nsf phd data

National Center for Science and Engineering Statistics

• All previous cycle years

The NSCG is a biennial survey that provides data on the characteristics of the nation's college graduates, with a focus on those in the science and engineering workforce.

Survey Info

• tag for use when URL is provided --> Methodology
• tag for use when URL is provided --> Data
• tag for use when URL is provided --> Analysis

The NSCG is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Areas of Interest

• Science and Engineering Workforce
• STEM Education

Survey Administration

This survey was conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics within the National Science Foundation.

Survey Details

• Survey Description (PDF 123 KB)
• Data Tables (PDF 2.1 MB)

Featured Survey Analysis

Effects of the COVID-19 Pandemic on Employment, Earnings, and Professional Engagement: New Insights from the 2021 National Survey of College Graduates

NSCG Overview

Data highlights, the share of u.s. college graduates employed full time trended downward between 2015 and 2021..

Unemployment increased across all levels of education between 2019 and 2021.

Methodology

Survey description, survey overview (2021 survey cycle).

The National Survey of College Graduates (NSCG)—sponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF)—provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering workforce. It samples individuals who are living in the United States during the survey reference week, have at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation.

Data collection authority

The information collected in the NSCG is solicited under the authority of the NSF Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Census Bureau collects the NSCG data under the authority of Title 13, Section 8 of the United States Code. The Office of Management and Budget control number is 3145-0141.

Major changes to recent survey cycle

The 2021 NSCG data collection instrument included new questions to gauge the effects of the coronavirus pandemic on employment, specifically on labor force status, number of hours worked per week, salary, benefits, telecommuting options, and total earned income.

Key Survey Information

Initial survey year, reference period.

The week of 1 February 2021.

Response unit

Individuals with at least a bachelor’s degree.

Sample or census

Population size.

Approximately 68.6 million individuals.

Sample size

Approximately 164,000 individuals.

Key variables

Key variables of interest are listed below.

• Demographics (e.g., age, race, sex, ethnicity, and citizenship)
• Educational history
• Employment status
• Field of degree

Survey Design

Target population.

The NSCG target population includes individuals who meet the following criteria:

• Earned a bachelor’s degree or higher prior to 1 January 2020,
• Are not institutionalized and reside in the United States or Puerto Rico as of 1 February 2021, and
• Are younger than 76 years as of 1 February 2021.

Sampling frame

The 2021 NSCG retains the four-panel rotating panel design that began with the 2010 NSCG. As part of this design, every new panel receives a baseline survey interview and three biennial follow-up interviews before rotating out of the survey.

The 2021 NSCG includes approximately 164,000 sample cases drawn from the following:

• Returning sample from the 2019 NSCG who were originally selected from the 2013 American Community Survey (ACS)
• Returning sample from the 2019 NSCG who were originally selected from the 2015 ACS
• Returning sample from the 2019 NSCG who were originally selected from the 2017 ACS
• New sample selected from the 2019 ACS

Approximately 90,000 cases were selected from the returning sample members for one of the three biennial follow-up interviews that are part of the rotating panel design. For the baseline survey interview, about 74,000 new sample cases were selected from the 2019 ACS.

Sample design

The NSCG uses a stratified sampling design to select its sample from the eligible sampling frame. Within the sampling strata, the NSCG uses probability proportional to size or systematic random sampling techniques to select the NSCG sample. The sampling strata were defined by the cross-classification of the following four variables:

• Young graduate oversample group eligibility indicator (2 levels)
• Demographic group (9 levels)
• Highest degree type (3 levels)
• Detailed occupation group (25 levels)

As has been the case since the 2013 NSCG, the 2021 NSCG includes an oversample of young graduates to improve the precision of estimates for this important population.

Data Collection and Processing

Data collection.

The NSCG uses a trimodal data collection approach: Web survey, mail survey, and computer-assisted telephone interview (CATI). The 2021 NSCG data collection effort lasted approximately 7 months.

Data processing

The data collected in the NSCG are subject to both editing and imputation procedures. The NSCG uses both logical imputation and statistical (hot deck) imputation as part of the data processing effort.

Estimation techniques

Because the NSCG is based on a complex sampling design and subject to nonresponse bias, sampling weights were created for each respondent to support unbiased population estimates. The final analysis weights account for several factors, including the following:

• Adjustments to account for undercoverage of recent immigrants and undercoverage of recent degree-earners
• Adjustment for incorrect names or incomplete address information on the sampling frame
• Differential sampling rates
• Adjustments to account for non-locatability and unit nonresponse
• Adjustments to align the sample distribution with population controls
• Trimming of extreme weights
• Overlap procedures to convert weights that reflect the population of each individual frame (2013 ACS, 2015 ACS, 2017 ACS, and 2019 ACS) into a final sample weight that reflects the 2021 NSCG target population.

The final sample weights enable data users to derive survey-based estimates of the NSCG target population.

Survey Quality Measures

Sampling error.

Estimates of sampling errors associated with this survey were calculated using the successive difference replication method. Please contact the NSCG Survey Manager to obtain the replicate weights.

Coverage error

Any missed housing units or missed individuals within sample households in the ACS would create undercoverage in the NSCG. Additional undercoverage errors may exist because of self-reporting errors in the NSCG sampling frame that led to incorrect classification of individuals as not having a bachelor’s degree or higher when in fact they held such a degree.

Nonresponse error

The weighted response rate for the 2021 NSCG was 65%. Analyses of NSCG nonresponse trends were used to develop nonresponse weighting adjustments to minimize the potential for nonresponse bias in the NSCG estimates. A hot deck imputation method was used to compensate for item nonresponse.

Measurement error

The NSCG is subject to reporting errors from differences in interpretation of questions and by modality (Web, mail, or CATI). To reduce measurement errors, the NSCG questionnaire items were pretested in focus groups and cognitive interviews.

Data Availability and Comparability

Data availability.

Data from 1993 to the present are available at the NSCG Web page .

Data comparability

Year-to-year comparisons can be made among the 1993 to 2021 NSCG survey cycles because many of the core questions remained the same. Small but notable differences exist across some survey years, such as the collection of occupation and education data based on more recent taxonomies. Also, because of the use of different reference months in some survey cycles, seasonal differences may occur when making comparisons across years.

There is overlap in the cases included in the 2010 NSCG through the 2017 NSCG, in the 2013 NSCG through the 2019 NSCG, and in the 2015 NSCG through the 2021 NSCG. This sample overlap consists of cases that originated in the 2013 ACS, 2015 ACS, 2017 ACS, or 2019 ACS. The overlap among cases allows for the ability to conduct longitudinal analysis of this subset of the NSCG sample. To reduce the risk of disclosure, longitudinal analyses can be conducted only within a restricted environment. See the NCSES Restricted-Use Data Licensing and Procedures page to learn more.

Data Products

Publications.

Data from the NSCG are published in NCSES InfoBriefs and data tables, available at https://www.nsf.gov/statistics/srvygrads/ .

Information from this survey is also included in Science and Engineering Indicators and Women, Minorities, and Persons with Disabilities in Science and Engineering .

Electronic access

The NSCG public use data through 2021 are available in the SESTAT data tool and in downloadable files through the NCSES data page . Data from 1993 to 2019 (2021 forthcoming) are also available in the new NCSES interactive data tool . The NSCG restricted use data are available through the Census Bureau’s Federal Statistical Research Data Centers .

Technical Notes

Survey overview, data collection and processing methods, data comparability and changes, definitions.

Purpose. The National Survey of College Graduates (NSCG) provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering (S&E) workforce. It samples individuals who are living in the United States during the survey reference week, have earned at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation.

The NSCG is designed to provide demographic, education, and career history information about college graduates and to complement another survey conducted by the National Center for Science and Engineering Statistics (NCSES): the Survey of Doctorate Recipients (SDR, https://www.nsf.gov/statistics/srvydoctoratework/ ). These two surveys share a common reference date, and they use similar questionnaires and data processing guidelines.

These technical notes provide an overview of the 2021 NSCG. Complete details are provided in the 2021 NSCG Methodology Report, available upon request from the NSCG Survey Manager.

Data collection authority. The information collected in the NSCG is solicited under the authority of the National Science Foundation Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Census Bureau collects the NSCG data, on behalf of NCSES, under the authority of Title 13, Section 8 of the United States Code. The Office of Management and Budget control number is 3145-0141.

Survey contractor. Census Bureau.

Survey sponsor. NCSES.

Frequency. Biennial.

Initial survey year. 1993.

Reference period. The week of 1 February 2021.

Response unit. Individual.

Sample or census. Sample.

Population size. Approximately 68.6 million individuals.

Sample size. Approximately 164,000 individuals.

Target population. The NSCG target population includes individuals who meet the following criteria:

• Earned a bachelor’s degree ​ Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. or higher prior to 1 January 2020
• Are not institutionalized and reside in the United States or Puerto Rico as of 1 February 2021
• Are younger than 76 years as of 1 February 2021

Sampling frame . Using a rotating panel design, the 2021 NSCG includes new sample cases from the 2019 American Community Survey (ACS) and returning sample cases from the 2019 NSCG.

