Wisconsin Nonprofits
U.S. · 39,309 locations
There are 39,309 Nonprofits in Wisconsin as of September 2026.
39,309 US nonprofit organisations. Built from IRS Business Master File, the Form 990/990-EZ/990-PF e-file XML, 990-N e-postcards, the IRS auto-revocation list, IRS Publication 78, the Federal Audit Clearinghouse and the US Census geocoder.
More about this list
82% carry a named officer, 35% a telephone number, 33% a website, 29% the mission statement they filed with the IRS. Every organisation carries an activity_status that was measured from both IRS filing channels — the full 990 e-file index and the 990-N postcard file — rather than inferred from registration alone. 73 columns per record.
Need just some of them? Ask in your own words.
●Where the rows are
≡Sample Data Preview
| name | city | state | zip | phone | website | ein | officer name | officer title | street | mission | activity status | ntee category | revenue amt | officer source | sort name | care of | ntee code | ntee subcategory | subsection | exempt status | org type | foundation type | deductible | ruling date | tax period | asset amt | income amt | revenue band | asset band | has financials | group exemption | filing requirement | source file | pulled at | exemption revoked | revocation date | last 990 filed | last 990 form | years 990 filed | form 990 object id | form 990 batch url | principal officer | last 990n filed | employees | volunteers | total expenses | net assets eoy | officers | top officer | top officer title | top officer comp | officer count | grants paid count | grants paid total | contact source | website source | pub78 verified | pub78 deductibility | ca may not operate | org structure | group ruling | county fips | census tract | msa code | msa name | msa type | a 133 audit performed | last single audit year | audit contact email | audit contact phone | geo precision | county | county note | address quality | latitude | longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NORTH AMERICAN LAKE MANAGEMENT SOCIETY | Madison | WI | 53705-0443 | (608) 233-2836 | WWW.NALMS.ORG | 010372129 | PHILIP FORSBERG | EXECUTIVE DIRECTOR | PO BOX 5443 | OUR MISSION IS TO FORGE PARTNERSHIPS AMONG CITIZENS, SCIENTISTS, AND PROFESSIONALS TO FOSTER THE MANAGEMENT AND PROTECTION OF LAKES AND RESERVOIRS FOR TODAY AND TOMORROW. | filing | Environment | 675527 | Form 990 Part VII | C033 | Professional Societies, Associations | 501(c)(3) | Unconditional Exemption | Corporation | Organization that normally receives no more than one-third of its support from gross investment income and unrelated business income and at the same time more than one-third of its support from contributions, fees, and gross receipts related to exempt purposes 509(a)(2) | yes | 1990-06 | 2024-12 | 888508 | 675527 | 500,000 to 999,999 | 500,000 to 999,999 | 1 | 990 (all other) or 990EZ return | eo2.csv | 2026-09-08 | 2024-12 | 990T | 5 | 202511919339313221 | https://apps.irs.gov/pub/epostcard/990/xml/2025/2025_TEOS_XML_07A.zip | 2 | 25 | 775027 | PHILIP FORSBERG (EXECUTIVE DIRECTOR); KELLIE MERRELL (PRESIDENT); KIYOKO YOKOTA (PAST PRESIDENT (LEFT NOVEMBER)); VICTORIA CHRAIBI (PRESIDENT-ELECT); JULIE CHAMBERS (PRESIDENT-ELECT (JOINED NOV)) | PHILIP FORSBERG | EXECUTIVE DIRECTOR | 72782 | 26 | Form 990 Part VII | Form 990 | 1 | PC | independent | 55025 | zip_centroid | Dane County | po_box | 43.074014 | -89.460509 | ||||||||||||||||||||||
| SIKH SOCIETY OF WISCONSIN-MADISON LTD | Middleton | WI | 53562-1732 | (608) 831-4401 | 311660981 | HARJINDER S PANGLI | PRESIDENT | 6970 CENTURY AVE | RELIGIOUS ORGANIZATION | filing | Religion-Related | 236503 | Form 990 Part VII | X99 | Religion Related N.E.C. | 501(c)(3) | Unconditional Exemption | Corporation | Church 170(b)(1)(A)(i) | yes | 1999-12 | 2024-12 | 1969773 | 269830 | 100,000 to 499,999 | 1,000,000 to 4,999,999 | 1 | 990 - Not required to file (church) | eo2.csv | 2026-09-08 | 2024-12 | 990 | 5 | 202542769349301664 | https://apps.irs.gov/pub/epostcard/990/xml/2025/2025_TEOS_XML_11B.zip | 2 | 1071618 | HARJINDER S PANGLI (PRESIDENT); SUNDEEP MANHANI (VICE-PRESIDENT); GURWATTAN MIRANPURI (SECRETARY); BOB SEERHA (TREASURER) | HARJINDER S PANGLI | PRESIDENT | 4 | Form 990 Part VII | 1 | PC | independent | 55025 | 011104 | 31540 | Madison, WI | Metropolitan Statistical Area | source | Dane County | complete | 43.108633781214 | -89.501150827751 | ||||||||||||||||||||||
