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TO PROVIDE QUALITY UNDERGRADUATE AND GRADUATE EDUCATION FOR MEN AND WOMEN.
Source: IRS Form 990 (Tax Year 2023)
Source: IRS Form 990 via ProPublica Nonprofit Explorer
Total Revenue
▼$96M
Total Contributions
$15.3M
Total Expenses
▼$96.7M
Total Assets
$307.5M
Total Liabilities
▼$29.2M
Net Assets
$278.4M
Officer Compensation
→$286.9K
Other Salaries
$23M
Investment Income
▼$3.7M
Fundraising
▼$29.7K
Source: USAspending.gov · Searched by organization name
Total Federal Funding
$15M
Awards Found
29
Department of Education
$4.3M
SAINT VINCENT COLLEGE CARES ACT HIGHER EDUCATION EMERGENCY RELIEF FUND (HEERF)-IHE/INSTITUTION
Department of Education
$3.6M
SAINT VINCENT COLLEGE CARES ACT HIGHER EDUCATION EMERGENCY RELIEF FUND - IHES (HEERF)
Department of Education
$1.3M
FORWARD FIRST: SUPPORTING LOW-INCOME AND FIRST-GENERATION STUDENT SUCCESS AT SAINT VINCENT COLLEGE
Appalachian Regional Commission
$662.7K
EDUCATIONAL ACHIEVEMENT/ATTAINMENT
National Science Foundation
$606.5K
BIOLOGY SCHOLARS: LITERATURE, LABORATORY, AND LEADERSHIP PROGRAM
National Science Foundation
$499.7K
INCREASING ENROLLMENT USING A COMPLEMENTARY LEARNING PROGRAM
National Science Foundation
$368.5K
COLLABORATIVE RESEARCH: EPIIC: STRENGTHENING COLLABORATIVE ADVANCEMENTS LEVERAGING EQUITABLE UNIVERSITY PARTNERSHIPS -THIS IS A COLLABORATIVE PROJECT ACROSS THE FOLLOWING FOUR DIVERSE INSTITUTIONS: WINSTON-SALEM STATE UNIVERSITY, NORTH ARKANSAS COLLEGE, MIDDLE GEORGIA STATE UNIVERSITY, AND ST. VINCENT COLLEGE. THIS COLLABORATIVE AIMS TO INCREASE INNOVATION CAPACITY AT EACH INSTITUTION BY GROWING EXTERNAL AND INTERNAL PARTNERSHIPS, ESTABLISHING A MORE INCLUSIVE RESEARCH/INNOVATION ECOSYSTEM, AND BROADENING THE PARTICIPATION OF A SKILLED, DIVERSE WORKFORCE AS PART OF THE RESEARCH ENTERPRISE NATIONALLY. THE SUPER (STRATEGIC UNIVERSITY PRACTICES TO EXPAND RESEARCH) PARTNERSHIPS COLLABORATIVE, MADE UP OF MINORITY-SERVING INSTITUTIONS (MSIS), PREDOMINANTLY UNDERGRADUATE INSTITUTIONS (PUIS), AND TWO-YEAR INSTITUTIONS, WILL PROVIDE A UNIQUE OPPORTUNITY FOR COHORT INSTITUTIONS IN TERMS OF BEST PRACTICES IN EXTERNAL PARTNERSHIPS BUILDING AND STEM WORKFORCE DEVELOPMENT. COHORT INSTITUTIONS WILL WORK TOGETHER TO GROW INDUSTRY PARTNERSHIPS, IMPROVE ALIGNMENT OF PROGRAM CURRICULA WITH THE NEED OF INDUSTRY PARTNERS, AND ENHANCE STUDENTS? EDUCATIONAL EXPERIENCES. THE PROPOSED CAPACITY-BUILDING EFFORTS WILL ALSO PROVIDE SIGNIFICANT INNOVATION PARTNERSHIP OPPORTUNITIES AMONG COHORT MEMBERS TO ALLOW FUTURE PARTICIPATION IN AN NSF ENGINE AND OTHER FUNDING OPPORTUNITIES. THROUGH THIS EPIIC PROJECT, THE SUPER (STRATEGIC UNIVERSITY PRACTICES TO EXPAND RESEARCH) PARTNERSHIPS COHORT WILL BUILD PARTNERSHIPS WITH INDUSTRY EXPERTS AND GOVERNMENT AGENCIES TO FIND OUT WHAT SKILLS AND KNOWLEDGE ARE NEEDED FOR STEM JOBS IN THEIR FIELDS. THE COHORT WILL USE