The NSCG sampling frame for new sample cases included the following eligibility requirements:

• Were residing in the United States or Puerto Rico as of the ACS interview date
• Were noninstitutionalized as of the ACS interview date
• Had earned at least a bachelor’s degree as of the ACS interview date
• Would be under the age of 76 as of 1 February 2021
• Did not have an inaccurate name or incomplete address on the ACS data file

Returning sample cases from the 2019 NSCG originated from three different frames (the 2013 ACS, 2015 ACS, and 2017 ACS) and had the following eligibility requirements:

• Were a complete interview or temporarily ineligible during their initial NSCG survey cycle
• During the 2019 NSCG survey cycle, did not refuse to participate and request to be excluded from future NSCG cycles

Sample design . The NSCG sample design is cross-sectional with a rotating panel element. As a cross-sectional study, the NSCG provides estimates of the size and characteristics of the college graduate population for a point in time. As part of the rotating panel design, every new panel receives a baseline survey interview and three biennial follow-up interviews before rotating out of the survey.

The NSCG uses a stratified sampling design to select its sample from the eligible sampling frame. In the new sample, cases were selected using systematic probability proportional to size (PPS) sampling. ​ With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. Among the returning sample, all eligible cases were selected. The sampling strata were defined by the cross-classification of the following four variables:

As has been the case since the 2013 NSCG, the 2021 NSCG includes an oversample of young graduates to improve the precision of estimates for this important population. The 2021 NSCG includes approximately 164,000 sample cases drawn from the following:

• Returning sample from the 2019 NSCG who were originally selected from the 2013 ACS

Data collection . The data collection period lasted approximately 7 months (8 April 2021 to 1 November 2021). The NSCG used a trimodal data collection approach: self-administered online survey (Web), self-administered paper questionnaire (via mail), and computer-assisted telephone interview (CATI). Individuals in the sample generally were started in the Web mode, depending on their available contact information and past preference. After an initial survey invitation, the data collection protocol included sequential contacts by postal mail, e-mail, and telephone that ran throughout the data collection period. At any time during data collection, sample members could choose to complete the survey using any of the three modes. Nonrespondents to the initial survey invitation received follow-up contacts via alternate modes.

Quality assurance procedures were in place at each data collection step (e.g., address updating, printing, package assembly and mailing, questionnaire receipt, data entry, CATI, coding, and post-data collection processing).

Mode . About 89% of the participants completed the survey by Web, 7% by mail, and 4% by CATI.

Response r ates . Response rates were calculated on complete responses, that is, from instruments with responses to all critical items. Critical items are those containing information needed to report labor force participation (including employment status, job title, and job description), college education (including degree type, degree date, and field of study), and location of residency on the reference date. The overall unweighted response rate was 67%; the weighted response rate was 65%. Of the roughly 164,000 persons in the 2021 NSCG sample, 106,279 completed the survey.

Data e diting. Response data had initial editing rules applied relative to the specific mode of capture to check internal consistency and valid range of response. The Web survey captured most of the survey responses and had internal editing controls where appropriate. A computer-assisted data entry (CADE) system was used to process the mailed paper forms. Responses from the three separate modes were merged for subsequent coding, editing, and cleaning necessary to create an analytical database.

Following established NCSES guidelines for coding NSCG survey data, including verbatim responses, staff were trained in conducting a standardized review and coding of occupation and education information, certifications, “other/specify” verbatim responses, state and country geographical information, and postsecondary institution information. For standardized coding of occupation (including auto-coding), the respondent's reported job title, duties and responsibilities, and other work-related information from the questionnaire were reviewed by specially trained coders who corrected respondents’ self-reporting errors to obtain the best occupation codes. For standardized coding of field of study associated with any reported degree (including auto-coding), the respondent’s reported department, degree level, and field of study information from the questionnaire were reviewed by specially trained coders who corrected respondents’ self-reporting errors to obtain the best field of study codes.

Imputation. Logical imputation was primarily accomplished as part of editing. In the editing phase, the answer to a question with missing data was sometimes determined by the answer to another question. In some circumstances, editing procedures found inconsistent data that were blanked out and therefore subject to statistical imputation.

The item nonresponse rates reflect data missing after logical imputation or editing but before statistical imputation. For key employment items—such as employment status, sector of employment, and primary work activity—the item nonresponse rates ranged from 0.0% to 1.1%. Nonresponse to questions deemed sensitive was higher: nonresponse to salary and earned income was 5.4% and 7.8%, respectively, for the new sample members and 4.7% and 6.8%, respectively, for the returning members. Personal demographic data of the new sample members had variable item nonresponse rates, with sex at 0.00%, birth year at 0.04%, marital status at 0.6%, citizenship at 0.4%, ethnicity at 1.4%, and race at 3.1%. The nonresponse rates for returning sample members were 0.8% for marital status and 0.7% for citizenship.

Item nonresponse was typically addressed using statistical imputation methods. Most NSCG variables were subjected to hot-deck imputation, with each variable having its own class and sort variables chosen by regression modeling to identify nearest neighbors for imputed information. For some variables, there was no set of class and sort variables that was reliably related to or suitable for predicting the missing value, such as day of birth. In these instances, random imputation was used, so that the distribution of imputed values was similar to the distribution of reported values without using class or sort variables.

Imputation was not performed on critical items or on verbatim-based variables. In addition, for some missing demographic information, the NSCG imported the corresponding data from the ACS, which had performed its own imputation.

Weighting. Because the NSCG is based on a complex sampling design and subject to nonresponse bias, sampling weights were created for each respondent to support unbiased population estimates. The final analysis weights account for several factors, including the following:

• Overlap procedures to convert weights that reflect the population of each individual frame (2013 ACS, 2015 ACS, 2017 ACS, and 2019 ACS) into a final sample weight that reflects the 2021 NSCG target population

The final sample weights enable data users to derive survey-based estimates of the NSCG target population. The variable name on the NSCG public use data files for the NSCG final sample weight is WTSURVY.

Variance estimation. The successive difference replication method (SDRM) was used to develop replicate weights for variance estimation. The theoretical basis for the SDRM is described in Wolter (1984) and in Fay and Train (1995). As with any replication method, successive difference replication involves constructing numerous subsamples (replicates) from the full sample and computing the statistic of interest for each replicate. The mean square error of the replicate estimates around their corresponding full sample estimate provides an estimate of the sampling variance of the statistic of interest. The 2021 NSCG produced 320 sets of replicate weights.

Disclosure protection. To protect against the disclosure of confidential information provided by NSCG respondents, the estimates presented in NSCG data tables are rounded to the nearest 1,000.

Data table cell values based on counts of respondents that fall below a predetermined threshold are deemed to be sensitive to potential disclosure, and the letter “D” indicates this type of suppression in a table cell.

Sampling error. NSCG estimates are subject to sampling errors. Estimates of sampling errors associated with this survey were calculated using replicate weights. Data table estimates with coefficients of variation (that is, the estimate divided by the standard error) that exceed a predetermined threshold are deemed unreliable and are suppressed. The letter “S” indicates this type of suppression in a table cell.

Coverage error. Coverage error occurs in sample estimates when the sampling frame does not accurately represent the target population and is a type of nonsampling error. Any missed housing units or missed individuals within sample households in the ACS would create undercoverage in the NSCG. Additional undercoverage errors may exist because of self-reporting errors in the NSCG sampling frame that led to incorrect classification of individuals as not having a bachelor's degree or higher when in fact they held such a degree.

Nonresponse error. The weighted response rate for the 2021 NSCG was 65%; the unweighted response rate was 67%. Analyses of NSCG nonresponse trends were used to develop nonresponse weighting adjustments to minimize the potential for nonresponse bias in the NSCG estimates. A hot deck imputation method was used to compensate for item nonresponse.

Measurement error. The NSCG is subject to reporting errors from differences in interpretation of questions and by modality (Web, mail, CATI). To reduce measurement errors, the NSCG questionnaire items were pretested in focus groups and cognitive interviews.

Data comparability. Year-to-year comparisons of the nation’s college-educated population can be made among the 1993, 2003, 2010, 2013, 2015, 2017, 2019, and 2021 survey cycles because many of the core questions remained the same. Since the 1995, 1997, 1999, 2006, and 2008 surveys do not provide full coverage of the nation’s college-educated population, any comparison between these cycles and other cycles should be limited to those individuals educated or employed in S&E fields.

Small but notable differences exist across some survey cycles, however, such as the collection of occupation and education data based on more recent taxonomies. Also, because of the use of different reference months in some survey cycles, seasonal differences may occur when making comparisons across years. Thus, use caution when interpreting cross-cycle comparisons.

There is overlap in the cases included in the 2010 NSCG through the 2017 NSCG, in the 2013 NSCG through the 2019 NSCG, and in the 2015 NSCG through the 2021 NSCG (see figure 1 ). The overlap among cases allows for longitudinal analysis of a subset of the NSCG sample using restricted use data files within NCSES’ Secure Data Access Facility (SDAF). Cases can be linked across survey years using a unique identification variable and single-frame weights are available for each survey year, allowing for the evaluation of estimates from each frame independently. If you are interested in applying for a license to access restricted use NSCG data via the SDAF, please visit NCSES Restricted-Use Data Procedures Guide . Moreover, the Census Bureau offers NSCG restricted use data files that include a few additional data elements. These files can be accessed via the Federal Statistical Research Data Centers .

• Share on X/Twitter
• Share on Facebook
• Share on LinkedIn
• Send as Email

Rotating panel design and sample sizes for the National Survey of College Graduates: 2010–21

ACS = American Community Survey; NSCG = National Survey of College Graduates; NSRCG = National Survey of Recent College Graduates.