| FAMILY TRAINING PROGRAM INC | Appleton | WI | 54914-8275 | (920) 722-5511 | WWW.FAMILYTRAININGPROGRAM.NET | 391452664 | TOM MAVES | PAST PRESIDE | 51 PARK PL STE 200 | PROVIDE IN-HOME TRAINING TO HELP PARENTS MANAGE THEIR CHILDREN'S BEHAVIOR THROUGH INDIVIDUALIZED PARENT EDUCATION PLANS. | filing | Human Services | 1248698 | Form 990 Part VII | AMMIE BORCHERT VP | P46Z | Family Counseling | 501(c)(3) | Unconditional Exemption | Corporation | Organization that receives a substantial part of its support from a governmental unit or the general public 170(b)(1)(A)(vi) | yes | 1987-05 | 2025-12 | 347537 | 1248698 | 1,000,000 to 4,999,999 | 100,000 to 499,999 | 1 | 990 (all other) or 990EZ return | eo2.csv | 2026-09-08 | 2025-12 | 990 | 6 | 202631709349300408 | https://apps.irs.gov/pub/epostcard/990/xml/2026/2026_TEOS_XML_06A.zip | 25 | 4 | 94994 | TOM MAVES (PAST PRESIDE); FRED LIGHTNER (SECRETARY); PATRICIA CLANCY (PRESIDENT); AMMIE BORCHERT (VICE PRESIDE); ELIZABETH DAVIS (TREASURER) | TOM MAVES | PAST PRESIDE | 66415 | 9 | Form 990 Part VII | Form 990 | 1 | PC | independent | 55087 | 012506 | 11540 | Appleton, WI | Metropolitan Statistical Area | source | Outagamie County | complete | 44.257677013758 | -88.467463656483 | |||||||||||||||||
| WAUNAKEE BAR BUDDIES INC | Waunakee | WI | 53597-2839 | 393273039 | Mary Horras | 804 SUNRISE BAY | filing | Public & Societal Benefit | Form 990-N e-postcard | % MARY HORRAS | W12 | Fund Raising and Fund Distribution | 501(c)(3) | Unconditional Exemption | Corporation | Organization that normally receives no more than one-third of its support from gross investment income and unrelated business income and at the same time more than one-third of its support from contributions, fees, and gross receipts related to exempt purposes 509(a)(2) | yes | 2025-08 | 2025-12 | 990 - Required to file Form 990-N - Income less than $50,000 per year | eo2.csv | 2026-09-08 | Mary Horras | 2025 | 990-N postcard | 1 | PC | independent | 55025 | 011301 | 31540 | Madison, WI | Metropolitan Statistical Area | source | Dane County | complete | 43.203737680179 | -89.46360577785 | |||||||||||||||||||||||||||||||||||||||
| INTERNATIONAL ASSOCIATION OF MACHINISTS & AEROSPACE WORKERS | Appleton | WI | 54913-8138 | 510160703 | Gary Vargo | 4721 EVERBREEZE CIRCLE | filing | Form 990-N e-postcard | 2575 | % GARY VARGO | 501(c)(5) | Unconditional Exemption | Association | All organizations except 501(c)(3) | no | 1940-12 | 2025-12 | 264 | 990 - Required to file Form 990-N - Income less than $50,000 per year | eo2.csv | 2026-09-08 | Gary Vargo | 2025 | 990-N postcard | subordinate | 264 | 55087 | 012504 | 11540 | Appleton, WI | Metropolitan Statistical Area | source | Outagamie County | complete | 44.309167029621 | -88.371717146496 |
{}What's in each row
| Field | What it is | Rows filled |
|---|---|---|
| ein | IRS Employer Identification Number — the primary key for every US nonprofit | 100% |
| name | Legal organisation name as registered with the IRS | 100% |
| city | City | 100% |
| state | Two-letter state code | 100% |
| officer_ | Officer name | 82% |
| officer_ | Officer title | 35% |
| phone | Telephone number as reported on the organisation's own Form 990 | 35% |
| website | Website as reported on the Form 990 or 990-N postcard | 33% |
| street | Street address | 100% |
| zip | ZIP code | 100% |
| mission | Mission statement as filed on Form 990 Part I/III | 29% |
| activity_ | filing, lapsed, revoked or no filing on record — measured, not assumed | 100% |
| ntee_ | NTEE major group — the IRS cause classification | 65% |
| revenue_ | Revenue as reported to the IRS | 28% |
| officer_ | Officer source | 82% |
| sort_ | Secondary or trading name where the IRS records one | 26% |
| care_ | Care of | 67% |
| ntee_ | Ntee code | 65% |
| ntee_ | NTEE detailed classification | 61% |
| subsection | 501(c) subsection, e.g. 501(c)(3) | 100% |
| exempt_ | Exempt status | 100% |
| org_ | Org type | 99% |
| foundation_ | IRS foundation classification — church, school, hospital, private foundation | 100% |
| deductible | Deductible | 100% |
| ruling_ | Month the IRS granted exempt status | 99% |
| tax_ | Tax period | 85% |
| asset_ | Total assets as reported to the IRS | 35% |
| income_ | Income amt | 34% |
| revenue_ | Revenue band | 35% |
| asset_ | Asset band | 35% |
| has_ | Has financials | 35% |
| group_ | Group exemption | 26% |
