· Public charity
Organization of Teratology Information Specialists
Dedicated to providing evidence-based information to parents, healthcare professionals, and the general public about medications and other exposures during pregnancy and while breastfeeding.
What you funded, over time
By grantee IRS cause code (NTEE).
A cause breakdown isn’t shown here: 73% of ORGANIZATION OF TERATOLOGY INFORMATION SPECIALISTS’s grantee dollars fall outside its nine largest cause areas, whether because the recipient carries no IRS cause code or because its cause sits in the long tail. A chart would be mostly one residual band and misrepresent the portfolio.
Where the money goes
Your grants by size, and where they go.
By grant size · FY2024
- $10k–50k2 grants · $62k
- $50k–250k10 grants · $727k
| Recipient | Amount |
|---|---|
| University of California San Diego | $115,891 |
| MH MISSION HOSPITAL | $81,637 |
| University of North Texas | $79,000 |
| University of TX Health Science Ctr | $74,900 |
| University of Nebraska Medical Cent | $70,050 |
| Emory University | $69,750 |
| University of South Florida | $63,137 |
| University of Connecticut Health Ce | $58,225 |
| State of Utah | $58,000 |
| University of Rochester | $56,750 |
| University of Arizona | $47,300 |
| Genesis Foundation for Children | $14,824 |
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 9%. 71% 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.
14 repeat relationships — 12 still active in FY2024, 2 since wound down.
How the two cohorts compare
Organizations
Total granted
Still filing today
New vs renewed · share of each year
In FY2024, 100% of grant dollars renewed an existing relationship; $0 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
- EUEmory University8× · 2017–2024 · $494k · revenue +80%
UNIVERSITY OF ROCHESTER8× · 2017–2024 · $471k · revenue +81%- TNThe Nebraska Medical Center8× · 2017–2024 · $461k · revenue +125%
Funded once
Repeat = funded in two or more distinct years; growth and survival are read from each grantee’s own subsequent IRS filings.
6 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. 6 of the 14 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.
The grantmakers whose grantees overlap with yours far more than size alone predicts. Each orbits closer the stronger the alignment; the arcs between them show where they also fund each other. Here it reads as a set of overlaps that mostly run through you, not between each other.
Open a dossier: Fidelity Investments Charitable Gift Fund
Affinity is a Gamma-Poisson posterior co-funding rate, re-centered on the typical rate, so thin evidence shrinks toward no signal. A research starting point: overlap is association, not proof of shared intent.
Government reliance of your grantees
Every dot is one organization Organization of Teratology Information Specialists 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.
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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How do I get to Organization of Teratology Information Specialists?
Find your warmest path to Organization of Teratology Information Specialists 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.