During a panel’s second survey cycle (in which it is part of the returning sample for the first time), its members include individuals who responded or who were temporarily ineligible during the first cycle. During a panel’s third and fourth cycles, its members include all respondents, nonrespondents, and temporarily ineligible cases from the preceding cycle. Beginning in 2013, the NSCG transitioned to a design that includes an oversample of young graduates to improve the precision of estimates for this important population.

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

Changes in survey coverage and population . None.

Changes in q uestio n naire

• 2021. To gauge the effects of the coronavirus pandemic on employment, the content of the NSCG questionnaire was modified for 2021 in two ways:
• The response options of long-standing items were revised to identify pandemic-related consequences: for example, reasons for not working, reasons for working part time, reasons for changing employment, and available job benefits.
• New items were added to understand the effects of the pandemic on salaries and earnings and to measure the prevalence of telework.
• 2019. The content of the 2019 NSCG questionnaire remained unchanged from the 2017 NSCG version.
• 2017. The 2017 NSCG questionnaire added two new questions about U.S. military veteran status that are asked on the ACS.
• 2015. The 2015 NSCG questionnaire added a section on professional certifications and licenses.
• 2013. The 2013 NSCG questionnaire added questions about attendance at community colleges, amounts borrowed to finance undergraduate and graduate degrees, and sources of financial support for undergraduate and graduate degrees. The 2013 questionnaire also differed from the 2010 questionnaire by splitting the first response category for the indicator of sample member location on the survey reference date into two categories. “United States, Puerto Rico, or another U.S. territory” became “United States or Puerto Rico” and “Another U.S. territory.”
• 2010. The 2010 NSCG questionnaire added items on components of job satisfaction, importance of job benefits, year of retirement, whether employer is a new business, and degree of difficulty concentrating, remembering, or making decisions.

Changes in reporting procedures or classification

• In past years, NSCG data were combined with data from the SDR and the NSRCG to form the Scientists and Engineers Statistical Data System (SESTAT). The last series of tables produced from SESTAT used 2013 NSCG data. Since then, NSCG data have been used in numerous tables for NCSES’s two congressionally mandated reports ( Science and Engineering Indicators and Women, Minorities, and Persons with Disabilities in Science and Engineering ).

Field of degree. NSCG respondents are asked to report each degree they have earned at the bachelor’s level or higher, along with the major field of study for each degree. The 2021 NSCG used a taxonomy of 142 “detailed” fields of study from which respondents could select the field that best represented their major. These 142 “detailed” fields of study were aggregated into 31 “minor” fields, 7 “major” fields, and 3 “broad” fields (S&E, S&E-related, and non-S&E). (See technical table A-1 for a list and classification of fields of study reported in the NSCG.)

Full-time and part-time employment. Full-time (working 35 hours or more per week) and part-time (working less than 35 hours per week) employment status is for the principal job only and not for all jobs held in the labor force. For example, an individual who works part time in his or her principal job but full time in the labor force would be tabulated as part time.

Highest degree level. NSCG respondents report the degrees they have earned at the bachelor’s level (e.g., BS, BA, AB), master’s level (e.g., MS, MA, MBA), and doctorate level (e.g., PhD, DSc, EdD), as well as other professional degrees (e.g., JD, LLB, MD, DDS, DVM). Because the NSCG is focused on the S&E workforce, the sampling strategy does not include a special effort to collect professional degrees. As such, there is not always sufficient data for the professional degrees to be displayed separately in the tables.

Occupation data. The occupational classification of the respondent was based on his or her principal job (including job title) held during the reference week—or on his or her last job held, if not employed in the reference week (survey questions A5 and A6 as well as A16 and A17). Also used in the occupational classification was a respondent-selected job code (survey questions A7 and A18). (See technical table A-2 for a list and classification of occupations reported in the NSCG.)

Race and ethnicity. Ethnicity is defined as Hispanic or Latino or not Hispanic or Latino. Values for those selecting a single race include American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, and White. Those persons who report more than one race and who are not of Hispanic or Latino ethnicity also have a separate value.

Salary. Median annual salaries are reported for the principal job, rounded to the nearest $1,000, and computed for individuals employed full time. For individuals employed by educational institutions, no accommodation was made to convert academic year salaries to calendar year salaries.

Sector of employment. Employment sector is a derived variable based on responses to questionnaire items A13, A14, and A15. In the data tables, the category 4-year educational institution includes 4-year colleges or universities, medical schools (including university-affiliated hospitals or medical centers), and university-affiliated research institutes. Two-year and pre-college institutions include community colleges, technical institutes, and other educational institutions (which respondents reported verbatim in the survey questionnaire). For-profit business or industry includes respondents who were self-employed in an incorporated business. Self-employed includes respondents who were self-employed or were a business owner in a non-incorporated business.

Fay RE, Train GF. 1995. Aspects of Survey and Model-Based Postcensal Estimation of Income and Poverty Characteristics for States and Counties. American Statistical Association Pro cee dings of the S ec tion on Go ve rnm e nt Statisti c s , 154–59.

Wolter K. 1984. An Investigation of Some Estimators of Variance for Systematic Sampling. J ournal of the Am e ri c an Statisti c al Asso c iation 79(388):781–90.

Technical Tables

Questionnaires, view archived questionnaires, key data tables.

Recommended data tables

Fields of study of college graduates

Occupations of college graduates, college graduates over time, data tables, work activities and job satisfaction of employed college graduates, median salaries of full-time employed college graduates, demographic characteristics of college graduates, general notes.

The National Survey of College Graduates, conducted by the National Center for Science and Engineering Statistics within the National Science Foundation, is a repeated cross-sectional biennial survey that collects information on the nation’s college-educated workforce. This survey is a unique source for examining the relationship between degree field and occupation, as well as for examining other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Acknowledgments and Suggested Citation

Acknowledgments, suggested citation.

Lynn Milan of the National Center for Science and Engineering Statistics (NCSES) developed and coordinated this report under the leadership of Emilda B. Rivers, NCSES Director; Vipin Arora, NCSES Deputy Director; and John Finamore, NCSES Chief Statistician. Jock Black (NCSES) reviewed the report.

The Census Bureau, under National Science Foundation interagency agreement number NCSE-2040211, collected and tabulated the data for the NSCG. The statistical data tables were compiled by Greg Orlofsky (Census) and verified by Nguyen Tu Tran (DMI). Data and publication processing support was provided by Devi Mishra, Christine Hamel, Tanya Gore, Joe Newman, and Rajinder Raut (NCSES).

NCSES thanks the college graduates who participated in the NSCG for their time and effort in generously contributing to the information included in this report.

National Center for Science and Engineering Statistics (NCSES). 2022. National Survey of College Graduates: 20 21 . NSF 23-306. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf23306/ .

Featured Analysis

Ncses data explorer, related content, related collections, survey contact.

For additional information about this survey or the methodology, contact

Get e-mail updates from NCSES

NCSES is an official statistical agency. Subscribe below to receive our latest news and announcements.

National Science Foundation - Where Discoveries Begin

• Home ✕
• Biological Sciences (BIO)
• Computer and Information Science and Engineering (CISE)
• Education and Human Resources (EHR)
• Engineering (ENG)
• Environmental Research and Education (ERE)
• Geosciences (GEO)
• Integrative Activities (OIA)
• International Science and Engineering (OISE)
• Mathematical and Physical Sciences (MPS)
• Social, Behavioral and Economic Sciences (SBE)
• Technology, Innovation and Partnerships (TIP)
• Related Links
• Interdisciplinary Research
• NSF Organization List
• Responsible and Ethical Conduct of Research
• Staff Directory
• Understanding NSF Research
• About Funding
• Archived Funding Search
• Find Funding
• Merit Review
• Policies and Procedures
• Preparing Proposals
• Recent Opportunities
• Transformative Research
• Proposal and Award Policies and Procedures Guide (PAPPG)
• Research.gov
• Funding Opportunities For
• Graduate Students
• K-12 Educators
• Postdoctoral Fellows
• Undergraduate Students
• Small Business
• About Awards
• Award Statistics (Budget Internet Info System)
• Award Conditions
• Managing Awards
• Presidential and Honorary Awards
• Search Awards
• Public Access Initiative
• All Documents
• National Center for Science and Engineering Statistics (NCSES)
• Obtaining Documents
• Search Documents
• For News Media
• Multimedia Gallery
• News Archive
• Search News
• Special Reports
• Speeches and Lectures
• About NSF Logo
• Broadening Participation/Diversity
• Budget and Performance
• Career Opportunities
• Contracting Opportunities
• National Science Board (NSB)
• NSF and Congress
• NSF Toolkit
• Office of Equity and Civil Rights
• Organization List
• Remote Participant Support
• Transparency and Accountability
• Academic Institution Profiles
• Academic Institution Profiles Home
• List of institutions
• Search by institution name
• Institution rankings most recent year
• Earned doctorates
• Full-time graduate students
• Total R&D expenditures
• Research Space
• Release notes
• Technical notes
• TableBuilder
• Contact NCSES

Stanford U.

U.S. doctorate awards

Each new cohort of doctorate recipients augments the supply of prospective scientists, engineers, researchers, and scholars. Data on the composition of these cohorts reveal changes in the presence of different demographic groups.