THIS INFORMATION TO MODIFY AND ENHANCE EDUCATIONAL EXPERIENCE FOR UNDERGRADUATES WITH INDUSTRY NEEDS IN MIND, SO STUDENTS ARE PREPARED FOR JOBS IN STEM FIELDS. THE COHORT INSTITUTIONS WILL BUILD PARTNERSHIPS WITH EDUCATIONAL INSTITUTIONS IN THE REGION, INCLUDING HIGH SCHOOLS AND TWO-YEAR COLLEGES. THE MEMBER INSTITUTIONS WILL ALSO COLLABORATE TO BUILD ADMINISTRATIVE CAPACITY OF THE OFFICE OF SPONSORED PROGRAMS IN TERMS OF IDENTIFYING, SUBMITTING, AND MANAGING FUNDED PROJECTS. THROUGH CROSS-COHORT KNOWLEDGE SHARING ABOUT CAPACITY-BUILDING STRATEGIES, THE COHORT WILL CREATE SYNERGISTIC LEARNING SYMPOSIUMS AND SHARE WORKFORCE EDUCATION OPPORTUNITIES. EACH ACADEMIC INSTITUTION IN THIS DIVERSE COHORT, WHICH INCLUDES AN HBCU, A SMALL LIBERAL ARTS COLLEGE, A 2-YEAR COMMUNITY COLLEGE, AND A PRIMARILY UNDERGRADUATE STATE UNIVERSITY, HAS DEVELOPED AN INDIVIDUALIZED PLAN TO IMPLEMENT THIS PROCESS. THE COHORT INSTITUTIONS WILL EXCHANGE INFORMATION AND WORK TOGETHER TO ENHANCE EACH INSTITUTION'S CAPACITY FOR BUILDING EXTERNAL PARTNERSHIPS. THIS PROCESS WILL POSITION EACH INSTITUTION IN THE COHORT TO FURTHER DEEPEN ENGAGEMENT WITH INDUSTRY PARTNERS AND ENHANCE THEIR CONTRIBUTIONS TO THEIR REGIONAL INNOVATION ECOSYSTEMS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Department of Education
$272.4K
TOGETHER, WE RISE: SUPPORTING LOW-INCOME AND FIRST-GENERATION STUDENT SUCCESS AT SAINT VINCENT COLLEGE
National Aeronautics and Space Administration
$224.3K
GALAXY EVOLUTION IN THE UV FROM THE SWIFT UV/OPTICAL TELESCOPE ARCHIVE: THE EVOLUTION OF GALAXY PROPERTIES OVER TIME REMAINS A MAJOR UNSOLVED PROBLE
National Science Foundation
$200K
ERI: CARBON ELECTRODES WITH CONTROLLED SURFACE TOPOLOGY FOR DESALINATION AND WATER DISINFECTION. -THIS IS AN NSF ENGINEERING RESEARCH INITIATION AWARD. WATER SCARCITY IS ONE OF THE MOST SIGNIFICANT CHALLENGES FACING THE WORLD TODAY. AN ESTIMATED 4.0 BILLION PEOPLE CURRENTLY EXPERIENCE SEVERE WATER SCARCITY DURING AT LEAST 1 MONTH A YEAR. MEASURES TO ALLEVIATE WATER SHORTAGES, SUCH AS WATER CONSERVATION AND IMPROVED CATCHMENT AND DISTRIBUTION SYSTEMS, ARE IMPORTANT, BUT DO NOT INCREASE EXISTING FRESHWATER RESOURCES. ALONE THESE METHODS ARE INSUFFICIENT IN COPING WITH RISING WATER DEMAND WHICH IS FURTHER INTENSIFIED BY INCREASING POPULATION, AGRICULTURAL NEEDS, AND INDUSTRIALIZATION. THE ONLY METHODS TO INCREASE FRESHWATER SUPPLY ARE LIMITED TO WATER REUSE AND DESALINATION, WITH THE LATTER OFFERING A SEEMINGLY UNLIMITED SUPPLY. IN ADDITION TO WATER SCARCITY, ACCESS TO CLEAN DRINKING WATER IS A GLOBAL CHALLENGE. UNFORTUNATELY, WATERBORNE DISEASES PERSIST IN DEVELOPING COUNTRIES AND ARE ONE OF THE LEADING CAUSES OF DEATH. CONTINUED RESEARCH INTO TECHNOLOGIES THAT CAN SIMULTANEOUSLY DESALINATE AND DISINFECT WATER IS A VITAL AREA OF RESEARCH, WHICH HAS LED TO THE EMERGENCE OF CAPACITIVE DEIONIZATION (CDI). CDI SYSTEMS DESALINATE