| filing_ | Filing requirement | 100% |
| source_ | Source file | 100% |
| pulled_ | Pulled at | 100% |
| last_ | Tax period of the most recent full Form 990 | 36% |
| last_ | Last 990 form | 36% |
| years_ | Years 990 filed | 36% |
| form_ | Form 990 object id | 36% |
| form_ | IRS bulk XML archive containing this organisation's return | 35% |
| principal_ | Principal officer from the 990-N postcard, where no full 990 exists | 55% |
| last_ | Year of the most recent 990-N postcard | 56% |
| employees | Employee count from Form 990 Part I | 10% |
| volunteers | Volunteer count from Form 990 Part I | 12% |
| total_ | Total expenses from Form 990 Part I | 17% |
| net_ | Net assets at end of year | 29% |
| officers | Up to five officers by compensation, with titles | 35% |
| top_ | Principal officer name from Form 990 Part VII | 35% |
| top_ | That officer's title | 35% |
| top_ | That officer's reportable compensation | 12% |
| officer_ | Officer count | 35% |
| grants_ | Number of grants this organisation reported paying | 8% |
| grants_ | Total value of grants paid | 8% |
| contact_ | Contact source | 82% |
| website_ | Website source | 33% |
| pub78_ | Listed in IRS Publication 78 as eligible to receive deductible contributions | 64% |
| pub78_ | Publication 78 deductibility code | 64% |
| org_ | parent, subordinate, intermediate or independent, from the IRS affiliation code | 99% |
| group_ | Group exemption number where the organisation files under one | 26% |
| latitude | Latitude, geocoded from the address via the US Census geocoder | 100% |
| longitude | Longitude, geocoded from the address via the US Census geocoder | 100% |
| county_ | County FIPS code | 100% |
| census_ | Census tract | 70% |
| msa_ | Msa code | 63% |
| msa_ | Metropolitan or micropolitan area | 63% |
| msa_ | Msa type | 63% |
| a_ | Single Audit on file with the Federal Audit Clearinghouse | 1% |
| last_ | Last single audit year | 1% |
| audit_ | Audit contact email | 1% |
| audit_ | Audit contact phone | 1% |
| geo_ | How exact the map point is: the source's own point, the street address, or the ZIP code's centre | 100% |
| county | County the location is in | 100% |
| county_ | How the county was found, where it was not given by the source | 10% |
| address_ | Whether the street address is complete, missing a house number, or a PO box | 100% |
Rows filled is measured on every row of the delivered file.
✓Where this data comes from
This dataset is built from the IRS's own public files — the Business Master File of exempt organisations, the Form 990/990-EZ/990-PF e-file XML, the 990-N e-postcard file, the auto-revocation list and Publication 78 — joined on EIN, with the Federal Audit Clearinghouse for single-audit detail and the US Census geocoder for coordinates. Nothing is collected from an organisation's own website: every field traces to a filing the organisation made or a determination the IRS published.
September 14, 2026
File built
78 fields
Columns in the file
Fill measured
On every row of the delivered file
Records are published as the IRS holds them — nothing is silently dropped for looking wrong, because on this file an empty field is itself information (a church that files no return is not a bad record). Coordinates are validated to US bounds, ZIPs and phone numbers to format, and every organisation carries an activity_status measured from both filing channels — the 990 e-file index and the 990-N postcard file — rather than inferred from registration. Fill rates vary sharply by slice and are stated on each dataset.
How we build and date every file: methodology.
Using ChatGPT, Claude or an API?
No setup needed. Ask ChatGPT or Claude your question about the Wisconsin Nonprofits and give it this page: it can open https://locationlists.com/find?dataset=wisconsin-nonprofits with the filters you need and show you the count and price.
Using ChatGPT or Claude? Tell it to answer from locationlists.com/find — no account, no connector.
How assistants ask
No connector needed. Any AI assistant can answer from this site with a web link, no connector, no account: open https://locationlists.com/find with the question as query parameters.
If a question needs filtering, distance, drive time, combining lists or a price, open a /find link: either put the question itself in q=, or build the structured link. The answer does not need an API, a connector or an account.
