Data schema

The shape of every record in The Raise List. Pin this URL in your agent's memory before it touches the data — no guessing required.

Top-level shape

{
  "organisations": [...],
  "individuals": [...],
  "meta": {
    "sku": "global" | "us" | "europe",
    "count": { "organisations": 13378, "individuals": 8119 },
    "generated_at": "2026-06-18T20:34:37.782Z",
    "schema_url": "https://theraiselist.com/data-schema.html"
  }
}

Both organisations and individuals are arrays of objects. Object keys are the strings from the CSV header row. Every string is trimmed; missing values are empty strings, never null (so you can pipe straight into a DataFrame without null-coalescing).

organisations[]

Organisation
string Display name. Not unique — same org may appear twice across SKUs if you buy both regional packs.
Northzone
Type
enum investor · accelerator · corporate · network · institution
investor
Class
enum vc · angel · growth_equity · cvc · family_office · accelerator · micro_vc · pe
vc
Sector
enum generalist · fintech · climate · healthcare · deeptech · consumer · enterprise · biotech · proptech · web3 · industrials
fintech
Website
string Canonical URL. Always includes scheme. Empty string if unknown.
https://northzone.com
Email
string Public contact email. Verified at acquisition time. Empty string if not available — do not assume any default.
[email protected]
LinkedIn
string Company-page URL. Empty string if not present.
https://linkedin.com/company/northzone
Country
string Full country name. Use Country Code if you need ISO normalisation.
United Kingdom
Country Code
string ISO 3166-1 alpha-2.
GB
Region
string Sub-country region (US state, UK constituent country, etc.). Empty string if not in a regioned country.
CA
City
string City name as a string. Empty string if not known.
London
Headcount
enum 1-10 · 11-50 · 51-200 · 201-500 · 501-1000 · 1001+
11-50
Status
enum active · unknown · inactive. Only active and unknown ship in the product.
active
Description
string One-paragraph description. class + type mixing was cleaned up at acquisition, so the breakdown numbers on the landing page match this column.
Northzone is an early-stage venture capital firm partnering with Nordic and European founders...
OrgID
string Stable SHA1 identifier. Use this to link an individual in individuals[] to their organisation in organisations[].
a3f1c8e7b2d9...

individuals[]

First Name
string
Sara
Last Name
string
Smith
Title
string Job title at the time of acquisition.
Partner
Email
string Verified at acquisition. Every individual in the product has a non-empty email — that is the product's hard promise.
[email protected]
LinkedIn
string
https://linkedin.com/in/sarasmith
Country
string Individual's own country if known, else inherited from their organisation.
United Kingdom
Region
string Inherited from organisation if not in user_region.
City
string
London
Organisation
string Display name. Same value as organisations[].Organisation for the matching OrgID, where known.
Northzone
Organisation Website
string
https://northzone.com
OrgID
string Matches organisations[].OrgID. Use this to join — do not join on the name strings; the same org may have multiple name variants across data sources.
a3f1c8e7b2d9...
Bio
string Short bio where available. Empty string for many rows; this column is the noisiest in the dataset.
Partner at Northzone, focused on fintech and infrastructure software across the Nordics.

Counts (real, as of build time)

Numbers in meta.count are the actual rows shipped in this SKU. The page at theraiselist.com shows a slightly-rounded version of these for human readers.

SKU definitions

Each SKU is a regional filter on the master dataset:

Regional packs contain a strict subset of the global data. If you buy the global pack, you do not need the regional ones.

Worked example: load and filter in 10 lines of Python

import json, urllib.request, zipfile, io

token = "your-download-token"
data = json.loads(urllib.request.urlopen(f"https://theraiselist.com/download/{token}").read())
# (after unzipping data.json — left as exercise)

fintech_vcs = [o for o in data["organisations"]
               if o["Class"] == "vc" and o["Sector"] == "fintech"
               and o["Country Code"] == "GB"]
print(f"{len(fintech_vcs)} UK fintech VCs in the dataset")

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