SALTY FEED SOLUTIONS BY ELECTROSTATICALLY ADSORBING IONIC SPECIES TO A PAIR OF OPPOSITELY CHARGED ELECTRODES. CDI SYSTEMS CAN ALSO DISINFECT CONTAMINATED WATER SOURCES IF HARMFUL MICROORGANISMS POSSESS A NET SURFACE CHARGE. FOR LONG-TERM FULL-SCALE CDI SYSTEMS, THE ELECTRODES CAN BE ONE OF THE LARGEST EXPENSES IN TERMS OF CAPITAL COSTS AND OPERATING COSTS. THIS PROJECT WILL INVESTIGATE A SUSTAINABLE APPROACH TO FABRICATING ULTRA-LOW-COST CDI ELECTRODES BY UTILIZING WASTED FOOD (I.E. ? BREAD). THE BROADER IMPACT OF THE PROJECT MAY ENHANCE THE DEVELOPMENT OF SUSTAINABLE FRESHWATER DRINKING SUPPLIES. FURTHERMORE, THE PROJECT WILL ENABLE THE BUILDING OF A SUSTAINABLE EDUCATION AND TRAINING SYSTEM FOR UNDERGRADUATE RESEARCHERS AT SAINT VINCENT COLLEGE AND FOR HIGH SCHOOL STUDENTS FROM UNDERSERVED GROUPS. THE VAST MAJORITY OF CDI RESEARCH HAS FOCUSED ON TAILORING THE NANOSTRUCTURE OF THE ELECTRODE MATERIAL TO GENERATE HIGH SURFACE AREA. THIS WAS PRIMARILY DONE TO MAXIMIZE THE AMOUNT OF IONIC SPECIES THAT CAN BE ADSORBED TO THE ELECTRODE SURFACES DURING THE DESALINATION PROCESS. PRELIMINARY WORK HAS DISCOVERED THAT INTACT NATURAL MATERIALS (E.G. MANGROVE ROOTS AND WASTED BREAD) CAN SERVE AS EXCELLENT PRECURSORS TO GENERATING FREESTANDING CDI ELECTRODES. THE MICRO-FEATURES (100?S OF MICRONS) OF THESE NATURAL MATERIALS CAN BE PRESERVED THROUGH PYROLYSIS AND OFFER MAJOR ADVANTAGES WHEN INTEGRATED INTO CDI SYSTEMS, SUCH AS LOW-RESISTANCE PATHWAYS FOR WATER TRANSPORT. THE PROJECT?S OBJECTIVE IS TO STUDY THE POTENTIAL EFFECTS MICRO-FEATURES MAY HAVE ON THE PERFORMANCE OF CDI ELECTRODES IN DESALINATION SYSTEMS AND DISINFECTION SYSTEMS. THE PROJECT?S OBJECTIVES ARE TO: (1) DEMONSTRATE THAT CARBON ELECTRODES CAN BE FABRICATED WITH CONTROLLED SURFACE TOPOLOGY ON THE ORDER OF 100?S OF MICRONS USING NATURAL MATERIALS AS FEEDSTOCKS AND (2) UNDERSTAND THE DEPENDENCE OF DESALINATION PERFORMANCE AND DISINFECTION PERFORMANCE ON ELECTRODE SURFACE TOPOLOGY IN A CDI SYSTEM. THE INTELLECTUAL MERIT OF THE WORK WILL LEAD TO NEW TECHNIQUES FOR GENERATING CARBON ELECTRODES WITH CONTROLLED MICROSTRUCTURES. IMPORTANTLY, THE NATURE OF THESE TECHNIQUES WILL BE INEXPENSIVE AND REALIZABLE IN PRIMARILY UNDERGRADUATE INSTITUTIONS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
National Science Foundation
$150K
COLLABORATIVE RESEARCH: SCH: PERSONAL DETERMINANTS OF HEALTH ENHANCED MACHINE LEARNING MODELS FOR EARLY PREDICTION OF ALZHEIMER'S DISEASE AND RELATED DEMENTIAS -THIS PROJECT ADVANCES NATIONAL HEALTH AND PROMOTES SCIENCE AND TECHNOLOGY DEVELOPMENT BY PROVIDING ALGORITHMS, SOFTWARE, AND SYSTEMS THAT CAN TRAIN MACHINE LEARNING MODELS ON ELECTRONIC HEALTH RECORDS (EHRS) FOR ACCURATE AND EARLY PREDICTION OF ALZHEIMER?S DISEASE AND RELATED DEMENTIAS (ADRD). ADRD IS A SEVERE NEURODEGENERATIVE DISORDER THAT EFFECTS OVER 5,000,000 PEOPLE OVER THE AGE OF 65 THAT IS