Overall trends

The number of research doctoral degrees awarded by U.S. institutions in 2021 decreased to 52,250, down from 55,224 in 2020, according to the Survey of Earned Doctorates (SED) ( figure 1 ). Since the survey’s inception in 1957, the number of doctorates awarded shows an upward trend—average annual growth of 3.0%—punctuated by periods of slow growth and even decline. However, the decline in 2021 is the second consecutive annual drop and the steepest in the history of the SED (-5.4%).

Since the SED began collecting data, the number of research doctorates awarded in science and engineering (S&E) fields has exceeded the number of non-S&E doctorates, and over time the gap has widened. From 1985 to 2021, the number of S&E doctorate recipients has more than doubled, while the number of non-S&E doctorate recipients in 2021 declined to just below the 1985 count. As a result, the proportion of S&E doctorates to all doctorates climbed from 62% in 1985 to 78% in 2021. The 2021 decline in the number of doctorate recipients was larger in S&E than in non-S&E fields, but the proportion was greater for non-S&E than for S&E fields.

• For grouped bar charts, Tab to the first data element (bar/line data point) which will bring up a pop-up with the data details
• To read the data in all groups Arrow-Down will go back and forth
• For bar/line chart data points are linear and not grouped, Arrow-Down will read each bar/line data points in order
• For line charts, Arrow-Left and Arrow-Right will move to the next set of data points after Tabbing to the first data point
• For stacked bars use the Arrow-Down key again after Tabbing to the first data bar
• Then use Arrow-Right and Arrow-Left to navigate the stacked bars within that stack
• Arrow-Down to advance to the next stack. Arrow-Up reverses
• Share on X/Twitter
• Share on Facebook
• Share on LinkedIn
• Send as Email

Doctorates awarded by U.S. colleges and universities: 1958–2021

S&E = science and engineering.

National Center for Science and Engineering Statistics, Survey of Earned Doctorates, 2021. Related detailed table 1-1 .

Citizenship

Trends in citizenship.

In 2021, the number of doctorates in S&E fields awarded to temporary visa holders was 15,216, down 784 from 2020 ( figure 2 ). Despite this decline, S&E doctorates awarded to temporary visa holders have increased 85% since 2001 and 25% since 2011. Over the past 20 years, the proportion of S&E doctorates awarded to temporary visa holders peaked at 41% in 2007, held steady at about 36% between 2010 and 2017, and increased to 39% in 2021.

In comparison, although starting from a larger base, the number of S&E doctorates awarded to U.S. citizens and permanent residents decreased by 1,464 in 2021; overall, it has experienced a slower relative increase over the past 20 years (35% since 2001 and 7% since 2011).

Doctorates awarded in S&E and non-S&E fields, by citizenship status: 2001–21

Excludes respondents who did not report citizenship. Counts of unreported citizenship fluctuated between 1,989 and 4,137.

National Center for Science and Engineering Statistics, Survey of Earned Doctorates, 2021. Related detailed table 1-6 and table 1-7 .

Countries or economies of foreign citizenship

The number of doctorate recipients on temporary visas is highly concentrated in a few places of origin. Between 2011 and 2021, 10 places accounted for 70% of the 181,446 doctorates awarded to temporary visa holders, and the top 3 countries—China, India, and South Korea—accounted for over half (53%) ( figure 3 ). Between 70% and 94% of doctorate recipients from each of these top 10 locations earned a doctorate in an S&E field.

Top 10 countries or economies of foreign citizenship for doctorate recipients with temporary visas: 2011–21

China includes Hong Kong. Ranking based on total number of doctorate recipients.

National Center for Science and Engineering Statistics, Survey of Earned Doctorates, 2021. Related detailed table 7-7 and table 7-8 .

Citizenship and sex

In 2021, women earned 46% of all doctorate awards. Since 2002, women have earned just over half of all doctorates awarded to U.S. citizens and permanent residents and more than 30% of doctorates awarded to temporary visa holders ( figure 4 ). In the past 10 years, the proportion of women has been stable in both citizenship categories (51%–52% for U.S. citizens and permanent residents and 35%–36% for temporary visa holders).

Doctorates awarded, by sex and citizenship: 2001–21

Excludes respondents who did not report sex or citizenship.

National Center for Science and Engineering Statistics, Survey of Earned Doctorates, 2021. Related detailed table 1-9 and table 1-10 .

In the past 20 years, most of the growth in the number of doctorates earned by both men and women has been in S&E fields ( figure 5 ). During this period, the number of female doctorate recipients in S&E fields increased by 72%, although starting from a smaller base, compared with a 40% increase in the number of male S&E doctorate recipients. The proportion of female doctorate recipients in S&E increased from 38% in 2001 to 42% in 2010, and it has remained fairly stable since then.

In non-S&E fields, women earned 58% of doctorates in 2021, a proportion that has changed little since the early 2000s. Between 2001 and 2021, the number of female non-S&E doctorate recipients declined by 14%, while the number of male doctorate recipients in those fields declined by 22%.

Between 2019 and 2021, the decline in the number and proportion of male S&E doctorate recipients was larger than the decline in female S&E doctorate recipients. In contrast, in non-S&E fields, the decline was larger among women than among men.

Doctorates awarded, by sex and field: 2001–21

Excludes respondents who did not report sex.

National Center for Science and Engineering Statistics, Survey of Earned Doctorates, 2021. Related detailed table 1-4 , table 1-5 , and table 3-2 .

Race and ethnicity

From 2001 to 2021, the proportion of doctorates earned by White U.S. citizens and permanent residents declined from 77% to 67%, and the proportion earned by Asian U.S. citizens and permanent residents increased from 7% to 10%. table 1-11 ." data-bs-content="For additional data on the race and ethnicity of doctorate recipients, see SED 2021 related detailed table 1-11 ." data-endnote-uuid="2d53c840-cb1b-494c-9894-b5907f3a0db6">​ For additional data on the race and ethnicity of doctorate recipients, see SED 2021 related detailed table 1-11 . The participation in doctoral education by Black or African American and Hispanic or Latino U.S. citizens and permanent residents increased, although starting from a small number.

In the past 20 years, the number of Hispanic or Latino doctorate recipients in S&E increased from 693 to 2,135 ( figure 6 ). As a result, the proportion of doctorates earned by this group among U.S. citizens and permanent residents grew from 4% in 2001 to 9% in 2021. The number of Black or African American doctorate recipients in S&E increased from 715 in 2001 to 1,392 in 2021, and the proportion of doctorates they earned among U.S. citizens and permanent residents increased from 4% to 6% during this period. Between 2019 (before the COVID-19 pandemic) and 2021, however, the number of Black or African American doctorate recipients in S&E declined by 73 and the number of Hispanic or Latino doctorate recipients in S&E increased by 57.

Between 2001 and 2021, the number of American Indian or Alaska Native doctorate recipients in S&E fluctuated between 49 and 86. Since 2019, it declined from 77 to 64, remaining under 0.5% of S&E doctorates awarded to U.S. citizens and permanent residents.

Doctorates earned by underrepresented minority U.S. citizens and permanent residents, by field: 2001–21

Excludes U.S. citizen and permanent resident respondents who did not report race or ethnicity. Counts of unreported race or ethnicity fluctuated between 434 and 982.

National Center for Science and Engineering Statistics, Survey of Earned Doctorates, 2021. Related detailed table 1-8 , table 1-11 , table 3-3 , and table 3-4 .

Graduate Research Fellowship Program

What is GRFP?

Fellowship benefits.

• Five year fellowship period with three years of financial support
• Annual stipend of $37,000
• Cost-of-education allowance of $16,000 to the institution
• No post-graduate study service requirement
• Access to supplemental funding to sustain research while on medical deferral (e.g. family leave)

Learn More »

Am I Eligible ?

To be eligible for the NSF GRFP, you must:

• be a US citizen, US national, or permanent resident
• intend to pursue a research-based Master’s or Ph.D. program in a GRFP-supported field
• be enrolled in an eligible program at an accredited United States graduate institution, with a US campus, by fall following selection
• be at an early stage in your graduate career
• have completed no more than one academic year of full-time graduate study (or the equivalent)
• Graduate students are limited to only one application to the GRFP, submitted either in the first year or in the second year of graduate school

Click here for more information

What's My Level?

Application level selection.

The GRFP Application requires you to select the academic level that best describes the stage of your academic career. Use the GRFP Academic Level Questionnaire to help you select the appropriate academic level in your application. Levels are determined as follows:

Level 1. You have not previously enrolled in a graduate degree-granting program, but plan to start graduate study next fall. Includes undergraduates in the final year of a bachelor’s degree program and individuals who previously earned a bachelor’s degree.

Level 2. First year graduate student currently enrolled in a graduate degree-granting program, who has never applied to GRFP before as a graduate student or returning graduate student, or a student currently enrolled in a joint bachelor’s-master’s degree program (must have completed three academic years in program).

Level 3. Second year graduate student who has completed no more than one academic year of graduate study while enrolled in any graduate degree-granting program, does not have a graduate degree, and has never applied to GRFP before as a graduate student or returning graduate student.

Level 4. Returning graduate student who is not currently enrolled in a degree-granting program, and may have more than one academic year in a graduate-degree granting program and/or a master’s or professional degree, followed by an interruption of at least two years just prior to the GRFP application deadline. Note: address the reasons for the interruption and why you should be considered to be in the early stages of your graduate education in the Personal, Relevant Background and Future Goals Statement.

