Skip to content
Lifenet Health at a glanceMostly funded, not a grantmaker: see who funds it →

· Public charity

Lifenet Health

LIFENET HEALTH, established in 1982, is a non-profit life sciences organization that provides allografts for use in most surgical disciplines; facilitates organ donation and transplantation; provides donated cells and tissues for biomedical research; and serves as a source of public advocacy and education for organ and tissue donation.…

$40k
Granted FY2021
1
Grants FY2021
1
States reached
$40k
Largest
01What it fundsRead
02The needRead
03Its fieldRead
01What it funds
01100% classified

What it funded, over time

Every grant placed by its stated purpose and the recipient’s mission, by year, FY2018 to 2021.

Public Safety & Disaster$70kEducation$56k
02FY2021 · 1 grant

Where the money goes

Grants by size, and the largest recipients.

$40,000
Median grant
1
States reached
$651M
Total assets
Largest grants
RecipientAmount
UNIVERSITY OF MARYLAND BALTIMORE FOUNDATION INC$40,000
02The need
03

Does the money reach high-need areas?

Each grant placed by the hardship in its grantee’s ZIP, against the area’s typical level.

show:

Dollar for dollar, its grants (FY18–21) land where the poverty rate runs at 19%, against an area that typically sits at 8%. 100% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 8%UNIVERSITY OF MARYLAND BALTIMORE FOUNDATION INC: $40k → 19%University of Maryland Baltimore Foundation Inc: $10k → 19%0%20%40%50%more need →
grant to an above-average-need area below average· circle size = grant amount
How this is measured
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty line in the grantee’s ZIP, averaged from the tracts in that ZIP.
How this is measured
US grants placed by each grantee’s ZIP, which can differ from where services are delivered. Need from public data (U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA), shown as context about the area, never attributed to the funder’s giving.
03Its field
04the grantee network

7 grantees tracked through their own filings, 2017–2025.

Each grant beside the grantee’s revenue from every source, year by year.

How this is measured

Counted here: distinct organizations funded across 2017 to 2025, not grant rows in a single year, so this will not match the grant count on the cover.

Each grantee is resolved to its own IRS returns. Association, dated; never a causal claim.

0
Load-bearing (≥25% of a budget)
2
Early backer (in before they grew)
7/7
Grantees still filing
6/7
Grew since first funded

Where the money sits, by cause and then by grantee

United Way of South Hampton Roads: $70,000 · PhilanthropyUnited Way of South Hampton RoadsUNIVERSITY OF MARYLAND BALTIMORE FOUNDATION INC: $50,000 · EducationUNIVERSITY OF MARYLAND BALTIMORE FOUNDATION …Old Dominion University: $6,000 · Other
Philanthropy$70,000Education$50,000Other$6,000

Each grantee by its size and the funder’s share of it; top left is load-bearing

25%50%75%100%grantee revenue →↑ funder's share of their budgetUnited Way of South Hampton Roads: $70,000 over 1y, 0.3% of budgetUNIVERSITY OF MARYLAND BALTIMORE FOUNDATION INC: $50,000 over 2y, 0.1% of budget
Grantee by grantee: a decade per organization, before and after funding

A decade per grantee: revenue shaded, grants as bars, all rows on one timeline.

Co-funders

Grantmakers whose grantees overlap with this funder’s far more than size alone predicts. Closer means stronger alignment; arcs show where they also fund each other. Here they form a tightly interlocked group: most of these funders back each other's grantees too.

American Online Givin…Donor Advised Charita…Fidelity Investments …Amazonsmile Foundation
American Online Giving Foundation IncDE7.7× affinity5 shared granteesties to 3 of 3Hover any node to trace its alignments.Compare side by side →

Open a dossier: American Online Giving Foundation Inc · Donor Advised Charitable Giving Inc · Fidelity Investments Charitable Gift Fund · Amazonsmile Foundation

How this is measured
Affinity is a Gamma-Poisson posterior co-funding rate, re-centered on the typical rate, so thin evidence shrinks toward no signal. Overlap is association, not proof of shared intent.

How much its grantees rely on government

Each dot is one organization Lifenet Health funds: left to right is the share of its income from government, bottom to top the share from Lifenet Health. Bigger dots raise more.

2025
’20’21’22’23’24’25
no gov · 10%0%1%3%5%funder's share of their income ↑0%1%3%4%5%share of the org’s income from government →
    no gov moneyreceives it· size = income
    1get no government money at all
    1report government grants on their 990 we could not trace to a source (not plotted)
    0rely on government for over half their income
    ⤢ axis zoomed · 0–5%
    typical government reliance, FY20–25
    How this is measured
    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 a grant caused the public funding. Coverage is precision-first, a floor rather than 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 to 24 months.

    On method. Every financial figure here is read from IRS e-file XML, the funder’s own 990/990-PF, and from the multi-year returns of the 3 grantees we resolved, each linked to its source. Grantee outcomes are each organization’s own program-service reporting (Form 990, Part III), read as association with funding, never as caused by it. Low-confidence name matches are suppressed rather than guessed. How we build these →

    How this is measured
    Each figure carries one of six labels. Filed is what the funder reported on its return. Official is another government record about an organization, such as a federal award or a charity register, joined by name where no shared identifier exists. Resolved is an identity we worked out where the filing named a recipient without an EIN, kept only above a measured confidence threshold. Computed is arithmetic over those, like themes, portfolio clusters and co-funder strength. Context is a statistic about a place rather than about an organization, which is what the need overlay is: it describes the area a grantee’s address sits in, not where its work lands. Inferred is drawn by a model from text, like the partnerships read out of public news and organization websites. Each is labeled where it appears.

    Generated from the IRS Form 990 e-file return for fiscal year 2021, released 2021. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

    Possibly out of date. A more recent filing (FY2024) is on record and reports $101k of grant expense, but none of it to a named recipient. On a Form 990 that can mean grants abroad, filed by region only (Schedule F), grants under $5,000, which are not itemized, or a schedule referenced as an attachment the e-file does not carry. The figures above are therefore from FY2021, the most recent year this funder named its grantees.

    Source object · view filing

    Play a game based on Lifenet Health’s filings →

    More from the funding graph

    The unnamed billions
    grants reported with no recipient in the public record
    The regranting layer
    51% of grant dollars land in the regranting layer
    The DAF merry-go-round
    $9.7B circulates between donor-advised funds
    The endowment market
    who holds foundation money, and what just moved
    The camps
    funders cluster by geography, not ideology
    By state
    where grant money is based, and what stays home