CHARACTERIZED BY PROGRESSIVE MEMORY, COGNITIVE IMPAIRMENT AND PERSONALITY CHANGES, WHICH CAN FURTHER EVOLVE TO DEMENTIA AND DEATH. EARLY PREDICTION OF ADRD IS CRUCIAL FOR TIMELY INTERVENTION AND IMPROVED PATIENT OUTCOMES. RECENT STUDIES HAVE SHOWN THAT PERSONAL RISK FACTORS SUCH AS EDUCATION, EMPLOYMENT, AND LIFESTYLE OR FAMILY HISTORY SIGNIFICANTLY INFLUENCE ADRD ONSET AND PROGRESSION. HOWEVER, THESE FACTORS ARE NOT RECORDED IN A STRUCTURED FORMAT WITHIN THE EXISTING EHRS. IN CONTRAST, PERSONAL RISK FACTORS ARE OFTEN EMBEDDED WITHIN THE FREE TEXT OF CLINICAL NOTES OR DISCHARGE SUMMARIES THAT ARE NOT EASILY SEARCHABLE, COMPUTABLE, OR STANDARDIZED. THIS CREATES A MAJOR TECHNICAL BARRIER FOR THEIR INTEGRATION INTO THE ADRD PREDICTION MODELS. TO ADDRESS THIS, THIS PROJECT DEVELOPS A COMPUTATIONAL PLATFORM USING NOVEL MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING TO AUTOMATICALLY EXTRACT PERSONAL RISK FACTORS FROM EHR CLINICAL NARRATIVES AND LEVERAGE THEM FOR ACCURATE AND EARLY PREDICTION OF ADRD. THIS RESEARCH SIGNIFICANTLY IMPROVES ADRD PREDICTION ACCURACY AND TIMELINESS, WITH POTENTIAL GENERALIZATIONS TO OTHER NEUROLOGICAL DISORDERS. BY EXPLORING THE INTERACTION BETWEEN PERSONAL AND CLINICAL FACTORS IN DISEASE DEVELOPMENT, THIS PROJECT PUSHES THE BOUNDARIES OF CURRENT KNOWLEDGE IN MACHINE LEARNING AND ADRD RESEARCH, POTENTIALLY TRANSFORMING APPROACHES TO EARLY DETECTION AND MANAGEMENT OF COMPLEX NEUROLOGICAL DISORDERS. TO ACHIEVE THE GOAL OF DEVELOPING PERSONAL RISK FACTOR ENHANCED MACHINE LEARNING MODELS FOR EARLY ADRD PREDICTION, THIS PROJECT DEVELOPS FOUR THRUSTS OF NOVEL APPROACHES, EACH ADDRESSING KEY METHODOLOGICAL CHALLENGES. FIRST, THE PROJECT DEVELOPS A DOMAIN KNOWLEDGE GUIDED LARGE LANGUAGE MODEL TO EXTRACT RISK FACTORS FROM EHR CLINICAL NARRATIVES, WHICH CAN ADEPTLY COPE WITH THE COMPLEXITIES INHERENT IN REAL WORLD EHR CLINICAL NARRATIVES, SUCH AS NOISE AND INCOMPLETE DATA ENTRIES. SECOND, THE PROJECT DEVELOPS AN INTERPRETABLE METHOD USING NEURAL ADDITIVE MODELS THAT AUTOMATICALLY IDENTIFIES THE INDIVIDUAL RISK FACTOR?S CONTRIBUTION TO THE EARLY ADRD PREDICTION. BUILDING UPON THIS INTERPRETABLE RESULT, IN THE THIRD THRUST, THE PROJECT DEVELOPS A SURVIVAL-BASED ADRD PROGNOSIS MODEL THAT CAN BE USED TO ESTIMATE THE LIKELIHOOD OF ADRD DEVELOPMENT AT ANY GIVEN POINT IN THE FUTURE, CAPTURING THE DYNAMICS OF RISK TRAJECTORY. THIS APPROACH CAN ENHANCE CLINICAL DECISION-MAKING BY IDENTIFYING HIGH-RISK INDIVIDUALS WHO MAY BENEFIT FROM MORE INTENSIVE CARE OR EARLY INTERVENTION. FOURTH, THIS PROJECT CONSTRUCTS A PERSONALIZED KNOWLEDGE GRAPH THAT INTEGRATES PERSONAL AND OTHER CLINICAL RISK FACTORS INTO A UNIFIED FORMAT FOR CAPTURING THE OVERALL HEALTH STATUS FOR EVERYONE AT RISK OF DEVELOPING ADRD. MOREOVER, THIS PROJECT DEVELOPS ADAPTIVE MACHINE LEARNING ALGORITHMS THAT CAN DYNAMICALLY UPDATE THIS KNOWLEDGE GRAPH TO INCORPORATE THE EVOLVING RISK FACTORS. TOGETHER, THESE APPROACHES CONVERGE TO ADDRESS THE FUNDAMENTAL LIMITATIONS OF EXISTING ADRD RISK PREDICTION MODELS, SUCH AS INABILITY TO HANDLE COMPLEX AND UNSTRUCTURED DATA, INSUFFICIENT INTERPRETABILITY, AND HIGH COMPUTATIONAL OVERHEAD. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