GRFP recognizes and supports outstanding graduate students who have demonstrated the potential to be high achieving scientists and engineers, early in their careers. Applicants must be pursuing full-time research-based master’s and doctoral degrees in science, technology, engineering, and mathematics (STEM) or in STEM education at accredited US institutions.

• Oct. 17, 2022 - Life Sciences
• Oct. 18, 2022 - Computer and Information Science and Engineering, Materials Research, Psychology, Social Sciences, STEM Education and Learning
• Oct. 20, 2022 - Engineering
• Oct. 21, 2022 - Chemistry, Geosciences, Mathematical Sciences, Physics and Astronomy
• Oct. 28, 2022 - 5:00 PM ET
• Aug. 31, 2022 - 11:15 AM to 1:15 PM ET Info for Applicants Webinar 1
• Sept. 21, 2022 - 11:15 AM to 1:15 PM ET Info for Applicants Webinar 2
• Sept. 28, 2022 - 11:15 AM to 1:15 PM ET Reading the Fine Points of the GRFP Solicitation Webinar

NSF welcomes scientists and engineers to serve as reviewers of GRFP applications. Serving as a GRFP Reviewer is an excellent opportunity to apply your research and career expertise to help identify future science and engineering leaders.

• Sep. 15, 2022 - 5:00 PM ET
• Nov. 1, 2022 - 11AM to 1 PM ET Reviewer Training Webinar #1
• Nov. 4, 2022 - 2 PM to 4 PM ET Reviewer Training Webinar #2
• Nov. 10, 2022 - 2 PM to 4 PM ET Reviewer Training Webinar #3
• Nov. 15, 2022 - 2 PM to 4 PM ET Reviewer Training Webinar #4

Reference Writers

Reference letters are a key component of a strong GRFP application package. The most effective reference letters provide detailed and specific information about how an applicant meets the NSF Merit Review Criteria of Intellectual Merit and Broader Impacts.

• Oct. 5, 2022 - 11:15 AM to 1:15 PM ET Info for Reference Writers & Research Mentors
• Oct. 6, 2022 - 11 AM to 1 PM ET Reference Writers Webinar

An official website of the United States government

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS. A lock ( Lock Locked padlock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Active funding opportunity

Nsf 24-560: data science corps, program solicitation, document information, document history.

• Posted: March 25, 2024
• Replaces: NSF 21-523

Program Solicitation NSF 24-560

Full Proposal Target Date(s) :

June 21, 2024

Important Information And Revision Notes

This is a revision of NSF 21-523 , the solicitation for Harnessing the Data Revolution (HDR): Data Science Corps (DSC) Building Capacity for HDR. As part of this revision, proposers are encouraged to pay particular attention to the following:

• The solicitation supports opportunities for undergraduates (including students from community colleges, Minority Serving Institutions, other emerging research institutions, and institutions in EPSCoR jurisdictions) and grade 6-12 teachers and students.
• Expected award size is revised.
• Prospective PIs must respond to one or more described mechanisms for data science education and training.
• Solicitation-specific criteria are revised.
• Prospective PIs are encouraged to plan activities over hybrid platforms that provide in-person and remote learning to further broaden the participation of diverse student cohorts.

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

Summary Of Program Requirements

General information.

Program Title:

Data Science Corps (DSC)

The objective of the Data Science Corps program is to help build a strong national data science infrastructure and workforce. The Data Science Corps program seeks to engage data science students in real-world data science implementation projects. This engagement will help bridge the data-to-knowledge gap in organizations and communities at all levels, including local, state, and national, and will empower better use of data for more effective decision making. Data Science Corps participants will be able to sharpen their skills in data science by working on real-world projects focused on specific community needs, including rural communities, urban communities, academia, industry, or government. This partnership between communities and data scientists will serve the nation by helping produce a workforce-ready cohort of data scientists and technologists, who have experience with data science in action in real-world settings. The program welcomes proposals that seek to broaden participation in science, technology, engineering and mathematics (STEM) and STEM education. This solicitation prompts the community to respond to one or more mechanisms by which to provide students with data science education and training, including in data science issues related to knowledge representation and creation and use of knowledge graphs. The solicitation supports opportunities for undergraduates (including students from community colleges, Minority Serving Institutions, other emerging research institutions as defined in the CHIPS and Science Act, https://www.congress.gov/bill/117th-congress/house-bill/4346 , and institutions in EPSCoR jurisdictions), and grade 6-12 teachers and students. When responding to this solicitation, even though proposals must be submitted through the Directorate for STEM Education, Division of Research on Learning in Formal and Informal Settings (EDU/DRL) , once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors This solicitation grew out of the NSF-wide activity known as Harnessing the Data Revolution (HDR), a national-scale activity to enable new modes of data-driven discovery addressing fundamental questions at the frontiers of science and engineering. HDR has supported an interrelated set of efforts in foundations of data science; data-intensive research in science and engineering; and education and workforce development. Contact Information: General inquiries may be addressed to [email protected] .

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

Cognizant Program Officer(s):

• Stephanie D. Teasley, Program Director, EDU/DRL, telephone: (703) 292-8752, email: [email protected]
• Sylvia Spengler, Program Director, CISE/IIS, telephone: (703) 292-7347, email: [email protected]
• Chaitanya Baru, Senior Advisor, TIP/OAD, telephone: (703) 292-4596, email: [email protected]
• Paul Tymann, Program Director, EHR/DUE, telephone: (703) 292-2832, email: [email protected]
• Jemin George, Program Officer, TIP/ITE, telephone: (703) 292-2251, email: [email protected]
• Raleigh Martin, Program Director, GEO/EAR, telephone: (703) 292-7199, email: [email protected]
• Jennifer Noll, Program Director, EDU/DRL, telephone: (703) 292-8117, email: [email protected]
• Christopher Stark, Program Director, MPS/DMS, telephone: (703) 292-4869, email: [email protected]
• Patricia Van Zandt, Program Director, SBE/BCS, telephone: (703) 292-7437, email: [email protected]
• 47.049 --- Mathematical and Physical Sciences
• 47.050 --- Geosciences
• 47.070 --- Computer and Information Science and Engineering
• 47.074 --- Biological Sciences
• 47.075 --- Social Behavioral and Economic Sciences
• 47.076 --- STEM Education
• 47.084 --- NSF Technology, Innovation and Partnerships

Award Information

Anticipated Type of Award: Standard Grant

Estimated Number of Awards: 10 to 15

Ten to fifteen awards are anticipated.

Anticipated Funding Amount: $10,000,000

Up to $10,000,000 is expected to be available, subject to availability of funds. Awards will typically be in the range of $800,000 to $1,200,000 for a duration of three years.

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following: Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus. Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities.

Who May Serve as PI:

There are no restrictions or limits.

Limit on Number of Proposals per Organization:

Limit on Number of Proposals per PI or co-PI: 1

An individual may participate in only one proposal as Principal Investigator, co-Principal Investigator, or Senior Personnel in any project. This eligibility constraint will be strictly enforced to treat everyone fairly and consistently. If an individual exceeds this limit, any proposal submitted after the first proposal is received at NSF will be returned without review. No exceptions will be made.

Proposal Preparation and Submission Instructions

A. proposal preparation instructions.

• Letters of Intent: Not required
• Preliminary Proposal Submission: Not required
• Full Proposals submitted via Research.gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .
• Full Proposals submitted via Grants.gov: NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov guidelines apply (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ).

B. Budgetary Information

C. due dates, proposal review information criteria.

Merit Review Criteria:

National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information

Award Conditions:

Additional award conditions apply. Please see the full text of this solicitation for further information.

Reporting Requirements:

Standard NSF reporting requirements apply.

I. Introduction

The objective of the Data Science Corps program is to help build a strong national data science infrastructure and workforce. The Data Science Corps program seeks to engage data science students in real-world data science implementation projects. This engagement will help bridge the data-to-knowledge gap in organizations and communities at all levels, including local, state, and national, and will empower better use of data for more effective decision making. Data Science Corps participants will be able to sharpen their skills in data science by working on real-world projects focused on specific community needs, including rural communities, urban communities, academia, industry, or government. This partnership between communities and data scientists will serve the nation by helping produce a workforce-ready cohort of data scientists and technologists, who have experience with data science in action in real-world settings.

II. Program Description

This solicitation has a primary focus on broadening participation in data science for undergraduates (including students in community college, Minority-Serving Institutions, other emerging research institutions, and institutions in EPSCoR jurisdictions), and teachers and students in grades 6 -12. Equitable access to data science education presents an opportunity to open doors to higher education, higher-paying careers, and support a more engaged citizenry. The DSC solicitation prompts the PI community to envision and implement diverse and creative mechanisms by which to provide all students with age and developmentally appropriate data science training to gain the expertise needed for understanding and interpreting data. The DSC funded projects should contribute to research and practice that supports data science literacy and practices, as well as creating and enhancing the theoretical and empirical foundations for effecting data science learning. Proposals responsive to this solicitation respond to and implement one or more of the four following mechanisms:

I. Learning in the Community: Effective data science education and training happens in the community. PIs are encouraged to engage students with stakeholder communities, so that students can obtain immersive educational and training experiences via hands-on training on real-world problems and data generated by and of importance to communities at all levels, thus expanding the supply of data science talent in support of local, regional, and national economies and society at large.

