· Public charity
Society of Nuclear Medicine and Molecular Imaging
The society is a multidisciplinary professional medical organization dedicated to improving human health by advancing nuclear medicine, molecular imaging and radionuclide therapy.
Read this first · the figures below need context
60% of Society of Nuclear Medicine and Molecular Imaging’s FY2024 grant dollars went to The Board Of Trustees Of The Leland Stanford Junior University, not to grantees.
Its money now reaches organizations through that sponsor, which does not report who recommended each grant. FY2024 names 5 organizations directly, so a portfolio cannot be read from it. Everything below is therefore FY2023, the last year Society of Nuclear Medicine and Molecular Imaging named its own grantees at scale: 6 organizations, $2M. It is dated, not current.
Organizations named directly on the return
Amber years are those where most dollars went to a sponsor.
What you funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year — across FY2017–2024.
Where the money goes
Your grants by size, and where they go.
The 7 grants below total $1,683,916 — the rows itemised in this filing. The $1,880,941 headline is the total grant expense reported on the return, so the remaining $197,025 is giving the schedule does not break out: grants under the $5,000 itemisation floor, grants to individuals, and grants reported on other schedules. Every figure below describes the itemised rows only.
By grant size · FY2023
- Under $10k1 grant · $6k
- $10k–50k2 grants · $37k
- $50k–250k1 grant · $58k
- $250k+3 grants · $1.6M
| Recipient | Amount |
|---|---|
| UNIVERSITY OF WISCONSIN | $598,830 |
| UNIVERSITY OF CALIFORNIA | $498,757 |
| SLOAN KETTERING INSTITUTE FOR CANCER RESEARCH | $485,091 |
| JOHNS HOPKINS UNIVERSITY | $58,238 |
| STANFORD UNIVERSITY | $20,000 |
| WASHINGTON UNIVERSITY IN SAINT LOUIS | $17,000 |
| UNIVERSITY OF ALABAMA AT BIRMINGHAM | $6,000 |
Do your dollars go where the need is?
Each grant placed by the hardship in its grantee’s ZIP, then read against the area’s typical level.
Dollar for dollar, your grants (FY17–24) land where the poverty rate runs at 12%, against an area that typically sits at 10%. 79% of your dollars go to grantees based in above-average-need neighborhoods. Most of your grants land in higher-need ZIPs.
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty linein the grantee’s ZIP, averaged from the tracts in that ZIP; grantee ZIP can differ from where services are delivered.
Which US states your grants reach
US grants placed by each grantee’s ZIP, which can differ from where services are actually delivered. Need from public data — U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA — shown as context about the area, never attributed to your giving.
Who you back again
Which organizations you funded more than once, and which you funded a single time. Each grantee is matched to its own filings.
And the relationships you keep tend to grow: grantees you re-up on have seen median revenue of +57% since the first grant, against +17% for the ones you funded once.
10 repeat relationships — 6 still active in FY2023, 4 since wound down; 2 grantees were first funded in FY2023 (too recent to call). Read against FY2023, the last year this funder named its own grantees directly; its giving has since run through a sponsor, which reports no donor behind each grant.
How the two cohorts compare
Organizations
Total granted
Median revenue growth · since first grant
Still filing today
New vs renewed · share of each year
In FY2024, 64% of grant dollars renewed an existing relationship; $120k went to new ones.
Where new relationships form · theme of each grantee’s first grant
First-time = a grantee’s first year in your filing window; renewed = funded in an earlier year too. As a portfolio matures the renewed share naturally climbs — once funded, an org stays “renewable” — so the signal is the years that buck it (a new-grantee intake wave). The earliest year is left-censored: relationships that predate the data read as “new.” Theme is the grantee’s IRS cause.
Backed again, and grew
MEMORIAL SLOAN-KETTERING CANCER CENTER3× · 2019–2024 · $605k · revenue +82%
WASHINGTON UNIVERSITY4× · 2021–2024 · $119k · revenue +21%- NYNew York University2× · 2017–2018 · $100k · revenue +76%
Funded once
- AAAMERICAN ASSOCIATION OF PHYSICISTS IN MEDICINEone grant, 2017 · $35k · revenue +17%
Dana-Farber Cancer Institute Incgraduatedone grant, 2020 · $20k · revenue +73%- SUSTATE UNIVERSITY OF NEW YORK COLLEGE AT PURCHASEone grant, 2021 · $20k
Repeat = funded in two or more distinct years; growth and survival are read from each grantee’s own subsequent IRS filings.
The map has a center of gravity: the average direction from the rest of the sector toward the organizations the foundation funds — the shape of its giving. Scoring every nonprofit along that direction surfaces the ones that look most like the portfolio. These are the closest matchesthat aren’t grantees — a resemblance in what they say they do, not a recommendation.
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For reference, the grantee most central to the portfolio’s shape is The Medstar-Georgetown Medical Center Inc and the most unlike its peers is Dana-Farber Cancer Institute Inc. Resemblance is measured on each organization’s own IRS 990 mission text; it reflects how work is described, not its quality or impact.
12 grantees tracked through their own filings, 2017–2025.
Each one resolved to its own IRS returns and tracked year by year — your grant beside their revenue from every source. Association, dated; never a causal claim.
Counted here: distinct organizations you funded across 2017–2025, not grant rows in a single year — so this will not match the grant count on the cover. 12 of the 19 grantees resolved in that span have returns of their own we could reconcile; the rest are funded organizations whose filings we could not track year by year.
Where your money sits — by cause, then by grantee
Each org by its size and your share of it — top-left is where you’re load-bearing
Go grantee by grantee — a decade per org, and how each moved after you funded them
A decade per grantee — revenue shaded, your grants as bars, all rows on one timeline.
- Who funds MEMORIAL SLOAN-KETTERING CANCER CENTER ↗
- Who funds THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY ↗
- Who funds WASHINGTON UNIVERSITY ↗
- Who funds New York University ↗
- Who funds IHC HEALTH SERVICES INC ↗
- Who funds Emory University ↗
- Who funds JOHNS HOPKINS UNIVERSITY ↗
- Who funds UNIVERSITY OF PITTSBURGH ↗
- Who funds AMERICAN ASSOCIATION OF PHYSICISTS IN MEDICINE ↗
- Who funds THE MEDSTAR-GEORGETOWN MEDICAL CENTER INC ↗
- Who funds Dana-Farber Cancer Institute Inc ↗
- Who funds SOCIETY OF RADIOPHARMACEUTICAL SCIENCES ↗
Government reliance of your grantees
Every dot is one organization Society of Nuclear Medicine and Molecular Imaging funds. Left–right is the share of its income from government; up–down is the share from you. Bigger dots raise more. Filter to federal or a single department — and drag the year to watch it move.
- Johns Hopkins University — 12% of income from government
- Dana-Farber Cancer Institute Inc — 7% of income from government
Government income is each org’s traced federal awards (USASpending — grants and contracts) plus state payments (open checkbooks) as a share of its total revenue (IRS Form 990); the self-reported government-grant line (990 line 1e) is carried for cross-check: a grantee that reports government grants we could not trace to a source is left off the chart rather than shown as receiving none. An association, not a claim that your grant caused the public funding. Coverage is precision-first — a floor, not a census; state records exist for 9 states, so a grantee outside them shows no state figure (dimmed) rather than a false zero. Federal award amounts are obligations, which can span years, so a single-year share is indicative. 990 filings lag 12–24 months.
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Find your warmest path to Society of Nuclear Medicine and Molecular Imaging through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.