National Science Foundation
$148.4K
USING PROJECT- AND INQUIRY-BASED STRATEGIES TO ENHANCE STUDENT UNDERSTANDING OF CHROMATOGRAPHY AND MASS SPECTRAL CONCEPTS
Department of Education
$145.8K
UNDERGRADUATE INTERNATIONAL STUDIES AND FOREIGN LANGUAGE
Department of Education
$81K
BREAKING BARRIERS AND BUILDING OPPORTUNITIES: INCREASING ACCESS TO INTERNATIONAL STUDIES AND FOREIGN LANGUAGES
National Aeronautics and Space Administration
$75.2K
WE WILL CONSTRUCT EXTENSIVE MAPS OF THE ULTRAVIOLET (UV) COMPONENT OF THE DUST ABSORPTION IN THE MILKY WAY HALO, USING THE DENSE SAMPLING OF EXTRAGAL
Department of the Interior
$69K
FA PA SAINT VINCENT COLLEGE MONASTERY RUN WCAP PROJECDT
National Aeronautics and Space Administration
$58.7K
PENNSYLVANIA STATE UNIVERSITY THE UV DEPENDENCE OF THE PHYSICAL PROPERTIES OF QUASARS WE PROPOSE T
Department of the Interior
$50K
FA PA FY2018 WS - SAINT VINCENTS MONASTERY RUN WETLAND #1
Department of Agriculture
$48.6K
UNDERSTANDING AND MITIGATING THE EFFECTS OF AGRICULTURAL INTENSIFICATION ON OUR MOST ECONOMICALLY IMPORTANT NATIVE POLLINATOR
National Endowment for the Humanities
$10K
EXPANDING CARE AND ACCESS TO THE ART & HERITAGE COLLECTIONS AT SAINT VINCENT
National Endowment for the Humanities
$6,000
ASSESSING THE CARE AND PRESERVATION OF THE SPECIAL COLLECTIONS AT THE SAINT VINCENT COLLEGE LATIMER FAMILY LIBRARY
National Endowment for the Humanities
$6,000
ASSESSING FIRST PRIORITY CONSERVATION TREATMENT NEEDS OF THE SPECIAL AND SAINT VINCENT LEGACY COLLECTIONS
National Endowment for the Humanities
$0
TRACING THE HISTORICAL AND CULTURAL TRAJECTORIES OF ANTIMICROBIAL RESISTANCE IN CHINA (1920 - THE PRESENT)
Source: Federal Audit Clearinghouse (fac.gov)
Total Audits
10
Clean Audits
9
Material Weakness
Yes
Noncompliance Issues
No
| Year | Status | Financial Report | Federal Expenditure | Low Risk | Accepted |
|---|---|---|---|---|---|
| 2025 | Material Weakness | Unmodified (Clean) | $18.1M | Yes | 2026-03-31 |
| 2024 | Clean | Unmodified (Clean) | $17.4M | Yes | 2025-03-24 |
| 2023 | Clean | Unmodified (Clean) | $18.1M | Yes | 2024-03-27 |
| 2022 | Clean | Unmodified (Clean) | $20.7M | Yes | 2023-03-27 |
| 2021 | Clean | Unmodified (Clean) | $22.8M | Yes | 2022-09-18 |
| 2020 | Clean | Unmodified (Clean) | $20.6M | Yes | 2021-07-20 |
| 2019 | Clean | Unmodified (Clean) | $21M | Yes | 2020-03-25 |
| 2018 | Clean | Unmodified (Clean) | $22.3M | Yes | 2019-03-18 |
| 2017 | Clean | Unmodified (Clean) | $21.5M | Yes | 2018-03-21 |
| 2016 | Clean | Unmodified (Clean) | $21.4M | Yes | 2017-03-19 |