II. Flexible Educational Pathways: Flexible educational pathways with multiple points of entry can be effective mechanisms to integrate and provide data science education and training to students with varied educational backgrounds and experiences, skill level, and technical maturity. Effective pathways provide students with data science expertise in a tiered manner in support of building a diverse workforce trained in data management, data analytics, and data-driven decision-making.

III. Across the Data Life Cycle: Foundational data science education and training needs to expose students to a variety of disciplinary approaches that track the full data life cycle, from data collection, processing, storage, to data management, analytics, and decision-making. PIs are encouraged to address the inherent interdisciplinarity of data science and bring multiple perspectives, including but not limited to computer science, statistics, mathematics, and information technology.

IV. Data Science in STEM: Today's very large datasets and modern data science tools are revolutionizing scientific inquiry knowledge generation, and advancement across the scientific disciplines. NSF welcomes proposals that provide data science training to students pursuing their primary studies in diverse scientific and engineering disciplines and so drive data-centric inquiry and innovation in the sciences.

Competitive proposals additionally establish the central role of ethics in data science training and are expected to instill and cultivate ethics across the proposed student experiences. Meaningful student experiences include exposure to FAIR (Findability, Accessibility, Interoperability, and Reuse of digital assets: https://www.go-fair.org/fair-principles/ ) and CARE principles (Collective Benefit, Authority to Control, Responsibility, and Ethics: https://www.gida-global.org/care ), in alignment with goals for the 2023 Federal Year of Open Science ( https://www.usgs.gov/special-topics/year-of-open-science/news/white-house-office-science-technology-policy-open-science ).

To support the development of a diverse STEM workforce, prospective PIs are encouraged to expand the participation of diverse students in the Data Science Corps program and broaden opportunities to student groups, Institutions of Higher Education, and geographic regions that are not yet fully represented in STEM disciplines. Projects responding to this solicitation should also support diversity among participating units and bring together diverse teams that engage in a tight and meaningful collaboration activity within or among educational institutions. These efforts should be described in the Management and Coordination Plan to be submitted as a Supplementary Document.

Program Structure

All Data Science Corps awards will interact and coordinate with one another. One or more award participants, including the project PI, will be expected to attend the annual PI meeting to exchange effective practices, curricula, assessment strategies, as well as challenges. Budget for such attendance should be included in each project.

While we encourage all proposed projects to envision in-person activities, we prompt the prospective PIs to consider leveraging hybrid platforms that combine virtual with in-person student participation to facilitate equitable participation in the Data Science Corps program by students of all backgrounds and needs, as well as facilitate student engagement with diverse data- and/or problem-providing communities.

Project Structure

Collaborative proposals may be submitted to NSF in one of two methods: as a single proposal, in which a single award is being requested (with subawards administered by the lead organization); or by simultaneous submission of proposals from different organizations, with each organization requesting a separate award. (See Proposal Preparation Instructions). Proposals focusing on under-served communities/organizations in K-12 and/or higher education, and involving collaborations led by or including Minority-Serving Institutions, other emerging research institutions, and institutions in EPSCoR jurisdictions are especially encouraged.

Where available, projects are encouraged to consult their institutional office of community engagement. Additional resources that may be useful to PIs preparing to respond to this solicitation include:

• The 2022 National Academies of Sciences, Engineering, and Medicine "Foundations of Data Science for Students in Grades K-12" ( https://www.nationalacademies.org/our-work/foundations-of-data-science-for-students-in-grades-k-12-a-workshop ) is a workshop that explored the rapidly growing field of K-12 data science education.
• The 2020 National Academies of Sciences, Engineering, and Medicine "The Future of Data Science" ( https://www.nationalacademies.org/event/10-28-2020/the-future-of-data-science ) is part of a colloquium series and explores various aspects of data science with an eye towards the future.
• The 2021 National Academies of Sciences, Engineering, and Medicine "Addressing the Underrepresentation of Women of Color in Tech" ( https://www.nationalacademies.org/our-work/addressing-the-underrepresentation-of-women-of-color-in-tech ). The associated consensus study report "Transforming Trajectories for Women of Color in Tech" addresses the critical need to increase the number of women of color in tech to building and maintaining a competitive workforce.
• The 2018 National Academies of Sciences, Engineering, and Medicine "Data Science for Undergraduates: Opportunities and Options" Consensus Study Report ( https://www.nap.edu/catalog/25104/data-science-for-undergraduates-opportunities-and-options ). The report additionally outlines some considerations and approaches for academic institutions and others in the broader data sciences communities to transform data science education at the undergraduate level and expand the supply of data science talent.
• The GoFair Initiative's "FAIR Principles" ( https://www.go-fair.org/fair-principles/ )
• The Research Data Alliance "Implementing the CARE Principles: The CARE-full Process" ( https://www.gida-global.org/care )
• The Committee on STEM Education of the National Science and Technology Council's five-year strategic plan, "Charting a Course for Success: America's Strategy for STEM Education" ( https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf ). The report is one of many resources available to prospective PIs to situate their activities within and building upon the scientific knowledge base of effective STEM education practices.
• Additional references can be obtained from searches of scholarly literature for publications on service-learning, community-based learning, data science education, and diversity, equity, and inclusion in STEM. The publications and other resources that provide the project's educational context should be included in the references.

It is expected that the collaborating units in a single-institution project or collaborating organizations in a multiple-organizations project will coordinate and work closely with one another. Closely coordinated activities include developing a common set of criteria for recruiting and incentivizing the participation of students at the various units or organizations, designing effective practices for preparing participants who have different levels of skill and technical expertise, as well as effective practices for development of Data Science Corps projects, and identifying and implementing suitable activities in response to one or more of the mechanisms outlined above. Collaborating activities should be described in the Management and Coordination Plan. This plan should also describe how the undertaken activities ensure broad representation across diverse groups and institutions underrepresented in STEM.

Units or organizations collaborating on a Data Science Corps project should identify a lead PI among them. The lead PI's unit or organization will undertake additional responsibilities related to coordination, monitoring, and evaluation of the award. In particular, the lead unit or organization should:

Work closely with the collaborating unit(s) or organization(s) to execute the overall award and develop criteria for recruiting and incentivizing the participation of students at the various organizations; and

Non-lead units or organization(s) should:

• Prepare students/participants for participation in the Data Science Corps projects, as well as mentor student participants while they are in the program and working on projects.
• Contribute to the curation and publication of information by the lead unit or organization, as described above.

Assessment of Student Learning and Program Evaluation

Data Science Corps awards must assess student learning and other outcomes, as well as evaluate overall project effectiveness. (Please see https://www.purdue.edu/research/docs/pdf/2010NSFuser-friendlyhandbookforprojectevaluation.pdf for more information about program evaluation).

Student assessment should aim to answer the following types of questions:

• What is the impact of the Data Science Corps experience on student gains in data science knowledge?
• What is the impact of the Data Science Corps experience on student gains in areas such as teamwork, entrepreneurship, critical thinking, communication, collaboration, creativity, and ethics?
• Has the Data Science Corps increased student persistence, employment pathways, and furthering education in a Science, Technology, Engineering, and Mathematics (STEM) related career path?

Project evaluation efforts should address issues related to the overall impact of the award . Examples of relevant questions include:

• Did the award reach its goals? Why or why not?
• Has the award been effective at developing models for communication and engagement among disciplines at the student level and among faculty?
• Are the materials reusable and generalizable?

Co-Funding Opportunities

The Eric and Wendy Schmidt Fund for Strategic Innovation has committed to providing an unrestricted donation to the NSF for the purpose of funding data science learning opportunities for students in grades 6 through 12. NSF and Schmidt Futures will provide funds toward awards in this age range. Schmidt representatives will have access to all the proposals targeting this age range, be invited to sit in on the NSF review panel's discussion of those proposals, and be able to discuss the reviews with the NSF Data Science Corps Program Directors.

III. Award Information

Iv. eligibility information.

Additional Eligibility Info:

Proposals from Minority Serving Institutions, other emerging research institutions, and institutions in EPSCoR jurisdictions are encouraged.

V. Proposal Preparation And Submission Instructions

Full Proposal Preparation Instructions : Proposers may opt to submit proposals in response to this Program Solicitation via Research.gov or Grants.gov.

• Full Proposals submitted via Research.gov: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] . The Prepare New Proposal setup will prompt you for the program solicitation number.
• Full proposals submitted via Grants.gov: Proposals submitted in response to this program solicitation via Grants.gov should be prepared and submitted in accordance with the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov . The complete text of the NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: ( https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ). To obtain copies of the Application Guide and Application Forms Package, click on the Apply tab on the Grants.gov site, then click on the Apply Step 1: Download a Grant Application Package and Application Instructions link and enter the funding opportunity number, (the program solicitation number without the NSF prefix) and press the Download Package button. Paper copies of the Grants.gov Application Guide also may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

In determining which method to utilize in the electronic preparation and submission of the proposal, please note the following:

Collaborative Proposals. All collaborative proposals submitted as separate submissions from multiple organizations must be submitted via Research.gov. PAPPG Chapter II.E.3 provides additional information on collaborative proposals.

See PAPPG Chapter II.D.2 for guidance on the required sections of a full research proposal submitted to NSF. Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions.

The following information supplements the guidelines and requirements in the NSF PAPPG:

1. Proposal Titles: Proposal titles must indicate the DSC program, followed by a colon, then the title of the project. For example, a DSC proposal title would be DSC: Title . Titles of collaborative proposals arranged as separate submissions from multiple organizations should begin with " Collaborative Research: DSC: " followed by the title of the project. For example, the title of each proposal for a collaborative set of proposals would be Collaborative Research: DSC: Title .