Simplest, the question itself: https://locationlists.com/find?q=<the question in plain words>
Or set the parts yourself: https://locationlists.com/find?dataset=<slug>&state=<ST>&city=<city>&county=<county>&zip=<zip>&near=<place or zip>&radius=<miles>&drive=<minutes>&where=<column>:<op>:<value>&total=<rows>
Leave out what the question does not need. Use radius for straight-line miles or drive for minutes of driving (5 to 60), not both; near can name several places separated by | to reach from any of them. To ask about several lists at once, use datasets with two or more names, or category with a kind of business. To compare two lists, add relate=nearest and the other list with b. in front of its parameters. To count by area, use areas=county (or zip, state, metro). To keep only places whose county, ZIP code, metro area or state meets a Census figure, add area_where (for example county:population>1000000); with areas, per=100000 gives counts per 100,000 residents.
Links an assistant can open, each showing how many rows match, a few preview rows and the price for just that part:
- Within 25 miles of Milwaukee, WI, the busiest area: https://locationlists.com/find?dataset=wisconsin-nonprofits&near=Milwaukee+WI&radius=25
- Within a 30-minute drive of Milwaukee, WI: https://locationlists.com/find?dataset=wisconsin-nonprofits&near=Milwaukee+WI&drive=30
- Only rows where deductible is yes: https://locationlists.com/find?dataset=wisconsin-nonprofits&where=deductible%3Aeq%3Ayes
- The whole list: add a city, state, ZIP code or distance:
https://locationlists.com/find?dataset=wisconsin-nonprofits - In plain words: "Wisconsin Nonprofits within 25 miles of Milwaukee, WI": https://locationlists.com/find?q=Wisconsin+Nonprofits+within+25+miles+of+Milwaukee%2C+WI
Optional: connect for repeat use or wallet payments:
Read https://locationlists.com/skill.md and follow the instructions to connect LocationLists. Then show me the Wisconsin Nonprofits list.Your assistant will walk you through setup and show you the price before you pay.
For developers
Every step below has a no-connector equivalent: https://locationlists.com/find?dataset=wisconsin-nonprofits with the same filters as query parameters.
- Connect your agent to
https://locationlists.com/mcp(no key, no signup). - Call
count_locationswithdataset: "wisconsin-nonprofits"and any filter — it is free, and returns how many rows match and what they cost. - Fetch the rows with
query_locations: $0.51 per 100 rows from this dataset. Above 19,655 rows, buying the whole file is cheaper.
Paid in USDC over x402 — the agent gets an HTTP 402 with the exact price, signs, and retries. Money moves only after rows are delivered, so a failed or empty query costs nothing. Setup for Claude, Cursor and VS Code
?Frequently Asked Questions
How many Nonprofits are there in Wisconsin?
There are 39,309 Nonprofits in Wisconsin as of September 2026.
Is this the same data as GuideStar or Candid?
It comes from the same public IRS sources Candid uses — they list them on candid.org/about/our-data/data-sources. Against Candid's published Essentials API schema we match 41 of the 66 fields it returns, which is 84% of the fields reproducible from public records. What we do not have is the part organisations submit to Candid directly: board rosters, DEI demographics, Seals of Transparency, awards and photos. If you need those, Candid is the right purchase. If you need the whole file as a CSV, this is.
Why is this so much cheaper than a Candid API subscription?
Candid sells a subscription starting at $4,800 a year. This is a one-off purchase — and you are not giving up API access to get the lower price. The same 39,309 records are available three ways: the bulk CSV, our MCP server for programmatic queries, and per-record pricing so an application or AI agent can pull just the rows it needs. What you are not paying for is an annual seat.
Do you have an API, or only CSV downloads?
Both. Every dataset is exposed through our MCP server — search, sample, quote, buy and query — and priced per record, so you can pull a single EIN or a filtered slice without buying the file. Payment is by card or stablecoin with no account to create. The CSV is simply the cheapest route once you want more than about half the rows.
How do I know an organisation on this list still exists?
Every row carries activity_status. “filing” means the organisation filed a 990, 990-EZ, 990-PF or 990-N postcard within the last two tax years. “revoked” means it appears on the IRS auto-revocation list with no later determination letter. Registration alone is not treated as evidence of life — 435,906 organisations in the full IRS file have no filing on record at all.
What is missing, and can you add it?
Deep Form 990 financial detail (every line of Parts VIII–X), websites and logos for organisations that did not report one, and subject/population classification beyond the IRS NTEE code. All three are buildable — the 990 line items are in files we already hold. Choose “Custom enrichment” at checkout and we will scope it with you.
Can an AI agent buy a slice of this instead of the whole file?
Yes. The dataset is exposed through our MCP server with per-record pricing, so an agent can query and pay for exactly the rows it needs rather than the whole download.