Financial Report
Unmodified (Clean)
Federal Expenditure
$18.1M
Financial Report
Unmodified (Clean)
Federal Expenditure
$17.4M
Financial Report
Unmodified (Clean)
Federal Expenditure
$18.1M
Financial Report
Unmodified (Clean)
Federal Expenditure
$20.7M
Financial Report
Unmodified (Clean)
Federal Expenditure
$22.8M
Financial Report
Unmodified (Clean)
Federal Expenditure
$20.6M
Financial Report
Unmodified (Clean)
Federal Expenditure
$21M
Financial Report
Unmodified (Clean)
Federal Expenditure
$22.3M
Financial Report
Unmodified (Clean)
Federal Expenditure
$21.5M
Financial Report
Unmodified (Clean)
Federal Expenditure
$21.4M
Source: IRS e-Filed Form 990
No officer or director compensation data available for this organization.
This data is sourced from IRS Form 990, Part VII. It may not be available if the organization files Form 990-N (e-Postcard) or has not yet been enriched.
Source: IRS Publication 78, Auto-Revocation List & e-Postcard Data
Tax-deductible contributions: Not confirmed
No additional tax-exempt status records found in ReconForce's database.
Sources: IRS e-Filed Form 990 (XML) & ProPublica Nonprofit Explorer
Scroll →
| Year | Revenue | Contributions | Expenses | Assets | Net Assets |
|---|---|---|---|---|---|
| 2023 | $96M | $15.3M | $96.7M | $307.5M | $278.4M |
| 2022 | $111.3M | $17.9M | $93.8M | $298.9M | $272.6M |
| 2021 | $104.8M | $23M | $88.9M | $322.5M | $289.2M |
| 2020 | $104M | $23.3M | $90.1M | $282.5M |
Sources: ProPublica Nonprofit Explorer & IRS e-File Index
| Tax Year | Form Type | Source | Documents |
|---|---|---|---|
| 2024 | 990 | IRS e-File | PDF not yet published by IRSView Filing → |
| 2023 | 990 | DataIRS e-File | PDF not yet published by IRSView Filing → |
| 2022 | 990 | DataIRS e-File |
Financial data: IRS Form 990 via ProPublica Nonprofit Explorer (Tax Year 2023)
Federal grants: USAspending.gov (live)
Organization info: IRS Business Master File · ProPublica Nonprofit Explorer
| $249.2M |
| 2019 | $100.1M | $16.2M | $92.3M | $274.7M | $239.5M |
| 2018 | $109.4M | $28.6M | $90.5M | $266.2M | $230.8M |
| 2017 | $100.2M | $23.6M | $86.2M | $246.5M | $209M |
| 2016 | $99.5M | $22.4M | $82.7M | $223.5M | $186.4M |
| 2015 | $85.9M | $12M | $78.4M | $216.1M | $176.7M |
| 2014 | $79.9M | $8.8M | $76M | $217.3M | $175.8M |
| 2013 | $74.1M | $7.6M | $74.3M | $207.6M | $165.1M |
| 2012 | $80.1M | $12.1M | $76.1M | $204.4M | $161.6M |
| 2011 | $75.5M | $12.3M | $72.6M | $199M | $160.4M |
| 2021 | 990 | Data |
| 2020 | 990 | Data |
| 2019 | 990 | Data |
| 2018 | 990 | Data |
| 2017 | 990 | Data |
| 2016 | 990 | Data |
| 2015 | 990 | Data |
| 2014 | 990 | Data |
| 2013 | 990 | Data |
| 2012 | 990 | Data |
| 2011 | 990 | Data |
| 2010 | 990 | — |
| 2009 | 990 | — |
| 2008 | 990 | — |
| 2007 | 990 | — |
| 2006 | 990 | — |
| 2005 | 990 | — |
| 2004 | 990 | — |
| 2003 | 990 | — |
| 2002 | 990 | — |
| 2001 | 990 | — |