2. Budget for participating organizations . Student participation costs should be budgeted under the Participant Support line of the budget. All other costs should be in the appropriate budget category.

3. Supplementary Documents

a. Management and Coordination Plan (page limit: three pages). Every proposal must contain a clearly labeled "Management and Coordination Plan" that describes in detail the timeline of activities, how the project will be managed across units or organizations, the specific roles of the PI, co-PIs, and other senior personnel at all participating units or organizations, and coordination mechanisms that convey a tight and meaningful collaboration between all participating units or organizations. Proposals missing the Management and Coordination Plan will be returned without review.

PIs are strongly encouraged to plan for hybrid environments of virtual and in-person meetings among the personnel of participating units or organizations. Specifically, they need to convey how activities can be effectively carried out over the three-year period.

b. Data Management and Sharing Plan (page limit: two pages) . Proposals must include a Supplementary document of no more than two pages, labeled "Data Management and Sharing Plan" and provided by the lead organization. This Supplementary Document should describe how the proposal conforms to the NSF policy on dissemination and sharing of research results. In particular, the document should describe how projects will make available for broad dissemination products of their Data Science Corps activity to ensure reproducibility of this activity.

For additional information, see: https://www.nsf.gov/bfa/dias/policy/dmp.jsp .

c. Human Subjects and Vertebrate Animals research . Documentation regarding research involving the use of human subjects, hazardous materials, vertebrate animals, or endangered species should be included where applicable. (See PAPPG Chapter II.E.4 and II.E.5.) Note that aspects of project evaluation or research about project effectiveness may involve gathering information from or about students and members of the community. Such work is likely to require IRB review.

d. A list of Project Personnel and Collaborating Organizations (Note: In separately submitted collaborative proposals, in which each institution submits a separate proposal, only the lead institution should collect and provide this information). Provide current, accurate information for all personnel and organizations involved in the project. NSF staff will use this information in the merit review process to manage reviewer selection. The list must include all PIs, co-PIs, Senior Personnel, funded/unfunded Consultants or Collaborators, Subawardees, Postdoctoral Researchers, and project-level advisory committee members. This list should be numbered and included (in this order) Full name, Organization(s), and Role in the project, with each item separated by a semi-colon. Each person listed should start a new numbered line. For example:

• Maria Hernandez; XYZ University; PI
• John Je; University of PQR; Senior Personnel
• Jane Brown; XYZ University; Postdoctoral Researcher
• Bob Adams; ABC Inc.; Paid Consultant
• Mary White; Welldone Institution; Unfunded Collaborator
• Tim Green; ZZZ University; Subawardee

Note the distinction between this numbered listing and Collaborators and Other Affiliations Information, is that a Single Copy Document is required for each individual identified as Senior/Key Personnel.

Cost Sharing:

Inclusion of voluntary committed cost sharing is prohibited.

Budget Preparation Instructions:

Budgets for all projects must include funding for one or more designated award representative(s) (PI/co-PI/Senior/Key Personnel or NSF-approved replacement) to attend the annual Data Science Corps meeting during the proposed lifetime of the award (per Schedule of Activities above). For budget preparation purposes, PIs should assume these meetings will be held each year in the Washington, DC area.

The FY 2024 competition is expected to be the only competition under this program.

D. Research.gov/Grants.gov Requirements

For Proposals Submitted Via Research.gov:

To prepare and submit a proposal via Research.gov, see detailed technical instructions available at: https://www.research.gov/research-portal/appmanager/base/desktop?_nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission.html . For Research.gov user support, call the Research.gov Help Desk at 1-800-381-1532 or e-mail [email protected] . The Research.gov Help Desk answers general technical questions related to the use of the Research.gov system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

For Proposals Submitted Via Grants.gov:

Before using Grants.gov for the first time, each organization must register to create an institutional profile. Once registered, the applicant's organization can then apply for any federal grant on the Grants.gov website. Comprehensive information about using Grants.gov is available on the Grants.gov Applicant Resources webpage: https://www.grants.gov/web/grants/applicants.html . In addition, the NSF Grants.gov Application Guide (see link in Section V.A) provides instructions regarding the technical preparation of proposals via Grants.gov. For Grants.gov user support, contact the Grants.gov Contact Center at 1-800-518-4726 or by email: [email protected] . The Grants.gov Contact Center answers general technical questions related to the use of Grants.gov. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation. Submitting the Proposal: Once all documents have been completed, the Authorized Organizational Representative (AOR) must submit the application to Grants.gov and verify the desired funding opportunity and agency to which the application is submitted. The AOR must then sign and submit the application to Grants.gov. The completed application will be transferred to Research.gov for further processing. The NSF Grants.gov Proposal Processing in Research.gov informational page provides submission guidance to applicants and links to helpful resources including the NSF Grants.gov Application Guide , Grants.gov Proposal Processing in Research.gov how-to guide , and Grants.gov Submitted Proposals Frequently Asked Questions . Grants.gov proposals must pass all NSF pre-check and post-check validations in order to be accepted by Research.gov at NSF. When submitting via Grants.gov, NSF strongly recommends applicants initiate proposal submission at least five business days in advance of a deadline to allow adequate time to address NSF compliance errors and resubmissions by 5:00 p.m. submitting organization's local time on the deadline. Please note that some errors cannot be corrected in Grants.gov. Once a proposal passes pre-checks but fails any post-check, an applicant can only correct and submit the in-progress proposal in Research.gov.

Proposers that submitted via Research.gov may use Research.gov to verify the status of their submission to NSF. For proposers that submitted via Grants.gov, until an application has been received and validated by NSF, the Authorized Organizational Representative may check the status of an application on Grants.gov. After proposers have received an e-mail notification from NSF, Research.gov should be used to check the status of an application.

VI. NSF Proposal Processing And Review Procedures

Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review. All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process. Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional. Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards. A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1.

A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www.nsf.gov/bfa/dias/policy/merit_review/ .

Proposers should also be aware of core strategies that are essential to the fulfillment of NSF's mission, as articulated in Leading the World in Discovery and Innovation, STEM Talent Development and the Delivery of Benefits from Research - NSF Strategic Plan for Fiscal Years (FY) 2022 - 2026 . These strategies are integrated in the program planning and implementation process, of which proposal review is one part. NSF's mission is particularly well-implemented through the integration of research and education and broadening participation in NSF programs, projects, and activities.

One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy. NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports.

A. Merit Review Principles and Criteria

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education. To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes." NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects.

1. Merit Review Principles

These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards. Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply:

• All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge.
• NSF projects, in the aggregate, should contribute more broadly to achieving societal goals. These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project activities may be based on previously established and/or innovative methods and approaches, but in either case must be well justified.
• Meaningful assessment and evaluation of NSF funded projects should be based on appropriate metrics, keeping in mind the likely correlation between the effect of broader impacts and the resources provided to implement projects. If the size of the activity is limited, evaluation of that activity in isolation is not likely to be meaningful. Thus, assessing the effectiveness of these activities may best be done at a higher, more aggregated, level than the individual project.

With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project. Thus, individual projects should include clearly stated goals, specific descriptions of the activities that the PI intends to do, and a plan in place to document the outputs of those activities.

These three merit review principles provide the basis for the merit review criteria, as well as a context within which the users of the criteria can better understand their intent.

2. Merit Review Criteria

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.D.2.d(i). contains additional information for use by proposers in development of the Project Description section of the proposal). Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.D.2.d(i), prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit: The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts: The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

• Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
• Benefit society or advance desired societal outcomes (Broader Impacts)?
• To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
• Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
• How well qualified is the individual, team, or organization to conduct the proposed activities?
• Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and other underrepresented groups in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the United States; and enhanced infrastructure for research and education.

Proposers are reminded that reviewers will also be asked to review the Data Management and Sharing Plan and the Mentoring Plan, as appropriate.

Additional Solicitation Specific Review Criteria

The proposals will also be evaluated using the following additional criteria:

• Unit or Organizational diversity. Whether the proposal has ensured diversity among partnering organizations, e.g., by including different types of Institutes of Higher Education, such as two- and four-year colleges, Minority-Serving Institutions, and research universities.
• Coordinated collaboration activities. Whether the proposal describes mechanisms that promote effective collaborations among all participating organizations.
• Assessment of Student Learning and Program Evaluation . Whether the proposal describes appropriate mechanisms to address the Assessment of Student Learning and Program Evaluation.

B. Review and Selection Process

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review.

Reviewers will be asked to evaluate proposals using two National Science Board approved merit review criteria and, if applicable, additional program specific criteria. A summary rating and accompanying narrative will generally be completed and submitted by each reviewer and/or panel. The Program Officer assigned to manage the proposal's review will consider the advice of reviewers and will formulate a recommendation.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF strives to be able to tell proposers whether their proposals have been declined or recommended for funding within six months. Large or particularly complex proposals or proposals from new recipients may require additional review and processing time. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director acts upon the Program Officer's recommendation.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements or the Division of Acquisition and Cooperative Support for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

VII. Award Administration Information

A. notification of the award.

Notification of the award is made to the submitting organization by an NSF Grants and Agreements Officer. Organizations whose proposals are declined will be advised as promptly as possible by the cognizant NSF Program administering the program. Verbatim copies of reviews, not including the identity of the reviewer, will be provided automatically to the Principal Investigator. (See Section VI.B. for additional information on the review process.)

