

demographic data as well as the other NASA Directorates (ARMD, ESDMD/SOMD, and STMD). 

Also, we will further examine combinations of different variables to the extent possible, such as 

gender and ethnicity. 

Finally, the Office of the Chief Scientist has developed a dashboard which makes a huge amount 

of demographic data for grant applicants and selections easily accessible. Filters are available 

to allow users to drill down into the demographic data for different team members (PI, Co-I, etc.), 

divisions, and across multiple years. The goal of the dashboard is to increase transparency, as it 

allows everyone to track and be aware of the data. This dashboard is currently available internally 

at NASA and will be made available to the public in the future. 

F CONCLUSIONS 49 
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Acronyms 

AIAN: American Indian or Alaska Native 

AO: Announcements of Opportunity 

Black: Black or African American 

Co-I: Co-investigator 

Hispanic: Hispanic or Latino 

MSI: Minority serving institutions 

NHOPI: Native Hawaiian or Other Pacific Islander 

OCS: Office of the Chief Scientist 

PI: Principal Investigator 

PNA: Prefer not to answer 

RNL: Race Not Listed 

RNO: Race and National Origin 

ROSES: Research Opportunities in Space and Earth Science 

SMD: Science Mission Directorate 

Contact 

For further information about this report, please contact Louis Barbier (louis.m.barbier@nasa.gov) 

and/or Caroline Wilson (caroline.wilson@nasa.gov). 

F CONCLUSIONS 50 
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ACKNOWLEDGEMENTS 

Thanks to Michael New, Heidi Jensen, Lorenzo Pappas, Nazifa Taha, Michele Ostovar, Jenny Mottar 

and Steven Bradley for all the help assembling this report. Thank you to Daniel Foley and Lynn Milan 

for explaining the NSF data and providing input and discussion on the analysis we performed. Any 

questions about the analysis of NSF data should be directed to caroline.wilson@nasa.gov. Michael 

Splane developed the Tableau dashboard which was used to extract the charts. Emily Chien at 

NRESS provided the initial data. 

REFERENCES 

National Center for Science and Engineering Statistics, National Survey of College Graduates, 2019, 

public use data file, accessed 3 January 2022 from https://ncsesdata.nsf.gov/datadownload/. 

Here we show the data from the NSF survey that is used in this report. This analysis was carried out 

by the Office of the Chief Scientist using raw NSF survey data. 

Employed Physical Scientists (excluding Chemistry), All Degree Levels: Gender by Career Stage 

(+/- 20 years from terminal degree) 

Weighted Percentage 

Female 

90% 

Confidence Interval 

Career Stage LL UL 

Less than 20 years 41 34 48 

20 or more 21 16 27 

Similar breakdown by those with PhDs: 

Weighted Percentage 

Female 

90% 

Confidence Interval 

Doctorate 

Less than 20 years 32 25 40 

20 or more 6 3 10 

Note the significant drop-off in women with PhDs in the 20+ year category. Clearly the percentage of 

women receiving PhDs in the physical sciences has been growing dramatically over the past 20 years. 

ACKNOWLEDGEMENTS AND REFERENCES 51 
NASA RESEARCHER DEMOGRAPHICS

APPENDIX A. EMPLOYED PHYSICAL SCIENTISTS: 

RACE AND ETHNICITY (NSF, 2019) 

ALL DEGREES AND DOCTORATES ONLY 

Asian Black Hispanic White 

Weighted % LL UL Weighted % LL UL Weighted % LL UL Weighted % LL UL 

Career Stage (Years from highest degree) 

Less than 20 years 14 11 18 5 2 9 8 5 12 71 66 76 

20 or more years 4 3 6 3 2 7 3 2 6 87 82 90 

Doctorates Only 

Less than 20 years 25 18 33 0.3 0.1 0.8 7 4 13 67 59 74 

20 or more years 9 5 13 0.7 0.2 2.5 2 1 6 88 82 92 

APPENDIX B. NASA’S PHYSICAL SCIENCES WORKFORCE 

AS OF DECEMBER 4, 2021 (FULL-TIME PERMANENT EMPLOYEES ONLY)

Job Series 

% of NASA FTP 

Physical Science 

Employees 

Male Female AAPI Black Hispanic Multiracial AIAN White 

1301 - Physical Scientist 47% 70% 30% 13% 3% 5% NR NR 79% 

1306 - Health Physicist NR NR NR NR NR NR NR NR NR 

1310 - Physicist 11% 81% 19% NR NR NR NR NR 80% 

1311 - Physical Science 

Technician NR NR NR NR NR NR NR NR NR 

1313 - Geophysicist 2% 93% NR NR NR NR NR NR 93% 

1320 - Chemist NR NR NR NR NR NR NR NR NR 

1330 - Space Scientist 35% 70% 30% 7% NR 7% NR NR 84% 

1340 - Meteorologist 2% 75% NR NR NR NR NR NR 80% 

1341 - Meteorological 

Technician NR NR NR NR NR NR NR NR NR 

1360 - Oceanographer 1% 93% NR NR NR NR NR NR 86% 

1370 - Cartographer NR NR NR NR NR NR NR NR NR 

1386 - Photographic 

Technologist NR NR NR NR NR NR NR NR NR 

13xx - Physical Sciences 100% 72% 28% 10% 3% 5% NR NR 81% 

APPENDICES 52 
National Aeronautics and Space Administration 

Office of the Chief Scientist 

NASA Headquarters 

300 E Street SW 

Washington, DC 20546 

http://www.nasa.gov/offices/ocs

www.nasa.gov 

NP-2023-04-3125-HQ