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Give to Research

The mission of innerscience research fund is to fund research on the biological and physiological effects of meditation on the human body, empowering individuals to heal and thrive.

$4.9M
Granted FY2025still arriving
4
Grants FY2025still arriving
3
States reached
$4.4M
Largest
01What it funds
01100% classified

What it funded, over time

Every grant placed by its stated purpose and the recipient’s mission, by year, FY2021 to 2024.

Science & Tech$5.9MHuman Services$50kHealth$23k
02FY2025 · 4 grants

Where the money goes

Grants by size, and the largest recipients.

By grant size · FY2025

  • $50k–250k3 grants · $481k
  • $250k+1 grant · $4.4M
$223,926
Median grant
3
States reached
$5.6M
Total assets
Largest grants
RecipientAmount
UNIVERSITY OF CALIFORNIA SAN DIEGO$4,425,000
INSTITUTE OF HEARTMATH$223,926
BETH ISRAEL DEACONESS MEDICAL CENTER$191,852
GIVE TO GIVE FOUNDATION$65,421
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:

Its human-services grants (FY24–25, $116k) land where the poverty rate runs at 17%, against an area that typically sits at 10%. 100% of those dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 10%GIVE TO GIVE FOUNDATION: $65k → 17%GIVE TO GIVE FOUNDATION: $50k → 17%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 grantees
repeat funding

Who it funds again

Organizations funded in more than one year, against those funded once.

96%of every dollar goes to organizations it has funded before.
$10M · 3 repeat orgs$449k to everyone else

Grantees it renews have grown median revenue 0% since the first grant, against -13% for those funded once.

3 repeat relationships: 3 still active in FY2025, 0 since wound down; 1 grantees were first funded in FY2025 (too recent to call).

How the two cohorts compare

Re-uppedFunded once

Organizations

3
2

Total granted

$10M
$257k

Median revenue growth · since first grant

0%
-13%

Still filing today

67%
100%

New vs renewed · share of each year

In FY2025, 96% of grant dollars renewed an existing relationship; $192k went to new ones.

50%100%’21’22’23’24’25
RenewedFirst-time

Where new relationships form · theme of each grantee’s first grant

’21’22’23’24’25
HealthScience & TechHuman ServicesOther
How this is measured
First-time means a grantee’s first year in the filing window; renewed means funded in an earlier year too. The renewed share climbs naturally as a portfolio matures, so the signal is a year that bucks it (a wave of new grantees). 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

  • RO
    REGENTS OF THE UNIVERSITY OF CALIFORNIA AT SAN DIEGO
    5× · 2021–2025 · $10M
  • IO
    INSTITUTE OF HEARTMATH
    2× · 2024–2025 · $246k · revenue 0%
  • TG
    THE GIVE TO GIVE FOUNDATION
    2× · 2024–2025 · $116k · revenue 0%

Funded once

  • FI
    Florida Institute for Human and Machine Cognition Inc
    one grant, 2024 · $156k · revenue -13%
  • IO
    INSTITUTE OF NOETIC SCIENCES
    one grant, 2024 · $102k · revenue -43%
How this is measured

Repeat means funded in two or more distinct years. Growth and survival are read from each grantee’s own subsequent IRS filings.

04Its field
04the grantee network

5 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. 5 of the 6 grantees resolved in that span have returns of their own we could reconcile.

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

0
Load-bearing (≥25% of a budget)
0
Early backer (in before they grew)
5/5
Grantees still filing
0/5
Grew since first funded

Where the money sits, by cause and then by grantee

REGENTS OF THE UNIVERSITY OF CALIFORNIA AT SAN DIEGO: $10,024,576 · OtherREGENTS OF THE UNIVERSITY OF CALIFORNIA AT SAN DIEGOINSTITUTE OF HEARTMATH: $246,426 · HealthBETH ISRAEL DEACONESS MEDICAL CENTER INC: $191,852 · HealthFlorida Institute for Human and Machine Cognition Inc: $155,560 · Science & TechINSTITUTE OF NOETIC SCIENCES: $101,750 · Science & TechTHE GIVE TO GIVE FOUNDATION: $115,741 · Human Services
Other$10,024,576Health$438,278Science & Tech$257,310Human Services$115,741

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

25%50%75%100%$10M$100Mgrantee revenue →↑ funder's share of their budgetINSTITUTE OF HEARTMATH: $246,426 over 2y, 0.6% of budgetBETH ISRAEL DEACONESS MEDICAL CENTER INC: $191,852 over 1y, 0.0% of budgetFlorida Institute for Human and Machine Cognition Inc: $155,560 over 1y, 0.4% of budgetTHE GIVE TO GIVE FOUNDATION: $115,741 over 2y, 1.8% of budgetINSTITUTE OF NOETIC SCIENCES: $101,750 over 1y, 2.4% 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.

How much its grantees rely on government

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

2024
’20’21’22’23’24’25
no gov · 00%0%1%3%5%funder's share of their income ↑0%19%38%56%75%share of the org’s income from government →median 23%
  • Florida Institute for Human and Machine Cognition Inc: 23% of income from government
no gov moneyreceives it· size = income
0get no government money at all
0rely on government for over half their income
⤢ axis zoomed · 0–75%
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.
05Through Plinth

Warm introductions · Powered by PlinthPlus

How do I get to Give to Research?

Find your warmest path to Give to Research 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.

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 6 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 2025, released 2025. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

Source object · view filing

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