B. Award Conditions

An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto; (2) the budget, which indicates the amounts, by categories of expense, on which NSF has based its support (or otherwise communicates any specific approvals or disapprovals of proposed expenditures); (3) the proposal referenced in the award notice; (4) the applicable award conditions, such as Grant General Conditions (GC-1)*; or Research Terms and Conditions* and (5) any announcement or other NSF issuance that may be incorporated by reference in the award notice. Cooperative agreements also are administered in accordance with NSF Cooperative Agreement Financial and Administrative Terms and Conditions (CA-FATC) and the applicable Programmatic Terms and Conditions. NSF awards are electronically signed by an NSF Grants and Agreements Officer and transmitted electronically to the organization via e-mail.

*These documents may be accessed electronically on NSF's Website at https://www.nsf.gov/awards/managing/award_conditions.jsp?org=NSF . Paper copies may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

More comprehensive information on NSF Award Conditions and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Administrative and National Policy Requirements

Build America, Buy America

As expressed in Executive Order 14005, Ensuring the Future is Made in All of America by All of America's Workers (86 FR 7475), it is the policy of the executive branch to use terms and conditions of Federal financial assistance awards to maximize, consistent with law, the use of goods, products, and materials produced in, and services offered in, the United States.

Consistent with the requirements of the Build America, Buy America Act (Pub. L. 117-58, Division G, Title IX, Subtitle A, November 15, 2021), no funding made available through this funding opportunity may be obligated for an award unless all iron, steel, manufactured products, and construction materials used in the project are produced in the United States. For additional information, visit NSF's Build America, Buy America webpage.

Special Award Conditions:

Recipients will be required to attend the annual DSC PI Meetings.

Recipients will be required to include appropriate acknowledgment of NSF support under the DSC in any publication (including World Wide Web pages) of any material based on or developed under the project, in the following terms:

"This material is based upon work supported by the National Science Foundation Data Science Corps under Grant No." (NSF award number.)

Recipients co-funded by NSF and Schmidt Futures under this funding opportunity will be required to include appropriate acknowledgment of the support of Schmidt Futures and NSF in reports and publications on work performed under this award. An example of such an acknowledgement would be:

"This material is based upon work supported by a joint Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt, and NSF activity under NSF Award No. [NSF award number]."

Recipients also will be required to acknowledge NSF support (and Schmidt Future if applicable) using the language specified above during all news media interviews, including popular media such as radio, television and news magazines, as well as in social media platforms.

C. Reporting Requirements

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period. (Some programs or awards require submission of more frequent project reports). No later than 120 days following expiration of a grant, the PI also is required to submit a final annual project report, and a project outcomes report for the general public.

Failure to provide the required annual or final annual project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award. PIs should examine the formats of the required reports in advance to assure availability of required data.

PIs are required to use NSF's electronic project-reporting system, available through Research.gov, for preparation and submission of annual and final annual project reports. Such reports provide information on accomplishments, project participants (individual and organizational), publications, and other specific products and impacts of the project. Submission of the report via Research.gov constitutes certification by the PI that the contents of the report are accurate and complete. The project outcomes report also must be prepared and submitted using Research.gov. This report serves as a brief summary, prepared specifically for the public, of the nature and outcomes of the project. This report will be posted on the NSF website exactly as it is submitted by the PI.

More comprehensive information on NSF Reporting Requirements and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

VIII. Agency Contacts

Contact Information:

General inquiries may be addressed to [email protected] .

For questions related to the use of NSF systems contact:

• NSF Help Desk: 1-800-381-1532
• Research.gov Help Desk e-mail: [email protected]

For questions relating to Grants.gov contact:

• Grants.gov Contact Center: If the Authorized Organizational Representatives (AOR) has not received a confirmation message from Grants.gov within 48 hours of submission of application, please contact via telephone: 1-800-518-4726; e-mail: [email protected] .

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences . Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website .

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov .

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.F.7 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

Privacy Act And Public Burden Statements

The information requested on proposal forms and project reports is solicited under the authority of the National Science Foundation Act of 1950, as amended. The information on proposal forms will be used in connection with the selection of qualified proposals; and project reports submitted by proposers will be used for program evaluation and reporting within the Executive Branch and to Congress. The information requested may be disclosed to qualified reviewers and staff assistants as part of the proposal review process; to proposer institutions/grantees to provide or obtain data regarding the proposal review process, award decisions, or the administration of awards; to government contractors, experts, volunteers and researchers and educators as necessary to complete assigned work; to other government agencies or other entities needing information regarding proposers or nominees as part of a joint application review process, or in order to coordinate programs or policy; and to another Federal agency, court, or party in a court or Federal administrative proceeding if the government is a party. Information about Principal Investigators may be added to the Reviewer file and used to select potential candidates to serve as peer reviewers or advisory committee members. See System of Record Notices , NSF-50 , "Principal Investigator/Proposal File and Associated Records," and NSF-51 , "Reviewer/Proposal File and Associated Records." Submission of the information is voluntary. Failure to provide full and complete information, however, may reduce the possibility of receiving an award.

An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0058. Public reporting burden for this collection of information is estimated to average 120 hours per response, including the time for reviewing instructions. Send comments regarding the burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to:

Suzanne H. Plimpton Reports Clearance Officer Policy Office, Division of Institution and Award Support Office of Budget, Finance, and Award Management National Science Foundation Alexandria, VA 22314

Natural Resources Job Board

OUR SPONSORS AND PARTNERS

M.S. Graduate Research Assistant, Auburn University, White-tailed deer population assessment using harvest data

Auburn University, College of Forestry, Wildlife and Environment (State)

Description

View location.

A member of Texas A&M AgriLife

Texas A&M AgriLife Extension Service | Texas A&M AgriLife Research | Texas A&M Forest Service | Texas A&M AgriLife Veterinary Medical Diagnostic Lab | College of Agriculture & Life Sciences

The Federal Register

The daily journal of the united states government, request access.

Due to aggressive automated scraping of FederalRegister.gov and eCFR.gov, programmatic access to these sites is limited to access to our extensive developer APIs.

If you are human user receiving this message, we can add your IP address to a set of IPs that can access FederalRegister.gov & eCFR.gov; complete the CAPTCHA (bot test) below and click "Request Access". This process will be necessary for each IP address you wish to access the site from, requests are valid for approximately one quarter (three months) after which the process may need to be repeated.

An official website of the United States government.

If you want to request a wider IP range, first request access for your current IP, and then use the "Site Feedback" button found in the lower left-hand side to make the request.

• The Graduate School >
• Graduate News >

UB distributes over 12,000 NSF eclipse glasses to local students

campus news

The National Science Foundation provided UB chemistry professor Steven Ray with more than 12,000 eclipse glasses, like these, to distribute to local schools and community centers. Photo: Steven Ray

By TOM DINKI

Published March 28, 2024

Thousands of Western New York kids will safely view next month’s total solar eclipse thanks to a collaboration between UB and the National Science Foundation. 

Steven Ray, associate professor of chemistry, College of Arts and Sciences, has helped distribute 12,500 pairs of eclipse glasses to local school districts and community centers ahead of the April 8 eclipse. The glasses were provided by the NSF for free and come with safety instructions.

“It’s a great opportunity to help kids enjoy a once-in-a-lifetime experience of being in the path of totality,” Ray says. “Being able to provide this at no cost to schools and parents is a big plus.”

The effort is part of the NSF’s plan to make 1 million eclipse glasses available to the public prior to April 8. In partnership with the National Oceanic and Atmospheric Administration and NASA, the foundation is distributing glasses nationwide, as well as at the National Mall in Washington, D.C., on the day of the eclipse.

Ray, who is currently principal investigator on two NSF grants related to plasma physics and mass spectrometry, applied for a small grant to distribute 12,000 of those glasses. 

“UB is at the center of research and scholarship in Western New York, so we were a natural partner for the NSF on this kind of outreach effort,” he says.

Ray worked with the Western Region of the New York State Parent Teacher Association, as well as the Buffalo Community PTA, to get the glasses into the hands of school officials. The glasses have been distributed to the Lackawanna, Orchard Park, Pembroke and Sweet Home school districts; Erie 1 BOCES; CHC Learning Center; as well as the Elmwood Village, Enterprise, Global Concepts, Persistence Preparatory Academy, Reach Academy, Buffalo Commons, West Buffalo, King Center, Buffalo United and Tapestry charter schools. Glasses have also been given to Delavan Grider Community Center and Resource Council of Western New York. 

With many school districts canceling classes on the day of the eclipse, it’s likely students will be sent home with the glasses. 

“I hope students get a sense of wonder from the eclipse and appreciate that science can predict exactly when it’s going to happen and explain why it’s happening,” Ray says. “This event is a great opportunity to instill a love of science in the next generation. Students sometimes view science as something of a dry subject, but here we can show them that it has real implications on their everyday lives.”

Specialized glasses, like those provided by the NSF, are required to safely view the eclipse. Glasses can be removed only during totality, the brief period when the moon completely blocks the sun.

Western New York will experience totality for over 3 minutes, from 3:18 to 3:21 p.m., but UB ophthalmologists suggest  only removing your glasses during the two minutes of peak totality, which will last from about 3:19 to 3:21 p.m.